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1 | # Ing. AVP | |
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2 | # 06/10/2021 | |
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3 | # ARCHIVO DE LECTURA | |
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4 | import os, sys | |
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5 | import datetime | |
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6 | import time | |
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7 | from schainpy.controller import Project | |
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8 | #### NOTA########################################### | |
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9 | # INPUT : | |
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10 | # VELOCIDAD PARAMETRO : V = 2Β°/seg | |
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11 | # MODO PULSE PAIR O MOMENTOS: 0 : Pulse Pair ,1 : Momentos | |
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12 | ###################################################### | |
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13 | ##### PROCESAMIENTO ################################## | |
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14 | ##### OJO TENER EN CUENTA EL n= para el Pulse Pair ## | |
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15 | ##### O EL n= nFFTPoints ### | |
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16 | ###################################################### | |
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17 | ######## BUSCAMOS EL numero de IPP equivalente 1Β°##### | |
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18 | ######## Sea V la velocidad del Pedestal en Β°/seg##### | |
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19 | ######## 1Β° sera Recorrido en un tiempo de 1/V ###### | |
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20 | ######## IPP del Radar 400 useg --> 60 Km ############ | |
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21 | ######## n = 1/(V(Β°/seg)*IPP(Km)) , NUMERO DE IPP ## | |
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22 | ######## n = 1/(V*IPP) ############################# | |
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23 | ######## VELOCIDAD DEL PEDESTAL ###################### | |
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24 | print("SETUP- RADAR METEOROLOGICO") | |
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25 | V = 10 | |
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26 | mode = 1 | |
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27 | #path = '/DATA_RM/23/6v' | |
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28 | #path = '/DATA_RM/TEST_INTEGRACION_2M' | |
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29 | path = '/DATA_RM/WR_20_OCT' | |
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30 | ||
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31 | #path_ped='/DATA_RM/TEST_PEDESTAL/P20211012-082745' | |
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32 | path_ped='/DATA_RM/TEST_PEDESTAL/P20211020-131248' | |
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33 | ||
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34 | figpath_pp = "/home/soporte/Pictures/TEST_PP" | |
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35 | figpath_mom = "/home/soporte/Pictures/TEST_MOM" | |
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36 | plot = 0 | |
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37 | integration = 1 | |
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38 | save = 0 | |
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39 | if save == 1: | |
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40 | if mode==0: | |
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41 | path_save = '/DATA_RM/TEST_HDF5_PP_23/6v' | |
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42 | path_save = '/DATA_RM/TEST_HDF5_PP' | |
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43 | path_save = '/DATA_RM/TEST_HDF5_PP_100' | |
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44 | else: | |
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45 | path_save = '/DATA_RM/TEST_HDF5_SPEC_23_V2/6v' | |
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46 | ||
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47 | print("* PATH data ADQ :", path) | |
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48 | print("* Velocidad Pedestal :",V,"Β°/seg") | |
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49 | ############################ NRO Perfiles PROCESAMIENTO ################### | |
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50 | V=V | |
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51 | IPP=400*1e-6 | |
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52 | n= int(1/(V*IPP)) | |
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53 | print("* n - NRO Perfiles Proc:", n ) | |
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54 | ################################## MODE ################################### | |
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55 | print("* Modo de Operacion :",mode) | |
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56 | if mode ==0: | |
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57 | print("* Met. Seleccionado : Pulse Pair") | |
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58 | else: | |
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59 | print("* Met. Momentos : Momentos") | |
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60 | ||
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61 | ################################## MODE ################################### | |
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62 | print("* Grabado de datos :",save) | |
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63 | if save ==1: | |
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64 | if mode==0: | |
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65 | ope= "Pulse Pair" | |
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66 | else: | |
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67 | ope= "Momentos" | |
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68 | print("* Path-Save Data -", ope , path_save) | |
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69 | ||
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70 | print("* Integracion de datos :",integration) | |
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71 | ||
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72 | time.sleep(15) | |
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73 | #remotefolder = "/home/wmaster/graficos" | |
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74 | ####################################################################### | |
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75 | ################# RANGO DE PLOTEO###################################### | |
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76 | dBmin = '1' | |
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77 | dBmax = '85' | |
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78 | xmin = '15' | |
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79 | xmax = '15.25' | |
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80 | ymin = '0' | |
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81 | ymax = '600' | |
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82 | ####################################################################### | |
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83 | ########################FECHA########################################## | |
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84 | str = datetime.date.today() | |
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85 | today = str.strftime("%Y/%m/%d") | |
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86 | str2 = str - datetime.timedelta(days=1) | |
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87 | yesterday = str2.strftime("%Y/%m/%d") | |
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88 | ####################################################################### | |
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89 | ########################SIGNAL CHAIN ################################## | |
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90 | ####################################################################### | |
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91 | desc = "USRP_test" | |
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92 | filename = "USRP_processing.xml" | |
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93 | controllerObj = Project() | |
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94 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) | |
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95 | ####################################################################### | |
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96 | ######################## UNIDAD DE LECTURA############################# | |
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97 | ####################################################################### | |
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98 | readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader', | |
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99 | path=path, | |
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100 | startDate="2021/01/01",#today, | |
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101 | endDate="2021/12/30",#today, | |
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102 | startTime='00:00:00', | |
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103 | endTime='23:59:59', | |
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104 | delay=0, | |
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105 | #set=0, | |
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106 | online=0, | |
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107 | walk=1, | |
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108 | ippKm = 60) | |
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109 | ||
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110 | opObj11 = readUnitConfObj.addOperation(name='printInfo') | |
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111 | ||
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112 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
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113 | ||
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114 | if mode ==0: | |
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115 | ####################### METODO PULSE PAIR ###################################################################### | |
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116 | opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other') | |
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117 | opObj11.addParameter(name='n', value=int(n), format='int')#10 VOY A USAR 250 DADO QUE LA VELOCIDAD ES 10 GRADOS | |
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118 | #opObj11.addParameter(name='removeDC', value=1, format='int') | |
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119 | ####################### METODO Parametros ###################################################################### | |
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120 | procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId()) | |
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121 | if plot==1: | |
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122 | opObj11 = procUnitConfObjB.addOperation(name='GenericRTIPlot',optype='external') | |
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123 | opObj11.addParameter(name='attr_data', value='dataPP_POW') | |
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124 | opObj11.addParameter(name='colormap', value='jet') | |
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125 | opObj11.addParameter(name='xmin', value=xmin) | |
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126 | opObj11.addParameter(name='xmax', value=xmax) | |
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127 | opObj11.addParameter(name='zmin', value=dBmin) | |
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128 | opObj11.addParameter(name='zmax', value=dBmax) | |
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129 | opObj11.addParameter(name='save', value=figpath_pp) | |
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130 | opObj11.addParameter(name='showprofile', value=0) | |
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131 | opObj11.addParameter(name='save_period', value=50) | |
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132 | ||
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133 | ####################### METODO ESCRITURA ####################################################################### | |
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134 | if save==1: | |
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135 | opObj10 = procUnitConfObjB.addOperation(name='HDFWriter') | |
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136 | opObj10.addParameter(name='path',value=path_save) | |
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137 | #opObj10.addParameter(name='mode',value=0) | |
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138 | opObj10.addParameter(name='blocksPerFile',value='100',format='int') | |
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139 | opObj10.addParameter(name='metadataList',value='utctimeInit,timeZone,paramInterval,profileIndex,channelList,heightList,flagDataAsBlock',format='list') | |
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140 | opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,utctime',format='list')#,format='list' | |
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141 | if integration==1: | |
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142 | V=10 | |
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143 | blocksPerfile=360 | |
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144 | print("* Velocidad del Pedestal:",V) | |
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145 | tmp_blocksPerfile = 100 | |
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146 | f_a_p= int(tmp_blocksPerfile/V) | |
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147 | ||
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148 | opObj11 = procUnitConfObjB.addOperation(name='PedestalInformation') | |
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149 | opObj11.addParameter(name='path_ped', value=path_ped) | |
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150 | #opObj11.addParameter(name='path_adq', value=path_adq) | |
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151 | opObj11.addParameter(name='t_Interval_p', value='0.01', format='float') | |
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152 | opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int') | |
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153 | opObj11.addParameter(name='n_Muestras_p', value='100', format='float') | |
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154 | opObj11.addParameter(name='f_a_p', value=f_a_p, format='int') | |
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155 | opObj11.addParameter(name='online', value='0', format='int') | |
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156 | ||
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157 | opObj11 = procUnitConfObjB.addOperation(name='Block360') | |
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158 | opObj11.addParameter(name='n', value='10', format='int') | |
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159 | opObj11.addParameter(name='mode', value=mode, format='int') | |
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160 | ||
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161 | # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180 | |
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162 | ||
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163 | opObj11= procUnitConfObjB.addOperation(name='WeatherPlot',optype='other') | |
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164 | ||
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165 | ||
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166 | else: | |
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167 | ####################### METODO SPECTROS ###################################################################### | |
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168 | procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId()) | |
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169 | procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int') | |
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170 | procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int') | |
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171 | ||
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172 | procUnitConfObjC = controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId()) | |
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173 | procUnitConfObjC.addOperation(name='SpectralMoments') | |
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174 | if plot==1: | |
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175 | dBmin = '1' | |
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176 | dBmax = '65' | |
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177 | opObj11 = procUnitConfObjC.addOperation(name='PowerPlot',optype='external') | |
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178 | opObj11.addParameter(name='xmin', value=xmin) | |
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179 | opObj11.addParameter(name='xmax', value=xmax) | |
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180 | opObj11.addParameter(name='zmin', value=dBmin) | |
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181 | opObj11.addParameter(name='zmax', value=dBmax) | |
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182 | opObj11.addParameter(name='save', value=figpath_mom) | |
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183 | opObj11.addParameter(name='showprofile', value=0) | |
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184 | opObj11.addParameter(name='save_period', value=100) | |
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185 | ||
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186 | if save==1: | |
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187 | opObj10 = procUnitConfObjC.addOperation(name='HDFWriter') | |
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188 | opObj10.addParameter(name='path',value=path_save) | |
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189 | #opObj10.addParameter(name='mode',value=0) | |
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190 | opObj10.addParameter(name='blocksPerFile',value='360',format='int') | |
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191 | #opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex | |
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192 | opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex | |
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193 | opObj10.addParameter(name='dataList',value='data_pow,data_dop,utctime',format='list')#,format='list' | |
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194 | ||
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195 | if integration==1: | |
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196 | V=10 | |
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197 | blocksPerfile=360 | |
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198 | print("* Velocidad del Pedestal:",V) | |
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199 | tmp_blocksPerfile = 100 | |
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200 | f_a_p= int(tmp_blocksPerfile/V) | |
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201 | ||
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202 | opObj11 = procUnitConfObjC.addOperation(name='PedestalInformation') | |
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203 | opObj11.addParameter(name='path_ped', value=path_ped) | |
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204 | #opObj11.addParameter(name='path_adq', value=path_adq) | |
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205 | opObj11.addParameter(name='t_Interval_p', value='0.01', format='float') | |
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206 | opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int') | |
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207 | opObj11.addParameter(name='n_Muestras_p', value='100', format='float') | |
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208 | opObj11.addParameter(name='f_a_p', value=f_a_p, format='int') | |
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209 | opObj11.addParameter(name='online', value='0', format='int') | |
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210 | ||
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211 | opObj11 = procUnitConfObjC.addOperation(name='Block360') | |
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212 | opObj11.addParameter(name='n', value='30', format='int') | |
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213 | opObj11.addParameter(name='mode', value=mode, format='int') | |
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214 | ||
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215 | # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180 | |
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216 | opObj11= procUnitConfObjC.addOperation(name='WeatherPlot',optype='other') | |
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217 | controllerObj.start() | |
| @@ -0,0 +1,13 | |||
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1 | ||
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2 | PULSE PAIR | |
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3 | help [[1.364 1.376 1.28 ... 1.352 1.372 1.332] | |
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4 | [2.912 3.012 3.06 ... 3.056 2.6 2.936]] | |
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5 | help [[1.256 1.304 1.212 ... 1.228 1.328 1.528] | |
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6 | [2.744 2.604 2.492 ... 2.544 2.916 2.644]] | |
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7 | help [[1.176 1.248 1.48 ... 1.388 1.396 1.216] | |
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8 | [2.564 2.524 2.756 ... 2.772 2.7 2.684]] | |
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9 | ||
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10 | help [[1.312 1.328 1.28 ... 1.28 1.444 1.532] | |
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11 | [3. 2.964 3.092 ... 3.104 3.016 2.984]] | |
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12 | ||
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13 | MOMENTOS | |
| @@ -0,0 +1,69 | |||
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1 | # Ing. AVP | |
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2 | # 06/10/2021 | |
|
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3 | # ARCHIVO DE LECTURA | |
|
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4 | import os, sys | |
|
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5 | import datetime | |
|
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6 | import time | |
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7 | from schainpy.controller import Project | |
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8 | ||
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9 | print("SETUP- RADAR METEOROLOGICO") | |
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10 | V = 10 | |
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11 | ####################################################################### | |
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12 | ################# RANGO DE PLOTEO###################################### | |
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13 | dBmin = '1' | |
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14 | dBmax = '65' | |
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15 | xmin = '13.2' | |
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16 | xmax = '13.5' | |
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17 | ymin = '0' | |
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18 | ymax = '60' | |
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19 | ||
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20 | path = '/DATA_RM/WR_20_OCT' | |
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21 | figpath_pp = "/home/soporte/Pictures/TEST_PP" | |
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22 | ||
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23 | ||
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24 | IPP=400*1e-6 | |
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25 | n= int(1/(V*IPP)) | |
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26 | #n=250 | |
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27 | print("* n - NRO Perfiles Proc:", n ) | |
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28 | ||
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29 | desc = "USRP_test" | |
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30 | filename = "USRP_processing.xml" | |
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31 | controllerObj = Project() | |
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32 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) | |
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33 | ####################################################################### | |
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34 | ######################## UNIDAD DE LECTURA############################# | |
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35 | ####################################################################### | |
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36 | readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader', | |
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37 | path=path, | |
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38 | startDate="2021/01/01",#today, | |
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39 | endDate="2021/12/30",#today, | |
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40 | startTime='00:00:00', | |
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41 | endTime='23:59:59', | |
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42 | delay=0, | |
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43 | #set=0, | |
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44 | online=0, | |
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45 | walk=1, | |
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46 | ippKm = 60) | |
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47 | ||
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48 | opObj11 = readUnitConfObj.addOperation(name='printInfo') | |
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49 | ||
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50 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
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51 | ||
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52 | opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other') | |
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53 | opObj11.addParameter(name='n', value=int(n), format='int')#10 VOY A USAR 250 DADO QUE LA VELOCIDAD ES 10 GRADOS | |
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54 | #opObj11.addParameter(name='removeDC', value=1, format='int') | |
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55 | ||
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56 | procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId()) | |
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57 | ||
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58 | opObj11 = procUnitConfObjB.addOperation(name='GenericRTIPlot',optype='external') | |
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59 | opObj11.addParameter(name='attr_data', value='dataPP_POWER') | |
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60 | opObj11.addParameter(name='colormap', value='jet') | |
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61 | opObj11.addParameter(name='xmin', value=xmin) | |
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62 | opObj11.addParameter(name='xmax', value=xmax) | |
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63 | opObj11.addParameter(name='zmin', value=dBmin) | |
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64 | opObj11.addParameter(name='zmax', value=dBmax) | |
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65 | opObj11.addParameter(name='save', value=figpath_pp) | |
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66 | opObj11.addParameter(name='showprofile', value=0) | |
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67 | #opObj11.addParameter(name='save_period', value=10) | |
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68 | ||
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69 | controllerObj.start() | |
| @@ -0,0 +1,80 | |||
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1 | # Ing. AVP | |
|
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2 | # 06/10/2021 | |
|
|
3 | # ARCHIVO DE LECTURA | |
|
|
4 | import os, sys | |
|
|
5 | import datetime | |
|
|
6 | import time | |
|
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7 | from schainpy.controller import Project | |
|
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8 | ||
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9 | print("SETUP- RADAR METEOROLOGICO") | |
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10 | V = 10 | |
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11 | ####################################################################### | |
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12 | ################# RANGO DE PLOTEO###################################### | |
|
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13 | dBmin = '1' | |
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14 | dBmax = '65' | |
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15 | xmin = '13.2' | |
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16 | xmax = '13.5' | |
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17 | ymin = '0' | |
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18 | ymax = '60' | |
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19 | ||
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20 | path = '/DATA_RM/WR_20_OCT' | |
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21 | figpath_spec = "/home/soporte/Pictures/TEST_MOM" | |
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22 | ||
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23 | ||
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24 | IPP=400*1e-6 | |
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25 | n= int(1/(V*IPP)) | |
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26 | print("* n - NRO Perfiles Proc:", n ) | |
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27 | time.sleep(5) | |
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28 | desc = "USRP_test" | |
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29 | filename = "USRP_processing.xml" | |
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30 | controllerObj = Project() | |
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31 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) | |
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32 | ####################################################################### | |
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33 | ######################## UNIDAD DE LECTURA############################# | |
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34 | ####################################################################### | |
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35 | readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader', | |
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36 | path=path, | |
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37 | startDate="2021/01/01",#today, | |
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38 | endDate="2021/12/30",#today, | |
|
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39 | startTime='00:00:00', | |
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40 | endTime='23:59:59', | |
|
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41 | delay=0, | |
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42 | #set=0, | |
|
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43 | online=0, | |
|
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44 | walk=1, | |
|
|
45 | ippKm = 60) | |
|
|
46 | ||
|
|
47 | opObj11 = readUnitConfObj.addOperation(name='printInfo') | |
|
|
48 | ||
|
|
49 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
|
|
50 | ||
|
|
51 | procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId()) | |
|
|
52 | procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int') | |
|
|
53 | procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int') | |
|
|
54 | ''' | |
|
|
55 | opObj11 = procUnitConfObjB.addOperation(name='RTIPlot', optype='external') | |
|
|
56 | #.addParameter(name='id', value='2', format='int') | |
|
|
57 | opObj11.addParameter(name='wintitle', value='RTIPlot', format='str') | |
|
|
58 | opObj11.addParameter(name='xmin', value=xmin) | |
|
|
59 | opObj11.addParameter(name='xmax', value=xmax) | |
|
|
60 | opObj11.addParameter(name='zmin', value=dBmin, format='int') | |
|
|
61 | opObj11.addParameter(name='zmax', value=dBmax, format='int') | |
|
|
62 | ''' | |
|
|
63 | #opObj13 = procUnitConfObjB.addOperation(name='removeDC') | |
|
|
64 | #opObj13.addParameter(name='mode', value='2', format='int') | |
|
|
65 | ||
|
|
66 | procUnitConfObjC = controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId()) | |
|
|
67 | procUnitConfObjC.addOperation(name='SpectralMoments') | |
|
|
68 | ||
|
|
69 | dBmin = '1' | |
|
|
70 | dBmax = '65' | |
|
|
71 | opObj11 = procUnitConfObjC.addOperation(name='PowerPlot',optype='external') | |
|
|
72 | opObj11.addParameter(name='xmin', value=xmin) | |
|
|
73 | opObj11.addParameter(name='xmax', value=xmax) | |
|
|
74 | opObj11.addParameter(name='zmin', value=dBmin) | |
|
|
75 | opObj11.addParameter(name='zmax', value=dBmax) | |
|
|
76 | opObj11.addParameter(name='save', value=figpath_spec) | |
|
|
77 | opObj11.addParameter(name='showprofile', value=0) | |
|
|
78 | #opObj11.addParameter(name='save_period', value=10) | |
|
|
79 | ||
|
|
80 | controllerObj.start() | |
| @@ -1,1069 +1,1068 | |||
|
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
|
5 | 5 | """Definition of diferent Data objects for different types of data |
|
|
6 | 6 | |
|
|
7 | 7 | Here you will find the diferent data objects for the different types |
|
|
8 | 8 | of data, this data objects must be used as dataIn or dataOut objects in |
|
|
9 | 9 | processing units and operations. Currently the supported data objects are: |
|
|
10 | 10 | Voltage, Spectra, SpectraHeis, Fits, Correlation and Parameters |
|
|
11 | 11 | """ |
|
|
12 | 12 | |
|
|
13 | 13 | import copy |
|
|
14 | 14 | import numpy |
|
|
15 | 15 | import datetime |
|
|
16 | 16 | import json |
|
|
17 | 17 | |
|
|
18 | 18 | import schainpy.admin |
|
|
19 | 19 | from schainpy.utils import log |
|
|
20 | 20 | from .jroheaderIO import SystemHeader, RadarControllerHeader |
|
|
21 | 21 | from schainpy.model.data import _noise |
|
|
22 | 22 | |
|
|
23 | 23 | |
|
|
24 | 24 | def getNumpyDtype(dataTypeCode): |
|
|
25 | 25 | |
|
|
26 | 26 | if dataTypeCode == 0: |
|
|
27 | 27 | numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
|
|
28 | 28 | elif dataTypeCode == 1: |
|
|
29 | 29 | numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
|
|
30 | 30 | elif dataTypeCode == 2: |
|
|
31 | 31 | numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
|
|
32 | 32 | elif dataTypeCode == 3: |
|
|
33 | 33 | numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
|
|
34 | 34 | elif dataTypeCode == 4: |
|
|
35 | 35 | numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
36 | 36 | elif dataTypeCode == 5: |
|
|
37 | 37 | numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
|
|
38 | 38 | else: |
|
|
39 | 39 | raise ValueError('dataTypeCode was not defined') |
|
|
40 | 40 | |
|
|
41 | 41 | return numpyDtype |
|
|
42 | 42 | |
|
|
43 | 43 | |
|
|
44 | 44 | def getDataTypeCode(numpyDtype): |
|
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45 | 45 | |
|
|
46 | 46 | if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]): |
|
|
47 | 47 | datatype = 0 |
|
|
48 | 48 | elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]): |
|
|
49 | 49 | datatype = 1 |
|
|
50 | 50 | elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]): |
|
|
51 | 51 | datatype = 2 |
|
|
52 | 52 | elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]): |
|
|
53 | 53 | datatype = 3 |
|
|
54 | 54 | elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]): |
|
|
55 | 55 | datatype = 4 |
|
|
56 | 56 | elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]): |
|
|
57 | 57 | datatype = 5 |
|
|
58 | 58 | else: |
|
|
59 | 59 | datatype = None |
|
|
60 | 60 | |
|
|
61 | 61 | return datatype |
|
|
62 | 62 | |
|
|
63 | 63 | |
|
|
64 | 64 | def hildebrand_sekhon(data, navg): |
|
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65 | 65 | """ |
|
|
66 | 66 | This method is for the objective determination of the noise level in Doppler spectra. This |
|
|
67 | 67 | implementation technique is based on the fact that the standard deviation of the spectral |
|
|
68 | 68 | densities is equal to the mean spectral density for white Gaussian noise |
|
|
69 | 69 | |
|
|
70 | 70 | Inputs: |
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|
71 | 71 | Data : heights |
|
|
72 | 72 | navg : numbers of averages |
|
|
73 | 73 | |
|
|
74 | 74 | Return: |
|
|
75 | 75 | mean : noise's level |
|
|
76 | 76 | """ |
|
|
77 | 77 | |
|
|
78 | 78 | sortdata = numpy.sort(data, axis=None) |
|
|
79 | 79 | ''' |
|
|
80 | 80 | lenOfData = len(sortdata) |
|
|
81 | 81 | nums_min = lenOfData*0.2 |
|
|
82 | 82 | |
|
|
83 | 83 | if nums_min <= 5: |
|
|
84 | 84 | |
|
|
85 | 85 | nums_min = 5 |
|
|
86 | 86 | |
|
|
87 | 87 | sump = 0. |
|
|
88 | 88 | sumq = 0. |
|
|
89 | 89 | |
|
|
90 | 90 | j = 0 |
|
|
91 | 91 | cont = 1 |
|
|
92 | 92 | |
|
|
93 | 93 | while((cont == 1)and(j < lenOfData)): |
|
|
94 | 94 | |
|
|
95 | 95 | sump += sortdata[j] |
|
|
96 | 96 | sumq += sortdata[j]**2 |
|
|
97 | 97 | |
|
|
98 | 98 | if j > nums_min: |
|
|
99 | 99 | rtest = float(j)/(j-1) + 1.0/navg |
|
|
100 | 100 | if ((sumq*j) > (rtest*sump**2)): |
|
|
101 | 101 | j = j - 1 |
|
|
102 | 102 | sump = sump - sortdata[j] |
|
|
103 | 103 | sumq = sumq - sortdata[j]**2 |
|
|
104 | 104 | cont = 0 |
|
|
105 | 105 | |
|
|
106 | 106 | j += 1 |
|
|
107 | 107 | |
|
|
108 | 108 | lnoise = sump / j |
|
|
109 | 109 | ''' |
|
|
110 | 110 | return _noise.hildebrand_sekhon(sortdata, navg) |
|
|
111 | 111 | |
|
|
112 | 112 | |
|
|
113 | 113 | class Beam: |
|
|
114 | 114 | |
|
|
115 | 115 | def __init__(self): |
|
|
116 | 116 | self.codeList = [] |
|
|
117 | 117 | self.azimuthList = [] |
|
|
118 | 118 | self.zenithList = [] |
|
|
119 | 119 | |
|
|
120 | 120 | |
|
|
121 | 121 | class GenericData(object): |
|
|
122 | 122 | |
|
|
123 | 123 | flagNoData = True |
|
|
124 | 124 | |
|
|
125 | 125 | def copy(self, inputObj=None): |
|
|
126 | 126 | |
|
|
127 | 127 | if inputObj == None: |
|
|
128 | 128 | return copy.deepcopy(self) |
|
|
129 | 129 | |
|
|
130 | 130 | for key in list(inputObj.__dict__.keys()): |
|
|
131 | 131 | |
|
|
132 | 132 | attribute = inputObj.__dict__[key] |
|
|
133 | 133 | |
|
|
134 | 134 | # If this attribute is a tuple or list |
|
|
135 | 135 | if type(inputObj.__dict__[key]) in (tuple, list): |
|
|
136 | 136 | self.__dict__[key] = attribute[:] |
|
|
137 | 137 | continue |
|
|
138 | 138 | |
|
|
139 | 139 | # If this attribute is another object or instance |
|
|
140 | 140 | if hasattr(attribute, '__dict__'): |
|
|
141 | 141 | self.__dict__[key] = attribute.copy() |
|
|
142 | 142 | continue |
|
|
143 | 143 | |
|
|
144 | 144 | self.__dict__[key] = inputObj.__dict__[key] |
|
|
145 | 145 | |
|
|
146 | 146 | def deepcopy(self): |
|
|
147 | 147 | |
|
|
148 | 148 | return copy.deepcopy(self) |
|
|
149 | 149 | |
|
|
150 | 150 | def isEmpty(self): |
|
|
151 | 151 | |
|
|
152 | 152 | return self.flagNoData |
|
|
153 | 153 | |
|
|
154 | 154 | def isReady(self): |
|
|
155 | 155 | |
|
|
156 | 156 | return not self.flagNoData |
|
|
157 | 157 | |
|
|
158 | 158 | |
|
|
159 | 159 | class JROData(GenericData): |
|
|
160 | 160 | |
|
|
161 | 161 | systemHeaderObj = SystemHeader() |
|
|
162 | 162 | radarControllerHeaderObj = RadarControllerHeader() |
|
|
163 | 163 | type = None |
|
|
164 | 164 | datatype = None # dtype but in string |
|
|
165 | 165 | nProfiles = None |
|
|
166 | 166 | heightList = None |
|
|
167 | 167 | channelList = None |
|
|
168 | 168 | flagDiscontinuousBlock = False |
|
|
169 | 169 | useLocalTime = False |
|
|
170 | 170 | utctime = None |
|
|
171 | 171 | timeZone = None |
|
|
172 | 172 | dstFlag = None |
|
|
173 | 173 | errorCount = None |
|
|
174 | 174 | blocksize = None |
|
|
175 | 175 | flagDecodeData = False # asumo q la data no esta decodificada |
|
|
176 | 176 | flagDeflipData = False # asumo q la data no esta sin flip |
|
|
177 | 177 | flagShiftFFT = False |
|
|
178 | 178 | nCohInt = None |
|
|
179 | 179 | windowOfFilter = 1 |
|
|
180 | 180 | C = 3e8 |
|
|
181 | 181 | frequency = 49.92e6 |
|
|
182 | 182 | realtime = False |
|
|
183 | 183 | beacon_heiIndexList = None |
|
|
184 | 184 | last_block = None |
|
|
185 | 185 | blocknow = None |
|
|
186 | 186 | azimuth = None |
|
|
187 | 187 | zenith = None |
|
|
188 | 188 | beam = Beam() |
|
|
189 | 189 | profileIndex = None |
|
|
190 | 190 | error = None |
|
|
191 | 191 | data = None |
|
|
192 | 192 | nmodes = None |
|
|
193 | 193 | metadata_list = ['heightList', 'timeZone', 'type'] |
|
|
194 | 194 | |
|
|
195 | 195 | def __str__(self): |
|
|
196 | 196 | |
|
|
197 | 197 | return '{} - {}'.format(self.type, self.datatime()) |
|
|
198 | 198 | |
|
|
199 | 199 | def getNoise(self): |
|
|
200 | 200 | |
|
|
201 | 201 | raise NotImplementedError |
|
|
202 | 202 | |
|
|
203 | 203 | @property |
|
|
204 | 204 | def nChannels(self): |
|
|
205 | 205 | |
|
|
206 | 206 | return len(self.channelList) |
|
|
207 | 207 | |
|
|
208 | 208 | @property |
|
|
209 | 209 | def channelIndexList(self): |
|
|
210 | 210 | |
|
|
211 | 211 | return list(range(self.nChannels)) |
|
|
212 | 212 | |
|
|
213 | 213 | @property |
|
|
214 | 214 | def nHeights(self): |
|
|
215 | 215 | |
|
|
216 | 216 | return len(self.heightList) |
|
|
217 | 217 | |
|
|
218 | 218 | def getDeltaH(self): |
|
|
219 | 219 | |
|
|
220 | 220 | return self.heightList[1] - self.heightList[0] |
|
|
221 | 221 | |
|
|
222 | 222 | @property |
|
|
223 | 223 | def ltctime(self): |
|
|
224 | 224 | |
|
|
225 | 225 | if self.useLocalTime: |
|
|
226 | 226 | return self.utctime - self.timeZone * 60 |
|
|
227 | 227 | |
|
|
228 | 228 | return self.utctime |
|
|
229 | 229 | |
|
|
230 | 230 | @property |
|
|
231 | 231 | def datatime(self): |
|
|
232 | 232 | |
|
|
233 | 233 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
234 | 234 | return datatimeValue |
|
|
235 | 235 | |
|
|
236 | 236 | def getTimeRange(self): |
|
|
237 | 237 | |
|
|
238 | 238 | datatime = [] |
|
|
239 | 239 | |
|
|
240 | 240 | datatime.append(self.ltctime) |
|
|
241 | 241 | datatime.append(self.ltctime + self.timeInterval + 1) |
|
|
242 | 242 | |
|
|
243 | 243 | datatime = numpy.array(datatime) |
|
|
244 | 244 | |
|
|
245 | 245 | return datatime |
|
|
246 | 246 | |
|
|
247 | 247 | def getFmaxTimeResponse(self): |
|
|
248 | 248 | |
|
|
249 | 249 | period = (10**-6) * self.getDeltaH() / (0.15) |
|
|
250 | 250 | |
|
|
251 | 251 | PRF = 1. / (period * self.nCohInt) |
|
|
252 | 252 | |
|
|
253 | 253 | fmax = PRF |
|
|
254 | 254 | |
|
|
255 | 255 | return fmax |
|
|
256 | 256 | |
|
|
257 | 257 | def getFmax(self): |
|
|
258 | 258 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
|
|
259 | 259 | |
|
|
260 | 260 | fmax = PRF |
|
|
261 | 261 | return fmax |
|
|
262 | 262 | |
|
|
263 | 263 | def getVmax(self): |
|
|
264 | 264 | |
|
|
265 | 265 | _lambda = self.C / self.frequency |
|
|
266 | 266 | |
|
|
267 | 267 | vmax = self.getFmax() * _lambda / 2 |
|
|
268 | 268 | |
|
|
269 | 269 | return vmax |
|
|
270 | 270 | |
|
|
271 | 271 | @property |
|
|
272 | 272 | def ippSeconds(self): |
|
|
273 | 273 | ''' |
|
|
274 | 274 | ''' |
|
|
275 | 275 | return self.radarControllerHeaderObj.ippSeconds |
|
|
276 | 276 | |
|
|
277 | 277 | @ippSeconds.setter |
|
|
278 | 278 | def ippSeconds(self, ippSeconds): |
|
|
279 | 279 | ''' |
|
|
280 | 280 | ''' |
|
|
281 | 281 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
|
|
282 | 282 | |
|
|
283 | 283 | @property |
|
|
284 | 284 | def code(self): |
|
|
285 | 285 | ''' |
|
|
286 | 286 | ''' |
|
|
287 | 287 | return self.radarControllerHeaderObj.code |
|
|
288 | 288 | |
|
|
289 | 289 | @code.setter |
|
|
290 | 290 | def code(self, code): |
|
|
291 | 291 | ''' |
|
|
292 | 292 | ''' |
|
|
293 | 293 | self.radarControllerHeaderObj.code = code |
|
|
294 | 294 | |
|
|
295 | 295 | @property |
|
|
296 | 296 | def nCode(self): |
|
|
297 | 297 | ''' |
|
|
298 | 298 | ''' |
|
|
299 | 299 | return self.radarControllerHeaderObj.nCode |
|
|
300 | 300 | |
|
|
301 | 301 | @nCode.setter |
|
|
302 | 302 | def nCode(self, ncode): |
|
|
303 | 303 | ''' |
|
|
304 | 304 | ''' |
|
|
305 | 305 | self.radarControllerHeaderObj.nCode = ncode |
|
|
306 | 306 | |
|
|
307 | 307 | @property |
|
|
308 | 308 | def nBaud(self): |
|
|
309 | 309 | ''' |
|
|
310 | 310 | ''' |
|
|
311 | 311 | return self.radarControllerHeaderObj.nBaud |
|
|
312 | 312 | |
|
|
313 | 313 | @nBaud.setter |
|
|
314 | 314 | def nBaud(self, nbaud): |
|
|
315 | 315 | ''' |
|
|
316 | 316 | ''' |
|
|
317 | 317 | self.radarControllerHeaderObj.nBaud = nbaud |
|
|
318 | 318 | |
|
|
319 | 319 | @property |
|
|
320 | 320 | def ipp(self): |
|
|
321 | 321 | ''' |
|
|
322 | 322 | ''' |
|
|
323 | 323 | return self.radarControllerHeaderObj.ipp |
|
|
324 | 324 | |
|
|
325 | 325 | @ipp.setter |
|
|
326 | 326 | def ipp(self, ipp): |
|
|
327 | 327 | ''' |
|
|
328 | 328 | ''' |
|
|
329 | 329 | self.radarControllerHeaderObj.ipp = ipp |
|
|
330 | 330 | |
|
|
331 | 331 | @property |
|
|
332 | 332 | def metadata(self): |
|
|
333 | 333 | ''' |
|
|
334 | 334 | ''' |
|
|
335 | 335 | |
|
|
336 | 336 | return {attr: getattr(self, attr) for attr in self.metadata_list} |
|
|
337 | 337 | |
|
|
338 | 338 | |
|
|
339 | 339 | class Voltage(JROData): |
|
|
340 | 340 | |
|
|
341 | 341 | dataPP_POW = None |
|
|
342 | 342 | dataPP_DOP = None |
|
|
343 | 343 | dataPP_WIDTH = None |
|
|
344 | 344 | dataPP_SNR = None |
|
|
345 | 345 | |
|
|
346 | 346 | def __init__(self): |
|
|
347 | 347 | ''' |
|
|
348 | 348 | Constructor |
|
|
349 | 349 | ''' |
|
|
350 | 350 | |
|
|
351 | 351 | self.useLocalTime = True |
|
|
352 | 352 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
353 | 353 | self.systemHeaderObj = SystemHeader() |
|
|
354 | 354 | self.type = "Voltage" |
|
|
355 | 355 | self.data = None |
|
|
356 | 356 | self.nProfiles = None |
|
|
357 | 357 | self.heightList = None |
|
|
358 | 358 | self.channelList = None |
|
|
359 | 359 | self.flagNoData = True |
|
|
360 | 360 | self.flagDiscontinuousBlock = False |
|
|
361 | 361 | self.utctime = None |
|
|
362 | 362 | self.timeZone = 0 |
|
|
363 | 363 | self.dstFlag = None |
|
|
364 | 364 | self.errorCount = None |
|
|
365 | 365 | self.nCohInt = None |
|
|
366 | 366 | self.blocksize = None |
|
|
367 | 367 | self.flagCohInt = False |
|
|
368 | 368 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
369 | 369 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
370 | 370 | self.flagShiftFFT = False |
|
|
371 | 371 | self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil |
|
|
372 | 372 | self.profileIndex = 0 |
|
|
373 | 373 | self.metadata_list = ['type', 'heightList', 'timeZone', 'nProfiles', 'channelList', 'nCohInt', |
|
|
374 | 374 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp'] |
|
|
375 | 375 | |
|
|
376 | 376 | def getNoisebyHildebrand(self, channel=None): |
|
|
377 | 377 | """ |
|
|
378 | 378 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
379 | 379 | |
|
|
380 | 380 | Return: |
|
|
381 | 381 | noiselevel |
|
|
382 | 382 | """ |
|
|
383 | 383 | |
|
|
384 | 384 | if channel != None: |
|
|
385 | 385 | data = self.data[channel] |
|
|
386 | 386 | nChannels = 1 |
|
|
387 | 387 | else: |
|
|
388 | 388 | data = self.data |
|
|
389 | 389 | nChannels = self.nChannels |
|
|
390 | 390 | |
|
|
391 | 391 | noise = numpy.zeros(nChannels) |
|
|
392 | 392 | power = data * numpy.conjugate(data) |
|
|
393 | 393 | |
|
|
394 | 394 | for thisChannel in range(nChannels): |
|
|
395 | 395 | if nChannels == 1: |
|
|
396 | 396 | daux = power[:].real |
|
|
397 | 397 | else: |
|
|
398 | 398 | daux = power[thisChannel, :].real |
|
|
399 | 399 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
|
|
400 | 400 | |
|
|
401 | 401 | return noise |
|
|
402 | 402 | |
|
|
403 | 403 | def getNoise(self, type=1, channel=None): |
|
|
404 | 404 | |
|
|
405 | 405 | if type == 1: |
|
|
406 | 406 | noise = self.getNoisebyHildebrand(channel) |
|
|
407 | 407 | |
|
|
408 | 408 | return noise |
|
|
409 | 409 | |
|
|
410 | 410 | def getPower(self, channel=None): |
|
|
411 | 411 | |
|
|
412 | 412 | if channel != None: |
|
|
413 | 413 | data = self.data[channel] |
|
|
414 | 414 | else: |
|
|
415 | 415 | data = self.data |
|
|
416 | 416 | |
|
|
417 | 417 | power = data * numpy.conjugate(data) |
|
|
418 | 418 | powerdB = 10 * numpy.log10(power.real) |
|
|
419 | 419 | powerdB = numpy.squeeze(powerdB) |
|
|
420 | 420 | |
|
|
421 | 421 | return powerdB |
|
|
422 | 422 | |
|
|
423 | 423 | @property |
|
|
424 | 424 | def timeInterval(self): |
|
|
425 | 425 | |
|
|
426 | 426 | return self.ippSeconds * self.nCohInt |
|
|
427 | 427 | |
|
|
428 | 428 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
429 | 429 | |
|
|
430 | 430 | |
|
|
431 | 431 | class Spectra(JROData): |
|
|
432 | 432 | |
|
|
433 | 433 | def __init__(self): |
|
|
434 | 434 | ''' |
|
|
435 | 435 | Constructor |
|
|
436 | 436 | ''' |
|
|
437 | 437 | |
|
|
438 | 438 | self.data_dc = None |
|
|
439 | 439 | self.data_spc = None |
|
|
440 | 440 | self.data_cspc = None |
|
|
441 | 441 | self.useLocalTime = True |
|
|
442 | 442 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
443 | 443 | self.systemHeaderObj = SystemHeader() |
|
|
444 | 444 | self.type = "Spectra" |
|
|
445 | 445 | self.timeZone = 0 |
|
|
446 | 446 | self.nProfiles = None |
|
|
447 | 447 | self.heightList = None |
|
|
448 | 448 | self.channelList = None |
|
|
449 | 449 | self.pairsList = None |
|
|
450 | 450 | self.flagNoData = True |
|
|
451 | 451 | self.flagDiscontinuousBlock = False |
|
|
452 | 452 | self.utctime = None |
|
|
453 | 453 | self.nCohInt = None |
|
|
454 | 454 | self.nIncohInt = None |
|
|
455 | 455 | self.blocksize = None |
|
|
456 | 456 | self.nFFTPoints = None |
|
|
457 | 457 | self.wavelength = None |
|
|
458 | 458 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
459 | 459 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
460 | 460 | self.flagShiftFFT = False |
|
|
461 | 461 | self.ippFactor = 1 |
|
|
462 | 462 | self.beacon_heiIndexList = [] |
|
|
463 | 463 | self.noise_estimation = None |
|
|
464 | 464 | self.metadata_list = ['type', 'heightList', 'timeZone', 'pairsList', 'channelList', 'nCohInt', |
|
|
465 | 465 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp','nIncohInt', 'nFFTPoints', 'nProfiles'] |
|
|
466 | 466 | |
|
|
467 | 467 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
468 | 468 | """ |
|
|
469 | 469 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
470 | 470 | |
|
|
471 | 471 | Return: |
|
|
472 | 472 | noiselevel |
|
|
473 | 473 | """ |
|
|
474 | 474 | |
|
|
475 | 475 | noise = numpy.zeros(self.nChannels) |
|
|
476 | 476 | |
|
|
477 | 477 | for channel in range(self.nChannels): |
|
|
478 | 478 | daux = self.data_spc[channel, |
|
|
479 | 479 | xmin_index:xmax_index, ymin_index:ymax_index] |
|
|
480 | 480 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
481 | 481 | |
|
|
482 | 482 | return noise |
|
|
483 | 483 | |
|
|
484 | 484 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
485 | 485 | |
|
|
486 | 486 | if self.noise_estimation is not None: |
|
|
487 | 487 | # this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
488 | 488 | return self.noise_estimation |
|
|
489 | 489 | else: |
|
|
490 | 490 | noise = self.getNoisebyHildebrand( |
|
|
491 | 491 | xmin_index, xmax_index, ymin_index, ymax_index) |
|
|
492 | 492 | return noise |
|
|
493 | 493 | |
|
|
494 | 494 | def getFreqRangeTimeResponse(self, extrapoints=0): |
|
|
495 | 495 | |
|
|
496 | 496 | deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor) |
|
|
497 | 497 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
498 | 498 | |
|
|
499 | 499 | return freqrange |
|
|
500 | 500 | |
|
|
501 | 501 | def getAcfRange(self, extrapoints=0): |
|
|
502 | 502 | |
|
|
503 | 503 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
504 | 504 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
505 | 505 | |
|
|
506 | 506 | return freqrange |
|
|
507 | 507 | |
|
|
508 | 508 | def getFreqRange(self, extrapoints=0): |
|
|
509 | 509 | |
|
|
510 | 510 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
511 | 511 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
512 | 512 | |
|
|
513 | 513 | return freqrange |
|
|
514 | 514 | |
|
|
515 | 515 | def getVelRange(self, extrapoints=0): |
|
|
516 | 516 | |
|
|
517 | 517 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
|
518 | 518 | velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) |
|
|
519 | 519 | |
|
|
520 | 520 | if self.nmodes: |
|
|
521 | 521 | return velrange/self.nmodes |
|
|
522 | 522 | else: |
|
|
523 | 523 | return velrange |
|
|
524 | 524 | |
|
|
525 | 525 | @property |
|
|
526 | 526 | def nPairs(self): |
|
|
527 | 527 | |
|
|
528 | 528 | return len(self.pairsList) |
|
|
529 | 529 | |
|
|
530 | 530 | @property |
|
|
531 | 531 | def pairsIndexList(self): |
|
|
532 | 532 | |
|
|
533 | 533 | return list(range(self.nPairs)) |
|
|
534 | 534 | |
|
|
535 | 535 | @property |
|
|
536 | 536 | def normFactor(self): |
|
|
537 | 537 | |
|
|
538 | 538 | pwcode = 1 |
|
|
539 | 539 | |
|
|
540 | 540 | if self.flagDecodeData: |
|
|
541 | 541 | pwcode = numpy.sum(self.code[0]**2) |
|
|
542 | 542 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
543 | 543 | normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter |
|
|
544 | 544 | |
|
|
545 | 545 | return normFactor |
|
|
546 | 546 | |
|
|
547 | 547 | @property |
|
|
548 | 548 | def flag_cspc(self): |
|
|
549 | 549 | |
|
|
550 | 550 | if self.data_cspc is None: |
|
|
551 | 551 | return True |
|
|
552 | 552 | |
|
|
553 | 553 | return False |
|
|
554 | 554 | |
|
|
555 | 555 | @property |
|
|
556 | 556 | def flag_dc(self): |
|
|
557 | 557 | |
|
|
558 | 558 | if self.data_dc is None: |
|
|
559 | 559 | return True |
|
|
560 | 560 | |
|
|
561 | 561 | return False |
|
|
562 | 562 | |
|
|
563 | 563 | @property |
|
|
564 | 564 | def timeInterval(self): |
|
|
565 | 565 | |
|
|
566 | 566 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor |
|
|
567 | 567 | if self.nmodes: |
|
|
568 | 568 | return self.nmodes*timeInterval |
|
|
569 | 569 | else: |
|
|
570 | 570 | return timeInterval |
|
|
571 | 571 | |
|
|
572 | 572 | def getPower(self): |
|
|
573 | 573 | |
|
|
574 | 574 | factor = self.normFactor |
|
|
575 | 575 | z = self.data_spc / factor |
|
|
576 | 576 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
577 | 577 | avg = numpy.average(z, axis=1) |
|
|
578 | ||
|
|
579 | 578 | return 10 * numpy.log10(avg) |
|
|
580 | 579 | |
|
|
581 | 580 | def getCoherence(self, pairsList=None, phase=False): |
|
|
582 | 581 | |
|
|
583 | 582 | z = [] |
|
|
584 | 583 | if pairsList is None: |
|
|
585 | 584 | pairsIndexList = self.pairsIndexList |
|
|
586 | 585 | else: |
|
|
587 | 586 | pairsIndexList = [] |
|
|
588 | 587 | for pair in pairsList: |
|
|
589 | 588 | if pair not in self.pairsList: |
|
|
590 | 589 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
|
591 | 590 | pair)) |
|
|
592 | 591 | pairsIndexList.append(self.pairsList.index(pair)) |
|
|
593 | 592 | for i in range(len(pairsIndexList)): |
|
|
594 | 593 | pair = self.pairsList[pairsIndexList[i]] |
|
|
595 | 594 | ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
|
596 | 595 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
|
597 | 596 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
|
598 | 597 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
599 | 598 | if phase: |
|
|
600 | 599 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
601 | 600 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
602 | 601 | else: |
|
|
603 | 602 | data = numpy.abs(avgcoherenceComplex) |
|
|
604 | 603 | |
|
|
605 | 604 | z.append(data) |
|
|
606 | 605 | |
|
|
607 | 606 | return numpy.array(z) |
|
|
608 | 607 | |
|
|
609 | 608 | def setValue(self, value): |
|
|
610 | 609 | |
|
|
611 | 610 | print("This property should not be initialized") |
|
|
612 | 611 | |
|
|
613 | 612 | return |
|
|
614 | 613 | |
|
|
615 | 614 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
|
616 | 615 | |
|
|
617 | 616 | |
|
|
618 | 617 | class SpectraHeis(Spectra): |
|
|
619 | 618 | |
|
|
620 | 619 | def __init__(self): |
|
|
621 | 620 | |
|
|
622 | 621 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
623 | 622 | self.systemHeaderObj = SystemHeader() |
|
|
624 | 623 | self.type = "SpectraHeis" |
|
|
625 | 624 | self.nProfiles = None |
|
|
626 | 625 | self.heightList = None |
|
|
627 | 626 | self.channelList = None |
|
|
628 | 627 | self.flagNoData = True |
|
|
629 | 628 | self.flagDiscontinuousBlock = False |
|
|
630 | 629 | self.utctime = None |
|
|
631 | 630 | self.blocksize = None |
|
|
632 | 631 | self.profileIndex = 0 |
|
|
633 | 632 | self.nCohInt = 1 |
|
|
634 | 633 | self.nIncohInt = 1 |
|
|
635 | 634 | |
|
|
636 | 635 | @property |
|
|
637 | 636 | def normFactor(self): |
|
|
638 | 637 | pwcode = 1 |
|
|
639 | 638 | if self.flagDecodeData: |
|
|
640 | 639 | pwcode = numpy.sum(self.code[0]**2) |
|
|
641 | 640 | |
|
|
642 | 641 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
|
643 | 642 | |
|
|
644 | 643 | return normFactor |
|
|
645 | 644 | |
|
|
646 | 645 | @property |
|
|
647 | 646 | def timeInterval(self): |
|
|
648 | 647 | |
|
|
649 | 648 | return self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
650 | 649 | |
|
|
651 | 650 | |
|
|
652 | 651 | class Fits(JROData): |
|
|
653 | 652 | |
|
|
654 | 653 | def __init__(self): |
|
|
655 | 654 | |
|
|
656 | 655 | self.type = "Fits" |
|
|
657 | 656 | self.nProfiles = None |
|
|
658 | 657 | self.heightList = None |
|
|
659 | 658 | self.channelList = None |
|
|
660 | 659 | self.flagNoData = True |
|
|
661 | 660 | self.utctime = None |
|
|
662 | 661 | self.nCohInt = 1 |
|
|
663 | 662 | self.nIncohInt = 1 |
|
|
664 | 663 | self.useLocalTime = True |
|
|
665 | 664 | self.profileIndex = 0 |
|
|
666 | 665 | self.timeZone = 0 |
|
|
667 | 666 | |
|
|
668 | 667 | def getTimeRange(self): |
|
|
669 | 668 | |
|
|
670 | 669 | datatime = [] |
|
|
671 | 670 | |
|
|
672 | 671 | datatime.append(self.ltctime) |
|
|
673 | 672 | datatime.append(self.ltctime + self.timeInterval) |
|
|
674 | 673 | |
|
|
675 | 674 | datatime = numpy.array(datatime) |
|
|
676 | 675 | |
|
|
677 | 676 | return datatime |
|
|
678 | 677 | |
|
|
679 | 678 | def getChannelIndexList(self): |
|
|
680 | 679 | |
|
|
681 | 680 | return list(range(self.nChannels)) |
|
|
682 | 681 | |
|
|
683 | 682 | def getNoise(self, type=1): |
|
|
684 | 683 | |
|
|
685 | 684 | |
|
|
686 | 685 | if type == 1: |
|
|
687 | 686 | noise = self.getNoisebyHildebrand() |
|
|
688 | 687 | |
|
|
689 | 688 | if type == 2: |
|
|
690 | 689 | noise = self.getNoisebySort() |
|
|
691 | 690 | |
|
|
692 | 691 | if type == 3: |
|
|
693 | 692 | noise = self.getNoisebyWindow() |
|
|
694 | 693 | |
|
|
695 | 694 | return noise |
|
|
696 | 695 | |
|
|
697 | 696 | @property |
|
|
698 | 697 | def timeInterval(self): |
|
|
699 | 698 | |
|
|
700 | 699 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
701 | 700 | |
|
|
702 | 701 | return timeInterval |
|
|
703 | 702 | |
|
|
704 | 703 | @property |
|
|
705 | 704 | def ippSeconds(self): |
|
|
706 | 705 | ''' |
|
|
707 | 706 | ''' |
|
|
708 | 707 | return self.ipp_sec |
|
|
709 | 708 | |
|
|
710 | 709 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
711 | 710 | |
|
|
712 | 711 | |
|
|
713 | 712 | class Correlation(JROData): |
|
|
714 | 713 | |
|
|
715 | 714 | def __init__(self): |
|
|
716 | 715 | ''' |
|
|
717 | 716 | Constructor |
|
|
718 | 717 | ''' |
|
|
719 | 718 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
720 | 719 | self.systemHeaderObj = SystemHeader() |
|
|
721 | 720 | self.type = "Correlation" |
|
|
722 | 721 | self.data = None |
|
|
723 | 722 | self.dtype = None |
|
|
724 | 723 | self.nProfiles = None |
|
|
725 | 724 | self.heightList = None |
|
|
726 | 725 | self.channelList = None |
|
|
727 | 726 | self.flagNoData = True |
|
|
728 | 727 | self.flagDiscontinuousBlock = False |
|
|
729 | 728 | self.utctime = None |
|
|
730 | 729 | self.timeZone = 0 |
|
|
731 | 730 | self.dstFlag = None |
|
|
732 | 731 | self.errorCount = None |
|
|
733 | 732 | self.blocksize = None |
|
|
734 | 733 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
735 | 734 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
736 | 735 | self.pairsList = None |
|
|
737 | 736 | self.nPoints = None |
|
|
738 | 737 | |
|
|
739 | 738 | def getPairsList(self): |
|
|
740 | 739 | |
|
|
741 | 740 | return self.pairsList |
|
|
742 | 741 | |
|
|
743 | 742 | def getNoise(self, mode=2): |
|
|
744 | 743 | |
|
|
745 | 744 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
746 | 745 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
747 | 746 | |
|
|
748 | 747 | jspectra0 = self.data_corr[:, :, indR, :] |
|
|
749 | 748 | jspectra = copy.copy(jspectra0) |
|
|
750 | 749 | |
|
|
751 | 750 | num_chan = jspectra.shape[0] |
|
|
752 | 751 | num_hei = jspectra.shape[2] |
|
|
753 | 752 | |
|
|
754 | 753 | freq_dc = jspectra.shape[1] / 2 |
|
|
755 | 754 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
756 | 755 | |
|
|
757 | 756 | if ind_vel[0] < 0: |
|
|
758 | 757 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
|
759 | 758 | range(0, 1))] + self.num_prof |
|
|
760 | 759 | |
|
|
761 | 760 | if mode == 1: |
|
|
762 | 761 | jspectra[:, freq_dc, :] = ( |
|
|
763 | 762 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
764 | 763 | |
|
|
765 | 764 | if mode == 2: |
|
|
766 | 765 | |
|
|
767 | 766 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
768 | 767 | xx = numpy.zeros([4, 4]) |
|
|
769 | 768 | |
|
|
770 | 769 | for fil in range(4): |
|
|
771 | 770 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
772 | 771 | |
|
|
773 | 772 | xx_inv = numpy.linalg.inv(xx) |
|
|
774 | 773 | xx_aux = xx_inv[0, :] |
|
|
775 | 774 | |
|
|
776 | 775 | for ich in range(num_chan): |
|
|
777 | 776 | yy = jspectra[ich, ind_vel, :] |
|
|
778 | 777 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
779 | 778 | |
|
|
780 | 779 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
781 | 780 | cjunkid = sum(junkid) |
|
|
782 | 781 | |
|
|
783 | 782 | if cjunkid.any(): |
|
|
784 | 783 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
785 | 784 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
786 | 785 | |
|
|
787 | 786 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
|
788 | 787 | |
|
|
789 | 788 | return noise |
|
|
790 | 789 | |
|
|
791 | 790 | @property |
|
|
792 | 791 | def timeInterval(self): |
|
|
793 | 792 | |
|
|
794 | 793 | return self.ippSeconds * self.nCohInt * self.nProfiles |
|
|
795 | 794 | |
|
|
796 | 795 | def splitFunctions(self): |
|
|
797 | 796 | |
|
|
798 | 797 | pairsList = self.pairsList |
|
|
799 | 798 | ccf_pairs = [] |
|
|
800 | 799 | acf_pairs = [] |
|
|
801 | 800 | ccf_ind = [] |
|
|
802 | 801 | acf_ind = [] |
|
|
803 | 802 | for l in range(len(pairsList)): |
|
|
804 | 803 | chan0 = pairsList[l][0] |
|
|
805 | 804 | chan1 = pairsList[l][1] |
|
|
806 | 805 | |
|
|
807 | 806 | # Obteniendo pares de Autocorrelacion |
|
|
808 | 807 | if chan0 == chan1: |
|
|
809 | 808 | acf_pairs.append(chan0) |
|
|
810 | 809 | acf_ind.append(l) |
|
|
811 | 810 | else: |
|
|
812 | 811 | ccf_pairs.append(pairsList[l]) |
|
|
813 | 812 | ccf_ind.append(l) |
|
|
814 | 813 | |
|
|
815 | 814 | data_acf = self.data_cf[acf_ind] |
|
|
816 | 815 | data_ccf = self.data_cf[ccf_ind] |
|
|
817 | 816 | |
|
|
818 | 817 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
|
819 | 818 | |
|
|
820 | 819 | @property |
|
|
821 | 820 | def normFactor(self): |
|
|
822 | 821 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
|
823 | 822 | acf_pairs = numpy.array(acf_pairs) |
|
|
824 | 823 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
|
825 | 824 | |
|
|
826 | 825 | for p in range(self.nPairs): |
|
|
827 | 826 | pair = self.pairsList[p] |
|
|
828 | 827 | |
|
|
829 | 828 | ch0 = pair[0] |
|
|
830 | 829 | ch1 = pair[1] |
|
|
831 | 830 | |
|
|
832 | 831 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
|
833 | 832 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
|
834 | 833 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
|
835 | 834 | |
|
|
836 | 835 | return normFactor |
|
|
837 | 836 | |
|
|
838 | 837 | |
|
|
839 | 838 | class Parameters(Spectra): |
|
|
840 | 839 | |
|
|
841 | 840 | groupList = None # List of Pairs, Groups, etc |
|
|
842 | 841 | data_param = None # Parameters obtained |
|
|
843 | 842 | data_pre = None # Data Pre Parametrization |
|
|
844 | 843 | data_SNR = None # Signal to Noise Ratio |
|
|
845 | 844 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
|
846 | 845 | utctimeInit = None # Initial UTC time |
|
|
847 | 846 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
|
848 | 847 | useLocalTime = True |
|
|
849 | 848 | # Fitting |
|
|
850 | 849 | data_error = None # Error of the estimation |
|
|
851 | 850 | constants = None |
|
|
852 | 851 | library = None |
|
|
853 | 852 | # Output signal |
|
|
854 | 853 | outputInterval = None # Time interval to calculate output signal in seconds |
|
|
855 | 854 | data_output = None # Out signal |
|
|
856 | 855 | nAvg = None |
|
|
857 | 856 | noise_estimation = None |
|
|
858 | 857 | GauSPC = None # Fit gaussian SPC |
|
|
859 | 858 | |
|
|
860 | 859 | def __init__(self): |
|
|
861 | 860 | ''' |
|
|
862 | 861 | Constructor |
|
|
863 | 862 | ''' |
|
|
864 | 863 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
865 | 864 | self.systemHeaderObj = SystemHeader() |
|
|
866 | 865 | self.type = "Parameters" |
|
|
867 | 866 | self.timeZone = 0 |
|
|
868 | 867 | |
|
|
869 | 868 | def getTimeRange1(self, interval): |
|
|
870 | 869 | |
|
|
871 | 870 | datatime = [] |
|
|
872 | 871 | |
|
|
873 | 872 | if self.useLocalTime: |
|
|
874 | 873 | time1 = self.utctimeInit - self.timeZone * 60 |
|
|
875 | 874 | else: |
|
|
876 | 875 | time1 = self.utctimeInit |
|
|
877 | 876 | |
|
|
878 | 877 | datatime.append(time1) |
|
|
879 | 878 | datatime.append(time1 + interval) |
|
|
880 | 879 | datatime = numpy.array(datatime) |
|
|
881 | 880 | |
|
|
882 | 881 | return datatime |
|
|
883 | 882 | |
|
|
884 | 883 | @property |
|
|
885 | 884 | def timeInterval(self): |
|
|
886 | 885 | |
|
|
887 | 886 | if hasattr(self, 'timeInterval1'): |
|
|
888 | 887 | return self.timeInterval1 |
|
|
889 | 888 | else: |
|
|
890 | 889 | return self.paramInterval |
|
|
891 | 890 | |
|
|
892 | 891 | def setValue(self, value): |
|
|
893 | 892 | |
|
|
894 | 893 | print("This property should not be initialized") |
|
|
895 | 894 | |
|
|
896 | 895 | return |
|
|
897 | 896 | |
|
|
898 | 897 | def getNoise(self): |
|
|
899 | 898 | |
|
|
900 | 899 | return self.spc_noise |
|
|
901 | 900 | |
|
|
902 | 901 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
|
903 | 902 | |
|
|
904 | 903 | |
|
|
905 | 904 | class PlotterData(object): |
|
|
906 | 905 | ''' |
|
|
907 | 906 | Object to hold data to be plotted |
|
|
908 | 907 | ''' |
|
|
909 | 908 | |
|
|
910 | 909 | MAXNUMX = 200 |
|
|
911 | 910 | MAXNUMY = 200 |
|
|
912 | 911 | |
|
|
913 | 912 | def __init__(self, code, exp_code, localtime=True): |
|
|
914 | 913 | |
|
|
915 | 914 | self.key = code |
|
|
916 | 915 | self.exp_code = exp_code |
|
|
917 | 916 | self.ready = False |
|
|
918 | 917 | self.flagNoData = False |
|
|
919 | 918 | self.localtime = localtime |
|
|
920 | 919 | self.data = {} |
|
|
921 | 920 | self.meta = {} |
|
|
922 | 921 | self.__heights = [] |
|
|
923 | 922 | |
|
|
924 | 923 | def __str__(self): |
|
|
925 | 924 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
926 | 925 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.times)) |
|
|
927 | 926 | |
|
|
928 | 927 | def __len__(self): |
|
|
929 | 928 | return len(self.data) |
|
|
930 | 929 | |
|
|
931 | 930 | def __getitem__(self, key): |
|
|
932 | 931 | if isinstance(key, int): |
|
|
933 | 932 | return self.data[self.times[key]] |
|
|
934 | 933 | elif isinstance(key, str): |
|
|
935 | 934 | ret = numpy.array([self.data[x][key] for x in self.times]) |
|
|
936 | 935 | if ret.ndim > 1: |
|
|
937 | 936 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
938 | 937 | return ret |
|
|
939 | 938 | |
|
|
940 | 939 | def __contains__(self, key): |
|
|
941 | 940 | return key in self.data[self.min_time] |
|
|
942 | 941 | |
|
|
943 | 942 | def setup(self): |
|
|
944 | 943 | ''' |
|
|
945 | 944 | Configure object |
|
|
946 | 945 | ''' |
|
|
947 | 946 | self.type = '' |
|
|
948 | 947 | self.ready = False |
|
|
949 | 948 | del self.data |
|
|
950 | 949 | self.data = {} |
|
|
951 | 950 | self.__heights = [] |
|
|
952 | 951 | self.__all_heights = set() |
|
|
953 | 952 | |
|
|
954 | 953 | def shape(self, key): |
|
|
955 | 954 | ''' |
|
|
956 | 955 | Get the shape of the one-element data for the given key |
|
|
957 | 956 | ''' |
|
|
958 | 957 | |
|
|
959 | 958 | if len(self.data[self.min_time][key]): |
|
|
960 | 959 | return self.data[self.min_time][key].shape |
|
|
961 | 960 | return (0,) |
|
|
962 | 961 | |
|
|
963 | 962 | def update(self, data, tm, meta={}): |
|
|
964 | 963 | ''' |
|
|
965 | 964 | Update data object with new dataOut |
|
|
966 | 965 | ''' |
|
|
967 | 966 | |
|
|
968 | 967 | self.data[tm] = data |
|
|
969 | 968 | |
|
|
970 | 969 | for key, value in meta.items(): |
|
|
971 | 970 | setattr(self, key, value) |
|
|
972 | 971 | |
|
|
973 | 972 | def normalize_heights(self): |
|
|
974 | 973 | ''' |
|
|
975 | 974 | Ensure same-dimension of the data for different heighList |
|
|
976 | 975 | ''' |
|
|
977 | 976 | |
|
|
978 | 977 | H = numpy.array(list(self.__all_heights)) |
|
|
979 | 978 | H.sort() |
|
|
980 | 979 | for key in self.data: |
|
|
981 | 980 | shape = self.shape(key)[:-1] + H.shape |
|
|
982 | 981 | for tm, obj in list(self.data[key].items()): |
|
|
983 | 982 | h = self.__heights[self.times.tolist().index(tm)] |
|
|
984 | 983 | if H.size == h.size: |
|
|
985 | 984 | continue |
|
|
986 | 985 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
987 | 986 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
988 | 987 | if len(shape) == 2: |
|
|
989 | 988 | dummy[:, index] = obj |
|
|
990 | 989 | else: |
|
|
991 | 990 | dummy[index] = obj |
|
|
992 | 991 | self.data[key][tm] = dummy |
|
|
993 | 992 | |
|
|
994 | 993 | self.__heights = [H for tm in self.times] |
|
|
995 | 994 | |
|
|
996 | 995 | def jsonify(self, tm, plot_name, plot_type, decimate=False): |
|
|
997 | 996 | ''' |
|
|
998 | 997 | Convert data to json |
|
|
999 | 998 | ''' |
|
|
1000 | 999 | |
|
|
1001 | 1000 | meta = {} |
|
|
1002 | 1001 | meta['xrange'] = [] |
|
|
1003 | 1002 | dy = int(len(self.yrange)/self.MAXNUMY) + 1 |
|
|
1004 | 1003 | tmp = self.data[tm][self.key] |
|
|
1005 | 1004 | shape = tmp.shape |
|
|
1006 | 1005 | if len(shape) == 2: |
|
|
1007 | 1006 | data = self.roundFloats(self.data[tm][self.key][::, ::dy].tolist()) |
|
|
1008 | 1007 | elif len(shape) == 3: |
|
|
1009 | 1008 | dx = int(self.data[tm][self.key].shape[1]/self.MAXNUMX) + 1 |
|
|
1010 | 1009 | data = self.roundFloats( |
|
|
1011 | 1010 | self.data[tm][self.key][::, ::dx, ::dy].tolist()) |
|
|
1012 | 1011 | meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
|
1013 | 1012 | else: |
|
|
1014 | 1013 | data = self.roundFloats(self.data[tm][self.key].tolist()) |
|
|
1015 | 1014 | |
|
|
1016 | 1015 | ret = { |
|
|
1017 | 1016 | 'plot': plot_name, |
|
|
1018 | 1017 | 'code': self.exp_code, |
|
|
1019 | 1018 | 'time': float(tm), |
|
|
1020 | 1019 | 'data': data, |
|
|
1021 | 1020 | } |
|
|
1022 | 1021 | meta['type'] = plot_type |
|
|
1023 | 1022 | meta['interval'] = float(self.interval) |
|
|
1024 | 1023 | meta['localtime'] = self.localtime |
|
|
1025 | 1024 | meta['yrange'] = self.roundFloats(self.yrange[::dy].tolist()) |
|
|
1026 | 1025 | meta.update(self.meta) |
|
|
1027 | 1026 | ret['metadata'] = meta |
|
|
1028 | 1027 | return json.dumps(ret) |
|
|
1029 | 1028 | |
|
|
1030 | 1029 | @property |
|
|
1031 | 1030 | def times(self): |
|
|
1032 | 1031 | ''' |
|
|
1033 | 1032 | Return the list of times of the current data |
|
|
1034 | 1033 | ''' |
|
|
1035 | 1034 | |
|
|
1036 | 1035 | ret = [t for t in self.data] |
|
|
1037 | 1036 | ret.sort() |
|
|
1038 | 1037 | return numpy.array(ret) |
|
|
1039 | 1038 | |
|
|
1040 | 1039 | @property |
|
|
1041 | 1040 | def min_time(self): |
|
|
1042 | 1041 | ''' |
|
|
1043 | 1042 | Return the minimun time value |
|
|
1044 | 1043 | ''' |
|
|
1045 | 1044 | |
|
|
1046 | 1045 | return self.times[0] |
|
|
1047 | 1046 | |
|
|
1048 | 1047 | @property |
|
|
1049 | 1048 | def max_time(self): |
|
|
1050 | 1049 | ''' |
|
|
1051 | 1050 | Return the maximun time value |
|
|
1052 | 1051 | ''' |
|
|
1053 | 1052 | |
|
|
1054 | 1053 | return self.times[-1] |
|
|
1055 | 1054 | |
|
|
1056 | 1055 | # @property |
|
|
1057 | 1056 | # def heights(self): |
|
|
1058 | 1057 | # ''' |
|
|
1059 | 1058 | # Return the list of heights of the current data |
|
|
1060 | 1059 | # ''' |
|
|
1061 | 1060 | |
|
|
1062 | 1061 | # return numpy.array(self.__heights[-1]) |
|
|
1063 | 1062 | |
|
|
1064 | 1063 | @staticmethod |
|
|
1065 | 1064 | def roundFloats(obj): |
|
|
1066 | 1065 | if isinstance(obj, list): |
|
|
1067 | 1066 | return list(map(PlotterData.roundFloats, obj)) |
|
|
1068 | 1067 | elif isinstance(obj, float): |
|
|
1069 | 1068 | return round(obj, 2) |
| @@ -1,519 +1,509 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | |
|
|
5 | 5 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
|
6 | 6 | from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot |
|
|
7 | 7 | from schainpy.utils import log |
|
|
8 | 8 | # libreria wradlib |
|
|
9 | 9 | import wradlib as wrl |
|
|
10 | 10 | |
|
|
11 | 11 | EARTH_RADIUS = 6.3710e3 |
|
|
12 | 12 | |
|
|
13 | 13 | |
|
|
14 | 14 | def ll2xy(lat1, lon1, lat2, lon2): |
|
|
15 | 15 | |
|
|
16 | 16 | p = 0.017453292519943295 |
|
|
17 | 17 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
|
18 | 18 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
|
19 | 19 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
|
20 | 20 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
|
21 | 21 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
|
22 | 22 | theta = -theta + numpy.pi/2 |
|
|
23 | 23 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
24 | 24 | |
|
|
25 | 25 | |
|
|
26 | 26 | def km2deg(km): |
|
|
27 | 27 | ''' |
|
|
28 | 28 | Convert distance in km to degrees |
|
|
29 | 29 | ''' |
|
|
30 | 30 | |
|
|
31 | 31 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
|
32 | 32 | |
|
|
33 | 33 | |
|
|
34 | 34 | |
|
|
35 | 35 | class SpectralMomentsPlot(SpectraPlot): |
|
|
36 | 36 | ''' |
|
|
37 | 37 | Plot for Spectral Moments |
|
|
38 | 38 | ''' |
|
|
39 | 39 | CODE = 'spc_moments' |
|
|
40 | 40 | # colormap = 'jet' |
|
|
41 | 41 | # plot_type = 'pcolor' |
|
|
42 | 42 | |
|
|
43 | 43 | class DobleGaussianPlot(SpectraPlot): |
|
|
44 | 44 | ''' |
|
|
45 | 45 | Plot for Double Gaussian Plot |
|
|
46 | 46 | ''' |
|
|
47 | 47 | CODE = 'gaussian_fit' |
|
|
48 | 48 | # colormap = 'jet' |
|
|
49 | 49 | # plot_type = 'pcolor' |
|
|
50 | 50 | |
|
|
51 | 51 | class DoubleGaussianSpectraCutPlot(SpectraCutPlot): |
|
|
52 | 52 | ''' |
|
|
53 | 53 | Plot SpectraCut with Double Gaussian Fit |
|
|
54 | 54 | ''' |
|
|
55 | 55 | CODE = 'cut_gaussian_fit' |
|
|
56 | 56 | |
|
|
57 | 57 | class SnrPlot(RTIPlot): |
|
|
58 | 58 | ''' |
|
|
59 | 59 | Plot for SNR Data |
|
|
60 | 60 | ''' |
|
|
61 | 61 | |
|
|
62 | 62 | CODE = 'snr' |
|
|
63 | 63 | colormap = 'jet' |
|
|
64 | 64 | |
|
|
65 | 65 | def update(self, dataOut): |
|
|
66 | 66 | |
|
|
67 | 67 | data = { |
|
|
68 | 68 | 'snr': 10*numpy.log10(dataOut.data_snr) |
|
|
69 | 69 | } |
|
|
70 | 70 | |
|
|
71 | 71 | return data, {} |
|
|
72 | 72 | |
|
|
73 | 73 | class DopplerPlot(RTIPlot): |
|
|
74 | 74 | ''' |
|
|
75 | 75 | Plot for DOPPLER Data (1st moment) |
|
|
76 | 76 | ''' |
|
|
77 | 77 | |
|
|
78 | 78 | CODE = 'dop' |
|
|
79 | 79 | colormap = 'jet' |
|
|
80 | 80 | |
|
|
81 | 81 | def update(self, dataOut): |
|
|
82 | 82 | |
|
|
83 | 83 | data = { |
|
|
84 | 84 | 'dop': 10*numpy.log10(dataOut.data_dop) |
|
|
85 | 85 | } |
|
|
86 | 86 | |
|
|
87 | 87 | return data, {} |
|
|
88 | 88 | |
|
|
89 | 89 | class PowerPlot(RTIPlot): |
|
|
90 | 90 | ''' |
|
|
91 | 91 | Plot for Power Data (0 moment) |
|
|
92 | 92 | ''' |
|
|
93 | 93 | |
|
|
94 | 94 | CODE = 'pow' |
|
|
95 | 95 | colormap = 'jet' |
|
|
96 | 96 | |
|
|
97 | 97 | def update(self, dataOut): |
|
|
98 | ||
|
|
99 | 98 | data = { |
|
|
100 | 99 | 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor) |
|
|
101 | 100 | } |
|
|
102 | ||
|
|
103 | 101 | return data, {} |
|
|
104 | 102 | |
|
|
105 | 103 | class SpectralWidthPlot(RTIPlot): |
|
|
106 | 104 | ''' |
|
|
107 | 105 | Plot for Spectral Width Data (2nd moment) |
|
|
108 | 106 | ''' |
|
|
109 | 107 | |
|
|
110 | 108 | CODE = 'width' |
|
|
111 | 109 | colormap = 'jet' |
|
|
112 | 110 | |
|
|
113 | 111 | def update(self, dataOut): |
|
|
114 | 112 | |
|
|
115 | 113 | data = { |
|
|
116 | 114 | 'width': dataOut.data_width |
|
|
117 | 115 | } |
|
|
118 | 116 | |
|
|
119 | 117 | return data, {} |
|
|
120 | 118 | |
|
|
121 | 119 | class SkyMapPlot(Plot): |
|
|
122 | 120 | ''' |
|
|
123 | 121 | Plot for meteors detection data |
|
|
124 | 122 | ''' |
|
|
125 | 123 | |
|
|
126 | 124 | CODE = 'param' |
|
|
127 | 125 | |
|
|
128 | 126 | def setup(self): |
|
|
129 | 127 | |
|
|
130 | 128 | self.ncols = 1 |
|
|
131 | 129 | self.nrows = 1 |
|
|
132 | 130 | self.width = 7.2 |
|
|
133 | 131 | self.height = 7.2 |
|
|
134 | 132 | self.nplots = 1 |
|
|
135 | 133 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
136 | 134 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
137 | 135 | self.polar = True |
|
|
138 | 136 | self.ymin = -180 |
|
|
139 | 137 | self.ymax = 180 |
|
|
140 | 138 | self.colorbar = False |
|
|
141 | 139 | |
|
|
142 | 140 | def plot(self): |
|
|
143 | 141 | |
|
|
144 | 142 | arrayParameters = numpy.concatenate(self.data['param']) |
|
|
145 | 143 | error = arrayParameters[:, -1] |
|
|
146 | 144 | indValid = numpy.where(error == 0)[0] |
|
|
147 | 145 | finalMeteor = arrayParameters[indValid, :] |
|
|
148 | 146 | finalAzimuth = finalMeteor[:, 3] |
|
|
149 | 147 | finalZenith = finalMeteor[:, 4] |
|
|
150 | 148 | |
|
|
151 | 149 | x = finalAzimuth * numpy.pi / 180 |
|
|
152 | 150 | y = finalZenith |
|
|
153 | 151 | |
|
|
154 | 152 | ax = self.axes[0] |
|
|
155 | 153 | |
|
|
156 | 154 | if ax.firsttime: |
|
|
157 | 155 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
158 | 156 | else: |
|
|
159 | 157 | ax.plot.set_data(x, y) |
|
|
160 | 158 | |
|
|
161 | 159 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
162 | 160 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
163 | 161 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
164 | 162 | dt2, |
|
|
165 | 163 | len(x)) |
|
|
166 | 164 | self.titles[0] = title |
|
|
167 | 165 | |
|
|
168 | 166 | |
|
|
169 | 167 | class GenericRTIPlot(Plot): |
|
|
170 | 168 | ''' |
|
|
171 | 169 | Plot for data_xxxx object |
|
|
172 | 170 | ''' |
|
|
173 | 171 | |
|
|
174 | 172 | CODE = 'param' |
|
|
175 | 173 | colormap = 'viridis' |
|
|
176 | 174 | plot_type = 'pcolorbuffer' |
|
|
177 | 175 | |
|
|
178 | 176 | def setup(self): |
|
|
179 | 177 | self.xaxis = 'time' |
|
|
180 | 178 | self.ncols = 1 |
|
|
181 | 179 | self.nrows = self.data.shape('param')[0] |
|
|
182 | 180 | self.nplots = self.nrows |
|
|
183 | 181 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95}) |
|
|
184 | 182 | |
|
|
185 | 183 | if not self.xlabel: |
|
|
186 | 184 | self.xlabel = 'Time' |
|
|
187 | 185 | |
|
|
188 | 186 | self.ylabel = 'Range [km]' |
|
|
189 | 187 | if not self.titles: |
|
|
190 | 188 | self.titles = ['Param {}'.format(x) for x in range(self.nrows)] |
|
|
191 | 189 | |
|
|
192 | 190 | def update(self, dataOut): |
|
|
193 | 191 | |
|
|
194 | 192 | data = { |
|
|
195 | 193 | 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0) |
|
|
196 | 194 | } |
|
|
197 | 195 | |
|
|
198 | 196 | meta = {} |
|
|
199 | 197 | |
|
|
200 | 198 | return data, meta |
|
|
201 | 199 | |
|
|
202 | 200 | def plot(self): |
|
|
203 | 201 | # self.data.normalize_heights() |
|
|
204 | 202 | self.x = self.data.times |
|
|
205 | 203 | self.y = self.data.yrange |
|
|
206 | 204 | self.z = self.data['param'] |
|
|
207 | ||
|
|
208 | 205 | self.z = 10*numpy.log10(self.z) |
|
|
209 | ||
|
|
210 | 206 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
211 | 207 | |
|
|
212 | 208 | if self.decimation is None: |
|
|
213 | 209 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
214 | 210 | else: |
|
|
215 | 211 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
216 | 212 | |
|
|
217 | 213 | for n, ax in enumerate(self.axes): |
|
|
218 | 214 | |
|
|
219 | 215 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
220 | 216 | self.z[n]) |
|
|
221 | 217 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
222 | 218 | self.z[n]) |
|
|
223 | 219 | |
|
|
224 | 220 | if ax.firsttime: |
|
|
225 | 221 | if self.zlimits is not None: |
|
|
226 | 222 | self.zmin, self.zmax = self.zlimits[n] |
|
|
227 | 223 | |
|
|
228 | 224 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
229 | 225 | vmin=self.zmin, |
|
|
230 | 226 | vmax=self.zmax, |
|
|
231 | 227 | cmap=self.cmaps[n] |
|
|
232 | 228 | ) |
|
|
233 | 229 | else: |
|
|
234 | 230 | if self.zlimits is not None: |
|
|
235 | 231 | self.zmin, self.zmax = self.zlimits[n] |
|
|
236 | 232 | ax.collections.remove(ax.collections[0]) |
|
|
237 | 233 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
238 | 234 | vmin=self.zmin, |
|
|
239 | 235 | vmax=self.zmax, |
|
|
240 | 236 | cmap=self.cmaps[n] |
|
|
241 | 237 | ) |
|
|
242 | 238 | |
|
|
243 | 239 | |
|
|
244 | 240 | class PolarMapPlot(Plot): |
|
|
245 | 241 | ''' |
|
|
246 | 242 | Plot for weather radar |
|
|
247 | 243 | ''' |
|
|
248 | 244 | |
|
|
249 | 245 | CODE = 'param' |
|
|
250 | 246 | colormap = 'seismic' |
|
|
251 | 247 | |
|
|
252 | 248 | def setup(self): |
|
|
253 | 249 | self.ncols = 1 |
|
|
254 | 250 | self.nrows = 1 |
|
|
255 | 251 | self.width = 9 |
|
|
256 | 252 | self.height = 8 |
|
|
257 | 253 | self.mode = self.data.meta['mode'] |
|
|
258 | 254 | if self.channels is not None: |
|
|
259 | 255 | self.nplots = len(self.channels) |
|
|
260 | 256 | self.nrows = len(self.channels) |
|
|
261 | 257 | else: |
|
|
262 | 258 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
263 | 259 | self.nrows = self.nplots |
|
|
264 | 260 | self.channels = list(range(self.nplots)) |
|
|
265 | 261 | if self.mode == 'E': |
|
|
266 | 262 | self.xlabel = 'Longitude' |
|
|
267 | 263 | self.ylabel = 'Latitude' |
|
|
268 | 264 | else: |
|
|
269 | 265 | self.xlabel = 'Range (km)' |
|
|
270 | 266 | self.ylabel = 'Height (km)' |
|
|
271 | 267 | self.bgcolor = 'white' |
|
|
272 | 268 | self.cb_labels = self.data.meta['units'] |
|
|
273 | 269 | self.lat = self.data.meta['latitude'] |
|
|
274 | 270 | self.lon = self.data.meta['longitude'] |
|
|
275 | 271 | self.xmin, self.xmax = float( |
|
|
276 | 272 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
|
|
277 | 273 | self.ymin, self.ymax = float( |
|
|
278 | 274 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
|
|
279 | 275 | # self.polar = True |
|
|
280 | 276 | |
|
|
281 | 277 | def plot(self): |
|
|
282 | 278 | |
|
|
283 | 279 | for n, ax in enumerate(self.axes): |
|
|
284 | 280 | data = self.data['param'][self.channels[n]] |
|
|
285 | 281 | |
|
|
286 | 282 | zeniths = numpy.linspace( |
|
|
287 | 283 | 0, self.data.meta['max_range'], data.shape[1]) |
|
|
288 | 284 | if self.mode == 'E': |
|
|
289 | 285 | azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2 |
|
|
290 | 286 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
291 | 287 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
|
|
292 | 288 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
|
|
293 | 289 | x = km2deg(x) + self.lon |
|
|
294 | 290 | y = km2deg(y) + self.lat |
|
|
295 | 291 | else: |
|
|
296 | 292 | azimuths = numpy.radians(self.data.yrange) |
|
|
297 | 293 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
298 | 294 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
299 | 295 | self.y = zeniths |
|
|
300 | 296 | |
|
|
301 | 297 | if ax.firsttime: |
|
|
302 | 298 | if self.zlimits is not None: |
|
|
303 | 299 | self.zmin, self.zmax = self.zlimits[n] |
|
|
304 | 300 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
305 | 301 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
306 | 302 | vmin=self.zmin, |
|
|
307 | 303 | vmax=self.zmax, |
|
|
308 | 304 | cmap=self.cmaps[n]) |
|
|
309 | 305 | else: |
|
|
310 | 306 | if self.zlimits is not None: |
|
|
311 | 307 | self.zmin, self.zmax = self.zlimits[n] |
|
|
312 | 308 | ax.collections.remove(ax.collections[0]) |
|
|
313 | 309 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
314 | 310 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
315 | 311 | vmin=self.zmin, |
|
|
316 | 312 | vmax=self.zmax, |
|
|
317 | 313 | cmap=self.cmaps[n]) |
|
|
318 | 314 | |
|
|
319 | 315 | if self.mode == 'A': |
|
|
320 | 316 | continue |
|
|
321 | 317 | |
|
|
322 | 318 | # plot district names |
|
|
323 | 319 | f = open('/data/workspace/schain_scripts/distrito.csv') |
|
|
324 | 320 | for line in f: |
|
|
325 | 321 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
|
|
326 | 322 | lat = float(lat) |
|
|
327 | 323 | lon = float(lon) |
|
|
328 | 324 | # ax.plot(lon, lat, '.b', ms=2) |
|
|
329 | 325 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
|
|
330 | 326 | va='bottom', size='8', color='black') |
|
|
331 | 327 | |
|
|
332 | 328 | # plot limites |
|
|
333 | 329 | limites = [] |
|
|
334 | 330 | tmp = [] |
|
|
335 | 331 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
|
|
336 | 332 | if '#' in line: |
|
|
337 | 333 | if tmp: |
|
|
338 | 334 | limites.append(tmp) |
|
|
339 | 335 | tmp = [] |
|
|
340 | 336 | continue |
|
|
341 | 337 | values = line.strip().split(',') |
|
|
342 | 338 | tmp.append((float(values[0]), float(values[1]))) |
|
|
343 | 339 | for points in limites: |
|
|
344 | 340 | ax.add_patch( |
|
|
345 | 341 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
|
|
346 | 342 | |
|
|
347 | 343 | # plot Cuencas |
|
|
348 | 344 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
|
|
349 | 345 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
|
|
350 | 346 | values = [line.strip().split(',') for line in f] |
|
|
351 | 347 | points = [(float(s[0]), float(s[1])) for s in values] |
|
|
352 | 348 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
|
|
353 | 349 | |
|
|
354 | 350 | # plot grid |
|
|
355 | 351 | for r in (15, 30, 45, 60): |
|
|
356 | 352 | ax.add_artist(plt.Circle((self.lon, self.lat), |
|
|
357 | 353 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
|
|
358 | 354 | ax.text( |
|
|
359 | 355 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
|
|
360 | 356 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
|
|
361 | 357 | '{}km'.format(r), |
|
|
362 | 358 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
|
|
363 | 359 | |
|
|
364 | 360 | if self.mode == 'E': |
|
|
365 | 361 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
|
366 | 362 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
|
367 | 363 | else: |
|
|
368 | 364 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
|
|
369 | 365 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
|
|
370 | 366 | |
|
|
371 | 367 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
|
|
372 | 368 | self.titles = ['{} {}'.format( |
|
|
373 | 369 | self.data.parameters[x], title) for x in self.channels] |
|
|
374 | 370 | |
|
|
375 | 371 | class WeatherPlot(Plot): |
|
|
376 | 372 | CODE = 'weather' |
|
|
377 | 373 | plot_name = 'weather' |
|
|
378 | 374 | plot_type = 'ppistyle' |
|
|
379 | 375 | buffering = False |
|
|
380 | 376 | |
|
|
381 | 377 | def setup(self): |
|
|
382 | 378 | self.ncols = 1 |
|
|
383 | 379 | self.nrows = 1 |
|
|
384 | 380 | self.nplots= 1 |
|
|
385 | 381 | self.ylabel= 'Range [Km]' |
|
|
386 | 382 | self.titles= ['Weather'] |
|
|
387 | 383 | self.colorbar=False |
|
|
388 | 384 | self.width =8 |
|
|
389 | 385 | self.height =8 |
|
|
390 | 386 | self.ini =0 |
|
|
391 | 387 | self.len_azi =0 |
|
|
392 | 388 | self.buffer_ini = None |
|
|
393 | 389 | self.buffer_azi = None |
|
|
394 | 390 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
|
|
395 | 391 | self.flag =0 |
|
|
396 | 392 | self.indicador= 0 |
|
|
397 | 393 | |
|
|
398 | 394 | def update(self, dataOut): |
|
|
399 | 395 | |
|
|
400 | 396 | data = {} |
|
|
401 | 397 | meta = {} |
|
|
402 | print("aprox",dataOut.data_360[0]) | |
|
|
403 | 398 | data['weather'] = 10*numpy.log10(dataOut.data_360[0]/(250.0)) |
|
|
404 | #print(data['weather']) | |
|
|
405 | 399 | data['azi'] = dataOut.data_azi |
|
|
406 | print("UPDATE",data['azi']) | |
|
|
407 | 400 | return data, meta |
|
|
408 | 401 | |
|
|
409 | 402 | def const_ploteo(self,data_weather,data_azi,step,res): |
|
|
410 | #print("data_weather",data_weather) | |
|
|
411 | print("data_azi",data_azi) | |
|
|
412 | print("step",step) | |
|
|
413 | 403 | if self.ini==0: |
|
|
414 | 404 | #------- AZIMUTH |
|
|
415 | 405 | n = (360/res)-len(data_azi) |
|
|
416 | 406 | start = data_azi[-1] + res |
|
|
417 | 407 | end = data_azi[0] - res |
|
|
418 | 408 | if start>end: |
|
|
419 | 409 | end = end + 360 |
|
|
420 | 410 | azi_vacia = numpy.linspace(start,end,int(n)) |
|
|
421 | 411 | azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia) |
|
|
422 | 412 | data_azi = numpy.hstack((data_azi,azi_vacia)) |
|
|
423 | 413 | # RADAR |
|
|
424 | 414 | val_mean = numpy.mean(data_weather[:,0]) |
|
|
425 | 415 | data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean |
|
|
426 | 416 | data_weather = numpy.vstack((data_weather,data_weather_cmp)) |
|
|
427 | 417 | else: |
|
|
428 | 418 | # azimuth |
|
|
429 | 419 | flag=0 |
|
|
430 | 420 | start_azi = self.res_azi[0] |
|
|
431 | 421 | start = data_azi[0] |
|
|
432 | 422 | end = data_azi[-1] |
|
|
433 | 423 | print("start",start) |
|
|
434 | 424 | print("end",end) |
|
|
435 | 425 | if start< start_azi: |
|
|
436 | 426 | start = start +360 |
|
|
437 | 427 | if end <start_azi: |
|
|
438 | 428 | end = end +360 |
|
|
439 | 429 | |
|
|
440 | 430 | print("start",start) |
|
|
441 | 431 | print("end",end) |
|
|
442 | 432 | #### AQUI SERA LA MAGIA |
|
|
443 | 433 | pos_ini = int((start-start_azi)/res) |
|
|
444 | 434 | len_azi = len(data_azi) |
|
|
445 | 435 | if (360-pos_ini)<len_azi: |
|
|
446 | 436 | if pos_ini+1==360: |
|
|
447 | 437 | pos_ini=0 |
|
|
448 | 438 | else: |
|
|
449 | 439 | flag=1 |
|
|
450 | 440 | dif= 360-pos_ini |
|
|
451 | 441 | comp= len_azi-dif |
|
|
452 | 442 | |
|
|
453 | 443 | print(pos_ini) |
|
|
454 | 444 | print(len_azi) |
|
|
455 | 445 | print("shape",self.res_azi.shape) |
|
|
456 | 446 | if flag==0: |
|
|
457 | 447 | # AZIMUTH |
|
|
458 | 448 | self.res_azi[pos_ini:pos_ini+len_azi] = data_azi |
|
|
459 | 449 | # RADAR |
|
|
460 | 450 | self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather |
|
|
461 | 451 | else: |
|
|
462 | 452 | # AZIMUTH |
|
|
463 | 453 | self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif] |
|
|
464 | 454 | self.res_azi[0:comp] = data_azi[dif:] |
|
|
465 | 455 | # RADAR |
|
|
466 | 456 | self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:] |
|
|
467 | 457 | self.res_weather[0:comp,:] = data_weather[dif:,:] |
|
|
468 | 458 | flag=0 |
|
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469 | 459 | data_azi = self.res_azi |
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470 | 460 | data_weather = self.res_weather |
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471 | 461 | |
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472 | 462 | return data_weather,data_azi |
|
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473 | 463 | |
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474 | 464 | def plot(self): |
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475 | 465 | print("--------------------------------------",self.ini,"-----------------------------------") |
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476 | 466 | #numpy.set_printoptions(suppress=True) |
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477 | 467 | #print(self.data.times) |
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478 | 468 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]) |
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479 | 469 | data = self.data[-1] |
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480 | 470 | # ALTURA altura_tmp_h |
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481 | 471 | altura_h = (data['weather'].shape[1])/10.0 |
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482 | 472 | stoprange = float(altura_h*1.5)#stoprange = float(33*1.5) por ahora 400 |
|
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483 | 473 | rangestep = float(0.15) |
|
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484 | 474 | r = numpy.arange(0, stoprange, rangestep) |
|
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485 | 475 | self.y = 2*r |
|
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486 | 476 | # RADAR |
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487 | 477 | #data_weather = data['weather'] |
|
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488 | 478 | # PEDESTAL |
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489 | 479 | #data_azi = data['azi'] |
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490 | 480 | res = 1 |
|
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491 | 481 | # STEP |
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492 | 482 | step = (360/(res*data['weather'].shape[0])) |
|
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493 | 483 | #print("shape wr_data", wr_data.shape) |
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494 | 484 | #print("shape wr_azi",wr_azi.shape) |
|
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495 | 485 | #print("step",step) |
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496 | 486 | print("Time---->",self.data.times[-1],thisDatetime) |
|
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497 | 487 | #print("alturas", len(self.y)) |
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498 | 488 | self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'],data_azi=data['azi'],step=step,res=res) |
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499 | 489 | #numpy.set_printoptions(suppress=True) |
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500 | 490 | #print("resultado",self.res_azi) |
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501 | 491 | ########################################################## |
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502 | 492 | ################# PLOTEO ################### |
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503 | 493 | ########################################################## |
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504 | 494 | |
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505 | 495 | for i,ax in enumerate(self.axes): |
|
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506 | 496 | if ax.firsttime: |
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507 | 497 | plt.clf() |
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508 | 498 | cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=1, vmax=60) |
|
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509 | 499 | else: |
|
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510 | 500 | plt.clf() |
|
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511 | 501 | cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=0, vmax=60) |
|
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512 | 502 | caax = cgax.parasites[0] |
|
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513 | 503 | paax = cgax.parasites[1] |
|
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514 | 504 | cbar = plt.gcf().colorbar(pm, pad=0.075) |
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515 | 505 | caax.set_xlabel('x_range [km]') |
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516 | 506 | caax.set_ylabel('y_range [km]') |
|
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517 | 507 | plt.text(1.0, 1.05, 'azimuth '+str(thisDatetime)+"step"+str(self.ini), transform=caax.transAxes, va='bottom',ha='right') |
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518 | 508 | |
|
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519 | 509 | self.ini= self.ini+1 |
| @@ -1,745 +1,743 | |||
|
|
1 | 1 | # Copyright (c) 2012-2021 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
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5 | 5 | """Classes to plot Spectra data |
|
|
6 | 6 | |
|
|
7 | 7 | """ |
|
|
8 | 8 | |
|
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9 | 9 | import os |
|
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10 | 10 | import numpy |
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11 | 11 | |
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12 | 12 | from schainpy.model.graphics.jroplot_base import Plot, plt, log |
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13 | 13 | |
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14 | 14 | |
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15 | 15 | class SpectraPlot(Plot): |
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16 | 16 | ''' |
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17 | 17 | Plot for Spectra data |
|
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18 | 18 | ''' |
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19 | 19 | |
|
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20 | 20 | CODE = 'spc' |
|
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21 | 21 | colormap = 'jet' |
|
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22 | 22 | plot_type = 'pcolor' |
|
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23 | 23 | buffering = False |
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24 | 24 | |
|
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25 | 25 | def setup(self): |
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26 | 26 | self.nplots = len(self.data.channels) |
|
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27 | 27 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
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28 | 28 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
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29 | 29 | self.height = 2.6 * self.nrows |
|
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30 | 30 | self.cb_label = 'dB' |
|
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31 | 31 | if self.showprofile: |
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32 | 32 | self.width = 4 * self.ncols |
|
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33 | 33 | else: |
|
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34 | 34 | self.width = 3.5 * self.ncols |
|
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35 | 35 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
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36 | 36 | self.ylabel = 'Range [km]' |
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37 | 37 | |
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38 | 38 | def update(self, dataOut): |
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39 | 39 | |
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40 | 40 | data = {} |
|
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41 | 41 | meta = {} |
|
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42 | 42 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
|
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43 | 43 | data['spc'] = spc |
|
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44 | 44 | data['rti'] = dataOut.getPower() |
|
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45 | 45 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
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46 | 46 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
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47 | 47 | |
|
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48 | 48 | if self.CODE == 'spc_moments': |
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49 | 49 | data['moments'] = dataOut.moments |
|
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50 | 50 | # data['spc'] = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) |
|
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51 | 51 | if self.CODE == 'gaussian_fit': |
|
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52 | 52 | # data['moments'] = dataOut.moments |
|
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53 | 53 | data['gaussfit'] = dataOut.DGauFitParams |
|
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54 | 54 | # data['spc'] = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) |
|
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55 | 55 | |
|
|
56 | 56 | return data, meta |
|
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57 | 57 | |
|
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58 | 58 | def plot(self): |
|
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59 | 59 | if self.xaxis == "frequency": |
|
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60 | 60 | x = self.data.xrange[0] |
|
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61 | 61 | self.xlabel = "Frequency (kHz)" |
|
|
62 | 62 | elif self.xaxis == "time": |
|
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63 | 63 | x = self.data.xrange[1] |
|
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64 | 64 | self.xlabel = "Time (ms)" |
|
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65 | 65 | else: |
|
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66 | 66 | x = self.data.xrange[2] |
|
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67 | 67 | self.xlabel = "Velocity (m/s)" |
|
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68 | 68 | |
|
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69 | 69 | if (self.CODE == 'spc_moments') | (self.CODE == 'gaussian_fit'): |
|
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70 | 70 | x = self.data.xrange[2] |
|
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71 | 71 | self.xlabel = "Velocity (m/s)" |
|
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72 | 72 | |
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|
73 | 73 | self.titles = [] |
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74 | 74 | |
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75 | 75 | y = self.data.yrange |
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76 | 76 | self.y = y |
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77 | 77 | |
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78 | 78 | data = self.data[-1] |
|
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79 | 79 | z = data['spc'] |
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80 | 80 | |
|
|
81 | 81 | for n, ax in enumerate(self.axes): |
|
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82 | 82 | noise = data['noise'][n] |
|
|
83 | 83 | if self.CODE == 'spc_moments': |
|
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84 | 84 | mean = data['moments'][n, 1] |
|
|
85 | 85 | if self.CODE == 'gaussian_fit': |
|
|
86 | 86 | # mean = data['moments'][n, 1] |
|
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87 | 87 | gau0 = data['gaussfit'][n][2,:,0] |
|
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88 | 88 | gau1 = data['gaussfit'][n][2,:,1] |
|
|
89 | 89 | if ax.firsttime: |
|
|
90 | 90 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
91 | 91 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
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92 | 92 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
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93 | 93 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
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94 | 94 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
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95 | 95 | vmin=self.zmin, |
|
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96 | 96 | vmax=self.zmax, |
|
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97 | 97 | cmap=plt.get_cmap(self.colormap) |
|
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98 | 98 | ) |
|
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99 | 99 | |
|
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100 | 100 | if self.showprofile: |
|
|
101 | 101 | ax.plt_profile = self.pf_axes[n].plot( |
|
|
102 | 102 | data['rti'][n], y)[0] |
|
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103 | 103 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
|
|
104 | 104 | color="k", linestyle="dashed", lw=1)[0] |
|
|
105 | 105 | if self.CODE == 'spc_moments': |
|
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106 | 106 | ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] |
|
|
107 | 107 | if self.CODE == 'gaussian_fit': |
|
|
108 | 108 | # ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] |
|
|
109 | 109 | ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0] |
|
|
110 | 110 | ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0] |
|
|
111 | 111 | else: |
|
|
112 | 112 | ax.plt.set_array(z[n].T.ravel()) |
|
|
113 | 113 | if self.showprofile: |
|
|
114 | 114 | ax.plt_profile.set_data(data['rti'][n], y) |
|
|
115 | 115 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
|
|
116 | 116 | if self.CODE == 'spc_moments': |
|
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117 | 117 | ax.plt_mean.set_data(mean, y) |
|
|
118 | 118 | if self.CODE == 'gaussian_fit': |
|
|
119 | 119 | # ax.plt_mean.set_data(mean, y) |
|
|
120 | 120 | ax.plt_gau0.set_data(gau0, y) |
|
|
121 | 121 | ax.plt_gau1.set_data(gau1, y) |
|
|
122 | 122 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
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123 | 123 | |
|
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124 | 124 | |
|
|
125 | 125 | class CrossSpectraPlot(Plot): |
|
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126 | 126 | |
|
|
127 | 127 | CODE = 'cspc' |
|
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128 | 128 | colormap = 'jet' |
|
|
129 | 129 | plot_type = 'pcolor' |
|
|
130 | 130 | zmin_coh = None |
|
|
131 | 131 | zmax_coh = None |
|
|
132 | 132 | zmin_phase = None |
|
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133 | 133 | zmax_phase = None |
|
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134 | 134 | |
|
|
135 | 135 | def setup(self): |
|
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136 | 136 | |
|
|
137 | 137 | self.ncols = 4 |
|
|
138 | 138 | self.nplots = len(self.data.pairs) * 2 |
|
|
139 | 139 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
140 | 140 | self.width = 3.1 * self.ncols |
|
|
141 | 141 | self.height = 2.6 * self.nrows |
|
|
142 | 142 | self.ylabel = 'Range [km]' |
|
|
143 | 143 | self.showprofile = False |
|
|
144 | 144 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
|
|
145 | 145 | |
|
|
146 | 146 | def update(self, dataOut): |
|
|
147 | 147 | |
|
|
148 | 148 | data = {} |
|
|
149 | 149 | meta = {} |
|
|
150 | 150 | |
|
|
151 | 151 | spc = dataOut.data_spc |
|
|
152 | 152 | cspc = dataOut.data_cspc |
|
|
153 | 153 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
154 | 154 | meta['pairs'] = dataOut.pairsList |
|
|
155 | 155 | |
|
|
156 | 156 | tmp = [] |
|
|
157 | 157 | |
|
|
158 | 158 | for n, pair in enumerate(meta['pairs']): |
|
|
159 | 159 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
|
|
160 | 160 | coh = numpy.abs(out) |
|
|
161 | 161 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
|
|
162 | 162 | tmp.append(coh) |
|
|
163 | 163 | tmp.append(phase) |
|
|
164 | 164 | |
|
|
165 | 165 | data['cspc'] = numpy.array(tmp) |
|
|
166 | 166 | |
|
|
167 | 167 | return data, meta |
|
|
168 | 168 | |
|
|
169 | 169 | def plot(self): |
|
|
170 | 170 | |
|
|
171 | 171 | if self.xaxis == "frequency": |
|
|
172 | 172 | x = self.data.xrange[0] |
|
|
173 | 173 | self.xlabel = "Frequency (kHz)" |
|
|
174 | 174 | elif self.xaxis == "time": |
|
|
175 | 175 | x = self.data.xrange[1] |
|
|
176 | 176 | self.xlabel = "Time (ms)" |
|
|
177 | 177 | else: |
|
|
178 | 178 | x = self.data.xrange[2] |
|
|
179 | 179 | self.xlabel = "Velocity (m/s)" |
|
|
180 | 180 | |
|
|
181 | 181 | self.titles = [] |
|
|
182 | 182 | |
|
|
183 | 183 | y = self.data.yrange |
|
|
184 | 184 | self.y = y |
|
|
185 | 185 | |
|
|
186 | 186 | data = self.data[-1] |
|
|
187 | 187 | cspc = data['cspc'] |
|
|
188 | 188 | |
|
|
189 | 189 | for n in range(len(self.data.pairs)): |
|
|
190 | 190 | pair = self.data.pairs[n] |
|
|
191 | 191 | coh = cspc[n*2] |
|
|
192 | 192 | phase = cspc[n*2+1] |
|
|
193 | 193 | ax = self.axes[2 * n] |
|
|
194 | 194 | if ax.firsttime: |
|
|
195 | 195 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
196 | 196 | vmin=0, |
|
|
197 | 197 | vmax=1, |
|
|
198 | 198 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
199 | 199 | ) |
|
|
200 | 200 | else: |
|
|
201 | 201 | ax.plt.set_array(coh.T.ravel()) |
|
|
202 | 202 | self.titles.append( |
|
|
203 | 203 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
204 | 204 | |
|
|
205 | 205 | ax = self.axes[2 * n + 1] |
|
|
206 | 206 | if ax.firsttime: |
|
|
207 | 207 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
208 | 208 | vmin=-180, |
|
|
209 | 209 | vmax=180, |
|
|
210 | 210 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
211 | 211 | ) |
|
|
212 | 212 | else: |
|
|
213 | 213 | ax.plt.set_array(phase.T.ravel()) |
|
|
214 | 214 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
215 | 215 | |
|
|
216 | 216 | |
|
|
217 | 217 | class RTIPlot(Plot): |
|
|
218 | 218 | ''' |
|
|
219 | 219 | Plot for RTI data |
|
|
220 | 220 | ''' |
|
|
221 | 221 | |
|
|
222 | 222 | CODE = 'rti' |
|
|
223 | 223 | colormap = 'jet' |
|
|
224 | 224 | plot_type = 'pcolorbuffer' |
|
|
225 | 225 | |
|
|
226 | 226 | def setup(self): |
|
|
227 | 227 | self.xaxis = 'time' |
|
|
228 | 228 | self.ncols = 1 |
|
|
229 | print("ch",self.data.channels) | |
|
|
230 | 229 | self.nrows = len(self.data.channels) |
|
|
231 | 230 | self.nplots = len(self.data.channels) |
|
|
232 | 231 | self.ylabel = 'Range [km]' |
|
|
233 | 232 | self.xlabel = 'Time' |
|
|
234 | 233 | self.cb_label = 'dB' |
|
|
235 | 234 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95}) |
|
|
236 | 235 | self.titles = ['{} Channel {}'.format( |
|
|
237 | 236 | self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
238 | 237 | |
|
|
239 | 238 | def update(self, dataOut): |
|
|
240 | 239 | |
|
|
241 | 240 | data = {} |
|
|
242 | 241 | meta = {} |
|
|
243 | 242 | data['rti'] = dataOut.getPower() |
|
|
244 | 243 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
245 | 244 | |
|
|
246 | 245 | return data, meta |
|
|
247 | 246 | |
|
|
248 | 247 | def plot(self): |
|
|
249 | 248 | self.x = self.data.times |
|
|
250 | 249 | self.y = self.data.yrange |
|
|
251 | 250 | self.z = self.data[self.CODE] |
|
|
252 | 251 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
253 | 252 | |
|
|
254 | 253 | if self.decimation is None: |
|
|
255 | 254 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
256 | 255 | else: |
|
|
257 | 256 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
258 | 257 | |
|
|
259 | 258 | for n, ax in enumerate(self.axes): |
|
|
260 | 259 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
261 | 260 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
262 | 261 | data = self.data[-1] |
|
|
263 | 262 | if ax.firsttime: |
|
|
264 | 263 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
265 | 264 | vmin=self.zmin, |
|
|
266 | 265 | vmax=self.zmax, |
|
|
267 | 266 | cmap=plt.get_cmap(self.colormap) |
|
|
268 | 267 | ) |
|
|
269 | 268 | if self.showprofile: |
|
|
270 | print("test-------------------------------------1") | |
|
|
271 | 269 | ax.plot_profile = self.pf_axes[n].plot( |
|
|
272 | 270 | data['rti'][n], self.y)[0] |
|
|
273 | 271 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, |
|
|
274 | 272 | color="k", linestyle="dashed", lw=1)[0] |
|
|
275 | 273 | else: |
|
|
276 | 274 | ax.collections.remove(ax.collections[0]) |
|
|
277 | 275 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
278 | 276 | vmin=self.zmin, |
|
|
279 | 277 | vmax=self.zmax, |
|
|
280 | 278 | cmap=plt.get_cmap(self.colormap) |
|
|
281 | 279 | ) |
|
|
282 | 280 | if self.showprofile: |
|
|
283 | 281 | ax.plot_profile.set_data(data['rti'][n], self.y) |
|
|
284 | 282 | ax.plot_noise.set_data(numpy.repeat( |
|
|
285 | 283 | data['noise'][n], len(self.y)), self.y) |
|
|
286 | 284 | |
|
|
287 | 285 | |
|
|
288 | 286 | class CoherencePlot(RTIPlot): |
|
|
289 | 287 | ''' |
|
|
290 | 288 | Plot for Coherence data |
|
|
291 | 289 | ''' |
|
|
292 | 290 | |
|
|
293 | 291 | CODE = 'coh' |
|
|
294 | 292 | |
|
|
295 | 293 | def setup(self): |
|
|
296 | 294 | self.xaxis = 'time' |
|
|
297 | 295 | self.ncols = 1 |
|
|
298 | 296 | self.nrows = len(self.data.pairs) |
|
|
299 | 297 | self.nplots = len(self.data.pairs) |
|
|
300 | 298 | self.ylabel = 'Range [km]' |
|
|
301 | 299 | self.xlabel = 'Time' |
|
|
302 | 300 | self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1,'right':0.95}) |
|
|
303 | 301 | if self.CODE == 'coh': |
|
|
304 | 302 | self.cb_label = '' |
|
|
305 | 303 | self.titles = [ |
|
|
306 | 304 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
307 | 305 | else: |
|
|
308 | 306 | self.cb_label = 'Degrees' |
|
|
309 | 307 | self.titles = [ |
|
|
310 | 308 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
311 | 309 | |
|
|
312 | 310 | def update(self, dataOut): |
|
|
313 | 311 | |
|
|
314 | 312 | data = {} |
|
|
315 | 313 | meta = {} |
|
|
316 | 314 | data['coh'] = dataOut.getCoherence() |
|
|
317 | 315 | meta['pairs'] = dataOut.pairsList |
|
|
318 | 316 | |
|
|
319 | 317 | return data, meta |
|
|
320 | 318 | |
|
|
321 | 319 | class PhasePlot(CoherencePlot): |
|
|
322 | 320 | ''' |
|
|
323 | 321 | Plot for Phase map data |
|
|
324 | 322 | ''' |
|
|
325 | 323 | |
|
|
326 | 324 | CODE = 'phase' |
|
|
327 | 325 | colormap = 'seismic' |
|
|
328 | 326 | |
|
|
329 | 327 | def update(self, dataOut): |
|
|
330 | 328 | |
|
|
331 | 329 | data = {} |
|
|
332 | 330 | meta = {} |
|
|
333 | 331 | data['phase'] = dataOut.getCoherence(phase=True) |
|
|
334 | 332 | meta['pairs'] = dataOut.pairsList |
|
|
335 | 333 | |
|
|
336 | 334 | return data, meta |
|
|
337 | 335 | |
|
|
338 | 336 | class NoisePlot(Plot): |
|
|
339 | 337 | ''' |
|
|
340 | 338 | Plot for noise |
|
|
341 | 339 | ''' |
|
|
342 | 340 | |
|
|
343 | 341 | CODE = 'noise' |
|
|
344 | 342 | plot_type = 'scatterbuffer' |
|
|
345 | 343 | |
|
|
346 | 344 | def setup(self): |
|
|
347 | 345 | self.xaxis = 'time' |
|
|
348 | 346 | self.ncols = 1 |
|
|
349 | 347 | self.nrows = 1 |
|
|
350 | 348 | self.nplots = 1 |
|
|
351 | 349 | self.ylabel = 'Intensity [dB]' |
|
|
352 | 350 | self.xlabel = 'Time' |
|
|
353 | 351 | self.titles = ['Noise'] |
|
|
354 | 352 | self.colorbar = False |
|
|
355 | 353 | self.plots_adjust.update({'right': 0.85 }) |
|
|
356 | 354 | |
|
|
357 | 355 | def update(self, dataOut): |
|
|
358 | 356 | |
|
|
359 | 357 | data = {} |
|
|
360 | 358 | meta = {} |
|
|
361 | 359 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1) |
|
|
362 | 360 | meta['yrange'] = numpy.array([]) |
|
|
363 | 361 | |
|
|
364 | 362 | return data, meta |
|
|
365 | 363 | |
|
|
366 | 364 | def plot(self): |
|
|
367 | 365 | |
|
|
368 | 366 | x = self.data.times |
|
|
369 | 367 | xmin = self.data.min_time |
|
|
370 | 368 | xmax = xmin + self.xrange * 60 * 60 |
|
|
371 | 369 | Y = self.data['noise'] |
|
|
372 | 370 | |
|
|
373 | 371 | if self.axes[0].firsttime: |
|
|
374 | 372 | self.ymin = numpy.nanmin(Y) - 5 |
|
|
375 | 373 | self.ymax = numpy.nanmax(Y) + 5 |
|
|
376 | 374 | for ch in self.data.channels: |
|
|
377 | 375 | y = Y[ch] |
|
|
378 | 376 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
379 | 377 | plt.legend(bbox_to_anchor=(1.18, 1.0)) |
|
|
380 | 378 | else: |
|
|
381 | 379 | for ch in self.data.channels: |
|
|
382 | 380 | y = Y[ch] |
|
|
383 | 381 | self.axes[0].lines[ch].set_data(x, y) |
|
|
384 | 382 | |
|
|
385 | 383 | |
|
|
386 | 384 | class PowerProfilePlot(Plot): |
|
|
387 | 385 | |
|
|
388 | 386 | CODE = 'pow_profile' |
|
|
389 | 387 | plot_type = 'scatter' |
|
|
390 | 388 | |
|
|
391 | 389 | def setup(self): |
|
|
392 | 390 | |
|
|
393 | 391 | self.ncols = 1 |
|
|
394 | 392 | self.nrows = 1 |
|
|
395 | 393 | self.nplots = 1 |
|
|
396 | 394 | self.height = 4 |
|
|
397 | 395 | self.width = 3 |
|
|
398 | 396 | self.ylabel = 'Range [km]' |
|
|
399 | 397 | self.xlabel = 'Intensity [dB]' |
|
|
400 | 398 | self.titles = ['Power Profile'] |
|
|
401 | 399 | self.colorbar = False |
|
|
402 | 400 | |
|
|
403 | 401 | def update(self, dataOut): |
|
|
404 | 402 | |
|
|
405 | 403 | data = {} |
|
|
406 | 404 | meta = {} |
|
|
407 | 405 | data[self.CODE] = dataOut.getPower() |
|
|
408 | 406 | |
|
|
409 | 407 | return data, meta |
|
|
410 | 408 | |
|
|
411 | 409 | def plot(self): |
|
|
412 | 410 | |
|
|
413 | 411 | y = self.data.yrange |
|
|
414 | 412 | self.y = y |
|
|
415 | 413 | |
|
|
416 | 414 | x = self.data[-1][self.CODE] |
|
|
417 | 415 | |
|
|
418 | 416 | if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9 |
|
|
419 | 417 | if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1 |
|
|
420 | 418 | |
|
|
421 | 419 | if self.axes[0].firsttime: |
|
|
422 | 420 | for ch in self.data.channels: |
|
|
423 | 421 | self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) |
|
|
424 | 422 | plt.legend() |
|
|
425 | 423 | else: |
|
|
426 | 424 | for ch in self.data.channels: |
|
|
427 | 425 | self.axes[0].lines[ch].set_data(x[ch], y) |
|
|
428 | 426 | |
|
|
429 | 427 | |
|
|
430 | 428 | class SpectraCutPlot(Plot): |
|
|
431 | 429 | |
|
|
432 | 430 | CODE = 'spc_cut' |
|
|
433 | 431 | plot_type = 'scatter' |
|
|
434 | 432 | buffering = False |
|
|
435 | 433 | |
|
|
436 | 434 | def setup(self): |
|
|
437 | 435 | |
|
|
438 | 436 | self.nplots = len(self.data.channels) |
|
|
439 | 437 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
440 | 438 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
441 | 439 | self.width = 3.4 * self.ncols + 1.5 |
|
|
442 | 440 | self.height = 3 * self.nrows |
|
|
443 | 441 | self.ylabel = 'Power [dB]' |
|
|
444 | 442 | self.colorbar = False |
|
|
445 | 443 | self.plots_adjust.update({'left':0.1, 'hspace':0.3, 'right': 0.75, 'bottom':0.08}) |
|
|
446 | 444 | |
|
|
447 | 445 | def update(self, dataOut): |
|
|
448 | 446 | |
|
|
449 | 447 | data = {} |
|
|
450 | 448 | meta = {} |
|
|
451 | 449 | spc = 10*numpy.log10(dataOut.data_pre[0]/dataOut.normFactor) |
|
|
452 | 450 | data['spc'] = spc |
|
|
453 | 451 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
454 | 452 | if self.CODE == 'cut_gaussian_fit': |
|
|
455 | 453 | data['gauss_fit0'] = 10*numpy.log10(dataOut.GaussFit0/dataOut.normFactor) |
|
|
456 | 454 | data['gauss_fit1'] = 10*numpy.log10(dataOut.GaussFit1/dataOut.normFactor) |
|
|
457 | 455 | return data, meta |
|
|
458 | 456 | |
|
|
459 | 457 | def plot(self): |
|
|
460 | 458 | if self.xaxis == "frequency": |
|
|
461 | 459 | x = self.data.xrange[0][1:] |
|
|
462 | 460 | self.xlabel = "Frequency (kHz)" |
|
|
463 | 461 | elif self.xaxis == "time": |
|
|
464 | 462 | x = self.data.xrange[1] |
|
|
465 | 463 | self.xlabel = "Time (ms)" |
|
|
466 | 464 | else: |
|
|
467 | 465 | x = self.data.xrange[2][:-1] |
|
|
468 | 466 | self.xlabel = "Velocity (m/s)" |
|
|
469 | 467 | |
|
|
470 | 468 | if self.CODE == 'cut_gaussian_fit': |
|
|
471 | 469 | x = self.data.xrange[2][:-1] |
|
|
472 | 470 | self.xlabel = "Velocity (m/s)" |
|
|
473 | 471 | |
|
|
474 | 472 | self.titles = [] |
|
|
475 | 473 | |
|
|
476 | 474 | y = self.data.yrange |
|
|
477 | 475 | data = self.data[-1] |
|
|
478 | 476 | z = data['spc'] |
|
|
479 | 477 | |
|
|
480 | 478 | if self.height_index: |
|
|
481 | 479 | index = numpy.array(self.height_index) |
|
|
482 | 480 | else: |
|
|
483 | 481 | index = numpy.arange(0, len(y), int((len(y))/9)) |
|
|
484 | 482 | |
|
|
485 | 483 | for n, ax in enumerate(self.axes): |
|
|
486 | 484 | if self.CODE == 'cut_gaussian_fit': |
|
|
487 | 485 | gau0 = data['gauss_fit0'] |
|
|
488 | 486 | gau1 = data['gauss_fit1'] |
|
|
489 | 487 | if ax.firsttime: |
|
|
490 | 488 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
491 | 489 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
492 | 490 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z) |
|
|
493 | 491 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z) |
|
|
494 | 492 | ax.plt = ax.plot(x, z[n, :, index].T, lw=0.25) |
|
|
495 | 493 | if self.CODE == 'cut_gaussian_fit': |
|
|
496 | 494 | ax.plt_gau0 = ax.plot(x, gau0[n, :, index].T, lw=1, linestyle='-.') |
|
|
497 | 495 | for i, line in enumerate(ax.plt_gau0): |
|
|
498 | 496 | line.set_color(ax.plt[i].get_color()) |
|
|
499 | 497 | ax.plt_gau1 = ax.plot(x, gau1[n, :, index].T, lw=1, linestyle='--') |
|
|
500 | 498 | for i, line in enumerate(ax.plt_gau1): |
|
|
501 | 499 | line.set_color(ax.plt[i].get_color()) |
|
|
502 | 500 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] |
|
|
503 | 501 | self.figures[0].legend(ax.plt, labels, loc='center right') |
|
|
504 | 502 | else: |
|
|
505 | 503 | for i, line in enumerate(ax.plt): |
|
|
506 | 504 | line.set_data(x, z[n, :, index[i]].T) |
|
|
507 | 505 | for i, line in enumerate(ax.plt_gau0): |
|
|
508 | 506 | line.set_data(x, gau0[n, :, index[i]].T) |
|
|
509 | 507 | line.set_color(ax.plt[i].get_color()) |
|
|
510 | 508 | for i, line in enumerate(ax.plt_gau1): |
|
|
511 | 509 | line.set_data(x, gau1[n, :, index[i]].T) |
|
|
512 | 510 | line.set_color(ax.plt[i].get_color()) |
|
|
513 | 511 | self.titles.append('CH {}'.format(n)) |
|
|
514 | 512 | |
|
|
515 | 513 | |
|
|
516 | 514 | class BeaconPhase(Plot): |
|
|
517 | 515 | |
|
|
518 | 516 | __isConfig = None |
|
|
519 | 517 | __nsubplots = None |
|
|
520 | 518 | |
|
|
521 | 519 | PREFIX = 'beacon_phase' |
|
|
522 | 520 | |
|
|
523 | 521 | def __init__(self): |
|
|
524 | 522 | Plot.__init__(self) |
|
|
525 | 523 | self.timerange = 24*60*60 |
|
|
526 | 524 | self.isConfig = False |
|
|
527 | 525 | self.__nsubplots = 1 |
|
|
528 | 526 | self.counter_imagwr = 0 |
|
|
529 | 527 | self.WIDTH = 800 |
|
|
530 | 528 | self.HEIGHT = 400 |
|
|
531 | 529 | self.WIDTHPROF = 120 |
|
|
532 | 530 | self.HEIGHTPROF = 0 |
|
|
533 | 531 | self.xdata = None |
|
|
534 | 532 | self.ydata = None |
|
|
535 | 533 | |
|
|
536 | 534 | self.PLOT_CODE = BEACON_CODE |
|
|
537 | 535 | |
|
|
538 | 536 | self.FTP_WEI = None |
|
|
539 | 537 | self.EXP_CODE = None |
|
|
540 | 538 | self.SUB_EXP_CODE = None |
|
|
541 | 539 | self.PLOT_POS = None |
|
|
542 | 540 | |
|
|
543 | 541 | self.filename_phase = None |
|
|
544 | 542 | |
|
|
545 | 543 | self.figfile = None |
|
|
546 | 544 | |
|
|
547 | 545 | self.xmin = None |
|
|
548 | 546 | self.xmax = None |
|
|
549 | 547 | |
|
|
550 | 548 | def getSubplots(self): |
|
|
551 | 549 | |
|
|
552 | 550 | ncol = 1 |
|
|
553 | 551 | nrow = 1 |
|
|
554 | 552 | |
|
|
555 | 553 | return nrow, ncol |
|
|
556 | 554 | |
|
|
557 | 555 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
558 | 556 | |
|
|
559 | 557 | self.__showprofile = showprofile |
|
|
560 | 558 | self.nplots = nplots |
|
|
561 | 559 | |
|
|
562 | 560 | ncolspan = 7 |
|
|
563 | 561 | colspan = 6 |
|
|
564 | 562 | self.__nsubplots = 2 |
|
|
565 | 563 | |
|
|
566 | 564 | self.createFigure(id = id, |
|
|
567 | 565 | wintitle = wintitle, |
|
|
568 | 566 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
569 | 567 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
570 | 568 | show=show) |
|
|
571 | 569 | |
|
|
572 | 570 | nrow, ncol = self.getSubplots() |
|
|
573 | 571 | |
|
|
574 | 572 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
575 | 573 | |
|
|
576 | 574 | def save_phase(self, filename_phase): |
|
|
577 | 575 | f = open(filename_phase,'w+') |
|
|
578 | 576 | f.write('\n\n') |
|
|
579 | 577 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
580 | 578 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
581 | 579 | f.close() |
|
|
582 | 580 | |
|
|
583 | 581 | def save_data(self, filename_phase, data, data_datetime): |
|
|
584 | 582 | f=open(filename_phase,'a') |
|
|
585 | 583 | timetuple_data = data_datetime.timetuple() |
|
|
586 | 584 | day = str(timetuple_data.tm_mday) |
|
|
587 | 585 | month = str(timetuple_data.tm_mon) |
|
|
588 | 586 | year = str(timetuple_data.tm_year) |
|
|
589 | 587 | hour = str(timetuple_data.tm_hour) |
|
|
590 | 588 | minute = str(timetuple_data.tm_min) |
|
|
591 | 589 | second = str(timetuple_data.tm_sec) |
|
|
592 | 590 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
593 | 591 | f.close() |
|
|
594 | 592 | |
|
|
595 | 593 | def plot(self): |
|
|
596 | 594 | log.warning('TODO: Not yet implemented...') |
|
|
597 | 595 | |
|
|
598 | 596 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
599 | 597 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
600 | 598 | timerange=None, |
|
|
601 | 599 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
602 | 600 | server=None, folder=None, username=None, password=None, |
|
|
603 | 601 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
604 | 602 | |
|
|
605 | 603 | if dataOut.flagNoData: |
|
|
606 | 604 | return dataOut |
|
|
607 | 605 | |
|
|
608 | 606 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
609 | 607 | return |
|
|
610 | 608 | |
|
|
611 | 609 | if pairsList == None: |
|
|
612 | 610 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
613 | 611 | else: |
|
|
614 | 612 | pairsIndexList = [] |
|
|
615 | 613 | for pair in pairsList: |
|
|
616 | 614 | if pair not in dataOut.pairsList: |
|
|
617 | 615 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
618 | 616 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
619 | 617 | |
|
|
620 | 618 | if pairsIndexList == []: |
|
|
621 | 619 | return |
|
|
622 | 620 | |
|
|
623 | 621 | # if len(pairsIndexList) > 4: |
|
|
624 | 622 | # pairsIndexList = pairsIndexList[0:4] |
|
|
625 | 623 | |
|
|
626 | 624 | hmin_index = None |
|
|
627 | 625 | hmax_index = None |
|
|
628 | 626 | |
|
|
629 | 627 | if hmin != None and hmax != None: |
|
|
630 | 628 | indexes = numpy.arange(dataOut.nHeights) |
|
|
631 | 629 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
632 | 630 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
633 | 631 | |
|
|
634 | 632 | if hmin_list.any(): |
|
|
635 | 633 | hmin_index = hmin_list[0] |
|
|
636 | 634 | |
|
|
637 | 635 | if hmax_list.any(): |
|
|
638 | 636 | hmax_index = hmax_list[-1]+1 |
|
|
639 | 637 | |
|
|
640 | 638 | x = dataOut.getTimeRange() |
|
|
641 | 639 | |
|
|
642 | 640 | thisDatetime = dataOut.datatime |
|
|
643 | 641 | |
|
|
644 | 642 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
645 | 643 | xlabel = "Local Time" |
|
|
646 | 644 | ylabel = "Phase (degrees)" |
|
|
647 | 645 | |
|
|
648 | 646 | update_figfile = False |
|
|
649 | 647 | |
|
|
650 | 648 | nplots = len(pairsIndexList) |
|
|
651 | 649 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
652 | 650 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
653 | 651 | for i in range(nplots): |
|
|
654 | 652 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
655 | 653 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
656 | 654 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
657 | 655 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
658 | 656 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
659 | 657 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
660 | 658 | |
|
|
661 | 659 | if dataOut.beacon_heiIndexList: |
|
|
662 | 660 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
663 | 661 | else: |
|
|
664 | 662 | phase_beacon[i] = numpy.average(phase) |
|
|
665 | 663 | |
|
|
666 | 664 | if not self.isConfig: |
|
|
667 | 665 | |
|
|
668 | 666 | nplots = len(pairsIndexList) |
|
|
669 | 667 | |
|
|
670 | 668 | self.setup(id=id, |
|
|
671 | 669 | nplots=nplots, |
|
|
672 | 670 | wintitle=wintitle, |
|
|
673 | 671 | showprofile=showprofile, |
|
|
674 | 672 | show=show) |
|
|
675 | 673 | |
|
|
676 | 674 | if timerange != None: |
|
|
677 | 675 | self.timerange = timerange |
|
|
678 | 676 | |
|
|
679 | 677 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
680 | 678 | |
|
|
681 | 679 | if ymin == None: ymin = 0 |
|
|
682 | 680 | if ymax == None: ymax = 360 |
|
|
683 | 681 | |
|
|
684 | 682 | self.FTP_WEI = ftp_wei |
|
|
685 | 683 | self.EXP_CODE = exp_code |
|
|
686 | 684 | self.SUB_EXP_CODE = sub_exp_code |
|
|
687 | 685 | self.PLOT_POS = plot_pos |
|
|
688 | 686 | |
|
|
689 | 687 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
690 | 688 | self.isConfig = True |
|
|
691 | 689 | self.figfile = figfile |
|
|
692 | 690 | self.xdata = numpy.array([]) |
|
|
693 | 691 | self.ydata = numpy.array([]) |
|
|
694 | 692 | |
|
|
695 | 693 | update_figfile = True |
|
|
696 | 694 | |
|
|
697 | 695 | #open file beacon phase |
|
|
698 | 696 | path = '%s%03d' %(self.PREFIX, self.id) |
|
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699 | 697 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
700 | 698 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
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701 | 699 | #self.save_phase(self.filename_phase) |
|
|
702 | 700 | |
|
|
703 | 701 | |
|
|
704 | 702 | #store data beacon phase |
|
|
705 | 703 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
706 | 704 | |
|
|
707 | 705 | self.setWinTitle(title) |
|
|
708 | 706 | |
|
|
709 | 707 | |
|
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710 | 708 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
711 | 709 | |
|
|
712 | 710 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
713 | 711 | |
|
|
714 | 712 | axes = self.axesList[0] |
|
|
715 | 713 | |
|
|
716 | 714 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
717 | 715 | |
|
|
718 | 716 | if len(self.ydata)==0: |
|
|
719 | 717 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
720 | 718 | else: |
|
|
721 | 719 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
722 | 720 | |
|
|
723 | 721 | |
|
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724 | 722 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
725 | 723 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
726 | 724 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
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727 | 725 | XAxisAsTime=True, grid='both' |
|
|
728 | 726 | ) |
|
|
729 | 727 | |
|
|
730 | 728 | self.draw() |
|
|
731 | 729 | |
|
|
732 | 730 | if dataOut.ltctime >= self.xmax: |
|
|
733 | 731 | self.counter_imagwr = wr_period |
|
|
734 | 732 | self.isConfig = False |
|
|
735 | 733 | update_figfile = True |
|
|
736 | 734 | |
|
|
737 | 735 | self.save(figpath=figpath, |
|
|
738 | 736 | figfile=figfile, |
|
|
739 | 737 | save=save, |
|
|
740 | 738 | ftp=ftp, |
|
|
741 | 739 | wr_period=wr_period, |
|
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742 | 740 | thisDatetime=thisDatetime, |
|
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743 | 741 | update_figfile=update_figfile) |
|
|
744 | 742 | |
|
|
745 | 743 | return dataOut |
|
|
1 | NO CONTENT: modified file | |
| The requested commit or file is too big and content was truncated. Show full diff | |||
| @@ -1,1627 +1,1628 | |||
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|
1 | 1 | import sys |
|
|
2 | 2 | import numpy,math |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
5 | 5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon |
|
|
6 | 6 | from schainpy.utils import log |
|
|
7 | 7 | from time import time |
|
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8 | 8 | |
|
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9 | 9 | |
|
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10 | 10 | |
|
|
11 | 11 | class VoltageProc(ProcessingUnit): |
|
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12 | 12 | |
|
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13 | 13 | def __init__(self): |
|
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14 | 14 | |
|
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15 | 15 | ProcessingUnit.__init__(self) |
|
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16 | 16 | |
|
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17 | 17 | self.dataOut = Voltage() |
|
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18 | 18 | self.flip = 1 |
|
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19 | 19 | self.setupReq = False |
|
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20 | 20 | |
|
|
21 | 21 | def run(self): |
|
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22 | 22 | |
|
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23 | 23 | if self.dataIn.type == 'AMISR': |
|
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24 | 24 | self.__updateObjFromAmisrInput() |
|
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25 | 25 | |
|
|
26 | 26 | if self.dataIn.type == 'Voltage': |
|
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27 | 27 | self.dataOut.copy(self.dataIn) |
|
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28 | 28 | |
|
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29 | 29 | def __updateObjFromAmisrInput(self): |
|
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30 | 30 | |
|
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31 | 31 | self.dataOut.timeZone = self.dataIn.timeZone |
|
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32 | 32 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
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33 | 33 | self.dataOut.errorCount = self.dataIn.errorCount |
|
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34 | 34 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
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35 | 35 | |
|
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36 | 36 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
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37 | 37 | self.dataOut.data = self.dataIn.data |
|
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38 | 38 | self.dataOut.utctime = self.dataIn.utctime |
|
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39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
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40 | 40 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
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41 | 41 | self.dataOut.heightList = self.dataIn.heightList |
|
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42 | 42 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
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43 | 43 | |
|
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44 | 44 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
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45 | 45 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
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46 | 46 | self.dataOut.frequency = self.dataIn.frequency |
|
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47 | 47 | |
|
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48 | 48 | self.dataOut.azimuth = self.dataIn.azimuth |
|
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49 | 49 | self.dataOut.zenith = self.dataIn.zenith |
|
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50 | 50 | |
|
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51 | 51 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
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52 | 52 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
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53 | 53 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
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54 | 54 | |
|
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55 | 55 | |
|
|
56 | 56 | class selectChannels(Operation): |
|
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57 | 57 | |
|
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58 | 58 | def run(self, dataOut, channelList): |
|
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59 | 59 | |
|
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60 | 60 | channelIndexList = [] |
|
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61 | 61 | self.dataOut = dataOut |
|
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62 | 62 | for channel in channelList: |
|
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63 | 63 | if channel not in self.dataOut.channelList: |
|
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64 | 64 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) |
|
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65 | 65 | |
|
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66 | 66 | index = self.dataOut.channelList.index(channel) |
|
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67 | 67 | channelIndexList.append(index) |
|
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68 | 68 | self.selectChannelsByIndex(channelIndexList) |
|
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69 | 69 | return self.dataOut |
|
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70 | 70 | |
|
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71 | 71 | def selectChannelsByIndex(self, channelIndexList): |
|
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72 | 72 | """ |
|
|
73 | 73 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
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74 | 74 | |
|
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75 | 75 | Input: |
|
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76 | 76 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
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77 | 77 | |
|
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78 | 78 | Affected: |
|
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79 | 79 | self.dataOut.data |
|
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80 | 80 | self.dataOut.channelIndexList |
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81 | 81 | self.dataOut.nChannels |
|
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82 | 82 | self.dataOut.m_ProcessingHeader.totalSpectra |
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83 | 83 | self.dataOut.systemHeaderObj.numChannels |
|
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84 | 84 | self.dataOut.m_ProcessingHeader.blockSize |
|
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85 | 85 | |
|
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86 | 86 | Return: |
|
|
87 | 87 | None |
|
|
88 | 88 | """ |
|
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89 | 89 | |
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90 | 90 | for channelIndex in channelIndexList: |
|
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91 | 91 | if channelIndex not in self.dataOut.channelIndexList: |
|
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92 | 92 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
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93 | 93 | |
|
|
94 | 94 | if self.dataOut.type == 'Voltage': |
|
|
95 | 95 | if self.dataOut.flagDataAsBlock: |
|
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96 | 96 | """ |
|
|
97 | 97 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
98 | 98 | """ |
|
|
99 | 99 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
100 | 100 | else: |
|
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101 | 101 | data = self.dataOut.data[channelIndexList,:] |
|
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102 | 102 | |
|
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103 | 103 | self.dataOut.data = data |
|
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104 | 104 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
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105 | 105 | self.dataOut.channelList = range(len(channelIndexList)) |
|
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106 | 106 | |
|
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107 | 107 | elif self.dataOut.type == 'Spectra': |
|
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108 | 108 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
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109 | 109 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
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110 | 110 | |
|
|
111 | 111 | self.dataOut.data_spc = data_spc |
|
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112 | 112 | self.dataOut.data_dc = data_dc |
|
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113 | 113 | |
|
|
114 | 114 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
115 | 115 | self.dataOut.channelList = range(len(channelIndexList)) |
|
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116 | 116 | self.__selectPairsByChannel(channelIndexList) |
|
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117 | 117 | |
|
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118 | 118 | return 1 |
|
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119 | 119 | |
|
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120 | 120 | def __selectPairsByChannel(self, channelList=None): |
|
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121 | 121 | |
|
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122 | 122 | if channelList == None: |
|
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123 | 123 | return |
|
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124 | 124 | |
|
|
125 | 125 | pairsIndexListSelected = [] |
|
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126 | 126 | for pairIndex in self.dataOut.pairsIndexList: |
|
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127 | 127 | # First pair |
|
|
128 | 128 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
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129 | 129 | continue |
|
|
130 | 130 | # Second pair |
|
|
131 | 131 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
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132 | 132 | continue |
|
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133 | 133 | |
|
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134 | 134 | pairsIndexListSelected.append(pairIndex) |
|
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135 | 135 | |
|
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136 | 136 | if not pairsIndexListSelected: |
|
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137 | 137 | self.dataOut.data_cspc = None |
|
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138 | 138 | self.dataOut.pairsList = [] |
|
|
139 | 139 | return |
|
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140 | 140 | |
|
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141 | 141 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
142 | 142 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
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143 | 143 | for i in pairsIndexListSelected] |
|
|
144 | 144 | |
|
|
145 | 145 | return |
|
|
146 | 146 | |
|
|
147 | 147 | class selectHeights(Operation): |
|
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148 | 148 | |
|
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149 | 149 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
|
150 | 150 | """ |
|
|
151 | 151 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
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152 | 152 | minHei <= height <= maxHei |
|
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153 | 153 | |
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154 | 154 | Input: |
|
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155 | 155 | minHei : valor minimo de altura a considerar |
|
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156 | 156 | maxHei : valor maximo de altura a considerar |
|
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157 | 157 | |
|
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158 | 158 | Affected: |
|
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159 | 159 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
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160 | 160 | |
|
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161 | 161 | Return: |
|
|
162 | 162 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
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163 | 163 | """ |
|
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164 | 164 | |
|
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165 | 165 | self.dataOut = dataOut |
|
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166 | 166 | |
|
|
167 | 167 | if minHei and maxHei: |
|
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168 | 168 | |
|
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169 | 169 | if (minHei < self.dataOut.heightList[0]): |
|
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170 | 170 | minHei = self.dataOut.heightList[0] |
|
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171 | 171 | |
|
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172 | 172 | if (maxHei > self.dataOut.heightList[-1]): |
|
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173 | 173 | maxHei = self.dataOut.heightList[-1] |
|
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174 | 174 | |
|
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175 | 175 | minIndex = 0 |
|
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176 | 176 | maxIndex = 0 |
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177 | 177 | heights = self.dataOut.heightList |
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178 | 178 | |
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179 | 179 | inda = numpy.where(heights >= minHei) |
|
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180 | 180 | indb = numpy.where(heights <= maxHei) |
|
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181 | 181 | |
|
|
182 | 182 | try: |
|
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183 | 183 | minIndex = inda[0][0] |
|
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184 | 184 | except: |
|
|
185 | 185 | minIndex = 0 |
|
|
186 | 186 | |
|
|
187 | 187 | try: |
|
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188 | 188 | maxIndex = indb[0][-1] |
|
|
189 | 189 | except: |
|
|
190 | 190 | maxIndex = len(heights) |
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191 | 191 | |
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192 | 192 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
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193 | 193 | |
|
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194 | 194 | return self.dataOut |
|
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195 | 195 | |
|
|
196 | 196 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
197 | 197 | """ |
|
|
198 | 198 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
199 | 199 | minIndex <= index <= maxIndex |
|
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200 | 200 | |
|
|
201 | 201 | Input: |
|
|
202 | 202 | minIndex : valor de indice minimo de altura a considerar |
|
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203 | 203 | maxIndex : valor de indice maximo de altura a considerar |
|
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204 | 204 | |
|
|
205 | 205 | Affected: |
|
|
206 | 206 | self.dataOut.data |
|
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207 | 207 | self.dataOut.heightList |
|
|
208 | 208 | |
|
|
209 | 209 | Return: |
|
|
210 | 210 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
211 | 211 | """ |
|
|
212 | 212 | |
|
|
213 | 213 | if self.dataOut.type == 'Voltage': |
|
|
214 | 214 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
215 | 215 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
216 | 216 | |
|
|
217 | 217 | if (maxIndex >= self.dataOut.nHeights): |
|
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218 | 218 | maxIndex = self.dataOut.nHeights |
|
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219 | 219 | |
|
|
220 | 220 | #voltage |
|
|
221 | 221 | if self.dataOut.flagDataAsBlock: |
|
|
222 | 222 | """ |
|
|
223 | 223 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
224 | 224 | """ |
|
|
225 | 225 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
226 | 226 | else: |
|
|
227 | 227 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
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228 | 228 | |
|
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229 | 229 | # firstHeight = self.dataOut.heightList[minIndex] |
|
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230 | 230 | |
|
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231 | 231 | self.dataOut.data = data |
|
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232 | 232 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
233 | 233 | |
|
|
234 | 234 | if self.dataOut.nHeights <= 1: |
|
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235 | 235 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
|
236 | 236 | elif self.dataOut.type == 'Spectra': |
|
|
237 | 237 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
238 | 238 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
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239 | 239 | minIndex, maxIndex)) |
|
|
240 | 240 | |
|
|
241 | 241 | if (maxIndex >= self.dataOut.nHeights): |
|
|
242 | 242 | maxIndex = self.dataOut.nHeights - 1 |
|
|
243 | 243 | |
|
|
244 | 244 | # Spectra |
|
|
245 | 245 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
246 | 246 | |
|
|
247 | 247 | data_cspc = None |
|
|
248 | 248 | if self.dataOut.data_cspc is not None: |
|
|
249 | 249 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
250 | 250 | |
|
|
251 | 251 | data_dc = None |
|
|
252 | 252 | if self.dataOut.data_dc is not None: |
|
|
253 | 253 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
254 | 254 | |
|
|
255 | 255 | self.dataOut.data_spc = data_spc |
|
|
256 | 256 | self.dataOut.data_cspc = data_cspc |
|
|
257 | 257 | self.dataOut.data_dc = data_dc |
|
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258 | 258 | |
|
|
259 | 259 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
260 | 260 | |
|
|
261 | 261 | return 1 |
|
|
262 | 262 | |
|
|
263 | 263 | |
|
|
264 | 264 | class filterByHeights(Operation): |
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265 | 265 | |
|
|
266 | 266 | def run(self, dataOut, window): |
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267 | 267 | |
|
|
268 | 268 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
269 | 269 | |
|
|
270 | 270 | if window == None: |
|
|
271 | 271 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
272 | 272 | |
|
|
273 | 273 | newdelta = deltaHeight * window |
|
|
274 | 274 | r = dataOut.nHeights % window |
|
|
275 | 275 | newheights = (dataOut.nHeights-r)/window |
|
|
276 | 276 | |
|
|
277 | 277 | if newheights <= 1: |
|
|
278 | 278 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
|
279 | 279 | |
|
|
280 | 280 | if dataOut.flagDataAsBlock: |
|
|
281 | 281 | """ |
|
|
282 | 282 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
283 | 283 | """ |
|
|
284 | 284 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
|
285 | 285 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
|
286 | 286 | buffer = numpy.sum(buffer,3) |
|
|
287 | 287 | |
|
|
288 | 288 | else: |
|
|
289 | 289 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
|
290 | 290 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
|
291 | 291 | buffer = numpy.sum(buffer,2) |
|
|
292 | 292 | |
|
|
293 | 293 | dataOut.data = buffer |
|
|
294 | 294 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
295 | 295 | dataOut.windowOfFilter = window |
|
|
296 | 296 | |
|
|
297 | 297 | return dataOut |
|
|
298 | 298 | |
|
|
299 | 299 | |
|
|
300 | 300 | class setH0(Operation): |
|
|
301 | 301 | |
|
|
302 | 302 | def run(self, dataOut, h0, deltaHeight = None): |
|
|
303 | 303 | |
|
|
304 | 304 | if not deltaHeight: |
|
|
305 | 305 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
306 | 306 | |
|
|
307 | 307 | nHeights = dataOut.nHeights |
|
|
308 | 308 | |
|
|
309 | 309 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
310 | 310 | |
|
|
311 | 311 | dataOut.heightList = newHeiRange |
|
|
312 | 312 | |
|
|
313 | 313 | return dataOut |
|
|
314 | 314 | |
|
|
315 | 315 | |
|
|
316 | 316 | class deFlip(Operation): |
|
|
317 | 317 | |
|
|
318 | 318 | def run(self, dataOut, channelList = []): |
|
|
319 | 319 | |
|
|
320 | 320 | data = dataOut.data.copy() |
|
|
321 | 321 | |
|
|
322 | 322 | if dataOut.flagDataAsBlock: |
|
|
323 | 323 | flip = self.flip |
|
|
324 | 324 | profileList = list(range(dataOut.nProfiles)) |
|
|
325 | 325 | |
|
|
326 | 326 | if not channelList: |
|
|
327 | 327 | for thisProfile in profileList: |
|
|
328 | 328 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
329 | 329 | flip *= -1.0 |
|
|
330 | 330 | else: |
|
|
331 | 331 | for thisChannel in channelList: |
|
|
332 | 332 | if thisChannel not in dataOut.channelList: |
|
|
333 | 333 | continue |
|
|
334 | 334 | |
|
|
335 | 335 | for thisProfile in profileList: |
|
|
336 | 336 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
337 | 337 | flip *= -1.0 |
|
|
338 | 338 | |
|
|
339 | 339 | self.flip = flip |
|
|
340 | 340 | |
|
|
341 | 341 | else: |
|
|
342 | 342 | if not channelList: |
|
|
343 | 343 | data[:,:] = data[:,:]*self.flip |
|
|
344 | 344 | else: |
|
|
345 | 345 | for thisChannel in channelList: |
|
|
346 | 346 | if thisChannel not in dataOut.channelList: |
|
|
347 | 347 | continue |
|
|
348 | 348 | |
|
|
349 | 349 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
350 | 350 | |
|
|
351 | 351 | self.flip *= -1. |
|
|
352 | 352 | |
|
|
353 | 353 | dataOut.data = data |
|
|
354 | 354 | |
|
|
355 | 355 | return dataOut |
|
|
356 | 356 | |
|
|
357 | 357 | |
|
|
358 | 358 | class setAttribute(Operation): |
|
|
359 | 359 | ''' |
|
|
360 | 360 | Set an arbitrary attribute(s) to dataOut |
|
|
361 | 361 | ''' |
|
|
362 | 362 | |
|
|
363 | 363 | def __init__(self): |
|
|
364 | 364 | |
|
|
365 | 365 | Operation.__init__(self) |
|
|
366 | 366 | self._ready = False |
|
|
367 | 367 | |
|
|
368 | 368 | def run(self, dataOut, **kwargs): |
|
|
369 | 369 | |
|
|
370 | 370 | for key, value in kwargs.items(): |
|
|
371 | 371 | setattr(dataOut, key, value) |
|
|
372 | 372 | |
|
|
373 | 373 | return dataOut |
|
|
374 | 374 | |
|
|
375 | 375 | |
|
|
376 | 376 | @MPDecorator |
|
|
377 | 377 | class printAttribute(Operation): |
|
|
378 | 378 | ''' |
|
|
379 | 379 | Print an arbitrary attribute of dataOut |
|
|
380 | 380 | ''' |
|
|
381 | 381 | |
|
|
382 | 382 | def __init__(self): |
|
|
383 | 383 | |
|
|
384 | 384 | Operation.__init__(self) |
|
|
385 | 385 | |
|
|
386 | 386 | def run(self, dataOut, attributes): |
|
|
387 | 387 | |
|
|
388 | 388 | if isinstance(attributes, str): |
|
|
389 | 389 | attributes = [attributes] |
|
|
390 | 390 | for attr in attributes: |
|
|
391 | 391 | if hasattr(dataOut, attr): |
|
|
392 | 392 | log.log(getattr(dataOut, attr), attr) |
|
|
393 | 393 | |
|
|
394 | 394 | |
|
|
395 | 395 | class interpolateHeights(Operation): |
|
|
396 | 396 | |
|
|
397 | 397 | def run(self, dataOut, topLim, botLim): |
|
|
398 | 398 | #69 al 72 para julia |
|
|
399 | 399 | #82-84 para meteoros |
|
|
400 | 400 | if len(numpy.shape(dataOut.data))==2: |
|
|
401 | 401 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
|
402 | 402 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
403 | 403 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
404 | 404 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
405 | 405 | else: |
|
|
406 | 406 | nHeights = dataOut.data.shape[2] |
|
|
407 | 407 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
408 | 408 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
|
409 | 409 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
410 | 410 | xnew = numpy.arange(botLim,topLim+1) |
|
|
411 | 411 | ynew = f(xnew) |
|
|
412 | 412 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
413 | 413 | |
|
|
414 | 414 | return dataOut |
|
|
415 | 415 | |
|
|
416 | 416 | |
|
|
417 | 417 | class CohInt(Operation): |
|
|
418 | 418 | |
|
|
419 | 419 | isConfig = False |
|
|
420 | 420 | __profIndex = 0 |
|
|
421 | 421 | __byTime = False |
|
|
422 | 422 | __initime = None |
|
|
423 | 423 | __lastdatatime = None |
|
|
424 | 424 | __integrationtime = None |
|
|
425 | 425 | __buffer = None |
|
|
426 | 426 | __bufferStride = [] |
|
|
427 | 427 | __dataReady = False |
|
|
428 | 428 | __profIndexStride = 0 |
|
|
429 | 429 | __dataToPutStride = False |
|
|
430 | 430 | n = None |
|
|
431 | 431 | |
|
|
432 | 432 | def __init__(self, **kwargs): |
|
|
433 | 433 | |
|
|
434 | 434 | Operation.__init__(self, **kwargs) |
|
|
435 | 435 | |
|
|
436 | 436 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
437 | 437 | """ |
|
|
438 | 438 | Set the parameters of the integration class. |
|
|
439 | 439 | |
|
|
440 | 440 | Inputs: |
|
|
441 | 441 | |
|
|
442 | 442 | n : Number of coherent integrations |
|
|
443 | 443 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
444 | 444 | overlapping : |
|
|
445 | 445 | """ |
|
|
446 | 446 | |
|
|
447 | 447 | self.__initime = None |
|
|
448 | 448 | self.__lastdatatime = 0 |
|
|
449 | 449 | self.__buffer = None |
|
|
450 | 450 | self.__dataReady = False |
|
|
451 | 451 | self.byblock = byblock |
|
|
452 | 452 | self.stride = stride |
|
|
453 | 453 | |
|
|
454 | 454 | if n == None and timeInterval == None: |
|
|
455 | 455 | raise ValueError("n or timeInterval should be specified ...") |
|
|
456 | 456 | |
|
|
457 | 457 | if n != None: |
|
|
458 | 458 | self.n = n |
|
|
459 | 459 | self.__byTime = False |
|
|
460 | 460 | else: |
|
|
461 | 461 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
462 | 462 | self.n = 9999 |
|
|
463 | 463 | self.__byTime = True |
|
|
464 | 464 | |
|
|
465 | 465 | if overlapping: |
|
|
466 | 466 | self.__withOverlapping = True |
|
|
467 | 467 | self.__buffer = None |
|
|
468 | 468 | else: |
|
|
469 | 469 | self.__withOverlapping = False |
|
|
470 | 470 | self.__buffer = 0 |
|
|
471 | 471 | |
|
|
472 | 472 | self.__profIndex = 0 |
|
|
473 | 473 | |
|
|
474 | 474 | def putData(self, data): |
|
|
475 | 475 | |
|
|
476 | 476 | """ |
|
|
477 | 477 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
478 | 478 | |
|
|
479 | 479 | """ |
|
|
480 | 480 | |
|
|
481 | 481 | if not self.__withOverlapping: |
|
|
482 | 482 | self.__buffer += data.copy() |
|
|
483 | 483 | self.__profIndex += 1 |
|
|
484 | 484 | return |
|
|
485 | 485 | |
|
|
486 | 486 | #Overlapping data |
|
|
487 | 487 | nChannels, nHeis = data.shape |
|
|
488 | 488 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
489 | 489 | |
|
|
490 | 490 | #If the buffer is empty then it takes the data value |
|
|
491 | 491 | if self.__buffer is None: |
|
|
492 | 492 | self.__buffer = data |
|
|
493 | 493 | self.__profIndex += 1 |
|
|
494 | 494 | return |
|
|
495 | 495 | |
|
|
496 | 496 | #If the buffer length is lower than n then stakcing the data value |
|
|
497 | 497 | if self.__profIndex < self.n: |
|
|
498 | 498 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
499 | 499 | self.__profIndex += 1 |
|
|
500 | 500 | return |
|
|
501 | 501 | |
|
|
502 | 502 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
503 | 503 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
504 | 504 | self.__buffer[self.n-1] = data |
|
|
505 | 505 | self.__profIndex = self.n |
|
|
506 | 506 | return |
|
|
507 | 507 | |
|
|
508 | 508 | |
|
|
509 | 509 | def pushData(self): |
|
|
510 | 510 | """ |
|
|
511 | 511 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
512 | 512 | |
|
|
513 | 513 | Affected: |
|
|
514 | 514 | |
|
|
515 | 515 | self.__profileIndex |
|
|
516 | 516 | |
|
|
517 | 517 | """ |
|
|
518 | 518 | |
|
|
519 | 519 | if not self.__withOverlapping: |
|
|
520 | 520 | data = self.__buffer |
|
|
521 | 521 | n = self.__profIndex |
|
|
522 | 522 | |
|
|
523 | 523 | self.__buffer = 0 |
|
|
524 | 524 | self.__profIndex = 0 |
|
|
525 | 525 | |
|
|
526 | 526 | return data, n |
|
|
527 | 527 | |
|
|
528 | 528 | #Integration with Overlapping |
|
|
529 | 529 | data = numpy.sum(self.__buffer, axis=0) |
|
|
530 | 530 | # print data |
|
|
531 | 531 | # raise |
|
|
532 | 532 | n = self.__profIndex |
|
|
533 | 533 | |
|
|
534 | 534 | return data, n |
|
|
535 | 535 | |
|
|
536 | 536 | def byProfiles(self, data): |
|
|
537 | 537 | |
|
|
538 | 538 | self.__dataReady = False |
|
|
539 | 539 | avgdata = None |
|
|
540 | 540 | # n = None |
|
|
541 | 541 | # print data |
|
|
542 | 542 | # raise |
|
|
543 | 543 | self.putData(data) |
|
|
544 | 544 | |
|
|
545 | 545 | if self.__profIndex == self.n: |
|
|
546 | 546 | avgdata, n = self.pushData() |
|
|
547 | 547 | self.__dataReady = True |
|
|
548 | 548 | |
|
|
549 | 549 | return avgdata |
|
|
550 | 550 | |
|
|
551 | 551 | def byTime(self, data, datatime): |
|
|
552 | 552 | |
|
|
553 | 553 | self.__dataReady = False |
|
|
554 | 554 | avgdata = None |
|
|
555 | 555 | n = None |
|
|
556 | 556 | |
|
|
557 | 557 | self.putData(data) |
|
|
558 | 558 | |
|
|
559 | 559 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
560 | 560 | avgdata, n = self.pushData() |
|
|
561 | 561 | self.n = n |
|
|
562 | 562 | self.__dataReady = True |
|
|
563 | 563 | |
|
|
564 | 564 | return avgdata |
|
|
565 | 565 | |
|
|
566 | 566 | def integrateByStride(self, data, datatime): |
|
|
567 | 567 | # print data |
|
|
568 | 568 | if self.__profIndex == 0: |
|
|
569 | 569 | self.__buffer = [[data.copy(), datatime]] |
|
|
570 | 570 | else: |
|
|
571 | 571 | self.__buffer.append([data.copy(),datatime]) |
|
|
572 | 572 | self.__profIndex += 1 |
|
|
573 | 573 | self.__dataReady = False |
|
|
574 | 574 | |
|
|
575 | 575 | if self.__profIndex == self.n * self.stride : |
|
|
576 | 576 | self.__dataToPutStride = True |
|
|
577 | 577 | self.__profIndexStride = 0 |
|
|
578 | 578 | self.__profIndex = 0 |
|
|
579 | 579 | self.__bufferStride = [] |
|
|
580 | 580 | for i in range(self.stride): |
|
|
581 | 581 | current = self.__buffer[i::self.stride] |
|
|
582 | 582 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
583 | 583 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
584 | 584 | # print data |
|
|
585 | 585 | self.__bufferStride.append((data, avgdatatime)) |
|
|
586 | 586 | |
|
|
587 | 587 | if self.__dataToPutStride: |
|
|
588 | 588 | self.__dataReady = True |
|
|
589 | 589 | self.__profIndexStride += 1 |
|
|
590 | 590 | if self.__profIndexStride == self.stride: |
|
|
591 | 591 | self.__dataToPutStride = False |
|
|
592 | 592 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
593 | 593 | # raise |
|
|
594 | 594 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
595 | 595 | |
|
|
596 | 596 | |
|
|
597 | 597 | return None, None |
|
|
598 | 598 | |
|
|
599 | 599 | def integrate(self, data, datatime=None): |
|
|
600 | 600 | |
|
|
601 | 601 | if self.__initime == None: |
|
|
602 | 602 | self.__initime = datatime |
|
|
603 | 603 | |
|
|
604 | 604 | if self.__byTime: |
|
|
605 | 605 | avgdata = self.byTime(data, datatime) |
|
|
606 | 606 | else: |
|
|
607 | 607 | avgdata = self.byProfiles(data) |
|
|
608 | 608 | |
|
|
609 | 609 | |
|
|
610 | 610 | self.__lastdatatime = datatime |
|
|
611 | 611 | |
|
|
612 | 612 | if avgdata is None: |
|
|
613 | 613 | return None, None |
|
|
614 | 614 | |
|
|
615 | 615 | avgdatatime = self.__initime |
|
|
616 | 616 | |
|
|
617 | 617 | deltatime = datatime - self.__lastdatatime |
|
|
618 | 618 | |
|
|
619 | 619 | if not self.__withOverlapping: |
|
|
620 | 620 | self.__initime = datatime |
|
|
621 | 621 | else: |
|
|
622 | 622 | self.__initime += deltatime |
|
|
623 | 623 | |
|
|
624 | 624 | return avgdata, avgdatatime |
|
|
625 | 625 | |
|
|
626 | 626 | def integrateByBlock(self, dataOut): |
|
|
627 | 627 | |
|
|
628 | 628 | times = int(dataOut.data.shape[1]/self.n) |
|
|
629 | 629 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
630 | 630 | |
|
|
631 | 631 | id_min = 0 |
|
|
632 | 632 | id_max = self.n |
|
|
633 | 633 | |
|
|
634 | 634 | for i in range(times): |
|
|
635 | 635 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
636 | 636 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
637 | 637 | id_min += self.n |
|
|
638 | 638 | id_max += self.n |
|
|
639 | 639 | |
|
|
640 | 640 | timeInterval = dataOut.ippSeconds*self.n |
|
|
641 | 641 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
642 | 642 | self.__dataReady = True |
|
|
643 | 643 | return avgdata, avgdatatime |
|
|
644 | 644 | |
|
|
645 | 645 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
646 | 646 | |
|
|
647 | 647 | if not self.isConfig: |
|
|
648 | 648 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
649 | 649 | self.isConfig = True |
|
|
650 | 650 | |
|
|
651 | 651 | if dataOut.flagDataAsBlock: |
|
|
652 | 652 | """ |
|
|
653 | 653 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
654 | 654 | """ |
|
|
655 | 655 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
656 | 656 | dataOut.nProfiles /= self.n |
|
|
657 | 657 | else: |
|
|
658 | 658 | if stride is None: |
|
|
659 | 659 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
660 | 660 | else: |
|
|
661 | 661 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
662 | 662 | |
|
|
663 | 663 | |
|
|
664 | 664 | # dataOut.timeInterval *= n |
|
|
665 | 665 | dataOut.flagNoData = True |
|
|
666 | 666 | |
|
|
667 | 667 | if self.__dataReady: |
|
|
668 | 668 | dataOut.data = avgdata |
|
|
669 | 669 | if not dataOut.flagCohInt: |
|
|
670 | 670 | dataOut.nCohInt *= self.n |
|
|
671 | 671 | dataOut.flagCohInt = True |
|
|
672 | 672 | dataOut.utctime = avgdatatime |
|
|
673 | 673 | # print avgdata, avgdatatime |
|
|
674 | 674 | # raise |
|
|
675 | 675 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
676 | 676 | dataOut.flagNoData = False |
|
|
677 | 677 | return dataOut |
|
|
678 | 678 | |
|
|
679 | 679 | class Decoder(Operation): |
|
|
680 | 680 | |
|
|
681 | 681 | isConfig = False |
|
|
682 | 682 | __profIndex = 0 |
|
|
683 | 683 | |
|
|
684 | 684 | code = None |
|
|
685 | 685 | |
|
|
686 | 686 | nCode = None |
|
|
687 | 687 | nBaud = None |
|
|
688 | 688 | |
|
|
689 | 689 | def __init__(self, **kwargs): |
|
|
690 | 690 | |
|
|
691 | 691 | Operation.__init__(self, **kwargs) |
|
|
692 | 692 | |
|
|
693 | 693 | self.times = None |
|
|
694 | 694 | self.osamp = None |
|
|
695 | 695 | # self.__setValues = False |
|
|
696 | 696 | self.isConfig = False |
|
|
697 | 697 | self.setupReq = False |
|
|
698 | 698 | def setup(self, code, osamp, dataOut): |
|
|
699 | 699 | |
|
|
700 | 700 | self.__profIndex = 0 |
|
|
701 | 701 | |
|
|
702 | 702 | self.code = code |
|
|
703 | 703 | |
|
|
704 | 704 | self.nCode = len(code) |
|
|
705 | 705 | self.nBaud = len(code[0]) |
|
|
706 | 706 | |
|
|
707 | 707 | if (osamp != None) and (osamp >1): |
|
|
708 | 708 | self.osamp = osamp |
|
|
709 | 709 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
710 | 710 | self.nBaud = self.nBaud*self.osamp |
|
|
711 | 711 | |
|
|
712 | 712 | self.__nChannels = dataOut.nChannels |
|
|
713 | 713 | self.__nProfiles = dataOut.nProfiles |
|
|
714 | 714 | self.__nHeis = dataOut.nHeights |
|
|
715 | 715 | |
|
|
716 | 716 | if self.__nHeis < self.nBaud: |
|
|
717 | 717 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
718 | 718 | |
|
|
719 | 719 | #Frequency |
|
|
720 | 720 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
721 | 721 | |
|
|
722 | 722 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
723 | 723 | |
|
|
724 | 724 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
725 | 725 | |
|
|
726 | 726 | if dataOut.flagDataAsBlock: |
|
|
727 | 727 | |
|
|
728 | 728 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
729 | 729 | |
|
|
730 | 730 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
731 | 731 | |
|
|
732 | 732 | else: |
|
|
733 | 733 | |
|
|
734 | 734 | #Time |
|
|
735 | 735 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
736 | 736 | |
|
|
737 | 737 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
738 | 738 | |
|
|
739 | 739 | def __convolutionInFreq(self, data): |
|
|
740 | 740 | |
|
|
741 | 741 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
742 | 742 | |
|
|
743 | 743 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
744 | 744 | |
|
|
745 | 745 | conv = fft_data*fft_code |
|
|
746 | 746 | |
|
|
747 | 747 | data = numpy.fft.ifft(conv,axis=1) |
|
|
748 | 748 | |
|
|
749 | 749 | return data |
|
|
750 | 750 | |
|
|
751 | 751 | def __convolutionInFreqOpt(self, data): |
|
|
752 | 752 | |
|
|
753 | 753 | raise NotImplementedError |
|
|
754 | 754 | |
|
|
755 | 755 | def __convolutionInTime(self, data): |
|
|
756 | 756 | |
|
|
757 | 757 | code = self.code[self.__profIndex] |
|
|
758 | 758 | for i in range(self.__nChannels): |
|
|
759 | 759 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
760 | 760 | |
|
|
761 | 761 | return self.datadecTime |
|
|
762 | 762 | |
|
|
763 | 763 | def __convolutionByBlockInTime(self, data): |
|
|
764 | 764 | |
|
|
765 | 765 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
766 | 766 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
767 | 767 | junk = junk.flatten() |
|
|
768 | 768 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
769 | 769 | profilesList = range(self.__nProfiles) |
|
|
770 | 770 | |
|
|
771 | 771 | for i in range(self.__nChannels): |
|
|
772 | 772 | for j in profilesList: |
|
|
773 | 773 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
774 | 774 | return self.datadecTime |
|
|
775 | 775 | |
|
|
776 | 776 | def __convolutionByBlockInFreq(self, data): |
|
|
777 | 777 | |
|
|
778 | 778 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
779 | 779 | |
|
|
780 | 780 | |
|
|
781 | 781 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
782 | 782 | |
|
|
783 | 783 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
784 | 784 | |
|
|
785 | 785 | conv = fft_data*fft_code |
|
|
786 | 786 | |
|
|
787 | 787 | data = numpy.fft.ifft(conv,axis=2) |
|
|
788 | 788 | |
|
|
789 | 789 | return data |
|
|
790 | 790 | |
|
|
791 | 791 | |
|
|
792 | 792 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
793 | 793 | |
|
|
794 | 794 | if dataOut.flagDecodeData: |
|
|
795 | 795 | print("This data is already decoded, recoding again ...") |
|
|
796 | 796 | |
|
|
797 | 797 | if not self.isConfig: |
|
|
798 | 798 | |
|
|
799 | 799 | if code is None: |
|
|
800 | 800 | if dataOut.code is None: |
|
|
801 | 801 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
802 | 802 | |
|
|
803 | 803 | code = dataOut.code |
|
|
804 | 804 | else: |
|
|
805 | 805 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
806 | 806 | self.setup(code, osamp, dataOut) |
|
|
807 | 807 | |
|
|
808 | 808 | self.isConfig = True |
|
|
809 | 809 | |
|
|
810 | 810 | if mode == 3: |
|
|
811 | 811 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
812 | 812 | |
|
|
813 | 813 | if times != None: |
|
|
814 | 814 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
815 | 815 | |
|
|
816 | 816 | if self.code is None: |
|
|
817 | 817 | print("Fail decoding: Code is not defined.") |
|
|
818 | 818 | return |
|
|
819 | 819 | |
|
|
820 | 820 | self.__nProfiles = dataOut.nProfiles |
|
|
821 | 821 | datadec = None |
|
|
822 | 822 | |
|
|
823 | 823 | if mode == 3: |
|
|
824 | 824 | mode = 0 |
|
|
825 | 825 | |
|
|
826 | 826 | if dataOut.flagDataAsBlock: |
|
|
827 | 827 | """ |
|
|
828 | 828 | Decoding when data have been read as block, |
|
|
829 | 829 | """ |
|
|
830 | 830 | |
|
|
831 | 831 | if mode == 0: |
|
|
832 | 832 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
833 | 833 | if mode == 1: |
|
|
834 | 834 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
835 | 835 | else: |
|
|
836 | 836 | """ |
|
|
837 | 837 | Decoding when data have been read profile by profile |
|
|
838 | 838 | """ |
|
|
839 | 839 | if mode == 0: |
|
|
840 | 840 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
841 | 841 | |
|
|
842 | 842 | if mode == 1: |
|
|
843 | 843 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
844 | 844 | |
|
|
845 | 845 | if mode == 2: |
|
|
846 | 846 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
847 | 847 | |
|
|
848 | 848 | if datadec is None: |
|
|
849 | 849 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
850 | 850 | |
|
|
851 | 851 | dataOut.code = self.code |
|
|
852 | 852 | dataOut.nCode = self.nCode |
|
|
853 | 853 | dataOut.nBaud = self.nBaud |
|
|
854 | 854 | |
|
|
855 | 855 | dataOut.data = datadec |
|
|
856 | 856 | |
|
|
857 | 857 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
858 | 858 | |
|
|
859 | 859 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
860 | 860 | |
|
|
861 | 861 | if self.__profIndex == self.nCode-1: |
|
|
862 | 862 | self.__profIndex = 0 |
|
|
863 | 863 | return dataOut |
|
|
864 | 864 | |
|
|
865 | 865 | self.__profIndex += 1 |
|
|
866 | 866 | |
|
|
867 | 867 | return dataOut |
|
|
868 | 868 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
869 | 869 | |
|
|
870 | 870 | |
|
|
871 | 871 | class ProfileConcat(Operation): |
|
|
872 | 872 | |
|
|
873 | 873 | isConfig = False |
|
|
874 | 874 | buffer = None |
|
|
875 | 875 | |
|
|
876 | 876 | def __init__(self, **kwargs): |
|
|
877 | 877 | |
|
|
878 | 878 | Operation.__init__(self, **kwargs) |
|
|
879 | 879 | self.profileIndex = 0 |
|
|
880 | 880 | |
|
|
881 | 881 | def reset(self): |
|
|
882 | 882 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
883 | 883 | self.start_index = 0 |
|
|
884 | 884 | self.times = 1 |
|
|
885 | 885 | |
|
|
886 | 886 | def setup(self, data, m, n=1): |
|
|
887 | 887 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
888 | 888 | self.nHeights = data.shape[1]#.nHeights |
|
|
889 | 889 | self.start_index = 0 |
|
|
890 | 890 | self.times = 1 |
|
|
891 | 891 | |
|
|
892 | 892 | def concat(self, data): |
|
|
893 | 893 | |
|
|
894 | 894 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
895 | 895 | self.start_index = self.start_index + self.nHeights |
|
|
896 | 896 | |
|
|
897 | 897 | def run(self, dataOut, m): |
|
|
898 | 898 | dataOut.flagNoData = True |
|
|
899 | 899 | |
|
|
900 | 900 | if not self.isConfig: |
|
|
901 | 901 | self.setup(dataOut.data, m, 1) |
|
|
902 | 902 | self.isConfig = True |
|
|
903 | 903 | |
|
|
904 | 904 | if dataOut.flagDataAsBlock: |
|
|
905 | 905 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
906 | 906 | |
|
|
907 | 907 | else: |
|
|
908 | 908 | self.concat(dataOut.data) |
|
|
909 | 909 | self.times += 1 |
|
|
910 | 910 | if self.times > m: |
|
|
911 | 911 | dataOut.data = self.buffer |
|
|
912 | 912 | self.reset() |
|
|
913 | 913 | dataOut.flagNoData = False |
|
|
914 | 914 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
915 | 915 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
916 | 916 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
917 | 917 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
918 | 918 | dataOut.ippSeconds *= m |
|
|
919 | 919 | return dataOut |
|
|
920 | 920 | |
|
|
921 | 921 | class ProfileSelector(Operation): |
|
|
922 | 922 | |
|
|
923 | 923 | profileIndex = None |
|
|
924 | 924 | # Tamanho total de los perfiles |
|
|
925 | 925 | nProfiles = None |
|
|
926 | 926 | |
|
|
927 | 927 | def __init__(self, **kwargs): |
|
|
928 | 928 | |
|
|
929 | 929 | Operation.__init__(self, **kwargs) |
|
|
930 | 930 | self.profileIndex = 0 |
|
|
931 | 931 | |
|
|
932 | 932 | def incProfileIndex(self): |
|
|
933 | 933 | |
|
|
934 | 934 | self.profileIndex += 1 |
|
|
935 | 935 | |
|
|
936 | 936 | if self.profileIndex >= self.nProfiles: |
|
|
937 | 937 | self.profileIndex = 0 |
|
|
938 | 938 | |
|
|
939 | 939 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
940 | 940 | |
|
|
941 | 941 | if profileIndex < minIndex: |
|
|
942 | 942 | return False |
|
|
943 | 943 | |
|
|
944 | 944 | if profileIndex > maxIndex: |
|
|
945 | 945 | return False |
|
|
946 | 946 | |
|
|
947 | 947 | return True |
|
|
948 | 948 | |
|
|
949 | 949 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
950 | 950 | |
|
|
951 | 951 | if profileIndex not in profileList: |
|
|
952 | 952 | return False |
|
|
953 | 953 | |
|
|
954 | 954 | return True |
|
|
955 | 955 | |
|
|
956 | 956 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
957 | 957 | |
|
|
958 | 958 | """ |
|
|
959 | 959 | ProfileSelector: |
|
|
960 | 960 | |
|
|
961 | 961 | Inputs: |
|
|
962 | 962 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
963 | 963 | |
|
|
964 | 964 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
965 | 965 | |
|
|
966 | 966 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
967 | 967 | |
|
|
968 | 968 | """ |
|
|
969 | 969 | |
|
|
970 | 970 | if rangeList is not None: |
|
|
971 | 971 | if type(rangeList[0]) not in (tuple, list): |
|
|
972 | 972 | rangeList = [rangeList] |
|
|
973 | 973 | |
|
|
974 | 974 | dataOut.flagNoData = True |
|
|
975 | 975 | |
|
|
976 | 976 | if dataOut.flagDataAsBlock: |
|
|
977 | 977 | """ |
|
|
978 | 978 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
979 | 979 | """ |
|
|
980 | 980 | if profileList != None: |
|
|
981 | 981 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
982 | 982 | |
|
|
983 | 983 | if profileRangeList != None: |
|
|
984 | 984 | minIndex = profileRangeList[0] |
|
|
985 | 985 | maxIndex = profileRangeList[1] |
|
|
986 | 986 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
987 | 987 | |
|
|
988 | 988 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
989 | 989 | |
|
|
990 | 990 | if rangeList != None: |
|
|
991 | 991 | |
|
|
992 | 992 | profileList = [] |
|
|
993 | 993 | |
|
|
994 | 994 | for thisRange in rangeList: |
|
|
995 | 995 | minIndex = thisRange[0] |
|
|
996 | 996 | maxIndex = thisRange[1] |
|
|
997 | 997 | |
|
|
998 | 998 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
999 | 999 | |
|
|
1000 | 1000 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | dataOut.nProfiles = len(profileList) |
|
|
1003 | 1003 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
1004 | 1004 | dataOut.flagNoData = False |
|
|
1005 | 1005 | |
|
|
1006 | 1006 | return dataOut |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | """ |
|
|
1009 | 1009 | data dimension = [nChannels, nHeis] |
|
|
1010 | 1010 | """ |
|
|
1011 | 1011 | |
|
|
1012 | 1012 | if profileList != None: |
|
|
1013 | 1013 | |
|
|
1014 | 1014 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
1015 | 1015 | |
|
|
1016 | 1016 | self.nProfiles = len(profileList) |
|
|
1017 | 1017 | dataOut.nProfiles = self.nProfiles |
|
|
1018 | 1018 | dataOut.profileIndex = self.profileIndex |
|
|
1019 | 1019 | dataOut.flagNoData = False |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | self.incProfileIndex() |
|
|
1022 | 1022 | return dataOut |
|
|
1023 | 1023 | |
|
|
1024 | 1024 | if profileRangeList != None: |
|
|
1025 | 1025 | |
|
|
1026 | 1026 | minIndex = profileRangeList[0] |
|
|
1027 | 1027 | maxIndex = profileRangeList[1] |
|
|
1028 | 1028 | |
|
|
1029 | 1029 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
1032 | 1032 | dataOut.nProfiles = self.nProfiles |
|
|
1033 | 1033 | dataOut.profileIndex = self.profileIndex |
|
|
1034 | 1034 | dataOut.flagNoData = False |
|
|
1035 | 1035 | |
|
|
1036 | 1036 | self.incProfileIndex() |
|
|
1037 | 1037 | return dataOut |
|
|
1038 | 1038 | |
|
|
1039 | 1039 | if rangeList != None: |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | nProfiles = 0 |
|
|
1042 | 1042 | |
|
|
1043 | 1043 | for thisRange in rangeList: |
|
|
1044 | 1044 | minIndex = thisRange[0] |
|
|
1045 | 1045 | maxIndex = thisRange[1] |
|
|
1046 | 1046 | |
|
|
1047 | 1047 | nProfiles += maxIndex - minIndex + 1 |
|
|
1048 | 1048 | |
|
|
1049 | 1049 | for thisRange in rangeList: |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | minIndex = thisRange[0] |
|
|
1052 | 1052 | maxIndex = thisRange[1] |
|
|
1053 | 1053 | |
|
|
1054 | 1054 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | self.nProfiles = nProfiles |
|
|
1057 | 1057 | dataOut.nProfiles = self.nProfiles |
|
|
1058 | 1058 | dataOut.profileIndex = self.profileIndex |
|
|
1059 | 1059 | dataOut.flagNoData = False |
|
|
1060 | 1060 | |
|
|
1061 | 1061 | self.incProfileIndex() |
|
|
1062 | 1062 | |
|
|
1063 | 1063 | break |
|
|
1064 | 1064 | |
|
|
1065 | 1065 | return dataOut |
|
|
1066 | 1066 | |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | if beam != None: #beam is only for AMISR data |
|
|
1069 | 1069 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
1070 | 1070 | dataOut.flagNoData = False |
|
|
1071 | 1071 | dataOut.profileIndex = self.profileIndex |
|
|
1072 | 1072 | |
|
|
1073 | 1073 | self.incProfileIndex() |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | return dataOut |
|
|
1076 | 1076 | |
|
|
1077 | 1077 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | |
|
|
1080 | 1080 | class Reshaper(Operation): |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | def __init__(self, **kwargs): |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | Operation.__init__(self, **kwargs) |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | self.__buffer = None |
|
|
1087 | 1087 | self.__nitems = 0 |
|
|
1088 | 1088 | |
|
|
1089 | 1089 | def __appendProfile(self, dataOut, nTxs): |
|
|
1090 | 1090 | |
|
|
1091 | 1091 | if self.__buffer is None: |
|
|
1092 | 1092 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
1093 | 1093 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
1094 | 1094 | |
|
|
1095 | 1095 | ini = dataOut.nHeights * self.__nitems |
|
|
1096 | 1096 | end = ini + dataOut.nHeights |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1099 | 1099 | |
|
|
1100 | 1100 | self.__nitems += 1 |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | return int(self.__nitems*nTxs) |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | def __getBuffer(self): |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | if self.__nitems == int(1./self.__nTxs): |
|
|
1107 | 1107 | |
|
|
1108 | 1108 | self.__nitems = 0 |
|
|
1109 | 1109 | |
|
|
1110 | 1110 | return self.__buffer.copy() |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | return None |
|
|
1113 | 1113 | |
|
|
1114 | 1114 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | if shape is None and nTxs is None: |
|
|
1117 | 1117 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
1118 | 1118 | |
|
|
1119 | 1119 | if nTxs: |
|
|
1120 | 1120 | if nTxs < 0: |
|
|
1121 | 1121 | raise ValueError("nTxs should be greater than 0") |
|
|
1122 | 1122 | |
|
|
1123 | 1123 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1124 | 1124 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1127 | 1127 | |
|
|
1128 | 1128 | return shape, nTxs |
|
|
1129 | 1129 | |
|
|
1130 | 1130 | if len(shape) != 2 and len(shape) != 3: |
|
|
1131 | 1131 | raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)) |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | if len(shape) == 2: |
|
|
1134 | 1134 | shape_tuple = [dataOut.nChannels] |
|
|
1135 | 1135 | shape_tuple.extend(shape) |
|
|
1136 | 1136 | else: |
|
|
1137 | 1137 | shape_tuple = list(shape) |
|
|
1138 | 1138 | |
|
|
1139 | 1139 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1140 | 1140 | |
|
|
1141 | 1141 | return shape_tuple, nTxs |
|
|
1142 | 1142 | |
|
|
1143 | 1143 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1144 | 1144 | |
|
|
1145 | 1145 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1146 | 1146 | |
|
|
1147 | 1147 | dataOut.flagNoData = True |
|
|
1148 | 1148 | profileIndex = None |
|
|
1149 | 1149 | |
|
|
1150 | 1150 | if dataOut.flagDataAsBlock: |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1153 | 1153 | dataOut.flagNoData = False |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | else: |
|
|
1158 | 1158 | |
|
|
1159 | 1159 | if self.__nTxs < 1: |
|
|
1160 | 1160 | |
|
|
1161 | 1161 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1162 | 1162 | new_data = self.__getBuffer() |
|
|
1163 | 1163 | |
|
|
1164 | 1164 | if new_data is not None: |
|
|
1165 | 1165 | dataOut.data = new_data |
|
|
1166 | 1166 | dataOut.flagNoData = False |
|
|
1167 | 1167 | |
|
|
1168 | 1168 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1169 | 1169 | |
|
|
1170 | 1170 | else: |
|
|
1171 | 1171 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
1172 | 1172 | |
|
|
1173 | 1173 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1174 | 1174 | |
|
|
1175 | 1175 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1176 | 1176 | |
|
|
1177 | 1177 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | dataOut.profileIndex = profileIndex |
|
|
1180 | 1180 | |
|
|
1181 | 1181 | dataOut.ippSeconds /= self.__nTxs |
|
|
1182 | 1182 | |
|
|
1183 | 1183 | return dataOut |
|
|
1184 | 1184 | |
|
|
1185 | 1185 | class SplitProfiles(Operation): |
|
|
1186 | 1186 | |
|
|
1187 | 1187 | def __init__(self, **kwargs): |
|
|
1188 | 1188 | |
|
|
1189 | 1189 | Operation.__init__(self, **kwargs) |
|
|
1190 | 1190 | |
|
|
1191 | 1191 | def run(self, dataOut, n): |
|
|
1192 | 1192 | |
|
|
1193 | 1193 | dataOut.flagNoData = True |
|
|
1194 | 1194 | profileIndex = None |
|
|
1195 | 1195 | |
|
|
1196 | 1196 | if dataOut.flagDataAsBlock: |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | #nchannels, nprofiles, nsamples |
|
|
1199 | 1199 | shape = dataOut.data.shape |
|
|
1200 | 1200 | |
|
|
1201 | 1201 | if shape[2] % n != 0: |
|
|
1202 | 1202 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
1203 | 1203 | |
|
|
1204 | 1204 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
1205 | 1205 | |
|
|
1206 | 1206 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1207 | 1207 | dataOut.flagNoData = False |
|
|
1208 | 1208 | |
|
|
1209 | 1209 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1210 | 1210 | |
|
|
1211 | 1211 | else: |
|
|
1212 | 1212 | |
|
|
1213 | 1213 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
1214 | 1214 | |
|
|
1215 | 1215 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1216 | 1216 | |
|
|
1217 | 1217 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1218 | 1218 | |
|
|
1219 | 1219 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1220 | 1220 | |
|
|
1221 | 1221 | dataOut.profileIndex = profileIndex |
|
|
1222 | 1222 | |
|
|
1223 | 1223 | dataOut.ippSeconds /= n |
|
|
1224 | 1224 | |
|
|
1225 | 1225 | return dataOut |
|
|
1226 | 1226 | |
|
|
1227 | 1227 | class CombineProfiles(Operation): |
|
|
1228 | 1228 | def __init__(self, **kwargs): |
|
|
1229 | 1229 | |
|
|
1230 | 1230 | Operation.__init__(self, **kwargs) |
|
|
1231 | 1231 | |
|
|
1232 | 1232 | self.__remData = None |
|
|
1233 | 1233 | self.__profileIndex = 0 |
|
|
1234 | 1234 | |
|
|
1235 | 1235 | def run(self, dataOut, n): |
|
|
1236 | 1236 | |
|
|
1237 | 1237 | dataOut.flagNoData = True |
|
|
1238 | 1238 | profileIndex = None |
|
|
1239 | 1239 | |
|
|
1240 | 1240 | if dataOut.flagDataAsBlock: |
|
|
1241 | 1241 | |
|
|
1242 | 1242 | #nchannels, nprofiles, nsamples |
|
|
1243 | 1243 | shape = dataOut.data.shape |
|
|
1244 | 1244 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1245 | 1245 | |
|
|
1246 | 1246 | if shape[1] % n != 0: |
|
|
1247 | 1247 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
1248 | 1248 | |
|
|
1249 | 1249 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1250 | 1250 | dataOut.flagNoData = False |
|
|
1251 | 1251 | |
|
|
1252 | 1252 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1253 | 1253 | |
|
|
1254 | 1254 | else: |
|
|
1255 | 1255 | |
|
|
1256 | 1256 | #nchannels, nsamples |
|
|
1257 | 1257 | if self.__remData is None: |
|
|
1258 | 1258 | newData = dataOut.data |
|
|
1259 | 1259 | else: |
|
|
1260 | 1260 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1261 | 1261 | |
|
|
1262 | 1262 | self.__profileIndex += 1 |
|
|
1263 | 1263 | |
|
|
1264 | 1264 | if self.__profileIndex < n: |
|
|
1265 | 1265 | self.__remData = newData |
|
|
1266 | 1266 | #continue |
|
|
1267 | 1267 | return |
|
|
1268 | 1268 | |
|
|
1269 | 1269 | self.__profileIndex = 0 |
|
|
1270 | 1270 | self.__remData = None |
|
|
1271 | 1271 | |
|
|
1272 | 1272 | dataOut.data = newData |
|
|
1273 | 1273 | dataOut.flagNoData = False |
|
|
1274 | 1274 | |
|
|
1275 | 1275 | profileIndex = dataOut.profileIndex/n |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | |
|
|
1278 | 1278 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1281 | 1281 | |
|
|
1282 | 1282 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1283 | 1283 | |
|
|
1284 | 1284 | dataOut.profileIndex = profileIndex |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | dataOut.ippSeconds *= n |
|
|
1287 | 1287 | |
|
|
1288 | 1288 | return dataOut |
|
|
1289 | 1289 | |
|
|
1290 | 1290 | class PulsePair(Operation): |
|
|
1291 | 1291 | ''' |
|
|
1292 | 1292 | Function PulsePair(Signal Power, Velocity) |
|
|
1293 | 1293 | The real component of Lag[0] provides Intensity Information |
|
|
1294 | 1294 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1295 | 1295 | |
|
|
1296 | 1296 | Configuration Parameters: |
|
|
1297 | 1297 | nPRF = Number of Several PRF |
|
|
1298 | 1298 | theta = Degree Azimuth angel Boundaries |
|
|
1299 | 1299 | |
|
|
1300 | 1300 | Input: |
|
|
1301 | 1301 | self.dataOut |
|
|
1302 | 1302 | lag[N] |
|
|
1303 | 1303 | Affected: |
|
|
1304 | 1304 | self.dataOut.spc |
|
|
1305 | 1305 | ''' |
|
|
1306 | 1306 | isConfig = False |
|
|
1307 | 1307 | __profIndex = 0 |
|
|
1308 | 1308 | __initime = None |
|
|
1309 | 1309 | __lastdatatime = None |
|
|
1310 | 1310 | __buffer = None |
|
|
1311 | 1311 | noise = None |
|
|
1312 | 1312 | __dataReady = False |
|
|
1313 | 1313 | n = None |
|
|
1314 | 1314 | __nch = 0 |
|
|
1315 | 1315 | __nHeis = 0 |
|
|
1316 | 1316 | removeDC = False |
|
|
1317 | 1317 | ipp = None |
|
|
1318 | 1318 | lambda_ = 0 |
|
|
1319 | 1319 | |
|
|
1320 | 1320 | def __init__(self,**kwargs): |
|
|
1321 | 1321 | Operation.__init__(self,**kwargs) |
|
|
1322 | 1322 | |
|
|
1323 | 1323 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1324 | 1324 | ''' |
|
|
1325 | 1325 | n= Numero de PRF's de entrada |
|
|
1326 | 1326 | ''' |
|
|
1327 | 1327 | print("[INICIO]-setup del METODO PULSE PAIR") |
|
|
1328 | 1328 | self.__initime = None |
|
|
1329 | 1329 | self.__lastdatatime = 0 |
|
|
1330 | 1330 | self.__dataReady = False |
|
|
1331 | 1331 | self.__buffer = 0 |
|
|
1332 | 1332 | self.__profIndex = 0 |
|
|
1333 | 1333 | self.noise = None |
|
|
1334 | 1334 | self.__nch = dataOut.nChannels |
|
|
1335 | 1335 | self.__nHeis = dataOut.nHeights |
|
|
1336 | 1336 | self.removeDC = removeDC |
|
|
1337 | 1337 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1338 | 1338 | self.ippSec = dataOut.ippSeconds |
|
|
1339 | 1339 | self.nCohInt = dataOut.nCohInt |
|
|
1340 | 1340 | print("IPPseconds",dataOut.ippSeconds) |
|
|
1341 | 1341 | |
|
|
1342 | 1342 | print("ELVALOR DE n es:", n) |
|
|
1343 | 1343 | if n == None: |
|
|
1344 | 1344 | raise ValueError("n should be specified.") |
|
|
1345 | 1345 | |
|
|
1346 | 1346 | if n != None: |
|
|
1347 | 1347 | if n<2: |
|
|
1348 | 1348 | raise ValueError("n should be greater than 2") |
|
|
1349 | 1349 | |
|
|
1350 | 1350 | self.n = n |
|
|
1351 | 1351 | self.__nProf = n |
|
|
1352 | 1352 | |
|
|
1353 | 1353 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1354 | 1354 | n, |
|
|
1355 | 1355 | dataOut.nHeights), |
|
|
1356 | 1356 | dtype='complex') |
|
|
1357 | 1357 | |
|
|
1358 | 1358 | def putData(self,data): |
|
|
1359 | 1359 | ''' |
|
|
1360 | 1360 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1361 | 1361 | ''' |
|
|
1362 | 1362 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1363 | 1363 | self.__profIndex += 1 |
|
|
1364 | 1364 | return |
|
|
1365 | 1365 | |
|
|
1366 | 1366 | def pushData(self,dataOut): |
|
|
1367 | 1367 | ''' |
|
|
1368 | 1368 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1369 | 1369 | Affected : self.__profileIndex |
|
|
1370 | 1370 | ''' |
|
|
1371 | 1371 | #----------------- Remove DC----------------------------------- |
|
|
1372 | 1372 | if self.removeDC==True: |
|
|
1373 | 1373 | mean = numpy.mean(self.__buffer,1) |
|
|
1374 | 1374 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1375 | 1375 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1376 | 1376 | self.__buffer = self.__buffer - dc |
|
|
1377 | 1377 | #------------------Calculo de Potencia ------------------------ |
|
|
1378 | 1378 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
|
1379 | 1379 | pair0 = pair0.real |
|
|
1380 | 1380 | lag_0 = numpy.sum(pair0,1) |
|
|
1381 | 1381 | #------------------Calculo de Ruido x canal-------------------- |
|
|
1382 | 1382 | self.noise = numpy.zeros(self.__nch) |
|
|
1383 | 1383 | for i in range(self.__nch): |
|
|
1384 | 1384 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
|
1385 | 1385 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
|
1386 | 1386 | |
|
|
1387 | 1387 | self.noise = self.noise.reshape(self.__nch,1) |
|
|
1388 | 1388 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
|
1389 | 1389 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
|
1390 | 1390 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
|
1391 | 1391 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
1392 | 1392 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
1393 | 1393 | #-------------------- Power -------------------------------------------------- |
|
|
1394 | 1394 | data_power = lag_0/(self.n*self.nCohInt) |
|
|
1395 | 1395 | #------------------ Senal --------------------------------------------------- |
|
|
1396 | 1396 | data_intensity = pair0 - noise_buffer |
|
|
1397 | 1397 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
|
1398 | 1398 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
1399 | 1399 | for i in range(self.__nch): |
|
|
1400 | 1400 | for j in range(self.__nHeis): |
|
|
1401 | 1401 | if data_intensity[i][j] < 0: |
|
|
1402 | 1402 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
1403 | 1403 | |
|
|
1404 | 1404 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
1405 | 1405 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1406 | 1406 | lag_1 = numpy.sum(pair1,1) |
|
|
1407 | 1407 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
|
1408 | 1408 | data_velocity = (self.lambda_/2.0)*data_freq |
|
|
1409 | 1409 | |
|
|
1410 | 1410 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
1411 | 1411 | lag_0 = lag_0/self.n |
|
|
1412 | 1412 | S = lag_0-self.noise |
|
|
1413 | 1413 | |
|
|
1414 | 1414 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
1415 | 1415 | lag_1 = lag_1/(self.n-1) |
|
|
1416 | 1416 | R1 = numpy.abs(lag_1) |
|
|
1417 | 1417 | |
|
|
1418 | 1418 | #---------------- Calculo del SNR---------------------------------- |
|
|
1419 | 1419 | data_snrPP = S/self.noise |
|
|
1420 | 1420 | for i in range(self.__nch): |
|
|
1421 | 1421 | for j in range(self.__nHeis): |
|
|
1422 | 1422 | if data_snrPP[i][j] < 1.e-20: |
|
|
1423 | 1423 | data_snrPP[i][j] = 1.e-20 |
|
|
1424 | 1424 | |
|
|
1425 | 1425 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
1426 | 1426 | L = S/R1 |
|
|
1427 | 1427 | L = numpy.where(L<0,1,L) |
|
|
1428 | 1428 | L = numpy.log(L) |
|
|
1429 | 1429 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1430 | 1430 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
|
1431 | 1431 | n = self.__profIndex |
|
|
1432 | 1432 | |
|
|
1433 | 1433 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1434 | 1434 | self.__profIndex = 0 |
|
|
1435 | 1435 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n |
|
|
1436 | 1436 | |
|
|
1437 | 1437 | |
|
|
1438 | 1438 | def pulsePairbyProfiles(self,dataOut): |
|
|
1439 | 1439 | |
|
|
1440 | 1440 | self.__dataReady = False |
|
|
1441 | 1441 | data_power = None |
|
|
1442 | 1442 | data_intensity = None |
|
|
1443 | 1443 | data_velocity = None |
|
|
1444 | 1444 | data_specwidth = None |
|
|
1445 | 1445 | data_snrPP = None |
|
|
1446 | 1446 | self.putData(data=dataOut.data) |
|
|
1447 | 1447 | if self.__profIndex == self.n: |
|
|
1448 | 1448 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) |
|
|
1449 | 1449 | self.__dataReady = True |
|
|
1450 | 1450 | |
|
|
1451 | 1451 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
|
1452 | 1452 | |
|
|
1453 | 1453 | |
|
|
1454 | 1454 | def pulsePairOp(self, dataOut, datatime= None): |
|
|
1455 | 1455 | |
|
|
1456 | 1456 | if self.__initime == None: |
|
|
1457 | 1457 | self.__initime = datatime |
|
|
1458 | 1458 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
|
1459 | 1459 | self.__lastdatatime = datatime |
|
|
1460 | 1460 | |
|
|
1461 | 1461 | if data_power is None: |
|
|
1462 | 1462 | return None, None, None,None,None,None |
|
|
1463 | 1463 | |
|
|
1464 | 1464 | avgdatatime = self.__initime |
|
|
1465 | 1465 | deltatime = datatime - self.__lastdatatime |
|
|
1466 | 1466 | self.__initime = datatime |
|
|
1467 | 1467 | |
|
|
1468 | 1468 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
|
1469 | 1469 | |
|
|
1470 | 1470 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1471 | 1471 | |
|
|
1472 | 1472 | if not self.isConfig: |
|
|
1473 | 1473 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1474 | 1474 | self.isConfig = True |
|
|
1475 | 1475 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
|
1476 | 1476 | dataOut.flagNoData = True |
|
|
1477 | 1477 | |
|
|
1478 | 1478 | if self.__dataReady: |
|
|
1479 | 1479 | dataOut.nCohInt *= self.n |
|
|
1480 | 1480 | dataOut.dataPP_POW = data_intensity # S |
|
|
1481 | print("help",data_power) | |
|
|
1481 | 1482 | dataOut.dataPP_POWER = data_power # P |
|
|
1482 | 1483 | dataOut.dataPP_DOP = data_velocity |
|
|
1483 | 1484 | dataOut.dataPP_SNR = data_snrPP |
|
|
1484 | 1485 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
1485 | 1486 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1486 | 1487 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1487 | 1488 | dataOut.utctime = avgdatatime |
|
|
1488 | 1489 | dataOut.flagNoData = False |
|
|
1489 | 1490 | return dataOut |
|
|
1490 | 1491 | |
|
|
1491 | 1492 | |
|
|
1492 | 1493 | |
|
|
1493 | 1494 | # import collections |
|
|
1494 | 1495 | # from scipy.stats import mode |
|
|
1495 | 1496 | # |
|
|
1496 | 1497 | # class Synchronize(Operation): |
|
|
1497 | 1498 | # |
|
|
1498 | 1499 | # isConfig = False |
|
|
1499 | 1500 | # __profIndex = 0 |
|
|
1500 | 1501 | # |
|
|
1501 | 1502 | # def __init__(self, **kwargs): |
|
|
1502 | 1503 | # |
|
|
1503 | 1504 | # Operation.__init__(self, **kwargs) |
|
|
1504 | 1505 | # # self.isConfig = False |
|
|
1505 | 1506 | # self.__powBuffer = None |
|
|
1506 | 1507 | # self.__startIndex = 0 |
|
|
1507 | 1508 | # self.__pulseFound = False |
|
|
1508 | 1509 | # |
|
|
1509 | 1510 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1510 | 1511 | # |
|
|
1511 | 1512 | # #Read data |
|
|
1512 | 1513 | # |
|
|
1513 | 1514 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1514 | 1515 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1515 | 1516 | # |
|
|
1516 | 1517 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1517 | 1518 | # |
|
|
1518 | 1519 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1519 | 1520 | # |
|
|
1520 | 1521 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1521 | 1522 | # |
|
|
1522 | 1523 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1523 | 1524 | # |
|
|
1524 | 1525 | # if maxValue < noisedB + 10: |
|
|
1525 | 1526 | # #No se encuentra ningun pulso de transmision |
|
|
1526 | 1527 | # return None |
|
|
1527 | 1528 | # |
|
|
1528 | 1529 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1529 | 1530 | # |
|
|
1530 | 1531 | # if len(maxValuesIndex) < 2: |
|
|
1531 | 1532 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1532 | 1533 | # return None |
|
|
1533 | 1534 | # |
|
|
1534 | 1535 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1535 | 1536 | # |
|
|
1536 | 1537 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1537 | 1538 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1538 | 1539 | # |
|
|
1539 | 1540 | # if len(pulseIndex) < 2: |
|
|
1540 | 1541 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1541 | 1542 | # return None |
|
|
1542 | 1543 | # |
|
|
1543 | 1544 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1544 | 1545 | # |
|
|
1545 | 1546 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1546 | 1547 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1547 | 1548 | # |
|
|
1548 | 1549 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1549 | 1550 | # |
|
|
1550 | 1551 | # if len(realIndex) < 2: |
|
|
1551 | 1552 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1552 | 1553 | # return None |
|
|
1553 | 1554 | # |
|
|
1554 | 1555 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1555 | 1556 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1556 | 1557 | # |
|
|
1557 | 1558 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1558 | 1559 | # |
|
|
1559 | 1560 | # print "IPP = %d samples" %period |
|
|
1560 | 1561 | # |
|
|
1561 | 1562 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1562 | 1563 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1563 | 1564 | # |
|
|
1564 | 1565 | # return 1 |
|
|
1565 | 1566 | # |
|
|
1566 | 1567 | # |
|
|
1567 | 1568 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1568 | 1569 | # |
|
|
1569 | 1570 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1570 | 1571 | # maxlen = buffer_size*nSamples) |
|
|
1571 | 1572 | # |
|
|
1572 | 1573 | # bufferList = [] |
|
|
1573 | 1574 | # |
|
|
1574 | 1575 | # for i in range(nChannels): |
|
|
1575 | 1576 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1576 | 1577 | # maxlen = buffer_size*nSamples) |
|
|
1577 | 1578 | # |
|
|
1578 | 1579 | # bufferList.append(bufferByChannel) |
|
|
1579 | 1580 | # |
|
|
1580 | 1581 | # self.__nSamples = nSamples |
|
|
1581 | 1582 | # self.__nChannels = nChannels |
|
|
1582 | 1583 | # self.__bufferList = bufferList |
|
|
1583 | 1584 | # |
|
|
1584 | 1585 | # def run(self, dataOut, channel = 0): |
|
|
1585 | 1586 | # |
|
|
1586 | 1587 | # if not self.isConfig: |
|
|
1587 | 1588 | # nSamples = dataOut.nHeights |
|
|
1588 | 1589 | # nChannels = dataOut.nChannels |
|
|
1589 | 1590 | # self.setup(nSamples, nChannels) |
|
|
1590 | 1591 | # self.isConfig = True |
|
|
1591 | 1592 | # |
|
|
1592 | 1593 | # #Append new data to internal buffer |
|
|
1593 | 1594 | # for thisChannel in range(self.__nChannels): |
|
|
1594 | 1595 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1595 | 1596 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1596 | 1597 | # |
|
|
1597 | 1598 | # if self.__pulseFound: |
|
|
1598 | 1599 | # self.__startIndex -= self.__nSamples |
|
|
1599 | 1600 | # |
|
|
1600 | 1601 | # #Finding Tx Pulse |
|
|
1601 | 1602 | # if not self.__pulseFound: |
|
|
1602 | 1603 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1603 | 1604 | # |
|
|
1604 | 1605 | # if indexFound == None: |
|
|
1605 | 1606 | # dataOut.flagNoData = True |
|
|
1606 | 1607 | # return |
|
|
1607 | 1608 | # |
|
|
1608 | 1609 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1609 | 1610 | # self.__pulseFound = True |
|
|
1610 | 1611 | # self.__startIndex = indexFound |
|
|
1611 | 1612 | # |
|
|
1612 | 1613 | # #If pulse was found ... |
|
|
1613 | 1614 | # for thisChannel in range(self.__nChannels): |
|
|
1614 | 1615 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1615 | 1616 | # #print self.__startIndex |
|
|
1616 | 1617 | # x = numpy.array(bufferByChannel) |
|
|
1617 | 1618 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1618 | 1619 | # |
|
|
1619 | 1620 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1620 | 1621 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1621 | 1622 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1622 | 1623 | # |
|
|
1623 | 1624 | # dataOut.data = self.__arrayBuffer |
|
|
1624 | 1625 | # |
|
|
1625 | 1626 | # self.__startIndex += self.__newNSamples |
|
|
1626 | 1627 | # |
|
|
1627 | 1628 | # return |
| @@ -1,157 +1,159 | |||
|
|
1 | 1 | #!python |
|
|
2 | 2 | ''' |
|
|
3 | 3 | ''' |
|
|
4 | 4 | |
|
|
5 | 5 | import os, sys |
|
|
6 | 6 | import datetime |
|
|
7 | 7 | import time |
|
|
8 | 8 | |
|
|
9 | 9 | #path = os.path.dirname(os.getcwd()) |
|
|
10 | 10 | #path = os.path.dirname(path) |
|
|
11 | 11 | #sys.path.insert(0, path) |
|
|
12 | 12 | |
|
|
13 | 13 | from schainpy.controller import Project |
|
|
14 | 14 | |
|
|
15 | 15 | desc = "USRP_test" |
|
|
16 | 16 | filename = "USRP_processing.xml" |
|
|
17 | 17 | controllerObj = Project() |
|
|
18 | 18 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) |
|
|
19 | 19 | |
|
|
20 | 20 | ############## USED TO PLOT IQ VOLTAGE, POWER AND SPECTRA ############# |
|
|
21 | 21 | |
|
|
22 | 22 | ####################################################################### |
|
|
23 | 23 | ######PATH DE LECTURA, ESCRITURA, GRAFICOS Y ENVIO WEB################# |
|
|
24 | 24 | ####################################################################### |
|
|
25 | 25 | #path = '/media/data/data/vientos/57.2063km/echoes/NCO_Woodman' |
|
|
26 | 26 | #path = '/DATA_RM/TEST_INTEGRACION' |
|
|
27 | 27 | #path = '/DATA_RM/TEST_ONLINE' |
|
|
28 | 28 | #path_pp = '/DATA_RM/TEST_HDF5' |
|
|
29 | 29 | |
|
|
30 | 30 | #figpath = '/home/soporte/Pictures/TEST_INTEGRACION_IMG' |
|
|
31 | 31 | ###path = '/DATA_RM/TEST_INTEGRACION/ADQ_OFFLINE/' |
|
|
32 | 32 | ###path_pp = '/DATA_RM/TEST_HDF5_SPEC' |
|
|
33 | 33 | |
|
|
34 | 34 | #path = '/DATA_RM/USRP_22' |
|
|
35 | path = '/DATA_RM/23/6v' | |
|
|
35 | ###path = '/DATA_RM/23/6v' | |
|
|
36 | path = '/DATA_RM/TEST_19OCTUBRE/10MHZ' | |
|
|
36 | 37 | #path_pp = '/DATA_RM/TEST_HDF5' |
|
|
38 | path_pp = '/DATA_RM/TEST_HDF5_19OCT' | |
|
|
37 | 39 | # UTIMO TEST 22 DE SEPTIEMBRE |
|
|
38 | 40 | #path_pp = '/DATA_RM/TEST_HDF5_SPEC_22' |
|
|
39 | 41 | #path_pp = '/DATA_RM/TEST_HDF5_SPEC_3v' |
|
|
40 | path_pp = '/DATA_RM/TEST_HDF5_SPEC_23/6v' | |
|
|
42 | ###path_pp = '/DATA_RM/TEST_HDF5_SPEC_23/6v' | |
|
|
41 | 43 | |
|
|
42 | 44 | |
|
|
43 | 45 | #remotefolder = "/home/wmaster/graficos" |
|
|
44 | 46 | ####################################################################### |
|
|
45 | 47 | ################# RANGO DE PLOTEO###################################### |
|
|
46 | 48 | ####################################################################### |
|
|
47 | 49 | dBmin = '-5' |
|
|
48 | 50 | dBmax = '20' |
|
|
49 | 51 | xmin = '0' |
|
|
50 | 52 | xmax ='24' |
|
|
51 | 53 | ymin = '0' |
|
|
52 | 54 | ymax = '600' |
|
|
53 | 55 | ####################################################################### |
|
|
54 | 56 | ########################FECHA########################################## |
|
|
55 | 57 | ####################################################################### |
|
|
56 | 58 | str = datetime.date.today() |
|
|
57 | 59 | today = str.strftime("%Y/%m/%d") |
|
|
58 | 60 | str2 = str - datetime.timedelta(days=1) |
|
|
59 | 61 | yesterday = str2.strftime("%Y/%m/%d") |
|
|
60 | 62 | ####################################################################### |
|
|
61 | 63 | ######################## UNIDAD DE LECTURA############################# |
|
|
62 | 64 | ####################################################################### |
|
|
63 | 65 | readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader', |
|
|
64 | 66 | path=path, |
|
|
65 | 67 | startDate="2021/01/01",#today, |
|
|
66 | 68 | endDate="2021/12/30",#today, |
|
|
67 | 69 | startTime='00:00:00', |
|
|
68 | 70 | endTime='23:59:59', |
|
|
69 | 71 | delay=0, |
|
|
70 | 72 | #set=0, |
|
|
71 | 73 | online=0, |
|
|
72 | 74 | walk=1, |
|
|
73 | 75 | ippKm = 60) |
|
|
74 | 76 | |
|
|
75 | 77 | opObj11 = readUnitConfObj.addOperation(name='printInfo') |
|
|
76 | 78 | #opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') |
|
|
77 | 79 | ####################################################################### |
|
|
78 | 80 | ################ OPERACIONES DOMINIO DEL TIEMPO######################## |
|
|
79 | 81 | ####################################################################### |
|
|
80 | 82 | |
|
|
81 | 83 | |
|
|
82 | V=6 | |
|
|
84 | V=10 | |
|
|
83 | 85 | IPP=400*1e-6 |
|
|
84 | 86 | n= int(1/(V*IPP)) |
|
|
85 | 87 | print("n numero de Perfiles a procesar con nFFTPoints ", n) |
|
|
86 | 88 | |
|
|
87 | 89 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) |
|
|
88 | 90 | |
|
|
89 | 91 | procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId()) |
|
|
90 | 92 | procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int') |
|
|
91 | 93 | procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int') |
|
|
92 | 94 | |
|
|
93 | 95 | |
|
|
94 | 96 | |
|
|
95 | 97 | # |
|
|
96 | 98 | # codigo64='1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1,1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0,'+\ |
|
|
97 | 99 | # '1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,1,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1' |
|
|
98 | 100 | |
|
|
99 | 101 | #opObj11 = procUnitConfObjA.addOperation(name='setRadarFrequency') |
|
|
100 | 102 | #opObj11.addParameter(name='frequency', value='70312500') |
|
|
101 | 103 | #opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other') |
|
|
102 | 104 | #opObj11.addParameter(name='n', value='625', format='int')#10 |
|
|
103 | 105 | #opObj11.addParameter(name='removeDC', value=1, format='int') |
|
|
104 | 106 | |
|
|
105 | 107 | # Ploteo TEST |
|
|
106 | 108 | ''' |
|
|
107 | 109 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairPowerPlot', optype='other') |
|
|
108 | 110 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairSignalPlot', optype='other') |
|
|
109 | 111 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other') |
|
|
110 | 112 | #opObj11.addParameter(name='xmax', value=8) |
|
|
111 | 113 | opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other') |
|
|
112 | 114 | ''' |
|
|
113 | 115 | # OJO SCOPE |
|
|
114 | 116 | #opObj10 = procUnitConfObjA.addOperation(name='ScopePlot', optype='external') |
|
|
115 | 117 | #opObj10.addParameter(name='buffer_sizeid', value='10', format='int') |
|
|
116 | 118 | ##opObj10.addParameter(name='xmin', value='0', format='int') |
|
|
117 | 119 | ##opObj10.addParameter(name='xmax', value='50', format='int') |
|
|
118 | 120 | #opObj10.addParameter(name='type', value='iq') |
|
|
119 | 121 | ##opObj10.addParameter(name='ymin', value='-5000', format='int') |
|
|
120 | 122 | ##opObj10.addParameter(name='ymax', value='8500', format='int') |
|
|
121 | 123 | #opObj11.addParameter(name='save', value=figpath, format='str') |
|
|
122 | 124 | #opObj11.addParameter(name='save_period', value=10, format='int') |
|
|
123 | 125 | |
|
|
124 | 126 | #opObj10 = procUnitConfObjA.addOperation(name='setH0') |
|
|
125 | 127 | #opObj10.addParameter(name='h0', value='-5000', format='float') |
|
|
126 | 128 | |
|
|
127 | 129 | #opObj11 = procUnitConfObjA.addOperation(name='filterByHeights') |
|
|
128 | 130 | #opObj11.addParameter(name='window', value='1', format='int') |
|
|
129 | 131 | |
|
|
130 | 132 | #codigo='1,1,-1,1,1,-1,1,-1,-1,1,-1,-1,-1,1,-1,-1,-1,1,-1,-1,-1,1,1,1,1,-1,-1,-1' |
|
|
131 | 133 | #opObj11 = procUnitConfObjSousy.addOperation(name='Decoder', optype='other') |
|
|
132 | 134 | #opObj11.addParameter(name='code', value=codigo, formatyesterday='floatlist') |
|
|
133 | 135 | #opObj11.addParameter(name='nCode', value='1', format='int') |
|
|
134 | 136 | #opObj11.addParameter(name='nBaud', value='28', format='int') |
|
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135 | 137 | |
|
|
136 | 138 | #opObj11 = procUnitConfObjA.addOperation(name='CohInt', optype='other') |
|
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137 | 139 | #opObj11.addParameter(name='n', value='100', format='int') |
|
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138 | 140 | |
|
|
139 | 141 | ####################################################################### |
|
|
140 | 142 | ########## OPERACIONES ParametersProc######################## |
|
|
141 | 143 | ####################################################################### |
|
|
142 | 144 | |
|
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143 | 145 | procUnitConfObjC= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId()) |
|
|
144 | 146 | |
|
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145 | 147 | procUnitConfObjC.addOperation(name='SpectralMoments') |
|
|
146 | 148 | |
|
|
147 | 149 | |
|
|
148 | 150 | opObj10 = procUnitConfObjC.addOperation(name='HDFWriter') |
|
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149 | 151 | opObj10.addParameter(name='path',value=path_pp) |
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150 | 152 | #opObj10.addParameter(name='mode',value=0) |
|
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151 | 153 | opObj10.addParameter(name='blocksPerFile',value='100',format='int') |
|
|
152 | 154 | #opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex |
|
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153 | 155 | opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex |
|
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154 | 156 | |
|
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155 | 157 | opObj10.addParameter(name='dataList',value='data_pow,data_dop,utctime',format='list')#,format='list' |
|
|
156 | 158 | |
|
|
157 | 159 | controllerObj.start() |
| @@ -1,213 +1,216 | |||
|
|
1 | 1 | # Ing. AVP |
|
|
2 | 2 | # 06/10/2021 |
|
|
3 | 3 | # ARCHIVO DE LECTURA |
|
|
4 | 4 | import os, sys |
|
|
5 | 5 | import datetime |
|
|
6 | 6 | import time |
|
|
7 | 7 | from schainpy.controller import Project |
|
|
8 | 8 | #### NOTA########################################### |
|
|
9 | 9 | # INPUT : |
|
|
10 | 10 | # VELOCIDAD PARAMETRO : V = 2Β°/seg |
|
|
11 | 11 | # MODO PULSE PAIR O MOMENTOS: 0 : Pulse Pair ,1 : Momentos |
|
|
12 | 12 | ###################################################### |
|
|
13 | 13 | ##### PROCESAMIENTO ################################## |
|
|
14 | 14 | ##### OJO TENER EN CUENTA EL n= para el Pulse Pair ## |
|
|
15 | 15 | ##### O EL n= nFFTPoints ### |
|
|
16 | 16 | ###################################################### |
|
|
17 | 17 | ######## BUSCAMOS EL numero de IPP equivalente 1Β°##### |
|
|
18 | 18 | ######## Sea V la velocidad del Pedestal en Β°/seg##### |
|
|
19 | 19 | ######## 1Β° sera Recorrido en un tiempo de 1/V ###### |
|
|
20 | 20 | ######## IPP del Radar 400 useg --> 60 Km ############ |
|
|
21 | 21 | ######## n = 1/(V(Β°/seg)*IPP(Km)) , NUMERO DE IPP ## |
|
|
22 | 22 | ######## n = 1/(V*IPP) ############################# |
|
|
23 | 23 | ######## VELOCIDAD DEL PEDESTAL ###################### |
|
|
24 | 24 | print("SETUP- RADAR METEOROLOGICO") |
|
|
25 | 25 | V = 10 |
|
|
26 | 26 | mode = 1 |
|
|
27 | 27 | #path = '/DATA_RM/23/6v' |
|
|
28 | path = '/DATA_RM/TEST_INTEGRACION_2M' | |
|
|
29 | path_ped='/DATA_RM/TEST_PEDESTAL/P20211012-082745' | |
|
|
28 | ####path = '/DATA_RM/TEST_INTEGRACION_2M' | |
|
|
29 | #path = '/DATA_RM/TEST_19OCTUBRE/10MHZ' | |
|
|
30 | path = '/DATA_RM/WR_20_OCT' | |
|
|
31 | #### path_ped='/DATA_RM/TEST_PEDESTAL/P20211012-082745' | |
|
|
32 | #### path_ped='/DATA_RM/TEST_PEDESTAL/P20211019-192244' | |
|
|
30 | 33 | figpath_pp = "/home/soporte/Pictures/TEST_PP" |
|
|
31 |
figpath_ |
|
|
|
32 |
plot = |
|
|
|
33 |
integration = |
|
|
|
34 | figpath_spec = "/home/soporte/Pictures/TEST_MOM" | |
|
|
35 | plot = 1 | |
|
|
36 | integration = 0 | |
|
|
34 | 37 | save = 0 |
|
|
35 | 38 | if save == 1: |
|
|
36 | 39 | if mode==0: |
|
|
37 | 40 | path_save = '/DATA_RM/TEST_HDF5_PP_23/6v' |
|
|
38 | 41 | path_save = '/DATA_RM/TEST_HDF5_PP' |
|
|
39 | 42 | path_save = '/DATA_RM/TEST_HDF5_PP_100' |
|
|
40 | 43 | else: |
|
|
41 | 44 | path_save = '/DATA_RM/TEST_HDF5_SPEC_23_V2/6v' |
|
|
42 | 45 | |
|
|
43 | 46 | print("* PATH data ADQ :", path) |
|
|
44 | 47 | print("* Velocidad Pedestal :",V,"Β°/seg") |
|
|
45 | 48 | ############################ NRO Perfiles PROCESAMIENTO ################### |
|
|
46 | 49 | V=V |
|
|
47 | 50 | IPP=400*1e-6 |
|
|
48 | 51 | n= int(1/(V*IPP)) |
|
|
49 | 52 | print("* n - NRO Perfiles Proc:", n ) |
|
|
50 | 53 | ################################## MODE ################################### |
|
|
51 | 54 | print("* Modo de Operacion :",mode) |
|
|
52 | 55 | if mode ==0: |
|
|
53 | 56 | print("* Met. Seleccionado : Pulse Pair") |
|
|
54 | 57 | else: |
|
|
55 | 58 | print("* Met. Momentos : Momentos") |
|
|
56 | 59 | |
|
|
57 | 60 | ################################## MODE ################################### |
|
|
58 | 61 | print("* Grabado de datos :",save) |
|
|
59 | 62 | if save ==1: |
|
|
60 | 63 | if mode==0: |
|
|
61 | 64 | ope= "Pulse Pair" |
|
|
62 | 65 | else: |
|
|
63 | 66 | ope= "Momentos" |
|
|
64 | 67 | print("* Path-Save Data -", ope , path_save) |
|
|
65 | 68 | |
|
|
66 | 69 | print("* Integracion de datos :",integration) |
|
|
67 | 70 | |
|
|
68 | 71 | time.sleep(15) |
|
|
69 | 72 | #remotefolder = "/home/wmaster/graficos" |
|
|
70 | 73 | ####################################################################### |
|
|
71 | 74 | ################# RANGO DE PLOTEO###################################### |
|
|
72 | 75 | dBmin = '1' |
|
|
73 |
dBmax = ' |
|
|
|
74 |
xmin = '1 |
|
|
|
75 |
xmax = '1 |
|
|
|
76 | dBmax = '65' | |
|
|
77 | xmin = '13.2' | |
|
|
78 | xmax = '13.5' | |
|
|
76 | 79 | ymin = '0' |
|
|
77 |
ymax = '60 |
|
|
|
80 | ymax = '60' | |
|
|
78 | 81 | ####################################################################### |
|
|
79 | 82 | ########################FECHA########################################## |
|
|
80 | 83 | str = datetime.date.today() |
|
|
81 | 84 | today = str.strftime("%Y/%m/%d") |
|
|
82 | 85 | str2 = str - datetime.timedelta(days=1) |
|
|
83 | 86 | yesterday = str2.strftime("%Y/%m/%d") |
|
|
84 | 87 | ####################################################################### |
|
|
85 | 88 | ########################SIGNAL CHAIN ################################## |
|
|
86 | 89 | ####################################################################### |
|
|
87 | 90 | desc = "USRP_test" |
|
|
88 | 91 | filename = "USRP_processing.xml" |
|
|
89 | 92 | controllerObj = Project() |
|
|
90 | 93 | controllerObj.setup(id = '191', name='Test_USRP', description=desc) |
|
|
91 | 94 | ####################################################################### |
|
|
92 | 95 | ######################## UNIDAD DE LECTURA############################# |
|
|
93 | 96 | ####################################################################### |
|
|
94 | 97 | readUnitConfObj = controllerObj.addReadUnit(datatype='DigitalRFReader', |
|
|
95 | 98 | path=path, |
|
|
96 | 99 | startDate="2021/01/01",#today, |
|
|
97 | 100 | endDate="2021/12/30",#today, |
|
|
98 | 101 | startTime='00:00:00', |
|
|
99 | 102 | endTime='23:59:59', |
|
|
100 | 103 | delay=0, |
|
|
101 | 104 | #set=0, |
|
|
102 | 105 | online=0, |
|
|
103 | 106 | walk=1, |
|
|
104 | 107 | ippKm = 60) |
|
|
105 | 108 | |
|
|
106 | 109 | opObj11 = readUnitConfObj.addOperation(name='printInfo') |
|
|
107 | 110 | |
|
|
108 | 111 | procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) |
|
|
109 | 112 | |
|
|
110 | 113 | if mode ==0: |
|
|
111 | 114 | ####################### METODO PULSE PAIR ###################################################################### |
|
|
112 | 115 | opObj11 = procUnitConfObjA.addOperation(name='PulsePair', optype='other') |
|
|
113 | 116 | opObj11.addParameter(name='n', value=int(n), format='int')#10 VOY A USAR 250 DADO QUE LA VELOCIDAD ES 10 GRADOS |
|
|
114 | 117 | #opObj11.addParameter(name='removeDC', value=1, format='int') |
|
|
115 | 118 | ####################### METODO Parametros ###################################################################### |
|
|
116 | 119 | procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId()) |
|
|
117 | 120 | if plot==1: |
|
|
118 | 121 | opObj11 = procUnitConfObjB.addOperation(name='GenericRTIPlot',optype='external') |
|
|
119 | opObj11.addParameter(name='attr_data', value='dataPP_POW') | |
|
|
122 | opObj11.addParameter(name='attr_data', value='dataPP_POWER') | |
|
|
120 | 123 | opObj11.addParameter(name='colormap', value='jet') |
|
|
121 | 124 | opObj11.addParameter(name='xmin', value=xmin) |
|
|
122 | 125 | opObj11.addParameter(name='xmax', value=xmax) |
|
|
123 | 126 | opObj11.addParameter(name='zmin', value=dBmin) |
|
|
124 | 127 | opObj11.addParameter(name='zmax', value=dBmax) |
|
|
125 | 128 | opObj11.addParameter(name='save', value=figpath_pp) |
|
|
126 | 129 | opObj11.addParameter(name='showprofile', value=0) |
|
|
127 |
opObj11.addParameter(name='save_period', value= |
|
|
|
130 | opObj11.addParameter(name='save_period', value=10) | |
|
|
128 | 131 | |
|
|
129 | 132 | ####################### METODO ESCRITURA ####################################################################### |
|
|
130 | 133 | if save==1: |
|
|
131 | 134 | opObj10 = procUnitConfObjB.addOperation(name='HDFWriter') |
|
|
132 | 135 | opObj10.addParameter(name='path',value=path_save) |
|
|
133 | 136 | #opObj10.addParameter(name='mode',value=0) |
|
|
134 | 137 | opObj10.addParameter(name='blocksPerFile',value='100',format='int') |
|
|
135 | 138 | opObj10.addParameter(name='metadataList',value='utctimeInit,timeZone,paramInterval,profileIndex,channelList,heightList,flagDataAsBlock',format='list') |
|
|
136 | opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,utctime',format='list')#,format='list' | |
|
|
139 | opObj10.addParameter(name='dataList',value='dataPP_POWER,dataPP_DOP,utctime',format='list')#,format='list' | |
|
|
137 | 140 | if integration==1: |
|
|
138 | 141 | V=10 |
|
|
139 | 142 | blocksPerfile=360 |
|
|
140 | 143 | print("* Velocidad del Pedestal:",V) |
|
|
141 | 144 | tmp_blocksPerfile = 100 |
|
|
142 | 145 | f_a_p= int(tmp_blocksPerfile/V) |
|
|
143 | 146 | |
|
|
144 | 147 | opObj11 = procUnitConfObjB.addOperation(name='PedestalInformation') |
|
|
145 | 148 | opObj11.addParameter(name='path_ped', value=path_ped) |
|
|
146 | 149 | #opObj11.addParameter(name='path_adq', value=path_adq) |
|
|
147 | 150 | opObj11.addParameter(name='t_Interval_p', value='0.01', format='float') |
|
|
148 | 151 | opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int') |
|
|
149 | 152 | opObj11.addParameter(name='n_Muestras_p', value='100', format='float') |
|
|
150 | 153 | opObj11.addParameter(name='f_a_p', value=f_a_p, format='int') |
|
|
151 | 154 | opObj11.addParameter(name='online', value='0', format='int') |
|
|
152 | 155 | |
|
|
153 | 156 | opObj11 = procUnitConfObjB.addOperation(name='Block360') |
|
|
154 | 157 | opObj11.addParameter(name='n', value='10', format='int') |
|
|
155 | 158 | opObj11.addParameter(name='mode', value=mode, format='int') |
|
|
156 | 159 | |
|
|
157 | 160 | # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180 |
|
|
158 | 161 | |
|
|
159 | 162 | opObj11= procUnitConfObjB.addOperation(name='WeatherPlot',optype='other') |
|
|
160 | 163 | |
|
|
161 | 164 | |
|
|
162 | 165 | else: |
|
|
163 | 166 | ####################### METODO SPECTROS ###################################################################### |
|
|
164 | 167 | procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId()) |
|
|
165 | 168 | procUnitConfObjB.addParameter(name='nFFTPoints', value=n, format='int') |
|
|
166 | 169 | procUnitConfObjB.addParameter(name='nProfiles' , value=n, format='int') |
|
|
167 | 170 | |
|
|
168 | 171 | procUnitConfObjC = controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId()) |
|
|
169 | 172 | procUnitConfObjC.addOperation(name='SpectralMoments') |
|
|
170 | 173 | if plot==1: |
|
|
171 | 174 | dBmin = '1' |
|
|
172 | 175 | dBmax = '65' |
|
|
173 | 176 | opObj11 = procUnitConfObjC.addOperation(name='PowerPlot',optype='external') |
|
|
174 | 177 | opObj11.addParameter(name='xmin', value=xmin) |
|
|
175 | 178 | opObj11.addParameter(name='xmax', value=xmax) |
|
|
176 | 179 | opObj11.addParameter(name='zmin', value=dBmin) |
|
|
177 | 180 | opObj11.addParameter(name='zmax', value=dBmax) |
|
|
178 |
opObj11.addParameter(name='save', value=figpath_ |
|
|
|
181 | opObj11.addParameter(name='save', value=figpath_spec) | |
|
|
179 | 182 | opObj11.addParameter(name='showprofile', value=0) |
|
|
180 |
opObj11.addParameter(name='save_period', value=10 |
|
|
|
183 | opObj11.addParameter(name='save_period', value=10) | |
|
|
181 | 184 | |
|
|
182 | 185 | if save==1: |
|
|
183 | 186 | opObj10 = procUnitConfObjC.addOperation(name='HDFWriter') |
|
|
184 | 187 | opObj10.addParameter(name='path',value=path_save) |
|
|
185 | 188 | #opObj10.addParameter(name='mode',value=0) |
|
|
186 | 189 | opObj10.addParameter(name='blocksPerFile',value='360',format='int') |
|
|
187 | 190 | #opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex |
|
|
188 | 191 | opObj10.addParameter(name='metadataList',value='utctimeInit,heightList,nIncohInt,nCohInt,nProfiles,channelList',format='list')#profileIndex |
|
|
189 | 192 | opObj10.addParameter(name='dataList',value='data_pow,data_dop,utctime',format='list')#,format='list' |
|
|
190 | 193 | |
|
|
191 | 194 | if integration==1: |
|
|
192 | 195 | V=10 |
|
|
193 | 196 | blocksPerfile=360 |
|
|
194 | 197 | print("* Velocidad del Pedestal:",V) |
|
|
195 | 198 | tmp_blocksPerfile = 100 |
|
|
196 | 199 | f_a_p= int(tmp_blocksPerfile/V) |
|
|
197 | 200 | |
|
|
198 | 201 | opObj11 = procUnitConfObjC.addOperation(name='PedestalInformation') |
|
|
199 | 202 | opObj11.addParameter(name='path_ped', value=path_ped) |
|
|
200 | 203 | #opObj11.addParameter(name='path_adq', value=path_adq) |
|
|
201 | 204 | opObj11.addParameter(name='t_Interval_p', value='0.01', format='float') |
|
|
202 | 205 | opObj11.addParameter(name='blocksPerfile', value=blocksPerfile, format='int') |
|
|
203 | 206 | opObj11.addParameter(name='n_Muestras_p', value='100', format='float') |
|
|
204 | 207 | opObj11.addParameter(name='f_a_p', value=f_a_p, format='int') |
|
|
205 | 208 | opObj11.addParameter(name='online', value='0', format='int') |
|
|
206 | 209 | |
|
|
207 | 210 | opObj11 = procUnitConfObjC.addOperation(name='Block360') |
|
|
208 | 211 | opObj11.addParameter(name='n', value='10', format='int') |
|
|
209 | 212 | opObj11.addParameter(name='mode', value=mode, format='int') |
|
|
210 | 213 | |
|
|
211 | 214 | # este bloque funciona bien con divisores de 360 no olvidar 0 10 20 30 40 60 90 120 180 |
|
|
212 | 215 | opObj11= procUnitConfObjC.addOperation(name='WeatherPlot',optype='other') |
|
|
213 | 216 | controllerObj.start() |
| @@ -1,112 +1,119 | |||
|
|
1 | 1 | # Ing-AlexanderValdez |
|
|
2 | 2 | # Monitoreo de Pedestal |
|
|
3 | 3 | |
|
|
4 | 4 | ############## IMPORTA LIBRERIAS ################### |
|
|
5 | 5 | import os,numpy,h5py |
|
|
6 | 6 | import sys,time |
|
|
7 | 7 | import matplotlib.pyplot as plt |
|
|
8 | 8 | #################################################### |
|
|
9 | path_ped = '/DATA_RM/TEST_PEDESTAL/P20211012-082745' | |
|
|
9 | ################################################################# | |
|
|
10 | # LA FECHA 21-10-20 CORRESPONDE A LAS PRUEBAS DEL DIA MIERCOLES | |
|
|
11 | # 1:15:51 pm hasta 3:49:32 pm | |
|
|
12 | ################################################################# | |
|
|
13 | ||
|
|
14 | #path_ped = '/DATA_RM/TEST_PEDESTAL/P20211012-082745' | |
|
|
15 | path_ped = '/DATA_RM/TEST_PEDESTAL/P20211020-131248' | |
|
|
10 | 16 | # Metodo para verificar numero |
|
|
11 | 17 | def isNumber(str): |
|
|
12 | 18 | try: |
|
|
13 | 19 | float(str) |
|
|
14 | 20 | return True |
|
|
15 | 21 | except: |
|
|
16 | 22 | return False |
|
|
17 | 23 | # Metodo para extraer el arreglo |
|
|
18 | 24 | def getDatavaluefromDirFilename(path,file,value): |
|
|
19 | 25 | dir_file= path+"/"+file |
|
|
20 | 26 | fp = h5py.File(dir_file,'r') |
|
|
21 | 27 | array = fp['Data'].get(value)[()] |
|
|
22 | 28 | fp.close() |
|
|
23 | 29 | return array |
|
|
24 | 30 | |
|
|
25 | 31 | # LISTA COMPLETA DE ARCHIVOS HDF5 Pedestal |
|
|
26 | 32 | LIST= sorted(os.listdir(path_ped)) |
|
|
27 | 33 | m=len(LIST) |
|
|
28 | 34 | print("TOTAL DE ARCHIVOS DE PEDESTAL:",m) |
|
|
29 | 35 | # Contadores temporales |
|
|
30 | 36 | k= 0 |
|
|
31 | 37 | l= 0 |
|
|
32 | 38 | t= 0 |
|
|
33 | 39 | # Marca de tiempo temporal |
|
|
34 | 40 | time_ = numpy.zeros([m]) |
|
|
35 | 41 | # creacion de |
|
|
36 | 42 | for i in range(m): |
|
|
43 | print("order:",i) | |
|
|
37 | 44 | tmp_azi_pos = getDatavaluefromDirFilename(path=path_ped,file=LIST[i],value="azi_pos") |
|
|
38 | 45 | tmp_ele_pos = getDatavaluefromDirFilename(path=path_ped,file=LIST[i],value="ele_pos") |
|
|
39 | 46 | tmp_azi_vel = getDatavaluefromDirFilename(path=path_ped,file=LIST[i],value="azi_vel") |
|
|
40 | 47 | tmp_ele_vel = getDatavaluefromDirFilename(path=path_ped,file=LIST[i],value="azi_vel")# nuevo :D |
|
|
41 | 48 | |
|
|
42 | 49 | time_[i] = getDatavaluefromDirFilename(path=path_ped,file=LIST[i],value="utc") |
|
|
43 | 50 | |
|
|
44 | 51 | k=k +tmp_azi_pos.shape[0] |
|
|
45 | 52 | l=l +tmp_ele_pos.shape[0] |
|
|
46 | 53 | t=t +tmp_azi_vel.shape[0] |
|
|
47 | 54 | |
|
|
48 | 55 | print("TOTAL DE MUESTRAS, ARCHIVOS X100:",k) |
|
|
49 | 56 | time.sleep(5) |
|
|
50 | 57 | ######CREACION DE ARREGLOS CANTIDAD DE VALORES POR MUESTRA################# |
|
|
51 | 58 | azi_pos = numpy.zeros([k]) |
|
|
52 | 59 | ele_pos = numpy.zeros([l]) |
|
|
53 | 60 | time_azi_pos= numpy.zeros([k]) |
|
|
54 | 61 | # Contadores temporales |
|
|
55 | 62 | p=0 |
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56 | 63 | r=0 |
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57 | 64 | z=0 |
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58 | 65 | # VARIABLES TMP para almacenar azimuth, elevacion y tiempo |
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59 | 66 | |
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60 | 67 | #for filename in sorted(os.listdir(path_ped)): |
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61 | 68 | # CONDICION POR LEER EN TIEMPO REAL NO OFFLINE |
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62 | 69 | |
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63 | 70 | for filename in LIST: |
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64 | 71 | tmp_azi_pos = getDatavaluefromDirFilename(path=path_ped,file=filename,value="azi_pos") |
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65 | 72 | tmp_ele_pos = getDatavaluefromDirFilename(path=path_ped,file=filename,value="ele_pos") |
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66 | 73 | # CONDICION POR LEER EN TIEMPO REAL NO OFFLINE |
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67 | 74 | |
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68 | 75 | if z==(m-1): |
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69 | 76 | tmp_azi_time=numpy.arange(time_[z],time_[z]+1,1/(tmp_azi_pos.shape[0])) |
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70 | 77 | else: |
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71 | 78 | tmp_azi_time=numpy.arange(time_[z],time_[z+1],(time_[z+1]-time_[z])/(tmp_azi_pos.shape[0])) |
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72 | 79 | |
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73 | 80 | print(filename,time_[z]) |
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74 | 81 | print(z,tmp_azi_pos.shape[0]) |
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75 | 82 | |
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76 | 83 | i=0 |
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77 | 84 | for i in range(tmp_azi_pos.shape[0]): |
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78 | 85 | index=p+i |
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79 | 86 | azi_pos[index]=tmp_azi_pos[i] |
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80 | 87 | time_azi_pos[index]=tmp_azi_time[i] |
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81 | 88 | p=p+tmp_azi_pos.shape[0] |
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82 | 89 | i=0 |
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83 | 90 | for i in range(tmp_ele_pos.shape[0]): |
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84 | 91 | index=r+i |
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85 | 92 | ele_pos[index]=tmp_ele_pos[i] |
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86 | 93 | r=r+tmp_ele_pos.shape[0] |
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87 | 94 | |
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88 | 95 | |
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89 | 96 | z+=1 |
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90 | 97 | |
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91 | 98 | |
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92 | 99 | ######## GRAFIQUEMOS Y VEAMOS LOS DATOS DEL Pedestal |
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93 | 100 | fig, ax = plt.subplots(figsize=(16,8)) |
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94 | 101 | print(time_azi_pos.shape) |
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95 | 102 | print(azi_pos.shape) |
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96 | 103 | t=numpy.arange(time_azi_pos.shape[0])*0.01/(60.0) |
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97 | 104 | plt.plot(t,azi_pos,label='AZIMUTH_POS',color='blue') |
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98 | 105 | |
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99 | 106 | # AQUI ESTOY ADICIONANDO LA POSICION EN elevaciont=numpy.arange(len(ele_pos))*0.01/60.0 |
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100 | 107 | t=numpy.arange(len(ele_pos))*0.01/60.0 |
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101 | 108 | plt.plot(t,ele_pos,label='ELEVATION_POS',color='red')#*10 |
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102 | 109 | |
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103 | 110 | #ax.set_xlim(0, 9) |
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104 | 111 | ax.set_ylim(-5, 400) |
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105 | 112 | plt.ylabel("Azimuth Position") |
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106 | 113 | plt.xlabel("Muestra") |
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107 | 114 | plt.title('Azimuth Position vs Muestra ', fontsize=20) |
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108 | 115 | axes = plt.gca() |
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109 | 116 | axes.yaxis.grid() |
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110 | 117 | plt.xticks(fontsize=16) |
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111 | 118 | plt.yticks(fontsize=16) |
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112 | 119 | plt.show() |
| @@ -1,78 +1,79 | |||
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1 | 1 | import os,sys,json |
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2 | 2 | import datetime |
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3 | 3 | import time |
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4 | 4 | from schainpy.controller import Project |
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5 | 5 | ''' |
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6 | 6 | NOTA: |
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7 | 7 | Este script de prueba. |
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8 | 8 | - Unidad del lectura 'HDFReader'. |
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9 | 9 | - Unidad de procesamiento ParametersProc |
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10 | 10 | - Operacion SpectralMomentsPlot |
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11 | 11 | |
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12 | 12 | ''' |
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13 | 13 | |
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14 | 14 | ####################################################################### |
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15 | 15 | ################# RANGO DE PLOTEO###################################### |
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16 | 16 | ####################################################################### |
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17 | 17 | dBmin = '1' |
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18 | 18 | dBmax = '65' |
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19 | 19 | xmin = '0' |
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20 | 20 | xmax ='24' |
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21 | 21 | #tmmin = 16.2 |
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22 | 22 | #tmmax = 16.25 |
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23 | 23 | tmmin =15 |
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24 | 24 | tmmax =15.5 |
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25 | 25 | ymin = '0' |
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26 | 26 | ymax = '600' |
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27 | 27 | ####################################################################### |
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28 | 28 | ####################################################################### |
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29 | 29 | ####################################################################### |
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30 | 30 | #path = '/DATA_RM/TEST_HDF5_SPEC' |
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31 | path = '/DATA_RM/TEST_HDF5_SPEC_23/6v/' | |
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31 | #path = '/DATA_RM/TEST_HDF5_SPEC_23/6v/' | |
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32 | path = '/DATA_RM/TEST_HDF5_19OCT' | |
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32 | 33 | figpath = '/home/soporte/Downloads/23/6v' |
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33 | 34 | desc = "Simulator Test" |
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34 | 35 | desc_data = { |
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35 | 36 | 'Data': { |
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36 | 37 | 'data_pow': ['Data/data_pow/channel00','Data/data_pow/channel01'], |
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37 | 38 | 'data_dop': ['Data/data_dop/channel00','Data/data_dop/channel01'], |
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38 | 39 | 'utctime':'Data/utctime' |
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39 | 40 | }, |
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40 | 41 | 'Metadata': { |
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41 | 42 | 'heightList':'Metadata/heightList', |
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42 | 43 | 'nIncohInt' :'Metadata/nIncohInt', |
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43 | 44 | 'nCohInt' :'Metadata/nCohInt', |
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44 | 45 | 'nProfiles' :'Metadata/nProfiles', |
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45 | 46 | 'channelList' :'Metadata/channelList', |
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46 | 47 | 'utctimeInit' :'Metadata/utctimeInit' |
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47 | 48 | |
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48 | 49 | } |
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49 | 50 | } |
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50 | 51 | |
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51 | 52 | controllerObj = Project() |
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52 | 53 | |
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53 | 54 | controllerObj.setup(id='10',name='Test Simulator',description=desc) |
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54 | 55 | |
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55 | 56 | readUnitConfObj = controllerObj.addReadUnit(datatype='HDFReader', |
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56 | 57 | path=path, |
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57 | 58 | startDate="2021/01/01", #"2020/01/01",#today, |
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58 | 59 | endDate= "2021/12/01", #"2020/12/30",#today, |
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59 | 60 | startTime='00:00:00', |
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60 | 61 | endTime='23:59:59', |
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61 | 62 | delay=0, |
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62 | 63 | #set=0, |
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63 | 64 | online=0, |
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64 | 65 | walk=1, |
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65 | 66 | description= json.dumps(desc_data))#1 |
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66 | 67 | |
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67 | 68 | procUnitConfObjA = controllerObj.addProcUnit(datatype='ParametersProc',inputId=readUnitConfObj.getId()) |
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68 | 69 | |
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69 | 70 | opObj11 = procUnitConfObjA.addOperation(name='PowerPlot',optype='external') |
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70 | 71 | opObj11.addParameter(name='xmin', value=tmmin) |
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71 | 72 | opObj11.addParameter(name='xmax', value=tmmax) |
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72 | 73 | opObj11.addParameter(name='zmin', value=dBmin) |
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73 | 74 | opObj11.addParameter(name='zmax', value=dBmax) |
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74 | 75 | opObj11.addParameter(name='save', value=figpath) |
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75 | 76 | opObj11.addParameter(name='showprofile', value=0) |
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76 | 77 | opObj11.addParameter(name='save_period', value=10) |
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77 | 78 | |
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78 | 79 | controllerObj.start() |
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