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1 | 1 | ''' |
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2 | 2 | Created on Jul 9, 2014 |
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3 | 3 | |
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4 | 4 | @author: roj-idl71 |
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5 | 5 | ''' |
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6 | 6 | import os |
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7 | 7 | import datetime |
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8 | 8 | import numpy |
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9 | 9 | |
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10 | 10 | from figure import Figure, isRealtime, isTimeInHourRange |
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11 | 11 | from plotting_codes import * |
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12 | 12 | |
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13 | 13 | |
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14 | 14 | class SpectraPlot(Figure): |
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15 | 15 | |
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16 | 16 | isConfig = None |
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17 | 17 | __nsubplots = None |
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18 | 18 | |
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19 | 19 | WIDTHPROF = None |
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20 | 20 | HEIGHTPROF = None |
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21 | 21 | PREFIX = 'spc' |
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22 | 22 | |
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23 | 23 | def __init__(self, **kwargs): |
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24 | 24 | Figure.__init__(self, **kwargs) |
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25 | 25 | self.isConfig = False |
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26 | 26 | self.__nsubplots = 1 |
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27 | 27 | |
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28 | 28 | self.WIDTH = 250 |
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29 | 29 | self.HEIGHT = 250 |
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30 | 30 | self.WIDTHPROF = 120 |
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31 | 31 | self.HEIGHTPROF = 0 |
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32 | 32 | self.counter_imagwr = 0 |
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33 | 33 | |
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34 | 34 | self.PLOT_CODE = SPEC_CODE |
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35 | 35 | |
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36 | 36 | self.FTP_WEI = None |
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37 | 37 | self.EXP_CODE = None |
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38 | 38 | self.SUB_EXP_CODE = None |
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39 | 39 | self.PLOT_POS = None |
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40 | 40 | |
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41 | 41 | self.__xfilter_ena = False |
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42 | 42 | self.__yfilter_ena = False |
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43 | 43 | |
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44 | 44 | def getSubplots(self): |
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45 | 45 | |
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46 | 46 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
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47 | 47 | nrow = int(self.nplots*1./ncol + 0.9) |
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48 | 48 | |
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49 | 49 | return nrow, ncol |
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50 | 50 | |
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51 | 51 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
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52 | 52 | |
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53 | 53 | self.__showprofile = showprofile |
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54 | 54 | self.nplots = nplots |
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55 | 55 | |
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56 | 56 | ncolspan = 1 |
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57 | 57 | colspan = 1 |
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58 | 58 | if showprofile: |
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59 | 59 | ncolspan = 3 |
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60 | 60 | colspan = 2 |
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61 | 61 | self.__nsubplots = 2 |
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62 | 62 | |
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63 | 63 | self.createFigure(id = id, |
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64 | 64 | wintitle = wintitle, |
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65 | 65 | widthplot = self.WIDTH + self.WIDTHPROF, |
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66 | 66 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
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67 | 67 | show=show) |
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68 | 68 | |
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69 | 69 | nrow, ncol = self.getSubplots() |
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70 | 70 | |
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71 | 71 | counter = 0 |
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72 | 72 | for y in range(nrow): |
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73 | 73 | for x in range(ncol): |
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74 | 74 | |
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75 | 75 | if counter >= self.nplots: |
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76 | 76 | break |
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77 | 77 | |
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78 | 78 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
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79 | 79 | |
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80 | 80 | if showprofile: |
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81 | 81 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
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82 | 82 | |
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83 | 83 | counter += 1 |
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84 | 84 | |
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85 | 85 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
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86 | 86 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
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87 | 87 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
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88 | 88 | server=None, folder=None, username=None, password=None, |
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89 | 89 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
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90 | 90 | xaxis="frequency", colormap='jet', normFactor=None): |
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91 | 91 | |
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92 | 92 | """ |
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93 | 93 | |
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94 | 94 | Input: |
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95 | 95 | dataOut : |
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96 | 96 | id : |
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97 | 97 | wintitle : |
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98 | 98 | channelList : |
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99 | 99 | showProfile : |
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100 | 100 | xmin : None, |
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101 | 101 | xmax : None, |
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102 | 102 | ymin : None, |
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103 | 103 | ymax : None, |
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104 | 104 | zmin : None, |
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105 | 105 | zmax : None |
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106 | 106 | """ |
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107 | 107 | if realtime: |
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108 | 108 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
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109 | 109 | print 'Skipping this plot function' |
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110 | 110 | return |
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111 | 111 | |
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112 | 112 | if channelList == None: |
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113 | 113 | channelIndexList = dataOut.channelIndexList |
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114 | 114 | else: |
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115 | 115 | channelIndexList = [] |
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116 | 116 | for channel in channelList: |
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117 | 117 | if channel not in dataOut.channelList: |
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118 | 118 | raise ValueError, "Channel %d is not in dataOut.channelList" %channel |
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119 | 119 | channelIndexList.append(dataOut.channelList.index(channel)) |
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120 | 120 | |
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121 | 121 | if normFactor is None: |
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122 | 122 | factor = dataOut.normFactor |
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123 | 123 | else: |
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124 | 124 | factor = normFactor |
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125 | 125 | if xaxis == "frequency": |
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126 | 126 | x = dataOut.getFreqRange(1)/1000. |
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127 | 127 | xlabel = "Frequency (kHz)" |
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128 | 128 | |
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129 | 129 | elif xaxis == "time": |
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130 | 130 | x = dataOut.getAcfRange(1) |
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131 | 131 | xlabel = "Time (ms)" |
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132 | 132 | |
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133 | 133 | else: |
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134 | 134 | x = dataOut.getVelRange(1) |
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135 | 135 | xlabel = "Velocity (m/s)" |
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136 | 136 | |
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137 | 137 | ylabel = "Range (Km)" |
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138 | 138 | |
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139 | 139 | y = dataOut.getHeiRange() |
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140 | 140 | |
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141 | 141 | z = dataOut.data_spc/factor |
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142 | 142 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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143 | 143 | zdB = 10*numpy.log10(z) |
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144 | 144 | |
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145 | 145 | #print "a000",dataOut.data_spc.dtype |
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146 | 146 | avg = numpy.average(z, axis=1) |
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147 | 147 | avgdB = 10*numpy.log10(avg) |
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148 | 148 | #print "before plot" |
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149 | 149 | noise = dataOut.getNoise()/factor |
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150 | 150 | noisedB = 10*numpy.log10(noise) |
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151 | 151 | |
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152 | 152 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
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153 | 153 | title = wintitle + " Spectra" |
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154 | 154 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
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155 | 155 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
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156 | 156 | |
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157 | 157 | if not self.isConfig: |
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158 | 158 | |
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159 | 159 | nplots = len(channelIndexList) |
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160 | 160 | |
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161 | 161 | self.setup(id=id, |
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162 | 162 | nplots=nplots, |
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163 | 163 | wintitle=wintitle, |
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164 | 164 | showprofile=showprofile, |
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165 | 165 | show=show) |
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166 | 166 | |
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167 | 167 | if xmin == None: xmin = numpy.nanmin(x) |
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168 | 168 | if xmax == None: xmax = numpy.nanmax(x) |
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169 | 169 | if ymin == None: ymin = numpy.nanmin(y) |
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170 | 170 | if ymax == None: ymax = numpy.nanmax(y) |
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171 | 171 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
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172 | 172 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
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173 | 173 | |
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174 | 174 | self.FTP_WEI = ftp_wei |
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175 | 175 | self.EXP_CODE = exp_code |
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176 | 176 | self.SUB_EXP_CODE = sub_exp_code |
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177 | 177 | self.PLOT_POS = plot_pos |
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178 | 178 | |
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179 | 179 | self.isConfig = True |
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180 | 180 | |
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181 | 181 | self.setWinTitle(title) |
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182 | 182 | |
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183 | 183 | for i in range(self.nplots): |
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184 | 184 | index = channelIndexList[i] |
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185 | 185 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
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186 | 186 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[index], noisedB[index], str_datetime) |
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187 | 187 | if len(dataOut.beam.codeList) != 0: |
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188 | 188 | title = "Ch%d:%4.2fdB,%2.2f,%2.2f:%s" %(dataOut.channelList[index], noisedB[index], dataOut.beam.azimuthList[index], dataOut.beam.zenithList[index], str_datetime) |
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189 | 189 | |
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190 | 190 | axes = self.axesList[i*self.__nsubplots] |
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191 | 191 | axes.pcolor(x, y, zdB[index,:,:], |
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192 | 192 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
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193 | 193 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=colormap, |
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194 | 194 | ticksize=9, cblabel='') |
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195 | 195 | |
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196 | 196 | if self.__showprofile: |
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197 | 197 | axes = self.axesList[i*self.__nsubplots +1] |
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198 | 198 | axes.pline(avgdB[index,:], y, |
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199 | 199 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
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200 | 200 | xlabel='dB', ylabel='', title='', |
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201 | 201 | ytick_visible=False, |
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202 | 202 | grid='x') |
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203 | 203 | |
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204 | 204 | noiseline = numpy.repeat(noisedB[index], len(y)) |
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205 | 205 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
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206 | 206 | |
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207 | 207 | self.draw() |
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208 | 208 | |
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209 | 209 | if figfile == None: |
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210 | 210 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
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211 | 211 | name = str_datetime |
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212 | 212 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
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213 | 213 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
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214 | 214 | figfile = self.getFilename(name) |
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215 | 215 | |
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216 | 216 | self.save(figpath=figpath, |
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217 | 217 | figfile=figfile, |
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218 | 218 | save=save, |
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219 | 219 | ftp=ftp, |
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220 | 220 | wr_period=wr_period, |
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221 | 221 | thisDatetime=thisDatetime) |
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222 | 222 | |
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223 | 223 | class ACFPlot(Figure): |
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224 | 224 | |
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225 | 225 | isConfig = None |
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226 | 226 | __nsubplots = None |
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227 | 227 | |
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228 | 228 | WIDTHPROF = None |
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229 | 229 | HEIGHTPROF = None |
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230 | 230 | PREFIX = 'acf' |
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231 | 231 | |
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232 | 232 | def __init__(self, **kwargs): |
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233 | 233 | Figure.__init__(self, **kwargs) |
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234 | 234 | self.isConfig = False |
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235 | 235 | self.__nsubplots = 1 |
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236 | 236 | |
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237 | 237 | self.PLOT_CODE = ACF_CODE |
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238 | 238 | |
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239 | 239 | self.WIDTH = 900 |
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240 | 240 | self.HEIGHT = 700 |
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241 | 241 | self.counter_imagwr = 0 |
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242 | 242 | |
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243 | 243 | def getSubplots(self): |
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244 | 244 | ncol = 1 |
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245 | 245 | nrow = 1 |
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246 | 246 | |
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247 | 247 | return nrow, ncol |
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248 | 248 | |
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249 | 249 | def setup(self, id, nplots, wintitle, show): |
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250 | 250 | |
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251 | 251 | self.nplots = nplots |
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252 | 252 | |
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253 | 253 | ncolspan = 1 |
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254 | 254 | colspan = 1 |
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255 | 255 | |
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256 | 256 | self.createFigure(id = id, |
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257 | 257 | wintitle = wintitle, |
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258 | 258 | widthplot = self.WIDTH, |
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259 | 259 | heightplot = self.HEIGHT, |
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260 | 260 | show=show) |
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261 | 261 | |
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262 | 262 | nrow, ncol = self.getSubplots() |
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263 | 263 | |
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264 | 264 | counter = 0 |
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265 | 265 | for y in range(nrow): |
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266 | 266 | for x in range(ncol): |
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267 | 267 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
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268 | 268 | |
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269 | 269 | def run(self, dataOut, id, wintitle="", channelList=None,channel=None,nSamples=None, |
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270 | 270 | nSampleList= None,resolutionFactor=None, |
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271 | 271 | xmin=None, xmax=None, ymin=None, ymax=None, |
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272 | 272 | save=False, figpath='./', figfile=None, show=True, |
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273 | 273 | ftp=False, wr_period=1, server=None, |
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274 | 274 | folder=None, username=None, password=None, |
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275 | 275 | xaxis="frequency"): |
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276 | 276 | |
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277 | 277 | channel0 = channel |
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278 | 278 | nSamples = nSamples |
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279 | 279 | resFactor = resolutionFactor |
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280 | 280 | |
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281 | 281 | if nSamples == None: |
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282 | 282 | nSamples = 20 |
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283 | 283 | |
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284 | 284 | if resFactor == None: |
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285 | 285 | resFactor = 5 |
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286 | 286 | #else: |
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287 | 287 | # if nSamples not in dataOut.channelList: |
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288 | 288 | # raise ValueError, "Channel %d is not in %s dataOut.channelList"%(channel0, dataOut.channelList) |
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289 | 289 | |
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290 | 290 | if channel0 == None: |
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291 | 291 | channel0 = 0 |
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292 | 292 | else: |
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293 | 293 | if channel0 not in dataOut.channelList: |
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294 | 294 | raise ValueError, "Channel %d is not in %s dataOut.channelList"%(channel0, dataOut.channelList) |
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295 | 295 | |
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296 | 296 | if channelList == None: |
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297 | 297 | channelIndexList = dataOut.channelIndexList |
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298 | 298 | channelList = dataOut.channelList |
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299 | 299 | else: |
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300 | 300 | channelIndexList = [] |
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301 | 301 | for channel in channelList: |
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302 | 302 | if channel not in dataOut.channelList: |
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303 | 303 | raise ValueError, "Channel %d is not in dataOut.channelList" |
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304 | 304 | channelIndexList.append(dataOut.channelList.index(channel)) |
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305 | 305 | |
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306 | 306 | #z = dataOut.data_spc/factor |
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307 | 307 | factor = dataOut.normFactor |
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308 | 308 | y = dataOut.getHeiRange() |
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309 | 309 | deltaHeight = dataOut.heightList[1]-dataOut.heightList[0] |
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310 | 310 | z = dataOut.data_acf |
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311 | ||
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312 | #import matplotlib.pyplot as plt | |
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313 | #plt.plot(z[0,:,85]) | |
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314 | #plt.show() | |
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315 | #import time | |
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316 | #time.sleep(20) | |
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317 | ||
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318 | ||
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311 | 319 | #z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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312 | 320 | shape = dataOut.data_acf.shape |
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313 | 321 | hei_index = numpy.arange(shape[2]) |
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314 | 322 | hei_plot = numpy.arange(nSamples)*resFactor |
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315 | 323 | |
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316 | 324 | if nSampleList is not None: |
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317 | 325 | for nsample in nSampleList: |
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318 | 326 | if nsample not in dataOut.heightList/deltaHeight: |
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319 | 327 | raise ValueError, "nsample %d is not in %s dataOut.heightList"%(nsample,dataOut.heightList) |
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320 | 328 | |
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321 | 329 | if nSampleList is not None: |
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322 | 330 | hei_plot = numpy.array(nSampleList)*resFactor |
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323 | 331 | |
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324 | 332 | if hei_plot[-1] >= hei_index[-1]: |
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325 | 333 | print ("La cantidad de puntos en altura es %d y la resolucion es %d Km"%(hei_plot.shape[0],deltaHeight*resFactor )) |
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326 | 334 | raise ValueError, "resFactor %d multiplicado por el valor de %d nSamples es mayor a %d cantidad total de puntos"%(resFactor,nSamples,hei_index[-1]) |
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327 | 335 | |
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328 | 336 | #escalamiento -1 a 1 a resolucion (factor de resolucion en altura)* deltaHeight |
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329 | 337 | min = numpy.min(z[0,:,0]) |
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330 | 338 | max =numpy.max(z[0,:,0]) |
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331 | 339 | for i in range(shape[0]): |
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332 | 340 | for j in range(shape[2]): |
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333 | 341 | z[i,:,j]= (((z[i,:,j]-min)/(max-min))*deltaHeight*resFactor + j*deltaHeight) |
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334 | 342 | |
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335 | 343 | if xaxis == "frequency": |
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336 | 344 | x = dataOut.getFreqRange()/1000. |
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337 | 345 | zdB = 10*numpy.log10(z[channel0,:,hei_plot]) |
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338 | 346 | xlabel = "Frequency (kHz)" |
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339 | 347 | ylabel = "Power (dB)" |
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340 | 348 | |
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341 | 349 | elif xaxis == "time": |
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342 | 350 | delta= dataOut.getAcfRange()[1]-dataOut.getAcfRange()[0] |
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343 | 351 | x = dataOut.getAcfRange()+delta/2.0 |
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344 | 352 | zdB = z[channel0,:,hei_plot] |
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345 | 353 | xlabel = "Time (ms)" |
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346 | 354 | ylabel = "ACF" |
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347 | 355 | |
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348 | 356 | else: |
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349 | 357 | x = dataOut.getVelRange() |
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350 | 358 | zdB = 10*numpy.log10(z[channel0,:,hei_plot]) |
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351 | 359 | xlabel = "Velocity (m/s)" |
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352 | 360 | ylabel = "Power (dB)" |
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353 | 361 | |
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354 | 362 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
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355 | 363 | title = wintitle + " ACF Plot Ch %s %s" %(channel0,thisDatetime.strftime("%d-%b-%Y")) |
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356 | 364 | |
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357 | 365 | if not self.isConfig: |
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358 | 366 | |
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359 | 367 | nplots = 1 |
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360 | 368 | |
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361 | 369 | self.setup(id=id, |
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362 | 370 | nplots=nplots, |
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363 | 371 | wintitle=wintitle, |
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364 | 372 | show=show) |
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365 | 373 | |
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366 | 374 | if xmin == None: xmin = numpy.nanmin(x)*0.9 |
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367 | 375 | if xmax == None: xmax = numpy.nanmax(x)*1.1 |
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368 | 376 | if ymin == None: ymin = numpy.nanmin(zdB) |
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369 | 377 | if ymax == None: ymax = numpy.nanmax(zdB) |
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370 | 378 | |
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371 | 379 | print ("El parametro resFactor es %d y la resolucion en altura es %d"%(resFactor,deltaHeight )) |
|
|
372 | 380 | print ("La cantidad de puntos en altura es %d y la nueva resolucion es %d Km"%(hei_plot.shape[0],deltaHeight*resFactor )) |
|
|
373 | 381 | print ("La altura maxima es %d Km"%(hei_plot[-1]*deltaHeight )) |
|
|
374 | 382 | |
|
|
375 | 383 | self.isConfig = True |
|
|
376 | 384 | |
|
|
377 | 385 | self.setWinTitle(title) |
|
|
378 | 386 | |
|
|
379 | 387 | title = "ACF Plot: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
380 | 388 | axes = self.axesList[0] |
|
|
381 | 389 | |
|
|
382 | 390 | legendlabels = ["Range = %dKm" %y[i] for i in hei_plot] |
|
|
383 | 391 | |
|
|
384 | 392 | axes.pmultilineyaxis( x, zdB, |
|
|
385 | 393 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
386 | 394 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
387 | 395 | ytick_visible=True, nxticks=5, |
|
|
388 | 396 | grid='x') |
|
|
389 | 397 | |
|
|
390 | 398 | self.draw() |
|
|
391 | 399 | |
|
|
392 | 400 | if figfile == None: |
|
|
393 | 401 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
394 | 402 | name = str_datetime |
|
|
395 | 403 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
396 | 404 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
397 | 405 | figfile = self.getFilename(name) |
|
|
398 | 406 | |
|
|
399 | 407 | self.save(figpath=figpath, |
|
|
400 | 408 | figfile=figfile, |
|
|
401 | 409 | save=save, |
|
|
402 | 410 | ftp=ftp, |
|
|
403 | 411 | wr_period=wr_period, |
|
|
404 | 412 | thisDatetime=thisDatetime) |
|
|
405 | 413 | |
|
|
406 | 414 | |
|
|
407 | 415 | |
|
|
408 | 416 | class CrossSpectraPlot(Figure): |
|
|
409 | 417 | |
|
|
410 | 418 | isConfig = None |
|
|
411 | 419 | __nsubplots = None |
|
|
412 | 420 | |
|
|
413 | 421 | WIDTH = None |
|
|
414 | 422 | HEIGHT = None |
|
|
415 | 423 | WIDTHPROF = None |
|
|
416 | 424 | HEIGHTPROF = None |
|
|
417 | 425 | PREFIX = 'cspc' |
|
|
418 | 426 | |
|
|
419 | 427 | def __init__(self, **kwargs): |
|
|
420 | 428 | Figure.__init__(self, **kwargs) |
|
|
421 | 429 | self.isConfig = False |
|
|
422 | 430 | self.__nsubplots = 4 |
|
|
423 | 431 | self.counter_imagwr = 0 |
|
|
424 | 432 | self.WIDTH = 250 |
|
|
425 | 433 | self.HEIGHT = 250 |
|
|
426 | 434 | self.WIDTHPROF = 0 |
|
|
427 | 435 | self.HEIGHTPROF = 0 |
|
|
428 | 436 | |
|
|
429 | 437 | self.PLOT_CODE = CROSS_CODE |
|
|
430 | 438 | self.FTP_WEI = None |
|
|
431 | 439 | self.EXP_CODE = None |
|
|
432 | 440 | self.SUB_EXP_CODE = None |
|
|
433 | 441 | self.PLOT_POS = None |
|
|
434 | 442 | |
|
|
435 | 443 | def getSubplots(self): |
|
|
436 | 444 | |
|
|
437 | 445 | ncol = 4 |
|
|
438 | 446 | nrow = self.nplots |
|
|
439 | 447 | |
|
|
440 | 448 | return nrow, ncol |
|
|
441 | 449 | |
|
|
442 | 450 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
443 | 451 | |
|
|
444 | 452 | self.__showprofile = showprofile |
|
|
445 | 453 | self.nplots = nplots |
|
|
446 | 454 | |
|
|
447 | 455 | ncolspan = 1 |
|
|
448 | 456 | colspan = 1 |
|
|
449 | 457 | |
|
|
450 | 458 | self.createFigure(id = id, |
|
|
451 | 459 | wintitle = wintitle, |
|
|
452 | 460 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
453 | 461 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
454 | 462 | show=True) |
|
|
455 | 463 | |
|
|
456 | 464 | nrow, ncol = self.getSubplots() |
|
|
457 | 465 | |
|
|
458 | 466 | counter = 0 |
|
|
459 | 467 | for y in range(nrow): |
|
|
460 | 468 | for x in range(ncol): |
|
|
461 | 469 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
462 | 470 | |
|
|
463 | 471 | counter += 1 |
|
|
464 | 472 | |
|
|
465 | 473 | def run(self, dataOut, id, wintitle="", pairsList=None, |
|
|
466 | 474 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
467 | 475 | coh_min=None, coh_max=None, phase_min=None, phase_max=None, |
|
|
468 | 476 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
469 | 477 | power_cmap='jet', coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
470 | 478 | server=None, folder=None, username=None, password=None, |
|
|
471 | 479 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, normFactor=None, |
|
|
472 | 480 | xaxis='frequency'): |
|
|
473 | 481 | |
|
|
474 | 482 | """ |
|
|
475 | 483 | |
|
|
476 | 484 | Input: |
|
|
477 | 485 | dataOut : |
|
|
478 | 486 | id : |
|
|
479 | 487 | wintitle : |
|
|
480 | 488 | channelList : |
|
|
481 | 489 | showProfile : |
|
|
482 | 490 | xmin : None, |
|
|
483 | 491 | xmax : None, |
|
|
484 | 492 | ymin : None, |
|
|
485 | 493 | ymax : None, |
|
|
486 | 494 | zmin : None, |
|
|
487 | 495 | zmax : None |
|
|
488 | 496 | """ |
|
|
489 | 497 | |
|
|
490 | 498 | if pairsList == None: |
|
|
491 | 499 | pairsIndexList = dataOut.pairsIndexList |
|
|
492 | 500 | else: |
|
|
493 | 501 | pairsIndexList = [] |
|
|
494 | 502 | for pair in pairsList: |
|
|
495 | 503 | if pair not in dataOut.pairsList: |
|
|
496 | 504 | raise ValueError, "Pair %s is not in dataOut.pairsList" %str(pair) |
|
|
497 | 505 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
498 | 506 | |
|
|
499 | 507 | if not pairsIndexList: |
|
|
500 | 508 | return |
|
|
501 | 509 | |
|
|
502 | 510 | if len(pairsIndexList) > 4: |
|
|
503 | 511 | pairsIndexList = pairsIndexList[0:4] |
|
|
504 | 512 | |
|
|
505 | 513 | if normFactor is None: |
|
|
506 | 514 | factor = dataOut.normFactor |
|
|
507 | 515 | else: |
|
|
508 | 516 | factor = normFactor |
|
|
509 | 517 | x = dataOut.getVelRange(1) |
|
|
510 | 518 | y = dataOut.getHeiRange() |
|
|
511 | 519 | z = dataOut.data_spc[:,:,:]/factor |
|
|
512 | 520 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
513 | 521 | |
|
|
514 | 522 | noise = dataOut.noise/factor |
|
|
515 | 523 | |
|
|
516 | 524 | zdB = 10*numpy.log10(z) |
|
|
517 | 525 | noisedB = 10*numpy.log10(noise) |
|
|
518 | 526 | |
|
|
519 | 527 | if coh_min == None: |
|
|
520 | 528 | coh_min = 0.0 |
|
|
521 | 529 | if coh_max == None: |
|
|
522 | 530 | coh_max = 1.0 |
|
|
523 | 531 | |
|
|
524 | 532 | if phase_min == None: |
|
|
525 | 533 | phase_min = -180 |
|
|
526 | 534 | if phase_max == None: |
|
|
527 | 535 | phase_max = 180 |
|
|
528 | 536 | |
|
|
529 | 537 | #thisDatetime = dataOut.datatime |
|
|
530 | 538 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
531 | 539 | title = wintitle + " Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
532 | 540 | # xlabel = "Velocity (m/s)" |
|
|
533 | 541 | ylabel = "Range (Km)" |
|
|
534 | 542 | |
|
|
535 | 543 | if xaxis == "frequency": |
|
|
536 | 544 | x = dataOut.getFreqRange(1)/1000. |
|
|
537 | 545 | xlabel = "Frequency (kHz)" |
|
|
538 | 546 | |
|
|
539 | 547 | elif xaxis == "time": |
|
|
540 | 548 | x = dataOut.getAcfRange(1) |
|
|
541 | 549 | xlabel = "Time (ms)" |
|
|
542 | 550 | |
|
|
543 | 551 | else: |
|
|
544 | 552 | x = dataOut.getVelRange(1) |
|
|
545 | 553 | xlabel = "Velocity (m/s)" |
|
|
546 | 554 | |
|
|
547 | 555 | if not self.isConfig: |
|
|
548 | 556 | |
|
|
549 | 557 | nplots = len(pairsIndexList) |
|
|
550 | 558 | |
|
|
551 | 559 | self.setup(id=id, |
|
|
552 | 560 | nplots=nplots, |
|
|
553 | 561 | wintitle=wintitle, |
|
|
554 | 562 | showprofile=False, |
|
|
555 | 563 | show=show) |
|
|
556 | 564 | |
|
|
557 | 565 | avg = numpy.abs(numpy.average(z, axis=1)) |
|
|
558 | 566 | avgdB = 10*numpy.log10(avg) |
|
|
559 | 567 | |
|
|
560 | 568 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
561 | 569 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
562 | 570 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
563 | 571 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
564 | 572 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
565 | 573 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
566 | 574 | |
|
|
567 | 575 | self.FTP_WEI = ftp_wei |
|
|
568 | 576 | self.EXP_CODE = exp_code |
|
|
569 | 577 | self.SUB_EXP_CODE = sub_exp_code |
|
|
570 | 578 | self.PLOT_POS = plot_pos |
|
|
571 | 579 | |
|
|
572 | 580 | self.isConfig = True |
|
|
573 | 581 | |
|
|
574 | 582 | self.setWinTitle(title) |
|
|
575 | 583 | |
|
|
576 | 584 | for i in range(self.nplots): |
|
|
577 | 585 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
578 | 586 | |
|
|
579 | 587 | chan_index0 = dataOut.channelList.index(pair[0]) |
|
|
580 | 588 | chan_index1 = dataOut.channelList.index(pair[1]) |
|
|
581 | 589 | |
|
|
582 | 590 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
583 | 591 | title = "Ch%d: %4.2fdB: %s" %(pair[0], noisedB[chan_index0], str_datetime) |
|
|
584 | 592 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index0,:,:]/factor) |
|
|
585 | 593 | axes0 = self.axesList[i*self.__nsubplots] |
|
|
586 | 594 | axes0.pcolor(x, y, zdB, |
|
|
587 | 595 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
588 | 596 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
589 | 597 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
590 | 598 | |
|
|
591 | 599 | title = "Ch%d: %4.2fdB: %s" %(pair[1], noisedB[chan_index1], str_datetime) |
|
|
592 | 600 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index1,:,:]/factor) |
|
|
593 | 601 | axes0 = self.axesList[i*self.__nsubplots+1] |
|
|
594 | 602 | axes0.pcolor(x, y, zdB, |
|
|
595 | 603 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
596 | 604 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
597 | 605 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
598 | 606 | |
|
|
599 | 607 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[chan_index0,:,:]*dataOut.data_spc[chan_index1,:,:]) |
|
|
600 | 608 | coherence = numpy.abs(coherenceComplex) |
|
|
601 | 609 | # phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi |
|
|
602 | 610 | phase = numpy.arctan2(coherenceComplex.imag, coherenceComplex.real)*180/numpy.pi |
|
|
603 | 611 | |
|
|
604 | 612 | title = "Coherence Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
605 | 613 | axes0 = self.axesList[i*self.__nsubplots+2] |
|
|
606 | 614 | axes0.pcolor(x, y, coherence, |
|
|
607 | 615 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=coh_min, zmax=coh_max, |
|
|
608 | 616 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
609 | 617 | ticksize=9, colormap=coherence_cmap, cblabel='') |
|
|
610 | 618 | |
|
|
611 | 619 | title = "Phase Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
612 | 620 | axes0 = self.axesList[i*self.__nsubplots+3] |
|
|
613 | 621 | axes0.pcolor(x, y, phase, |
|
|
614 | 622 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
615 | 623 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
616 | 624 | ticksize=9, colormap=phase_cmap, cblabel='') |
|
|
617 | 625 | |
|
|
618 | 626 | |
|
|
619 | 627 | |
|
|
620 | 628 | self.draw() |
|
|
621 | 629 | |
|
|
622 | 630 | self.save(figpath=figpath, |
|
|
623 | 631 | figfile=figfile, |
|
|
624 | 632 | save=save, |
|
|
625 | 633 | ftp=ftp, |
|
|
626 | 634 | wr_period=wr_period, |
|
|
627 | 635 | thisDatetime=thisDatetime) |
|
|
628 | 636 | |
|
|
629 | 637 | |
|
|
630 | 638 | class RTIPlot(Figure): |
|
|
631 | 639 | |
|
|
632 | 640 | __isConfig = None |
|
|
633 | 641 | __nsubplots = None |
|
|
634 | 642 | |
|
|
635 | 643 | WIDTHPROF = None |
|
|
636 | 644 | HEIGHTPROF = None |
|
|
637 | 645 | PREFIX = 'rti' |
|
|
638 | 646 | |
|
|
639 | 647 | def __init__(self, **kwargs): |
|
|
640 | 648 | |
|
|
641 | 649 | Figure.__init__(self, **kwargs) |
|
|
642 | 650 | self.timerange = None |
|
|
643 | 651 | self.isConfig = False |
|
|
644 | 652 | self.__nsubplots = 1 |
|
|
645 | 653 | |
|
|
646 | 654 | self.WIDTH = 800 |
|
|
647 | 655 | self.HEIGHT = 180 |
|
|
648 | 656 | self.WIDTHPROF = 120 |
|
|
649 | 657 | self.HEIGHTPROF = 0 |
|
|
650 | 658 | self.counter_imagwr = 0 |
|
|
651 | 659 | |
|
|
652 | 660 | self.PLOT_CODE = RTI_CODE |
|
|
653 | 661 | |
|
|
654 | 662 | self.FTP_WEI = None |
|
|
655 | 663 | self.EXP_CODE = None |
|
|
656 | 664 | self.SUB_EXP_CODE = None |
|
|
657 | 665 | self.PLOT_POS = None |
|
|
658 | 666 | self.tmin = None |
|
|
659 | 667 | self.tmax = None |
|
|
660 | 668 | |
|
|
661 | 669 | self.xmin = None |
|
|
662 | 670 | self.xmax = None |
|
|
663 | 671 | |
|
|
664 | 672 | self.figfile = None |
|
|
665 | 673 | |
|
|
666 | 674 | def getSubplots(self): |
|
|
667 | 675 | |
|
|
668 | 676 | ncol = 1 |
|
|
669 | 677 | nrow = self.nplots |
|
|
670 | 678 | |
|
|
671 | 679 | return nrow, ncol |
|
|
672 | 680 | |
|
|
673 | 681 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
674 | 682 | |
|
|
675 | 683 | self.__showprofile = showprofile |
|
|
676 | 684 | self.nplots = nplots |
|
|
677 | 685 | |
|
|
678 | 686 | ncolspan = 1 |
|
|
679 | 687 | colspan = 1 |
|
|
680 | 688 | if showprofile: |
|
|
681 | 689 | ncolspan = 7 |
|
|
682 | 690 | colspan = 6 |
|
|
683 | 691 | self.__nsubplots = 2 |
|
|
684 | 692 | |
|
|
685 | 693 | self.createFigure(id = id, |
|
|
686 | 694 | wintitle = wintitle, |
|
|
687 | 695 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
688 | 696 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
689 | 697 | show=show) |
|
|
690 | 698 | |
|
|
691 | 699 | nrow, ncol = self.getSubplots() |
|
|
692 | 700 | |
|
|
693 | 701 | counter = 0 |
|
|
694 | 702 | for y in range(nrow): |
|
|
695 | 703 | for x in range(ncol): |
|
|
696 | 704 | |
|
|
697 | 705 | if counter >= self.nplots: |
|
|
698 | 706 | break |
|
|
699 | 707 | |
|
|
700 | 708 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
701 | 709 | |
|
|
702 | 710 | if showprofile: |
|
|
703 | 711 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
704 | 712 | |
|
|
705 | 713 | counter += 1 |
|
|
706 | 714 | |
|
|
707 | 715 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
708 | 716 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
709 | 717 | timerange=None, colormap='jet', |
|
|
710 | 718 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
711 | 719 | server=None, folder=None, username=None, password=None, |
|
|
712 | 720 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, normFactor=None, HEIGHT=None): |
|
|
713 | 721 | |
|
|
714 | 722 | """ |
|
|
715 | 723 | |
|
|
716 | 724 | Input: |
|
|
717 | 725 | dataOut : |
|
|
718 | 726 | id : |
|
|
719 | 727 | wintitle : |
|
|
720 | 728 | channelList : |
|
|
721 | 729 | showProfile : |
|
|
722 | 730 | xmin : None, |
|
|
723 | 731 | xmax : None, |
|
|
724 | 732 | ymin : None, |
|
|
725 | 733 | ymax : None, |
|
|
726 | 734 | zmin : None, |
|
|
727 | 735 | zmax : None |
|
|
728 | 736 | """ |
|
|
729 | 737 | |
|
|
730 | 738 | #colormap = kwargs.get('colormap', 'jet') |
|
|
731 | 739 | if HEIGHT is not None: |
|
|
732 | 740 | self.HEIGHT = HEIGHT |
|
|
733 | 741 | |
|
|
734 | 742 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
735 | 743 | return |
|
|
736 | 744 | |
|
|
737 | 745 | if channelList == None: |
|
|
738 | 746 | channelIndexList = dataOut.channelIndexList |
|
|
739 | 747 | else: |
|
|
740 | 748 | channelIndexList = [] |
|
|
741 | 749 | for channel in channelList: |
|
|
742 | 750 | if channel not in dataOut.channelList: |
|
|
743 | 751 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
744 | 752 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
745 | 753 | |
|
|
746 | 754 | if normFactor is None: |
|
|
747 | 755 | factor = dataOut.normFactor |
|
|
748 | 756 | else: |
|
|
749 | 757 | factor = normFactor |
|
|
750 | 758 | |
|
|
751 | 759 | # factor = dataOut.normFactor |
|
|
752 | 760 | x = dataOut.getTimeRange() |
|
|
753 | 761 | y = dataOut.getHeiRange() |
|
|
754 | 762 | |
|
|
755 | 763 | z = dataOut.data_spc/factor |
|
|
756 | 764 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
757 | 765 | avg = numpy.average(z, axis=1) |
|
|
758 | 766 | avgdB = 10.*numpy.log10(avg) |
|
|
759 | 767 | # avgdB = dataOut.getPower() |
|
|
760 | 768 | |
|
|
761 | 769 | |
|
|
762 | 770 | thisDatetime = dataOut.datatime |
|
|
763 | 771 | # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
764 | 772 | title = wintitle + " RTI" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
765 | 773 | xlabel = "" |
|
|
766 | 774 | ylabel = "Range (Km)" |
|
|
767 | 775 | |
|
|
768 | 776 | update_figfile = False |
|
|
769 | 777 | |
|
|
770 | 778 | if dataOut.ltctime >= self.xmax: |
|
|
771 | 779 | self.counter_imagwr = wr_period |
|
|
772 | 780 | self.isConfig = False |
|
|
773 | 781 | update_figfile = True |
|
|
774 | 782 | |
|
|
775 | 783 | if not self.isConfig: |
|
|
776 | 784 | |
|
|
777 | 785 | nplots = len(channelIndexList) |
|
|
778 | 786 | |
|
|
779 | 787 | self.setup(id=id, |
|
|
780 | 788 | nplots=nplots, |
|
|
781 | 789 | wintitle=wintitle, |
|
|
782 | 790 | showprofile=showprofile, |
|
|
783 | 791 | show=show) |
|
|
784 | 792 | |
|
|
785 | 793 | if timerange != None: |
|
|
786 | 794 | self.timerange = timerange |
|
|
787 | 795 | |
|
|
788 | 796 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
789 | 797 | |
|
|
790 | 798 | noise = dataOut.noise/factor |
|
|
791 | 799 | noisedB = 10*numpy.log10(noise) |
|
|
792 | 800 | |
|
|
793 | 801 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
794 | 802 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
795 | 803 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
796 | 804 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
797 | 805 | |
|
|
798 | 806 | self.FTP_WEI = ftp_wei |
|
|
799 | 807 | self.EXP_CODE = exp_code |
|
|
800 | 808 | self.SUB_EXP_CODE = sub_exp_code |
|
|
801 | 809 | self.PLOT_POS = plot_pos |
|
|
802 | 810 | |
|
|
803 | 811 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
804 | 812 | self.isConfig = True |
|
|
805 | 813 | self.figfile = figfile |
|
|
806 | 814 | update_figfile = True |
|
|
807 | 815 | |
|
|
808 | 816 | self.setWinTitle(title) |
|
|
809 | 817 | |
|
|
810 | 818 | for i in range(self.nplots): |
|
|
811 | 819 | index = channelIndexList[i] |
|
|
812 | 820 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
813 | 821 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
814 | 822 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
815 | 823 | axes = self.axesList[i*self.__nsubplots] |
|
|
816 | 824 | zdB = avgdB[index].reshape((1,-1)) |
|
|
817 | 825 | axes.pcolorbuffer(x, y, zdB, |
|
|
818 | 826 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
819 | 827 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
820 | 828 | ticksize=9, cblabel='', cbsize="1%", colormap=colormap) |
|
|
821 | 829 | |
|
|
822 | 830 | if self.__showprofile: |
|
|
823 | 831 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
824 | 832 | axes.pline(avgdB[index], y, |
|
|
825 | 833 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
826 | 834 | xlabel='dB', ylabel='', title='', |
|
|
827 | 835 | ytick_visible=False, |
|
|
828 | 836 | grid='x') |
|
|
829 | 837 | |
|
|
830 | 838 | self.draw() |
|
|
831 | 839 | |
|
|
832 | 840 | self.save(figpath=figpath, |
|
|
833 | 841 | figfile=figfile, |
|
|
834 | 842 | save=save, |
|
|
835 | 843 | ftp=ftp, |
|
|
836 | 844 | wr_period=wr_period, |
|
|
837 | 845 | thisDatetime=thisDatetime, |
|
|
838 | 846 | update_figfile=update_figfile) |
|
|
839 | 847 | |
|
|
840 | 848 | class CoherenceMap(Figure): |
|
|
841 | 849 | isConfig = None |
|
|
842 | 850 | __nsubplots = None |
|
|
843 | 851 | |
|
|
844 | 852 | WIDTHPROF = None |
|
|
845 | 853 | HEIGHTPROF = None |
|
|
846 | 854 | PREFIX = 'cmap' |
|
|
847 | 855 | |
|
|
848 | 856 | def __init__(self, **kwargs): |
|
|
849 | 857 | Figure.__init__(self, **kwargs) |
|
|
850 | 858 | self.timerange = 2*60*60 |
|
|
851 | 859 | self.isConfig = False |
|
|
852 | 860 | self.__nsubplots = 1 |
|
|
853 | 861 | |
|
|
854 | 862 | self.WIDTH = 800 |
|
|
855 | 863 | self.HEIGHT = 180 |
|
|
856 | 864 | self.WIDTHPROF = 120 |
|
|
857 | 865 | self.HEIGHTPROF = 0 |
|
|
858 | 866 | self.counter_imagwr = 0 |
|
|
859 | 867 | |
|
|
860 | 868 | self.PLOT_CODE = COH_CODE |
|
|
861 | 869 | |
|
|
862 | 870 | self.FTP_WEI = None |
|
|
863 | 871 | self.EXP_CODE = None |
|
|
864 | 872 | self.SUB_EXP_CODE = None |
|
|
865 | 873 | self.PLOT_POS = None |
|
|
866 | 874 | self.counter_imagwr = 0 |
|
|
867 | 875 | |
|
|
868 | 876 | self.xmin = None |
|
|
869 | 877 | self.xmax = None |
|
|
870 | 878 | |
|
|
871 | 879 | def getSubplots(self): |
|
|
872 | 880 | ncol = 1 |
|
|
873 | 881 | nrow = self.nplots*2 |
|
|
874 | 882 | |
|
|
875 | 883 | return nrow, ncol |
|
|
876 | 884 | |
|
|
877 | 885 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
878 | 886 | self.__showprofile = showprofile |
|
|
879 | 887 | self.nplots = nplots |
|
|
880 | 888 | |
|
|
881 | 889 | ncolspan = 1 |
|
|
882 | 890 | colspan = 1 |
|
|
883 | 891 | if showprofile: |
|
|
884 | 892 | ncolspan = 7 |
|
|
885 | 893 | colspan = 6 |
|
|
886 | 894 | self.__nsubplots = 2 |
|
|
887 | 895 | |
|
|
888 | 896 | self.createFigure(id = id, |
|
|
889 | 897 | wintitle = wintitle, |
|
|
890 | 898 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
891 | 899 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
892 | 900 | show=True) |
|
|
893 | 901 | |
|
|
894 | 902 | nrow, ncol = self.getSubplots() |
|
|
895 | 903 | |
|
|
896 | 904 | for y in range(nrow): |
|
|
897 | 905 | for x in range(ncol): |
|
|
898 | 906 | |
|
|
899 | 907 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
900 | 908 | |
|
|
901 | 909 | if showprofile: |
|
|
902 | 910 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
903 | 911 | |
|
|
904 | 912 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
905 | 913 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
906 | 914 | timerange=None, phase_min=None, phase_max=None, |
|
|
907 | 915 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
908 | 916 | coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
909 | 917 | server=None, folder=None, username=None, password=None, |
|
|
910 | 918 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
911 | 919 | |
|
|
912 | 920 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
913 | 921 | return |
|
|
914 | 922 | |
|
|
915 | 923 | if pairsList == None: |
|
|
916 | 924 | pairsIndexList = dataOut.pairsIndexList |
|
|
917 | 925 | else: |
|
|
918 | 926 | pairsIndexList = [] |
|
|
919 | 927 | for pair in pairsList: |
|
|
920 | 928 | if pair not in dataOut.pairsList: |
|
|
921 | 929 | raise ValueError, "Pair %s is not in dataOut.pairsList" %(pair) |
|
|
922 | 930 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
923 | 931 | |
|
|
924 | 932 | if pairsIndexList == []: |
|
|
925 | 933 | return |
|
|
926 | 934 | |
|
|
927 | 935 | if len(pairsIndexList) > 4: |
|
|
928 | 936 | pairsIndexList = pairsIndexList[0:4] |
|
|
929 | 937 | |
|
|
930 | 938 | if phase_min == None: |
|
|
931 | 939 | phase_min = -180 |
|
|
932 | 940 | if phase_max == None: |
|
|
933 | 941 | phase_max = 180 |
|
|
934 | 942 | |
|
|
935 | 943 | x = dataOut.getTimeRange() |
|
|
936 | 944 | y = dataOut.getHeiRange() |
|
|
937 | 945 | |
|
|
938 | 946 | thisDatetime = dataOut.datatime |
|
|
939 | 947 | |
|
|
940 | 948 | title = wintitle + " CoherenceMap" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
941 | 949 | xlabel = "" |
|
|
942 | 950 | ylabel = "Range (Km)" |
|
|
943 | 951 | update_figfile = False |
|
|
944 | 952 | |
|
|
945 | 953 | if not self.isConfig: |
|
|
946 | 954 | nplots = len(pairsIndexList) |
|
|
947 | 955 | self.setup(id=id, |
|
|
948 | 956 | nplots=nplots, |
|
|
949 | 957 | wintitle=wintitle, |
|
|
950 | 958 | showprofile=showprofile, |
|
|
951 | 959 | show=show) |
|
|
952 | 960 | |
|
|
953 | 961 | if timerange != None: |
|
|
954 | 962 | self.timerange = timerange |
|
|
955 | 963 | |
|
|
956 | 964 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
957 | 965 | |
|
|
958 | 966 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
959 | 967 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
960 | 968 | if zmin == None: zmin = 0. |
|
|
961 | 969 | if zmax == None: zmax = 1. |
|
|
962 | 970 | |
|
|
963 | 971 | self.FTP_WEI = ftp_wei |
|
|
964 | 972 | self.EXP_CODE = exp_code |
|
|
965 | 973 | self.SUB_EXP_CODE = sub_exp_code |
|
|
966 | 974 | self.PLOT_POS = plot_pos |
|
|
967 | 975 | |
|
|
968 | 976 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
969 | 977 | |
|
|
970 | 978 | self.isConfig = True |
|
|
971 | 979 | update_figfile = True |
|
|
972 | 980 | |
|
|
973 | 981 | self.setWinTitle(title) |
|
|
974 | 982 | |
|
|
975 | 983 | for i in range(self.nplots): |
|
|
976 | 984 | |
|
|
977 | 985 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
978 | 986 | |
|
|
979 | 987 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i],:,:],axis=0) |
|
|
980 | 988 | powa = numpy.average(dataOut.data_spc[pair[0],:,:],axis=0) |
|
|
981 | 989 | powb = numpy.average(dataOut.data_spc[pair[1],:,:],axis=0) |
|
|
982 | 990 | |
|
|
983 | 991 | |
|
|
984 | 992 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
985 | 993 | coherence = numpy.abs(avgcoherenceComplex) |
|
|
986 | 994 | |
|
|
987 | 995 | z = coherence.reshape((1,-1)) |
|
|
988 | 996 | |
|
|
989 | 997 | counter = 0 |
|
|
990 | 998 | |
|
|
991 | 999 | title = "Coherence Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
992 | 1000 | axes = self.axesList[i*self.__nsubplots*2] |
|
|
993 | 1001 | axes.pcolorbuffer(x, y, z, |
|
|
994 | 1002 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
995 | 1003 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
996 | 1004 | ticksize=9, cblabel='', colormap=coherence_cmap, cbsize="1%") |
|
|
997 | 1005 | |
|
|
998 | 1006 | if self.__showprofile: |
|
|
999 | 1007 | counter += 1 |
|
|
1000 | 1008 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
1001 | 1009 | axes.pline(coherence, y, |
|
|
1002 | 1010 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
1003 | 1011 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
1004 | 1012 | ytick_visible=False, nxticks=5, |
|
|
1005 | 1013 | grid='x') |
|
|
1006 | 1014 | |
|
|
1007 | 1015 | counter += 1 |
|
|
1008 | 1016 | |
|
|
1009 | 1017 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
1010 | 1018 | |
|
|
1011 | 1019 | z = phase.reshape((1,-1)) |
|
|
1012 | 1020 | |
|
|
1013 | 1021 | title = "Phase Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1014 | 1022 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
1015 | 1023 | axes.pcolorbuffer(x, y, z, |
|
|
1016 | 1024 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
1017 | 1025 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1018 | 1026 | ticksize=9, cblabel='', colormap=phase_cmap, cbsize="1%") |
|
|
1019 | 1027 | |
|
|
1020 | 1028 | if self.__showprofile: |
|
|
1021 | 1029 | counter += 1 |
|
|
1022 | 1030 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
1023 | 1031 | axes.pline(phase, y, |
|
|
1024 | 1032 | xmin=phase_min, xmax=phase_max, ymin=ymin, ymax=ymax, |
|
|
1025 | 1033 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
1026 | 1034 | ytick_visible=False, nxticks=4, |
|
|
1027 | 1035 | grid='x') |
|
|
1028 | 1036 | |
|
|
1029 | 1037 | self.draw() |
|
|
1030 | 1038 | |
|
|
1031 | 1039 | if dataOut.ltctime >= self.xmax: |
|
|
1032 | 1040 | self.counter_imagwr = wr_period |
|
|
1033 | 1041 | self.isConfig = False |
|
|
1034 | 1042 | update_figfile = True |
|
|
1035 | 1043 | |
|
|
1036 | 1044 | self.save(figpath=figpath, |
|
|
1037 | 1045 | figfile=figfile, |
|
|
1038 | 1046 | save=save, |
|
|
1039 | 1047 | ftp=ftp, |
|
|
1040 | 1048 | wr_period=wr_period, |
|
|
1041 | 1049 | thisDatetime=thisDatetime, |
|
|
1042 | 1050 | update_figfile=update_figfile) |
|
|
1043 | 1051 | |
|
|
1044 | 1052 | class PowerProfilePlot(Figure): |
|
|
1045 | 1053 | |
|
|
1046 | 1054 | isConfig = None |
|
|
1047 | 1055 | __nsubplots = None |
|
|
1048 | 1056 | |
|
|
1049 | 1057 | WIDTHPROF = None |
|
|
1050 | 1058 | HEIGHTPROF = None |
|
|
1051 | 1059 | PREFIX = 'spcprofile' |
|
|
1052 | 1060 | |
|
|
1053 | 1061 | def __init__(self, **kwargs): |
|
|
1054 | 1062 | Figure.__init__(self, **kwargs) |
|
|
1055 | 1063 | self.isConfig = False |
|
|
1056 | 1064 | self.__nsubplots = 1 |
|
|
1057 | 1065 | |
|
|
1058 | 1066 | self.PLOT_CODE = POWER_CODE |
|
|
1059 | 1067 | |
|
|
1060 | 1068 | self.WIDTH = 300 |
|
|
1061 | 1069 | self.HEIGHT = 500 |
|
|
1062 | 1070 | self.counter_imagwr = 0 |
|
|
1063 | 1071 | |
|
|
1064 | 1072 | def getSubplots(self): |
|
|
1065 | 1073 | ncol = 1 |
|
|
1066 | 1074 | nrow = 1 |
|
|
1067 | 1075 | |
|
|
1068 | 1076 | return nrow, ncol |
|
|
1069 | 1077 | |
|
|
1070 | 1078 | def setup(self, id, nplots, wintitle, show): |
|
|
1071 | 1079 | |
|
|
1072 | 1080 | self.nplots = nplots |
|
|
1073 | 1081 | |
|
|
1074 | 1082 | ncolspan = 1 |
|
|
1075 | 1083 | colspan = 1 |
|
|
1076 | 1084 | |
|
|
1077 | 1085 | self.createFigure(id = id, |
|
|
1078 | 1086 | wintitle = wintitle, |
|
|
1079 | 1087 | widthplot = self.WIDTH, |
|
|
1080 | 1088 | heightplot = self.HEIGHT, |
|
|
1081 | 1089 | show=show) |
|
|
1082 | 1090 | |
|
|
1083 | 1091 | nrow, ncol = self.getSubplots() |
|
|
1084 | 1092 | |
|
|
1085 | 1093 | counter = 0 |
|
|
1086 | 1094 | for y in range(nrow): |
|
|
1087 | 1095 | for x in range(ncol): |
|
|
1088 | 1096 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1089 | 1097 | |
|
|
1090 | 1098 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1091 | 1099 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1092 | 1100 | save=False, figpath='./', figfile=None, show=True, |
|
|
1093 | 1101 | ftp=False, wr_period=1, server=None, |
|
|
1094 | 1102 | folder=None, username=None, password=None): |
|
|
1095 | 1103 | |
|
|
1096 | 1104 | |
|
|
1097 | 1105 | if channelList == None: |
|
|
1098 | 1106 | channelIndexList = dataOut.channelIndexList |
|
|
1099 | 1107 | channelList = dataOut.channelList |
|
|
1100 | 1108 | else: |
|
|
1101 | 1109 | channelIndexList = [] |
|
|
1102 | 1110 | for channel in channelList: |
|
|
1103 | 1111 | if channel not in dataOut.channelList: |
|
|
1104 | 1112 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
1105 | 1113 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1106 | 1114 | |
|
|
1107 | 1115 | factor = dataOut.normFactor |
|
|
1108 | 1116 | |
|
|
1109 | 1117 | y = dataOut.getHeiRange() |
|
|
1110 | 1118 | |
|
|
1111 | 1119 | #for voltage |
|
|
1112 | 1120 | if dataOut.type == 'Voltage': |
|
|
1113 | 1121 | x = dataOut.data[channelIndexList,:] * numpy.conjugate(dataOut.data[channelIndexList,:]) |
|
|
1114 | 1122 | x = x.real |
|
|
1115 | 1123 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
1116 | 1124 | |
|
|
1117 | 1125 | #for spectra |
|
|
1118 | 1126 | if dataOut.type == 'Spectra': |
|
|
1119 | 1127 | x = dataOut.data_spc[channelIndexList,:,:]/factor |
|
|
1120 | 1128 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
1121 | 1129 | x = numpy.average(x, axis=1) |
|
|
1122 | 1130 | |
|
|
1123 | 1131 | |
|
|
1124 | 1132 | xdB = 10*numpy.log10(x) |
|
|
1125 | 1133 | |
|
|
1126 | 1134 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1127 | 1135 | title = wintitle + " Power Profile %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1128 | 1136 | xlabel = "dB" |
|
|
1129 | 1137 | ylabel = "Range (Km)" |
|
|
1130 | 1138 | |
|
|
1131 | 1139 | if not self.isConfig: |
|
|
1132 | 1140 | |
|
|
1133 | 1141 | nplots = 1 |
|
|
1134 | 1142 | |
|
|
1135 | 1143 | self.setup(id=id, |
|
|
1136 | 1144 | nplots=nplots, |
|
|
1137 | 1145 | wintitle=wintitle, |
|
|
1138 | 1146 | show=show) |
|
|
1139 | 1147 | |
|
|
1140 | 1148 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1141 | 1149 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1142 | 1150 | if xmin == None: xmin = numpy.nanmin(xdB)*0.9 |
|
|
1143 | 1151 | if xmax == None: xmax = numpy.nanmax(xdB)*1.1 |
|
|
1144 | 1152 | |
|
|
1145 | 1153 | self.isConfig = True |
|
|
1146 | 1154 | |
|
|
1147 | 1155 | self.setWinTitle(title) |
|
|
1148 | 1156 | |
|
|
1149 | 1157 | title = "Power Profile: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1150 | 1158 | axes = self.axesList[0] |
|
|
1151 | 1159 | |
|
|
1152 | 1160 | legendlabels = ["channel %d"%x for x in channelList] |
|
|
1153 | 1161 | axes.pmultiline(xdB, y, |
|
|
1154 | 1162 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1155 | 1163 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
1156 | 1164 | ytick_visible=True, nxticks=5, |
|
|
1157 | 1165 | grid='x') |
|
|
1158 | 1166 | |
|
|
1159 | 1167 | self.draw() |
|
|
1160 | 1168 | |
|
|
1161 | 1169 | self.save(figpath=figpath, |
|
|
1162 | 1170 | figfile=figfile, |
|
|
1163 | 1171 | save=save, |
|
|
1164 | 1172 | ftp=ftp, |
|
|
1165 | 1173 | wr_period=wr_period, |
|
|
1166 | 1174 | thisDatetime=thisDatetime) |
|
|
1167 | 1175 | |
|
|
1168 | 1176 | class SpectraCutPlot(Figure): |
|
|
1169 | 1177 | |
|
|
1170 | 1178 | isConfig = None |
|
|
1171 | 1179 | __nsubplots = None |
|
|
1172 | 1180 | |
|
|
1173 | 1181 | WIDTHPROF = None |
|
|
1174 | 1182 | HEIGHTPROF = None |
|
|
1175 | 1183 | PREFIX = 'spc_cut' |
|
|
1176 | 1184 | |
|
|
1177 | 1185 | def __init__(self, **kwargs): |
|
|
1178 | 1186 | Figure.__init__(self, **kwargs) |
|
|
1179 | 1187 | self.isConfig = False |
|
|
1180 | 1188 | self.__nsubplots = 1 |
|
|
1181 | 1189 | |
|
|
1182 | 1190 | self.PLOT_CODE = POWER_CODE |
|
|
1183 | 1191 | |
|
|
1184 | 1192 | self.WIDTH = 700 |
|
|
1185 | 1193 | self.HEIGHT = 500 |
|
|
1186 | 1194 | self.counter_imagwr = 0 |
|
|
1187 | 1195 | |
|
|
1188 | 1196 | def getSubplots(self): |
|
|
1189 | 1197 | ncol = 1 |
|
|
1190 | 1198 | nrow = 1 |
|
|
1191 | 1199 | |
|
|
1192 | 1200 | return nrow, ncol |
|
|
1193 | 1201 | |
|
|
1194 | 1202 | def setup(self, id, nplots, wintitle, show): |
|
|
1195 | 1203 | |
|
|
1196 | 1204 | self.nplots = nplots |
|
|
1197 | 1205 | |
|
|
1198 | 1206 | ncolspan = 1 |
|
|
1199 | 1207 | colspan = 1 |
|
|
1200 | 1208 | |
|
|
1201 | 1209 | self.createFigure(id = id, |
|
|
1202 | 1210 | wintitle = wintitle, |
|
|
1203 | 1211 | widthplot = self.WIDTH, |
|
|
1204 | 1212 | heightplot = self.HEIGHT, |
|
|
1205 | 1213 | show=show) |
|
|
1206 | 1214 | |
|
|
1207 | 1215 | nrow, ncol = self.getSubplots() |
|
|
1208 | 1216 | |
|
|
1209 | 1217 | counter = 0 |
|
|
1210 | 1218 | for y in range(nrow): |
|
|
1211 | 1219 | for x in range(ncol): |
|
|
1212 | 1220 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1213 | 1221 | |
|
|
1214 | 1222 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1215 | 1223 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1216 | 1224 | save=False, figpath='./', figfile=None, show=True, |
|
|
1217 | 1225 | ftp=False, wr_period=1, server=None, |
|
|
1218 | 1226 | folder=None, username=None, password=None, |
|
|
1219 | 1227 | xaxis="frequency"): |
|
|
1220 | 1228 | |
|
|
1221 | 1229 | |
|
|
1222 | 1230 | if channelList == None: |
|
|
1223 | 1231 | channelIndexList = dataOut.channelIndexList |
|
|
1224 | 1232 | channelList = dataOut.channelList |
|
|
1225 | 1233 | else: |
|
|
1226 | 1234 | channelIndexList = [] |
|
|
1227 | 1235 | for channel in channelList: |
|
|
1228 | 1236 | if channel not in dataOut.channelList: |
|
|
1229 | 1237 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
1230 | 1238 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1231 | 1239 | |
|
|
1232 | 1240 | factor = dataOut.normFactor |
|
|
1233 | 1241 | |
|
|
1234 | 1242 | y = dataOut.getHeiRange() |
|
|
1235 | 1243 | |
|
|
1236 | 1244 | z = dataOut.data_spc/factor |
|
|
1237 | 1245 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1238 | 1246 | |
|
|
1239 | 1247 | hei_index = numpy.arange(25)*3 + 20 |
|
|
1240 | 1248 | |
|
|
1241 | 1249 | if xaxis == "frequency": |
|
|
1242 | 1250 | x = dataOut.getFreqRange()/1000. |
|
|
1243 | 1251 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1244 | 1252 | xlabel = "Frequency (kHz)" |
|
|
1245 | 1253 | ylabel = "Power (dB)" |
|
|
1246 | 1254 | |
|
|
1247 | 1255 | elif xaxis == "time": |
|
|
1248 | 1256 | x = dataOut.getAcfRange() |
|
|
1249 | 1257 | zdB = z[0,:,hei_index] |
|
|
1250 | 1258 | xlabel = "Time (ms)" |
|
|
1251 | 1259 | ylabel = "ACF" |
|
|
1252 | 1260 | |
|
|
1253 | 1261 | else: |
|
|
1254 | 1262 | x = dataOut.getVelRange() |
|
|
1255 | 1263 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1256 | 1264 | xlabel = "Velocity (m/s)" |
|
|
1257 | 1265 | ylabel = "Power (dB)" |
|
|
1258 | 1266 | |
|
|
1259 | 1267 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1260 | 1268 | title = wintitle + " Range Cuts %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1261 | 1269 | |
|
|
1262 | 1270 | if not self.isConfig: |
|
|
1263 | 1271 | |
|
|
1264 | 1272 | nplots = 1 |
|
|
1265 | 1273 | |
|
|
1266 | 1274 | self.setup(id=id, |
|
|
1267 | 1275 | nplots=nplots, |
|
|
1268 | 1276 | wintitle=wintitle, |
|
|
1269 | 1277 | show=show) |
|
|
1270 | 1278 | |
|
|
1271 | 1279 | if xmin == None: xmin = numpy.nanmin(x)*0.9 |
|
|
1272 | 1280 | if xmax == None: xmax = numpy.nanmax(x)*1.1 |
|
|
1273 | 1281 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1274 | 1282 | if ymax == None: ymax = numpy.nanmax(zdB) |
|
|
1275 | 1283 | |
|
|
1276 | 1284 | self.isConfig = True |
|
|
1277 | 1285 | |
|
|
1278 | 1286 | self.setWinTitle(title) |
|
|
1279 | 1287 | |
|
|
1280 | 1288 | title = "Spectra Cuts: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1281 | 1289 | axes = self.axesList[0] |
|
|
1282 | 1290 | |
|
|
1283 | 1291 | legendlabels = ["Range = %dKm" %y[i] for i in hei_index] |
|
|
1284 | 1292 | |
|
|
1285 | 1293 | axes.pmultilineyaxis( x, zdB, |
|
|
1286 | 1294 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1287 | 1295 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
1288 | 1296 | ytick_visible=True, nxticks=5, |
|
|
1289 | 1297 | grid='x') |
|
|
1290 | 1298 | |
|
|
1291 | 1299 | self.draw() |
|
|
1292 | 1300 | |
|
|
1293 | 1301 | self.save(figpath=figpath, |
|
|
1294 | 1302 | figfile=figfile, |
|
|
1295 | 1303 | save=save, |
|
|
1296 | 1304 | ftp=ftp, |
|
|
1297 | 1305 | wr_period=wr_period, |
|
|
1298 | 1306 | thisDatetime=thisDatetime) |
|
|
1299 | 1307 | |
|
|
1300 | 1308 | class Noise(Figure): |
|
|
1301 | 1309 | |
|
|
1302 | 1310 | isConfig = None |
|
|
1303 | 1311 | __nsubplots = None |
|
|
1304 | 1312 | |
|
|
1305 | 1313 | PREFIX = 'noise' |
|
|
1306 | 1314 | |
|
|
1307 | 1315 | |
|
|
1308 | 1316 | def __init__(self, **kwargs): |
|
|
1309 | 1317 | Figure.__init__(self, **kwargs) |
|
|
1310 | 1318 | self.timerange = 24*60*60 |
|
|
1311 | 1319 | self.isConfig = False |
|
|
1312 | 1320 | self.__nsubplots = 1 |
|
|
1313 | 1321 | self.counter_imagwr = 0 |
|
|
1314 | 1322 | self.WIDTH = 800 |
|
|
1315 | 1323 | self.HEIGHT = 400 |
|
|
1316 | 1324 | self.WIDTHPROF = 120 |
|
|
1317 | 1325 | self.HEIGHTPROF = 0 |
|
|
1318 | 1326 | self.xdata = None |
|
|
1319 | 1327 | self.ydata = None |
|
|
1320 | 1328 | |
|
|
1321 | 1329 | self.PLOT_CODE = NOISE_CODE |
|
|
1322 | 1330 | |
|
|
1323 | 1331 | self.FTP_WEI = None |
|
|
1324 | 1332 | self.EXP_CODE = None |
|
|
1325 | 1333 | self.SUB_EXP_CODE = None |
|
|
1326 | 1334 | self.PLOT_POS = None |
|
|
1327 | 1335 | self.figfile = None |
|
|
1328 | 1336 | |
|
|
1329 | 1337 | self.xmin = None |
|
|
1330 | 1338 | self.xmax = None |
|
|
1331 | 1339 | |
|
|
1332 | 1340 | def getSubplots(self): |
|
|
1333 | 1341 | |
|
|
1334 | 1342 | ncol = 1 |
|
|
1335 | 1343 | nrow = 1 |
|
|
1336 | 1344 | |
|
|
1337 | 1345 | return nrow, ncol |
|
|
1338 | 1346 | |
|
|
1339 | 1347 | def openfile(self, filename): |
|
|
1340 | 1348 | dirname = os.path.dirname(filename) |
|
|
1341 | 1349 | |
|
|
1342 | 1350 | if not os.path.exists(dirname): |
|
|
1343 | 1351 | os.mkdir(dirname) |
|
|
1344 | 1352 | |
|
|
1345 | 1353 | f = open(filename,'w+') |
|
|
1346 | 1354 | f.write('\n\n') |
|
|
1347 | 1355 | f.write('JICAMARCA RADIO OBSERVATORY - Noise \n') |
|
|
1348 | 1356 | f.write('DD MM YYYY HH MM SS Channel0 Channel1 Channel2 Channel3\n\n' ) |
|
|
1349 | 1357 | f.close() |
|
|
1350 | 1358 | |
|
|
1351 | 1359 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1352 | 1360 | |
|
|
1353 | 1361 | f=open(filename_phase,'a') |
|
|
1354 | 1362 | |
|
|
1355 | 1363 | timetuple_data = data_datetime.timetuple() |
|
|
1356 | 1364 | day = str(timetuple_data.tm_mday) |
|
|
1357 | 1365 | month = str(timetuple_data.tm_mon) |
|
|
1358 | 1366 | year = str(timetuple_data.tm_year) |
|
|
1359 | 1367 | hour = str(timetuple_data.tm_hour) |
|
|
1360 | 1368 | minute = str(timetuple_data.tm_min) |
|
|
1361 | 1369 | second = str(timetuple_data.tm_sec) |
|
|
1362 | 1370 | |
|
|
1363 | 1371 | data_msg = '' |
|
|
1364 | 1372 | for i in range(len(data)): |
|
|
1365 | 1373 | data_msg += str(data[i]) + ' ' |
|
|
1366 | 1374 | |
|
|
1367 | 1375 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' ' + data_msg + '\n') |
|
|
1368 | 1376 | f.close() |
|
|
1369 | 1377 | |
|
|
1370 | 1378 | |
|
|
1371 | 1379 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1372 | 1380 | |
|
|
1373 | 1381 | self.__showprofile = showprofile |
|
|
1374 | 1382 | self.nplots = nplots |
|
|
1375 | 1383 | |
|
|
1376 | 1384 | ncolspan = 7 |
|
|
1377 | 1385 | colspan = 6 |
|
|
1378 | 1386 | self.__nsubplots = 2 |
|
|
1379 | 1387 | |
|
|
1380 | 1388 | self.createFigure(id = id, |
|
|
1381 | 1389 | wintitle = wintitle, |
|
|
1382 | 1390 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1383 | 1391 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1384 | 1392 | show=show) |
|
|
1385 | 1393 | |
|
|
1386 | 1394 | nrow, ncol = self.getSubplots() |
|
|
1387 | 1395 | |
|
|
1388 | 1396 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1389 | 1397 | |
|
|
1390 | 1398 | |
|
|
1391 | 1399 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
1392 | 1400 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1393 | 1401 | timerange=None, |
|
|
1394 | 1402 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1395 | 1403 | server=None, folder=None, username=None, password=None, |
|
|
1396 | 1404 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1397 | 1405 | |
|
|
1398 | 1406 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1399 | 1407 | return |
|
|
1400 | 1408 | |
|
|
1401 | 1409 | if channelList == None: |
|
|
1402 | 1410 | channelIndexList = dataOut.channelIndexList |
|
|
1403 | 1411 | channelList = dataOut.channelList |
|
|
1404 | 1412 | else: |
|
|
1405 | 1413 | channelIndexList = [] |
|
|
1406 | 1414 | for channel in channelList: |
|
|
1407 | 1415 | if channel not in dataOut.channelList: |
|
|
1408 | 1416 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
1409 | 1417 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1410 | 1418 | |
|
|
1411 | 1419 | x = dataOut.getTimeRange() |
|
|
1412 | 1420 | #y = dataOut.getHeiRange() |
|
|
1413 | 1421 | factor = dataOut.normFactor |
|
|
1414 | 1422 | noise = dataOut.noise[channelIndexList]/factor |
|
|
1415 | 1423 | noisedB = 10*numpy.log10(noise) |
|
|
1416 | 1424 | |
|
|
1417 | 1425 | thisDatetime = dataOut.datatime |
|
|
1418 | 1426 | |
|
|
1419 | 1427 | title = wintitle + " Noise" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1420 | 1428 | xlabel = "" |
|
|
1421 | 1429 | ylabel = "Intensity (dB)" |
|
|
1422 | 1430 | update_figfile = False |
|
|
1423 | 1431 | |
|
|
1424 | 1432 | if not self.isConfig: |
|
|
1425 | 1433 | |
|
|
1426 | 1434 | nplots = 1 |
|
|
1427 | 1435 | |
|
|
1428 | 1436 | self.setup(id=id, |
|
|
1429 | 1437 | nplots=nplots, |
|
|
1430 | 1438 | wintitle=wintitle, |
|
|
1431 | 1439 | showprofile=showprofile, |
|
|
1432 | 1440 | show=show) |
|
|
1433 | 1441 | |
|
|
1434 | 1442 | if timerange != None: |
|
|
1435 | 1443 | self.timerange = timerange |
|
|
1436 | 1444 | |
|
|
1437 | 1445 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1438 | 1446 | |
|
|
1439 | 1447 | if ymin == None: ymin = numpy.floor(numpy.nanmin(noisedB)) - 10.0 |
|
|
1440 | 1448 | if ymax == None: ymax = numpy.nanmax(noisedB) + 10.0 |
|
|
1441 | 1449 | |
|
|
1442 | 1450 | self.FTP_WEI = ftp_wei |
|
|
1443 | 1451 | self.EXP_CODE = exp_code |
|
|
1444 | 1452 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1445 | 1453 | self.PLOT_POS = plot_pos |
|
|
1446 | 1454 | |
|
|
1447 | 1455 | |
|
|
1448 | 1456 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1449 | 1457 | self.isConfig = True |
|
|
1450 | 1458 | self.figfile = figfile |
|
|
1451 | 1459 | self.xdata = numpy.array([]) |
|
|
1452 | 1460 | self.ydata = numpy.array([]) |
|
|
1453 | 1461 | |
|
|
1454 | 1462 | update_figfile = True |
|
|
1455 | 1463 | |
|
|
1456 | 1464 | #open file beacon phase |
|
|
1457 | 1465 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1458 | 1466 | noise_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1459 | 1467 | self.filename_noise = os.path.join(figpath,noise_file) |
|
|
1460 | 1468 | |
|
|
1461 | 1469 | self.setWinTitle(title) |
|
|
1462 | 1470 | |
|
|
1463 | 1471 | title = "Noise %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1464 | 1472 | |
|
|
1465 | 1473 | legendlabels = ["channel %d"%(idchannel) for idchannel in channelList] |
|
|
1466 | 1474 | axes = self.axesList[0] |
|
|
1467 | 1475 | |
|
|
1468 | 1476 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1469 | 1477 | |
|
|
1470 | 1478 | if len(self.ydata)==0: |
|
|
1471 | 1479 | self.ydata = noisedB.reshape(-1,1) |
|
|
1472 | 1480 | else: |
|
|
1473 | 1481 | self.ydata = numpy.hstack((self.ydata, noisedB.reshape(-1,1))) |
|
|
1474 | 1482 | |
|
|
1475 | 1483 | |
|
|
1476 | 1484 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1477 | 1485 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1478 | 1486 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1479 | 1487 | XAxisAsTime=True, grid='both' |
|
|
1480 | 1488 | ) |
|
|
1481 | 1489 | |
|
|
1482 | 1490 | self.draw() |
|
|
1483 | 1491 | |
|
|
1484 | 1492 | if dataOut.ltctime >= self.xmax: |
|
|
1485 | 1493 | self.counter_imagwr = wr_period |
|
|
1486 | 1494 | self.isConfig = False |
|
|
1487 | 1495 | update_figfile = True |
|
|
1488 | 1496 | |
|
|
1489 | 1497 | self.save(figpath=figpath, |
|
|
1490 | 1498 | figfile=figfile, |
|
|
1491 | 1499 | save=save, |
|
|
1492 | 1500 | ftp=ftp, |
|
|
1493 | 1501 | wr_period=wr_period, |
|
|
1494 | 1502 | thisDatetime=thisDatetime, |
|
|
1495 | 1503 | update_figfile=update_figfile) |
|
|
1496 | 1504 | |
|
|
1497 | 1505 | #store data beacon phase |
|
|
1498 | 1506 | if save: |
|
|
1499 | 1507 | self.save_data(self.filename_noise, noisedB, thisDatetime) |
|
|
1500 | 1508 | |
|
|
1501 | 1509 | class BeaconPhase(Figure): |
|
|
1502 | 1510 | |
|
|
1503 | 1511 | __isConfig = None |
|
|
1504 | 1512 | __nsubplots = None |
|
|
1505 | 1513 | |
|
|
1506 | 1514 | PREFIX = 'beacon_phase' |
|
|
1507 | 1515 | |
|
|
1508 | 1516 | def __init__(self, **kwargs): |
|
|
1509 | 1517 | Figure.__init__(self, **kwargs) |
|
|
1510 | 1518 | self.timerange = 24*60*60 |
|
|
1511 | 1519 | self.isConfig = False |
|
|
1512 | 1520 | self.__nsubplots = 1 |
|
|
1513 | 1521 | self.counter_imagwr = 0 |
|
|
1514 | 1522 | self.WIDTH = 800 |
|
|
1515 | 1523 | self.HEIGHT = 400 |
|
|
1516 | 1524 | self.WIDTHPROF = 120 |
|
|
1517 | 1525 | self.HEIGHTPROF = 0 |
|
|
1518 | 1526 | self.xdata = None |
|
|
1519 | 1527 | self.ydata = None |
|
|
1520 | 1528 | |
|
|
1521 | 1529 | self.PLOT_CODE = BEACON_CODE |
|
|
1522 | 1530 | |
|
|
1523 | 1531 | self.FTP_WEI = None |
|
|
1524 | 1532 | self.EXP_CODE = None |
|
|
1525 | 1533 | self.SUB_EXP_CODE = None |
|
|
1526 | 1534 | self.PLOT_POS = None |
|
|
1527 | 1535 | |
|
|
1528 | 1536 | self.filename_phase = None |
|
|
1529 | 1537 | |
|
|
1530 | 1538 | self.figfile = None |
|
|
1531 | 1539 | |
|
|
1532 | 1540 | self.xmin = None |
|
|
1533 | 1541 | self.xmax = None |
|
|
1534 | 1542 | |
|
|
1535 | 1543 | def getSubplots(self): |
|
|
1536 | 1544 | |
|
|
1537 | 1545 | ncol = 1 |
|
|
1538 | 1546 | nrow = 1 |
|
|
1539 | 1547 | |
|
|
1540 | 1548 | return nrow, ncol |
|
|
1541 | 1549 | |
|
|
1542 | 1550 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1543 | 1551 | |
|
|
1544 | 1552 | self.__showprofile = showprofile |
|
|
1545 | 1553 | self.nplots = nplots |
|
|
1546 | 1554 | |
|
|
1547 | 1555 | ncolspan = 7 |
|
|
1548 | 1556 | colspan = 6 |
|
|
1549 | 1557 | self.__nsubplots = 2 |
|
|
1550 | 1558 | |
|
|
1551 | 1559 | self.createFigure(id = id, |
|
|
1552 | 1560 | wintitle = wintitle, |
|
|
1553 | 1561 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1554 | 1562 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1555 | 1563 | show=show) |
|
|
1556 | 1564 | |
|
|
1557 | 1565 | nrow, ncol = self.getSubplots() |
|
|
1558 | 1566 | |
|
|
1559 | 1567 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1560 | 1568 | |
|
|
1561 | 1569 | def save_phase(self, filename_phase): |
|
|
1562 | 1570 | f = open(filename_phase,'w+') |
|
|
1563 | 1571 | f.write('\n\n') |
|
|
1564 | 1572 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
1565 | 1573 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
1566 | 1574 | f.close() |
|
|
1567 | 1575 | |
|
|
1568 | 1576 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1569 | 1577 | f=open(filename_phase,'a') |
|
|
1570 | 1578 | timetuple_data = data_datetime.timetuple() |
|
|
1571 | 1579 | day = str(timetuple_data.tm_mday) |
|
|
1572 | 1580 | month = str(timetuple_data.tm_mon) |
|
|
1573 | 1581 | year = str(timetuple_data.tm_year) |
|
|
1574 | 1582 | hour = str(timetuple_data.tm_hour) |
|
|
1575 | 1583 | minute = str(timetuple_data.tm_min) |
|
|
1576 | 1584 | second = str(timetuple_data.tm_sec) |
|
|
1577 | 1585 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
1578 | 1586 | f.close() |
|
|
1579 | 1587 | |
|
|
1580 | 1588 | |
|
|
1581 | 1589 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1582 | 1590 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
1583 | 1591 | timerange=None, |
|
|
1584 | 1592 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1585 | 1593 | server=None, folder=None, username=None, password=None, |
|
|
1586 | 1594 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1587 | 1595 | |
|
|
1588 | 1596 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1589 | 1597 | return |
|
|
1590 | 1598 | |
|
|
1591 | 1599 | if pairsList == None: |
|
|
1592 | 1600 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
1593 | 1601 | else: |
|
|
1594 | 1602 | pairsIndexList = [] |
|
|
1595 | 1603 | for pair in pairsList: |
|
|
1596 | 1604 | if pair not in dataOut.pairsList: |
|
|
1597 | 1605 | raise ValueError, "Pair %s is not in dataOut.pairsList" %(pair) |
|
|
1598 | 1606 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
1599 | 1607 | |
|
|
1600 | 1608 | if pairsIndexList == []: |
|
|
1601 | 1609 | return |
|
|
1602 | 1610 | |
|
|
1603 | 1611 | # if len(pairsIndexList) > 4: |
|
|
1604 | 1612 | # pairsIndexList = pairsIndexList[0:4] |
|
|
1605 | 1613 | |
|
|
1606 | 1614 | hmin_index = None |
|
|
1607 | 1615 | hmax_index = None |
|
|
1608 | 1616 | |
|
|
1609 | 1617 | if hmin != None and hmax != None: |
|
|
1610 | 1618 | indexes = numpy.arange(dataOut.nHeights) |
|
|
1611 | 1619 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
1612 | 1620 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
1613 | 1621 | |
|
|
1614 | 1622 | if hmin_list.any(): |
|
|
1615 | 1623 | hmin_index = hmin_list[0] |
|
|
1616 | 1624 | |
|
|
1617 | 1625 | if hmax_list.any(): |
|
|
1618 | 1626 | hmax_index = hmax_list[-1]+1 |
|
|
1619 | 1627 | |
|
|
1620 | 1628 | x = dataOut.getTimeRange() |
|
|
1621 | 1629 | #y = dataOut.getHeiRange() |
|
|
1622 | 1630 | |
|
|
1623 | 1631 | |
|
|
1624 | 1632 | thisDatetime = dataOut.datatime |
|
|
1625 | 1633 | |
|
|
1626 | 1634 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1627 | 1635 | xlabel = "Local Time" |
|
|
1628 | 1636 | ylabel = "Phase (degrees)" |
|
|
1629 | 1637 | |
|
|
1630 | 1638 | update_figfile = False |
|
|
1631 | 1639 | |
|
|
1632 | 1640 | nplots = len(pairsIndexList) |
|
|
1633 | 1641 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1634 | 1642 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
1635 | 1643 | for i in range(nplots): |
|
|
1636 | 1644 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
1637 | 1645 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
1638 | 1646 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
1639 | 1647 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
1640 | 1648 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
1641 | 1649 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
1642 | 1650 | |
|
|
1643 | 1651 | #print "Phase %d%d" %(pair[0], pair[1]) |
|
|
1644 | 1652 | #print phase[dataOut.beacon_heiIndexList] |
|
|
1645 | 1653 | |
|
|
1646 | 1654 | if dataOut.beacon_heiIndexList: |
|
|
1647 | 1655 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
1648 | 1656 | else: |
|
|
1649 | 1657 | phase_beacon[i] = numpy.average(phase) |
|
|
1650 | 1658 | |
|
|
1651 | 1659 | if not self.isConfig: |
|
|
1652 | 1660 | |
|
|
1653 | 1661 | nplots = len(pairsIndexList) |
|
|
1654 | 1662 | |
|
|
1655 | 1663 | self.setup(id=id, |
|
|
1656 | 1664 | nplots=nplots, |
|
|
1657 | 1665 | wintitle=wintitle, |
|
|
1658 | 1666 | showprofile=showprofile, |
|
|
1659 | 1667 | show=show) |
|
|
1660 | 1668 | |
|
|
1661 | 1669 | if timerange != None: |
|
|
1662 | 1670 | self.timerange = timerange |
|
|
1663 | 1671 | |
|
|
1664 | 1672 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1665 | 1673 | |
|
|
1666 | 1674 | if ymin == None: ymin = 0 |
|
|
1667 | 1675 | if ymax == None: ymax = 360 |
|
|
1668 | 1676 | |
|
|
1669 | 1677 | self.FTP_WEI = ftp_wei |
|
|
1670 | 1678 | self.EXP_CODE = exp_code |
|
|
1671 | 1679 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1672 | 1680 | self.PLOT_POS = plot_pos |
|
|
1673 | 1681 | |
|
|
1674 | 1682 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1675 | 1683 | self.isConfig = True |
|
|
1676 | 1684 | self.figfile = figfile |
|
|
1677 | 1685 | self.xdata = numpy.array([]) |
|
|
1678 | 1686 | self.ydata = numpy.array([]) |
|
|
1679 | 1687 | |
|
|
1680 | 1688 | update_figfile = True |
|
|
1681 | 1689 | |
|
|
1682 | 1690 | #open file beacon phase |
|
|
1683 | 1691 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1684 | 1692 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1685 | 1693 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1686 | 1694 | #self.save_phase(self.filename_phase) |
|
|
1687 | 1695 | |
|
|
1688 | 1696 | |
|
|
1689 | 1697 | #store data beacon phase |
|
|
1690 | 1698 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1691 | 1699 | |
|
|
1692 | 1700 | self.setWinTitle(title) |
|
|
1693 | 1701 | |
|
|
1694 | 1702 | |
|
|
1695 | 1703 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1696 | 1704 | |
|
|
1697 | 1705 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
1698 | 1706 | |
|
|
1699 | 1707 | axes = self.axesList[0] |
|
|
1700 | 1708 | |
|
|
1701 | 1709 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1702 | 1710 | |
|
|
1703 | 1711 | if len(self.ydata)==0: |
|
|
1704 | 1712 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1705 | 1713 | else: |
|
|
1706 | 1714 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1707 | 1715 | |
|
|
1708 | 1716 | |
|
|
1709 | 1717 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1710 | 1718 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1711 | 1719 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1712 | 1720 | XAxisAsTime=True, grid='both' |
|
|
1713 | 1721 | ) |
|
|
1714 | 1722 | |
|
|
1715 | 1723 | self.draw() |
|
|
1716 | 1724 | |
|
|
1717 | 1725 | if dataOut.ltctime >= self.xmax: |
|
|
1718 | 1726 | self.counter_imagwr = wr_period |
|
|
1719 | 1727 | self.isConfig = False |
|
|
1720 | 1728 | update_figfile = True |
|
|
1721 | 1729 | |
|
|
1722 | 1730 | self.save(figpath=figpath, |
|
|
1723 | 1731 | figfile=figfile, |
|
|
1724 | 1732 | save=save, |
|
|
1725 | 1733 | ftp=ftp, |
|
|
1726 | 1734 | wr_period=wr_period, |
|
|
1727 | 1735 | thisDatetime=thisDatetime, |
|
|
1728 | 1736 | update_figfile=update_figfile) |
| @@ -1,754 +1,793 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | |
|
|
3 | 3 | from jroproc_base import ProcessingUnit, Operation |
|
|
4 | 4 | from schainpy.model.data.jrodata import Spectra |
|
|
5 | 5 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
|
6 | 6 | |
|
|
7 | 7 | class SpectraAFCProc(ProcessingUnit): |
|
|
8 | 8 | |
|
|
9 | 9 | def __init__(self, **kwargs): |
|
|
10 | 10 | |
|
|
11 | 11 | ProcessingUnit.__init__(self, **kwargs) |
|
|
12 | 12 | |
|
|
13 | 13 | self.buffer = None |
|
|
14 | 14 | self.firstdatatime = None |
|
|
15 | 15 | self.profIndex = 0 |
|
|
16 | 16 | self.dataOut = Spectra() |
|
|
17 | 17 | self.id_min = None |
|
|
18 | 18 | self.id_max = None |
|
|
19 | 19 | |
|
|
20 | 20 | def __updateSpecFromVoltage(self): |
|
|
21 | 21 | |
|
|
22 | 22 | self.dataOut.plotting = "spectra_acf" |
|
|
23 | 23 | |
|
|
24 | 24 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
25 | 25 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
26 | 26 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
27 | 27 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
28 | 28 | |
|
|
29 | 29 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
30 | 30 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
31 | 31 | self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15 |
|
|
32 | 32 | |
|
|
33 | 33 | self.dataOut.channelList = self.dataIn.channelList |
|
|
34 | 34 | self.dataOut.heightList = self.dataIn.heightList |
|
|
35 | 35 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
36 | 36 | |
|
|
37 | 37 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
38 | 38 | self.dataOut.nCode = self.dataIn.nCode |
|
|
39 | 39 | self.dataOut.code = self.dataIn.code |
|
|
40 | 40 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
41 | 41 | |
|
|
42 | 42 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
43 | 43 | self.dataOut.utctime = self.firstdatatime |
|
|
44 | 44 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
45 | 45 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
46 | 46 | self.dataOut.flagShiftFFT = False |
|
|
47 | 47 | |
|
|
48 | 48 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
49 | 49 | self.dataOut.nIncohInt = 1 |
|
|
50 | 50 | |
|
|
51 | 51 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
52 | 52 | |
|
|
53 | 53 | self.dataOut.frequency = self.dataIn.frequency |
|
|
54 | 54 | self.dataOut.realtime = self.dataIn.realtime |
|
|
55 | 55 | |
|
|
56 | 56 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
57 | 57 | self.dataOut.zenith = self.dataIn.zenith |
|
|
58 | 58 | |
|
|
59 | 59 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
60 | 60 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
61 | 61 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
62 | 62 | |
|
|
63 | 63 | def __decodeData(self, nProfiles, code): |
|
|
64 | 64 | |
|
|
65 | 65 | if code is None: |
|
|
66 | 66 | return |
|
|
67 | 67 | |
|
|
68 | 68 | for i in range(nProfiles): |
|
|
69 | 69 | self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i] |
|
|
70 | 70 | |
|
|
71 | 71 | def __getFft(self): |
|
|
72 | 72 | """ |
|
|
73 | 73 | Convierte valores de Voltaje a Spectra |
|
|
74 | 74 | |
|
|
75 | 75 | Affected: |
|
|
76 | 76 | self.dataOut.data_spc |
|
|
77 | 77 | self.dataOut.data_cspc |
|
|
78 | 78 | self.dataOut.data_dc |
|
|
79 | 79 | self.dataOut.heightList |
|
|
80 | 80 | self.profIndex |
|
|
81 | 81 | self.buffer |
|
|
82 | 82 | self.dataOut.flagNoData |
|
|
83 | 83 | """ |
|
|
84 | 84 | nsegments = self.dataOut.nHeights |
|
|
85 | 85 | |
|
|
86 | 86 | _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex') |
|
|
87 | 87 | |
|
|
88 | 88 | for i in range(nsegments): |
|
|
89 | 89 | try: |
|
|
90 | 90 | _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles] |
|
|
91 | 91 | |
|
|
92 | 92 | if self.code is not None: |
|
|
93 | 93 | _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0] |
|
|
94 | 94 | except: |
|
|
95 | 95 | pass |
|
|
96 | 96 | |
|
|
97 | 97 | fft_volt = numpy.fft.fft(_fft_buffer, n=self.dataOut.nFFTPoints, axis=1) |
|
|
98 | 98 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
|
99 | 99 | dc = fft_volt[:,0,:] |
|
|
100 | 100 | |
|
|
101 | 101 | #calculo de self-spectra |
|
|
102 | 102 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
|
103 | 103 | data = numpy.fft.ifft(spc, axis=1) |
|
|
104 | 104 | data = numpy.fft.fftshift(data, axes=(1,)) |
|
|
105 | 105 | |
|
|
106 | 106 | spc = data |
|
|
107 | 107 | |
|
|
108 | 108 | blocksize = 0 |
|
|
109 | 109 | blocksize += dc.size |
|
|
110 | 110 | blocksize += spc.size |
|
|
111 | 111 | |
|
|
112 | 112 | cspc = None |
|
|
113 | 113 | pairIndex = 0 |
|
|
114 | 114 | |
|
|
115 | 115 | if self.dataOut.pairsList != None: |
|
|
116 | 116 | #calculo de cross-spectra |
|
|
117 | 117 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
|
118 | 118 | for pair in self.dataOut.pairsList: |
|
|
119 | 119 | if pair[0] not in self.dataOut.channelList: |
|
|
120 | 120 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
121 | 121 | if pair[1] not in self.dataOut.channelList: |
|
|
122 | 122 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
123 | 123 | |
|
|
124 | 124 | chan_index0 = self.dataOut.channelList.index(pair[0]) |
|
|
125 | 125 | chan_index1 = self.dataOut.channelList.index(pair[1]) |
|
|
126 | 126 | |
|
|
127 | 127 | cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:]) |
|
|
128 | 128 | pairIndex += 1 |
|
|
129 | 129 | blocksize += cspc.size |
|
|
130 | 130 | |
|
|
131 | 131 | self.dataOut.data_spc = spc |
|
|
132 | 132 | self.dataOut.data_cspc = cspc |
|
|
133 | 133 | self.dataOut.data_dc = dc |
|
|
134 | 134 | self.dataOut.blockSize = blocksize |
|
|
135 | 135 | self.dataOut.flagShiftFFT = True |
|
|
136 | 136 | |
|
|
137 | 137 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1,real= None, imag=None): |
|
|
138 | 138 | |
|
|
139 | 139 | self.dataOut.flagNoData = True |
|
|
140 | 140 | |
|
|
141 | 141 | if self.dataIn.type == "Spectra": |
|
|
142 | 142 | self.dataOut.copy(self.dataIn) |
|
|
143 | 143 | spc = self.dataOut.data_spc |
|
|
144 | 144 | data = numpy.fft.ifft(spc, axis=1) |
|
|
145 | 145 | data = numpy.fft.fftshift(data, axes=(1,)) |
|
|
146 | 146 | acf = numpy.abs(data) # Autocorrelacion LLAMAR A ESTE VALOR ACF |
|
|
147 | 147 | if real: |
|
|
148 | 148 | acf = data.real |
|
|
149 | 149 | if imag: |
|
|
150 | 150 | acf = data.imag |
|
|
151 | 151 | shape = acf.shape # nchannels, nprofiles, nsamples |
|
|
152 | 152 | |
|
|
153 | #import matplotlib.pyplot as plt | |
|
|
154 | #acf_tmp=acf[0,:,85] | |
|
|
155 | #plt.plot(acf_tmp) | |
|
|
156 | #plt.show() | |
|
|
157 | ||
|
|
158 | for i in range(shape[1]): | |
|
|
159 | tmp = numpy.argmax(acf[0,:,i]) | |
|
|
160 | if i>30: | |
|
|
161 | value = (acf[0,:,i][tmp+3]+acf[0,:,i][tmp+4])/2.0 | |
|
|
162 | acf[0,:,i][tmp] = value | |
|
|
163 | acf[0,:,i][tmp-1] = value | |
|
|
164 | acf[0,:,i][tmp+1] = value | |
|
|
165 | acf[0,:,i][tmp-2] = value | |
|
|
166 | acf[0,:,i][tmp+2] = value | |
|
|
167 | ||
|
|
168 | import scipy as sp | |
|
|
169 | from scipy import signal | |
|
|
170 | acf[0,:,i] = sp.signal.medfilt(acf[0,:,i],21) | |
|
|
171 | ||
|
|
172 | ||
|
|
173 | ||
|
|
174 | #print numpy.argmax(acf[0,:,85]) | |
|
|
175 | #import matplotlib.pyplot as plt | |
|
|
176 | #plt.plot(acf[0,:,85]) | |
|
|
177 | #a= acf[0,:,85] | |
|
|
178 | #b= acf[0,:,0] | |
|
|
179 | #print a[200],a[198],a[199], a[201],a[202],a[203] | |
|
|
180 | #plt.show() | |
|
|
181 | #import time | |
|
|
182 | #time.sleep(10) | |
|
|
183 | ||
|
|
184 | ||
|
|
153 | 185 | # Normalizando |
|
|
154 | 186 | for i in range(shape[0]): |
|
|
155 | 187 | for j in range(shape[2]): |
|
|
156 | 188 | acf[i,:,j]= acf[i,:,j] / numpy.max(numpy.abs(acf[i,:,j])) |
|
|
157 | 189 | |
|
|
190 | #import matplotlib.pyplot as plt | |
|
|
191 | #plt.plot(acf[0,:,85]) | |
|
|
192 | #plt.show() | |
|
|
193 | #import time | |
|
|
194 | #time.sleep(20) | |
|
|
195 | ||
|
|
196 | ||
|
|
158 | 197 | self.dataOut.data_acf = acf |
|
|
159 | 198 | return True |
|
|
160 | 199 | |
|
|
161 | 200 | if code is not None: |
|
|
162 | 201 | self.code = numpy.array(code).reshape(nCode,nBaud) |
|
|
163 | 202 | else: |
|
|
164 | 203 | self.code = None |
|
|
165 | 204 | |
|
|
166 | 205 | if self.dataIn.type == "Voltage": |
|
|
167 | 206 | |
|
|
168 | 207 | if nFFTPoints == None: |
|
|
169 | 208 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" |
|
|
170 | 209 | |
|
|
171 | 210 | if nProfiles == None: |
|
|
172 | 211 | nProfiles = nFFTPoints |
|
|
173 | 212 | |
|
|
174 | 213 | self.dataOut.ippFactor = 1 |
|
|
175 | 214 | |
|
|
176 | 215 | self.dataOut.nFFTPoints = nFFTPoints |
|
|
177 | 216 | self.dataOut.nProfiles = nProfiles |
|
|
178 | 217 | self.dataOut.pairsList = pairsList |
|
|
179 | 218 | |
|
|
180 | 219 | # if self.buffer is None: |
|
|
181 | 220 | # self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights), |
|
|
182 | 221 | # dtype='complex') |
|
|
183 | 222 | |
|
|
184 | 223 | if not self.dataIn.flagDataAsBlock: |
|
|
185 | 224 | self.buffer = self.dataIn.data.copy() |
|
|
186 | 225 | |
|
|
187 | 226 | # for i in range(self.dataIn.nHeights): |
|
|
188 | 227 | # self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex] |
|
|
189 | 228 | # |
|
|
190 | 229 | # self.profIndex += 1 |
|
|
191 | 230 | |
|
|
192 | 231 | else: |
|
|
193 | 232 | raise ValueError, "" |
|
|
194 | 233 | |
|
|
195 | 234 | self.firstdatatime = self.dataIn.utctime |
|
|
196 | 235 | |
|
|
197 | 236 | self.profIndex == nProfiles |
|
|
198 | 237 | |
|
|
199 | 238 | self.__updateSpecFromVoltage() |
|
|
200 | 239 | |
|
|
201 | 240 | self.__getFft() |
|
|
202 | 241 | |
|
|
203 | 242 | self.dataOut.flagNoData = False |
|
|
204 | 243 | |
|
|
205 | 244 | return True |
|
|
206 | 245 | |
|
|
207 | 246 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) |
|
|
208 | 247 | |
|
|
209 | 248 | def __selectPairs(self, pairsList): |
|
|
210 | 249 | |
|
|
211 | 250 | if channelList == None: |
|
|
212 | 251 | return |
|
|
213 | 252 | |
|
|
214 | 253 | pairsIndexListSelected = [] |
|
|
215 | 254 | |
|
|
216 | 255 | for thisPair in pairsList: |
|
|
217 | 256 | |
|
|
218 | 257 | if thisPair not in self.dataOut.pairsList: |
|
|
219 | 258 | continue |
|
|
220 | 259 | |
|
|
221 | 260 | pairIndex = self.dataOut.pairsList.index(thisPair) |
|
|
222 | 261 | |
|
|
223 | 262 | pairsIndexListSelected.append(pairIndex) |
|
|
224 | 263 | |
|
|
225 | 264 | if not pairsIndexListSelected: |
|
|
226 | 265 | self.dataOut.data_cspc = None |
|
|
227 | 266 | self.dataOut.pairsList = [] |
|
|
228 | 267 | return |
|
|
229 | 268 | |
|
|
230 | 269 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
231 | 270 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
232 | 271 | |
|
|
233 | 272 | return |
|
|
234 | 273 | |
|
|
235 | 274 | def __selectPairsByChannel(self, channelList=None): |
|
|
236 | 275 | |
|
|
237 | 276 | if channelList == None: |
|
|
238 | 277 | return |
|
|
239 | 278 | |
|
|
240 | 279 | pairsIndexListSelected = [] |
|
|
241 | 280 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
242 | 281 | #First pair |
|
|
243 | 282 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
244 | 283 | continue |
|
|
245 | 284 | #Second pair |
|
|
246 | 285 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
247 | 286 | continue |
|
|
248 | 287 | |
|
|
249 | 288 | pairsIndexListSelected.append(pairIndex) |
|
|
250 | 289 | |
|
|
251 | 290 | if not pairsIndexListSelected: |
|
|
252 | 291 | self.dataOut.data_cspc = None |
|
|
253 | 292 | self.dataOut.pairsList = [] |
|
|
254 | 293 | return |
|
|
255 | 294 | |
|
|
256 | 295 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
257 | 296 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
258 | 297 | |
|
|
259 | 298 | return |
|
|
260 | 299 | |
|
|
261 | 300 | def selectChannels(self, channelList): |
|
|
262 | 301 | |
|
|
263 | 302 | channelIndexList = [] |
|
|
264 | 303 | |
|
|
265 | 304 | for channel in channelList: |
|
|
266 | 305 | if channel not in self.dataOut.channelList: |
|
|
267 | 306 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) |
|
|
268 | 307 | |
|
|
269 | 308 | index = self.dataOut.channelList.index(channel) |
|
|
270 | 309 | channelIndexList.append(index) |
|
|
271 | 310 | |
|
|
272 | 311 | self.selectChannelsByIndex(channelIndexList) |
|
|
273 | 312 | |
|
|
274 | 313 | def selectChannelsByIndex(self, channelIndexList): |
|
|
275 | 314 | """ |
|
|
276 | 315 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
277 | 316 | |
|
|
278 | 317 | Input: |
|
|
279 | 318 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
280 | 319 | |
|
|
281 | 320 | Affected: |
|
|
282 | 321 | self.dataOut.data_spc |
|
|
283 | 322 | self.dataOut.channelIndexList |
|
|
284 | 323 | self.dataOut.nChannels |
|
|
285 | 324 | |
|
|
286 | 325 | Return: |
|
|
287 | 326 | None |
|
|
288 | 327 | """ |
|
|
289 | 328 | |
|
|
290 | 329 | for channelIndex in channelIndexList: |
|
|
291 | 330 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
292 | 331 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) |
|
|
293 | 332 | |
|
|
294 | 333 | # nChannels = len(channelIndexList) |
|
|
295 | 334 | |
|
|
296 | 335 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
|
|
297 | 336 | data_dc = self.dataOut.data_dc[channelIndexList,:] |
|
|
298 | 337 | |
|
|
299 | 338 | self.dataOut.data_spc = data_spc |
|
|
300 | 339 | self.dataOut.data_dc = data_dc |
|
|
301 | 340 | |
|
|
302 | 341 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
303 | 342 | # self.dataOut.nChannels = nChannels |
|
|
304 | 343 | |
|
|
305 | 344 | self.__selectPairsByChannel(self.dataOut.channelList) |
|
|
306 | 345 | |
|
|
307 | 346 | return 1 |
|
|
308 | 347 | |
|
|
309 | 348 | def selectHeights(self, minHei, maxHei): |
|
|
310 | 349 | """ |
|
|
311 | 350 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
312 | 351 | minHei <= height <= maxHei |
|
|
313 | 352 | |
|
|
314 | 353 | Input: |
|
|
315 | 354 | minHei : valor minimo de altura a considerar |
|
|
316 | 355 | maxHei : valor maximo de altura a considerar |
|
|
317 | 356 | |
|
|
318 | 357 | Affected: |
|
|
319 | 358 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
320 | 359 | |
|
|
321 | 360 | Return: |
|
|
322 | 361 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
323 | 362 | """ |
|
|
324 | 363 | |
|
|
325 | 364 | if (minHei > maxHei): |
|
|
326 | 365 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) |
|
|
327 | 366 | |
|
|
328 | 367 | if (minHei < self.dataOut.heightList[0]): |
|
|
329 | 368 | minHei = self.dataOut.heightList[0] |
|
|
330 | 369 | |
|
|
331 | 370 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
332 | 371 | maxHei = self.dataOut.heightList[-1] |
|
|
333 | 372 | |
|
|
334 | 373 | minIndex = 0 |
|
|
335 | 374 | maxIndex = 0 |
|
|
336 | 375 | heights = self.dataOut.heightList |
|
|
337 | 376 | |
|
|
338 | 377 | inda = numpy.where(heights >= minHei) |
|
|
339 | 378 | indb = numpy.where(heights <= maxHei) |
|
|
340 | 379 | |
|
|
341 | 380 | try: |
|
|
342 | 381 | minIndex = inda[0][0] |
|
|
343 | 382 | except: |
|
|
344 | 383 | minIndex = 0 |
|
|
345 | 384 | |
|
|
346 | 385 | try: |
|
|
347 | 386 | maxIndex = indb[0][-1] |
|
|
348 | 387 | except: |
|
|
349 | 388 | maxIndex = len(heights) |
|
|
350 | 389 | |
|
|
351 | 390 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
352 | 391 | |
|
|
353 | 392 | return 1 |
|
|
354 | 393 | |
|
|
355 | 394 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): |
|
|
356 | 395 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
357 | 396 | |
|
|
358 | 397 | if hei_ref != None: |
|
|
359 | 398 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
360 | 399 | |
|
|
361 | 400 | minIndex = min(newheis[0]) |
|
|
362 | 401 | maxIndex = max(newheis[0]) |
|
|
363 | 402 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
364 | 403 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
365 | 404 | |
|
|
366 | 405 | # determina indices |
|
|
367 | 406 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) |
|
|
368 | 407 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) |
|
|
369 | 408 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
370 | 409 | beacon_heiIndexList = [] |
|
|
371 | 410 | for val in avg_dB.tolist(): |
|
|
372 | 411 | if val >= beacon_dB[0]: |
|
|
373 | 412 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
|
374 | 413 | |
|
|
375 | 414 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
|
|
376 | 415 | data_cspc = None |
|
|
377 | 416 | if self.dataOut.data_cspc is not None: |
|
|
378 | 417 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
379 | 418 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
380 | 419 | |
|
|
381 | 420 | data_dc = None |
|
|
382 | 421 | if self.dataOut.data_dc is not None: |
|
|
383 | 422 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
384 | 423 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
385 | 424 | |
|
|
386 | 425 | self.dataOut.data_spc = data_spc |
|
|
387 | 426 | self.dataOut.data_cspc = data_cspc |
|
|
388 | 427 | self.dataOut.data_dc = data_dc |
|
|
389 | 428 | self.dataOut.heightList = heightList |
|
|
390 | 429 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
391 | 430 | |
|
|
392 | 431 | return 1 |
|
|
393 | 432 | |
|
|
394 | 433 | |
|
|
395 | 434 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
396 | 435 | """ |
|
|
397 | 436 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
398 | 437 | minIndex <= index <= maxIndex |
|
|
399 | 438 | |
|
|
400 | 439 | Input: |
|
|
401 | 440 | minIndex : valor de indice minimo de altura a considerar |
|
|
402 | 441 | maxIndex : valor de indice maximo de altura a considerar |
|
|
403 | 442 | |
|
|
404 | 443 | Affected: |
|
|
405 | 444 | self.dataOut.data_spc |
|
|
406 | 445 | self.dataOut.data_cspc |
|
|
407 | 446 | self.dataOut.data_dc |
|
|
408 | 447 | self.dataOut.heightList |
|
|
409 | 448 | |
|
|
410 | 449 | Return: |
|
|
411 | 450 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
412 | 451 | """ |
|
|
413 | 452 | |
|
|
414 | 453 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
415 | 454 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
416 | 455 | |
|
|
417 | 456 | if (maxIndex >= self.dataOut.nHeights): |
|
|
418 | 457 | maxIndex = self.dataOut.nHeights-1 |
|
|
419 | 458 | |
|
|
420 | 459 | #Spectra |
|
|
421 | 460 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
422 | 461 | |
|
|
423 | 462 | data_cspc = None |
|
|
424 | 463 | if self.dataOut.data_cspc is not None: |
|
|
425 | 464 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
426 | 465 | |
|
|
427 | 466 | data_dc = None |
|
|
428 | 467 | if self.dataOut.data_dc is not None: |
|
|
429 | 468 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
430 | 469 | |
|
|
431 | 470 | self.dataOut.data_spc = data_spc |
|
|
432 | 471 | self.dataOut.data_cspc = data_cspc |
|
|
433 | 472 | self.dataOut.data_dc = data_dc |
|
|
434 | 473 | |
|
|
435 | 474 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
436 | 475 | |
|
|
437 | 476 | return 1 |
|
|
438 | 477 | |
|
|
439 | 478 | def removeDC(self, mode = 2): |
|
|
440 | 479 | jspectra = self.dataOut.data_spc |
|
|
441 | 480 | jcspectra = self.dataOut.data_cspc |
|
|
442 | 481 | |
|
|
443 | 482 | |
|
|
444 | 483 | num_chan = jspectra.shape[0] |
|
|
445 | 484 | num_hei = jspectra.shape[2] |
|
|
446 | 485 | |
|
|
447 | 486 | if jcspectra is not None: |
|
|
448 | 487 | jcspectraExist = True |
|
|
449 | 488 | num_pairs = jcspectra.shape[0] |
|
|
450 | 489 | else: jcspectraExist = False |
|
|
451 | 490 | |
|
|
452 | 491 | freq_dc = jspectra.shape[1]/2 |
|
|
453 | 492 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
454 | 493 | |
|
|
455 | 494 | if ind_vel[0]<0: |
|
|
456 | 495 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
457 | 496 | |
|
|
458 | 497 | if mode == 1: |
|
|
459 | 498 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
460 | 499 | |
|
|
461 | 500 | if jcspectraExist: |
|
|
462 | 501 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 |
|
|
463 | 502 | |
|
|
464 | 503 | if mode == 2: |
|
|
465 | 504 | |
|
|
466 | 505 | vel = numpy.array([-2,-1,1,2]) |
|
|
467 | 506 | xx = numpy.zeros([4,4]) |
|
|
468 | 507 | |
|
|
469 | 508 | for fil in range(4): |
|
|
470 | 509 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
471 | 510 | |
|
|
472 | 511 | xx_inv = numpy.linalg.inv(xx) |
|
|
473 | 512 | xx_aux = xx_inv[0,:] |
|
|
474 | 513 | |
|
|
475 | 514 | for ich in range(num_chan): |
|
|
476 | 515 | yy = jspectra[ich,ind_vel,:] |
|
|
477 | 516 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
478 | 517 | |
|
|
479 | 518 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
480 | 519 | cjunkid = sum(junkid) |
|
|
481 | 520 | |
|
|
482 | 521 | if cjunkid.any(): |
|
|
483 | 522 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
484 | 523 | |
|
|
485 | 524 | if jcspectraExist: |
|
|
486 | 525 | for ip in range(num_pairs): |
|
|
487 | 526 | yy = jcspectra[ip,ind_vel,:] |
|
|
488 | 527 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
489 | 528 | |
|
|
490 | 529 | |
|
|
491 | 530 | self.dataOut.data_spc = jspectra |
|
|
492 | 531 | self.dataOut.data_cspc = jcspectra |
|
|
493 | 532 | |
|
|
494 | 533 | return 1 |
|
|
495 | 534 | |
|
|
496 | 535 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
497 | 536 | |
|
|
498 | 537 | jspectra = self.dataOut.data_spc |
|
|
499 | 538 | jcspectra = self.dataOut.data_cspc |
|
|
500 | 539 | jnoise = self.dataOut.getNoise() |
|
|
501 | 540 | num_incoh = self.dataOut.nIncohInt |
|
|
502 | 541 | |
|
|
503 | 542 | num_channel = jspectra.shape[0] |
|
|
504 | 543 | num_prof = jspectra.shape[1] |
|
|
505 | 544 | num_hei = jspectra.shape[2] |
|
|
506 | 545 | |
|
|
507 | 546 | #hei_interf |
|
|
508 | 547 | if hei_interf is None: |
|
|
509 | 548 | count_hei = num_hei/2 #Como es entero no importa |
|
|
510 | 549 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei |
|
|
511 | 550 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
512 | 551 | #nhei_interf |
|
|
513 | 552 | if (nhei_interf == None): |
|
|
514 | 553 | nhei_interf = 5 |
|
|
515 | 554 | if (nhei_interf < 1): |
|
|
516 | 555 | nhei_interf = 1 |
|
|
517 | 556 | if (nhei_interf > count_hei): |
|
|
518 | 557 | nhei_interf = count_hei |
|
|
519 | 558 | if (offhei_interf == None): |
|
|
520 | 559 | offhei_interf = 0 |
|
|
521 | 560 | |
|
|
522 | 561 | ind_hei = range(num_hei) |
|
|
523 | 562 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
524 | 563 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
525 | 564 | mask_prof = numpy.asarray(range(num_prof)) |
|
|
526 | 565 | num_mask_prof = mask_prof.size |
|
|
527 | 566 | comp_mask_prof = [0, num_prof/2] |
|
|
528 | 567 | |
|
|
529 | 568 | |
|
|
530 | 569 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
531 | 570 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
532 | 571 | jnoise = numpy.nan |
|
|
533 | 572 | noise_exist = jnoise[0] < numpy.Inf |
|
|
534 | 573 | |
|
|
535 | 574 | #Subrutina de Remocion de la Interferencia |
|
|
536 | 575 | for ich in range(num_channel): |
|
|
537 | 576 | #Se ordena los espectros segun su potencia (menor a mayor) |
|
|
538 | 577 | power = jspectra[ich,mask_prof,:] |
|
|
539 | 578 | power = power[:,hei_interf] |
|
|
540 | 579 | power = power.sum(axis = 0) |
|
|
541 | 580 | psort = power.ravel().argsort() |
|
|
542 | 581 | |
|
|
543 | 582 | #Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
544 | 583 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
545 | 584 | |
|
|
546 | 585 | if noise_exist: |
|
|
547 | 586 | # tmp_noise = jnoise[ich] / num_prof |
|
|
548 | 587 | tmp_noise = jnoise[ich] |
|
|
549 | 588 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
550 | 589 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
551 | 590 | |
|
|
552 | 591 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf |
|
|
553 | 592 | jspc_interf = jspc_interf.transpose() |
|
|
554 | 593 | #Calculando el espectro de interferencia promedio |
|
|
555 | 594 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
556 | 595 | noiseid = noiseid[0] |
|
|
557 | 596 | cnoiseid = noiseid.size |
|
|
558 | 597 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
559 | 598 | interfid = interfid[0] |
|
|
560 | 599 | cinterfid = interfid.size |
|
|
561 | 600 | |
|
|
562 | 601 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 |
|
|
563 | 602 | |
|
|
564 | 603 | #Expandiendo los perfiles a limpiar |
|
|
565 | 604 | if (cinterfid > 0): |
|
|
566 | 605 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof |
|
|
567 | 606 | new_interfid = numpy.asarray(new_interfid) |
|
|
568 | 607 | new_interfid = {x for x in new_interfid} |
|
|
569 | 608 | new_interfid = numpy.array(list(new_interfid)) |
|
|
570 | 609 | new_cinterfid = new_interfid.size |
|
|
571 | 610 | else: new_cinterfid = 0 |
|
|
572 | 611 | |
|
|
573 | 612 | for ip in range(new_cinterfid): |
|
|
574 | 613 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() |
|
|
575 | 614 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] |
|
|
576 | 615 | |
|
|
577 | 616 | |
|
|
578 | 617 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices |
|
|
579 | 618 | |
|
|
580 | 619 | #Removiendo la interferencia del punto de mayor interferencia |
|
|
581 | 620 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
582 | 621 | maxid = ListAux.index(max(ListAux)) |
|
|
583 | 622 | |
|
|
584 | 623 | |
|
|
585 | 624 | if cinterfid > 0: |
|
|
586 | 625 | for ip in range(cinterfid*(interf == 2) - 1): |
|
|
587 | 626 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() |
|
|
588 | 627 | cind = len(ind) |
|
|
589 | 628 | |
|
|
590 | 629 | if (cind > 0): |
|
|
591 | 630 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) |
|
|
592 | 631 | |
|
|
593 | 632 | ind = numpy.array([-2,-1,1,2]) |
|
|
594 | 633 | xx = numpy.zeros([4,4]) |
|
|
595 | 634 | |
|
|
596 | 635 | for id1 in range(4): |
|
|
597 | 636 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
598 | 637 | |
|
|
599 | 638 | xx_inv = numpy.linalg.inv(xx) |
|
|
600 | 639 | xx = xx_inv[:,0] |
|
|
601 | 640 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
602 | 641 | yy = jspectra[ich,mask_prof[ind],:] |
|
|
603 | 642 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
604 | 643 | |
|
|
605 | 644 | |
|
|
606 | 645 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() |
|
|
607 | 646 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) |
|
|
608 | 647 | |
|
|
609 | 648 | #Remocion de Interferencia en el Cross Spectra |
|
|
610 | 649 | if jcspectra is None: return jspectra, jcspectra |
|
|
611 | 650 | num_pairs = jcspectra.size/(num_prof*num_hei) |
|
|
612 | 651 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
613 | 652 | |
|
|
614 | 653 | for ip in range(num_pairs): |
|
|
615 | 654 | |
|
|
616 | 655 | #------------------------------------------- |
|
|
617 | 656 | |
|
|
618 | 657 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) |
|
|
619 | 658 | cspower = cspower[:,hei_interf] |
|
|
620 | 659 | cspower = cspower.sum(axis = 0) |
|
|
621 | 660 | |
|
|
622 | 661 | cspsort = cspower.ravel().argsort() |
|
|
623 | 662 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
624 | 663 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
625 | 664 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf |
|
|
626 | 665 | |
|
|
627 | 666 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
628 | 667 | |
|
|
629 | 668 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
630 | 669 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
631 | 670 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) |
|
|
632 | 671 | |
|
|
633 | 672 | for iprof in range(num_prof): |
|
|
634 | 673 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() |
|
|
635 | 674 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] |
|
|
636 | 675 | |
|
|
637 | 676 | #Removiendo la Interferencia |
|
|
638 | 677 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf |
|
|
639 | 678 | |
|
|
640 | 679 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
641 | 680 | maxid = ListAux.index(max(ListAux)) |
|
|
642 | 681 | |
|
|
643 | 682 | ind = numpy.array([-2,-1,1,2]) |
|
|
644 | 683 | xx = numpy.zeros([4,4]) |
|
|
645 | 684 | |
|
|
646 | 685 | for id1 in range(4): |
|
|
647 | 686 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
648 | 687 | |
|
|
649 | 688 | xx_inv = numpy.linalg.inv(xx) |
|
|
650 | 689 | xx = xx_inv[:,0] |
|
|
651 | 690 | |
|
|
652 | 691 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
653 | 692 | yy = jcspectra[ip,mask_prof[ind],:] |
|
|
654 | 693 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
655 | 694 | |
|
|
656 | 695 | #Guardar Resultados |
|
|
657 | 696 | self.dataOut.data_spc = jspectra |
|
|
658 | 697 | self.dataOut.data_cspc = jcspectra |
|
|
659 | 698 | |
|
|
660 | 699 | return 1 |
|
|
661 | 700 | |
|
|
662 | 701 | def setRadarFrequency(self, frequency=None): |
|
|
663 | 702 | |
|
|
664 | 703 | if frequency != None: |
|
|
665 | 704 | self.dataOut.frequency = frequency |
|
|
666 | 705 | |
|
|
667 | 706 | return 1 |
|
|
668 | 707 | |
|
|
669 | 708 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
670 | 709 | #validacion de rango |
|
|
671 | 710 | if minHei == None: |
|
|
672 | 711 | minHei = self.dataOut.heightList[0] |
|
|
673 | 712 | |
|
|
674 | 713 | if maxHei == None: |
|
|
675 | 714 | maxHei = self.dataOut.heightList[-1] |
|
|
676 | 715 | |
|
|
677 | 716 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
678 | 717 | print 'minHei: %.2f is out of the heights range'%(minHei) |
|
|
679 | 718 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) |
|
|
680 | 719 | minHei = self.dataOut.heightList[0] |
|
|
681 | 720 | |
|
|
682 | 721 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
683 | 722 | print 'maxHei: %.2f is out of the heights range'%(maxHei) |
|
|
684 | 723 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) |
|
|
685 | 724 | maxHei = self.dataOut.heightList[-1] |
|
|
686 | 725 | |
|
|
687 | 726 | # validacion de velocidades |
|
|
688 | 727 | velrange = self.dataOut.getVelRange(1) |
|
|
689 | 728 | |
|
|
690 | 729 | if minVel == None: |
|
|
691 | 730 | minVel = velrange[0] |
|
|
692 | 731 | |
|
|
693 | 732 | if maxVel == None: |
|
|
694 | 733 | maxVel = velrange[-1] |
|
|
695 | 734 | |
|
|
696 | 735 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
697 | 736 | print 'minVel: %.2f is out of the velocity range'%(minVel) |
|
|
698 | 737 | print 'minVel is setting to %.2f'%(velrange[0]) |
|
|
699 | 738 | minVel = velrange[0] |
|
|
700 | 739 | |
|
|
701 | 740 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
702 | 741 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) |
|
|
703 | 742 | print 'maxVel is setting to %.2f'%(velrange[-1]) |
|
|
704 | 743 | maxVel = velrange[-1] |
|
|
705 | 744 | |
|
|
706 | 745 | # seleccion de indices para rango |
|
|
707 | 746 | minIndex = 0 |
|
|
708 | 747 | maxIndex = 0 |
|
|
709 | 748 | heights = self.dataOut.heightList |
|
|
710 | 749 | |
|
|
711 | 750 | inda = numpy.where(heights >= minHei) |
|
|
712 | 751 | indb = numpy.where(heights <= maxHei) |
|
|
713 | 752 | |
|
|
714 | 753 | try: |
|
|
715 | 754 | minIndex = inda[0][0] |
|
|
716 | 755 | except: |
|
|
717 | 756 | minIndex = 0 |
|
|
718 | 757 | |
|
|
719 | 758 | try: |
|
|
720 | 759 | maxIndex = indb[0][-1] |
|
|
721 | 760 | except: |
|
|
722 | 761 | maxIndex = len(heights) |
|
|
723 | 762 | |
|
|
724 | 763 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
725 | 764 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
726 | 765 | |
|
|
727 | 766 | if (maxIndex >= self.dataOut.nHeights): |
|
|
728 | 767 | maxIndex = self.dataOut.nHeights-1 |
|
|
729 | 768 | |
|
|
730 | 769 | # seleccion de indices para velocidades |
|
|
731 | 770 | indminvel = numpy.where(velrange >= minVel) |
|
|
732 | 771 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
733 | 772 | try: |
|
|
734 | 773 | minIndexVel = indminvel[0][0] |
|
|
735 | 774 | except: |
|
|
736 | 775 | minIndexVel = 0 |
|
|
737 | 776 | |
|
|
738 | 777 | try: |
|
|
739 | 778 | maxIndexVel = indmaxvel[0][-1] |
|
|
740 | 779 | except: |
|
|
741 | 780 | maxIndexVel = len(velrange) |
|
|
742 | 781 | |
|
|
743 | 782 | #seleccion del espectro |
|
|
744 | 783 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] |
|
|
745 | 784 | #estimacion de ruido |
|
|
746 | 785 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
747 | 786 | |
|
|
748 | 787 | for channel in range(self.dataOut.nChannels): |
|
|
749 | 788 | daux = data_spc[channel,:,:] |
|
|
750 | 789 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) |
|
|
751 | 790 | |
|
|
752 | 791 | self.dataOut.noise_estimation = noise.copy() |
|
|
753 | 792 | |
|
|
754 | 793 | return 1 |
| @@ -1,1395 +1,1520 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | from schainpy import cSchain |
|
|
5 | 5 | from jroproc_base import ProcessingUnit, Operation |
|
|
6 | 6 | from schainpy.model.data.jrodata import Voltage |
|
|
7 | 7 | from time import time |
|
|
8 | 8 | |
|
|
9 | import math | |
|
|
10 | ||
|
|
11 | def rep_seq(x, rep=10): | |
|
|
12 | L = len(x) * rep | |
|
|
13 | res = numpy.zeros(L, dtype=x.dtype) | |
|
|
14 | idx = numpy.arange(len(x)) * rep | |
|
|
15 | for i in numpy.arange(rep): | |
|
|
16 | res[idx + i] = x | |
|
|
17 | return(res) | |
|
|
18 | ||
|
|
19 | ||
|
|
20 | def create_pseudo_random_code(clen=10000, seed=0): | |
|
|
21 | """ | |
|
|
22 | seed is a way of reproducing the random code without | |
|
|
23 | having to store all actual codes. the seed can then | |
|
|
24 | act as a sort of station_id. | |
|
|
25 | ||
|
|
26 | """ | |
|
|
27 | numpy.random.seed(seed) | |
|
|
28 | phases = numpy.array( | |
|
|
29 | numpy.exp(1.0j * 2.0 * math.pi * numpy.random.random(clen)), | |
|
|
30 | dtype=numpy.complex64, | |
|
|
31 | ) | |
|
|
32 | return(phases) | |
|
|
33 | ||
|
|
34 | ||
|
|
35 | def periodic_convolution_matrix(envelope, rmin=0, rmax=100): | |
|
|
36 | """ | |
|
|
37 | we imply that the number of measurements is equal to the number of elements | |
|
|
38 | in code | |
|
|
39 | ||
|
|
40 | """ | |
|
|
41 | L = len(envelope) | |
|
|
42 | ridx = numpy.arange(rmin, rmax) | |
|
|
43 | A = numpy.zeros([L, rmax-rmin], dtype=numpy.complex64) | |
|
|
44 | for i in numpy.arange(L): | |
|
|
45 | A[i, :] = envelope[(i-ridx) % L] | |
|
|
46 | result = {} | |
|
|
47 | result['A'] = A | |
|
|
48 | result['ridx'] = ridx | |
|
|
49 | return(result) | |
|
|
50 | ||
|
|
51 | ||
|
|
52 | B_cache = 0 | |
|
|
53 | r_cache = 0 | |
|
|
54 | B_cached = False | |
|
|
55 | def create_estimation_matrix(code, rmin=0, rmax=1000, cache=True): | |
|
|
56 | global B_cache | |
|
|
57 | global r_cache | |
|
|
58 | global B_cached | |
|
|
59 | ||
|
|
60 | if not cache or not B_cached: | |
|
|
61 | r_cache = periodic_convolution_matrix( | |
|
|
62 | envelope=code, rmin=rmin, rmax=rmax, | |
|
|
63 | ) | |
|
|
64 | A = r_cache['A'] | |
|
|
65 | Ah = numpy.transpose(numpy.conjugate(A)) | |
|
|
66 | B_cache = numpy.dot(numpy.linalg.inv(numpy.dot(Ah, A)), Ah) | |
|
|
67 | r_cache['B'] = B_cache | |
|
|
68 | B_cached = True | |
|
|
69 | return(r_cache) | |
|
|
70 | else: | |
|
|
71 | return(r_cache) | |
|
|
72 | ||
|
|
9 | 73 | class VoltageProc(ProcessingUnit): |
|
|
10 | 74 | |
|
|
11 | 75 | |
|
|
12 | 76 | def __init__(self, **kwargs): |
|
|
13 | 77 | |
|
|
14 | 78 | ProcessingUnit.__init__(self, **kwargs) |
|
|
15 | 79 | |
|
|
16 | 80 | # self.objectDict = {} |
|
|
17 | 81 | self.dataOut = Voltage() |
|
|
18 | 82 | self.flip = 1 |
|
|
19 | 83 | |
|
|
20 | 84 | def run(self): |
|
|
21 | 85 | if self.dataIn.type == 'AMISR': |
|
|
22 | 86 | self.__updateObjFromAmisrInput() |
|
|
23 | 87 | |
|
|
24 | 88 | if self.dataIn.type == 'Voltage': |
|
|
25 | 89 | self.dataOut.copy(self.dataIn) |
|
|
26 | 90 | |
|
|
27 | 91 | # self.dataOut.copy(self.dataIn) |
|
|
28 | 92 | |
|
|
29 | 93 | def __updateObjFromAmisrInput(self): |
|
|
30 | 94 | |
|
|
31 | 95 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
32 | 96 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
33 | 97 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
34 | 98 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
35 | 99 | |
|
|
36 | 100 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
37 | 101 | self.dataOut.data = self.dataIn.data |
|
|
38 | 102 | self.dataOut.utctime = self.dataIn.utctime |
|
|
39 | 103 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 104 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
41 | 105 | self.dataOut.heightList = self.dataIn.heightList |
|
|
42 | 106 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
43 | 107 | |
|
|
44 | 108 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
45 | 109 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
46 | 110 | self.dataOut.frequency = self.dataIn.frequency |
|
|
47 | 111 | |
|
|
48 | 112 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
49 | 113 | self.dataOut.zenith = self.dataIn.zenith |
|
|
50 | 114 | |
|
|
51 | 115 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
52 | 116 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
53 | 117 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
54 | 118 | # |
|
|
55 | 119 | # pass# |
|
|
56 | 120 | # |
|
|
57 | 121 | # def init(self): |
|
|
58 | 122 | # |
|
|
59 | 123 | # |
|
|
60 | 124 | # if self.dataIn.type == 'AMISR': |
|
|
61 | 125 | # self.__updateObjFromAmisrInput() |
|
|
62 | 126 | # |
|
|
63 | 127 | # if self.dataIn.type == 'Voltage': |
|
|
64 | 128 | # self.dataOut.copy(self.dataIn) |
|
|
65 | 129 | # # No necesita copiar en cada init() los atributos de dataIn |
|
|
66 | 130 | # # la copia deberia hacerse por cada nuevo bloque de datos |
|
|
67 | 131 | |
|
|
68 | 132 | def selectChannels(self, channelList): |
|
|
69 | 133 | |
|
|
70 | 134 | channelIndexList = [] |
|
|
71 | 135 | |
|
|
72 | 136 | for channel in channelList: |
|
|
73 | 137 | if channel not in self.dataOut.channelList: |
|
|
74 | 138 | raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList)) |
|
|
75 | 139 | |
|
|
76 | 140 | index = self.dataOut.channelList.index(channel) |
|
|
77 | 141 | channelIndexList.append(index) |
|
|
78 | 142 | |
|
|
79 | 143 | self.selectChannelsByIndex(channelIndexList) |
|
|
80 | 144 | |
|
|
81 | 145 | def selectChannelsByIndex(self, channelIndexList): |
|
|
82 | 146 | """ |
|
|
83 | 147 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
84 | 148 | |
|
|
85 | 149 | Input: |
|
|
86 | 150 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
87 | 151 | |
|
|
88 | 152 | Affected: |
|
|
89 | 153 | self.dataOut.data |
|
|
90 | 154 | self.dataOut.channelIndexList |
|
|
91 | 155 | self.dataOut.nChannels |
|
|
92 | 156 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
93 | 157 | self.dataOut.systemHeaderObj.numChannels |
|
|
94 | 158 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
95 | 159 | |
|
|
96 | 160 | Return: |
|
|
97 | 161 | None |
|
|
98 | 162 | """ |
|
|
99 | 163 | |
|
|
100 | 164 | for channelIndex in channelIndexList: |
|
|
101 | 165 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
102 | 166 | print channelIndexList |
|
|
103 | 167 | raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex |
|
|
104 | 168 | |
|
|
105 | 169 | if self.dataOut.flagDataAsBlock: |
|
|
106 | 170 | """ |
|
|
107 | 171 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
108 | 172 | """ |
|
|
109 | 173 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
110 | 174 | else: |
|
|
111 | 175 | data = self.dataOut.data[channelIndexList,:] |
|
|
112 | 176 | |
|
|
113 | 177 | self.dataOut.data = data |
|
|
114 | 178 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
115 | 179 | # self.dataOut.nChannels = nChannels |
|
|
116 | 180 | |
|
|
117 | 181 | return 1 |
|
|
118 | 182 | |
|
|
119 | 183 | def selectHeights(self, minHei=None, maxHei=None): |
|
|
120 | 184 | """ |
|
|
121 | 185 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
122 | 186 | minHei <= height <= maxHei |
|
|
123 | 187 | |
|
|
124 | 188 | Input: |
|
|
125 | 189 | minHei : valor minimo de altura a considerar |
|
|
126 | 190 | maxHei : valor maximo de altura a considerar |
|
|
127 | 191 | |
|
|
128 | 192 | Affected: |
|
|
129 | 193 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
130 | 194 | |
|
|
131 | 195 | Return: |
|
|
132 | 196 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
133 | 197 | """ |
|
|
134 | 198 | |
|
|
135 | 199 | if minHei == None: |
|
|
136 | 200 | minHei = self.dataOut.heightList[0] |
|
|
137 | 201 | |
|
|
138 | 202 | if maxHei == None: |
|
|
139 | 203 | maxHei = self.dataOut.heightList[-1] |
|
|
140 | 204 | |
|
|
141 | 205 | if (minHei < self.dataOut.heightList[0]): |
|
|
142 | 206 | minHei = self.dataOut.heightList[0] |
|
|
143 | 207 | |
|
|
144 | 208 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
145 | 209 | maxHei = self.dataOut.heightList[-1] |
|
|
146 | 210 | |
|
|
147 | 211 | minIndex = 0 |
|
|
148 | 212 | maxIndex = 0 |
|
|
149 | 213 | heights = self.dataOut.heightList |
|
|
150 | 214 | |
|
|
151 | 215 | inda = numpy.where(heights >= minHei) |
|
|
152 | 216 | indb = numpy.where(heights <= maxHei) |
|
|
153 | 217 | |
|
|
154 | 218 | try: |
|
|
155 | 219 | minIndex = inda[0][0] |
|
|
156 | 220 | except: |
|
|
157 | 221 | minIndex = 0 |
|
|
158 | 222 | |
|
|
159 | 223 | try: |
|
|
160 | 224 | maxIndex = indb[0][-1] |
|
|
161 | 225 | except: |
|
|
162 | 226 | maxIndex = len(heights) |
|
|
163 | 227 | |
|
|
164 | 228 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
165 | 229 | |
|
|
166 | 230 | return 1 |
|
|
167 | 231 | |
|
|
168 | 232 | |
|
|
169 | 233 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
170 | 234 | """ |
|
|
171 | 235 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
172 | 236 | minIndex <= index <= maxIndex |
|
|
173 | 237 | |
|
|
174 | 238 | Input: |
|
|
175 | 239 | minIndex : valor de indice minimo de altura a considerar |
|
|
176 | 240 | maxIndex : valor de indice maximo de altura a considerar |
|
|
177 | 241 | |
|
|
178 | 242 | Affected: |
|
|
179 | 243 | self.dataOut.data |
|
|
180 | 244 | self.dataOut.heightList |
|
|
181 | 245 | |
|
|
182 | 246 | Return: |
|
|
183 | 247 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
184 | 248 | """ |
|
|
185 | 249 | |
|
|
186 | 250 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
187 | 251 | raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
188 | 252 | |
|
|
189 | 253 | if (maxIndex >= self.dataOut.nHeights): |
|
|
190 | 254 | maxIndex = self.dataOut.nHeights |
|
|
191 | 255 | |
|
|
192 | 256 | #voltage |
|
|
193 | 257 | if self.dataOut.flagDataAsBlock: |
|
|
194 | 258 | """ |
|
|
195 | 259 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
196 | 260 | """ |
|
|
197 | 261 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
198 | 262 | else: |
|
|
199 | 263 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
200 | 264 | |
|
|
201 | 265 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
202 | 266 | |
|
|
203 | 267 | self.dataOut.data = data |
|
|
204 | 268 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
205 | 269 | |
|
|
206 | 270 | if self.dataOut.nHeights <= 1: |
|
|
207 | 271 | raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights) |
|
|
208 | 272 | |
|
|
209 | 273 | return 1 |
|
|
210 | 274 | |
|
|
211 | 275 | |
|
|
212 | 276 | def filterByHeights(self, window): |
|
|
213 | 277 | |
|
|
214 | 278 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
215 | 279 | |
|
|
216 | 280 | if window == None: |
|
|
217 | 281 | window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
218 | 282 | |
|
|
219 | 283 | newdelta = deltaHeight * window |
|
|
220 | 284 | r = self.dataOut.nHeights % window |
|
|
221 | 285 | newheights = (self.dataOut.nHeights-r)/window |
|
|
222 | 286 | |
|
|
223 | 287 | if newheights <= 1: |
|
|
224 | 288 | raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window) |
|
|
225 | 289 | |
|
|
226 | 290 | if self.dataOut.flagDataAsBlock: |
|
|
227 | 291 | """ |
|
|
228 | 292 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
229 | 293 | """ |
|
|
230 | 294 | buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r] |
|
|
231 | 295 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window) |
|
|
232 | 296 | buffer = numpy.sum(buffer,3) |
|
|
233 | 297 | |
|
|
234 | 298 | else: |
|
|
235 | 299 | buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r] |
|
|
236 | 300 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window) |
|
|
237 | 301 | buffer = numpy.sum(buffer,2) |
|
|
238 | 302 | |
|
|
239 | 303 | self.dataOut.data = buffer |
|
|
240 | 304 | self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
241 | 305 | self.dataOut.windowOfFilter = window |
|
|
242 | 306 | |
|
|
243 | 307 | def setH0(self, h0, deltaHeight = None): |
|
|
244 | 308 | |
|
|
245 | 309 | if not deltaHeight: |
|
|
246 | 310 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
247 | 311 | |
|
|
248 | 312 | nHeights = self.dataOut.nHeights |
|
|
249 | 313 | |
|
|
250 | 314 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
251 | 315 | |
|
|
252 | 316 | self.dataOut.heightList = newHeiRange |
|
|
253 | 317 | |
|
|
254 | 318 | def deFlip(self, channelList = []): |
|
|
255 | 319 | |
|
|
256 | 320 | data = self.dataOut.data.copy() |
|
|
257 | 321 | |
|
|
258 | 322 | if self.dataOut.flagDataAsBlock: |
|
|
259 | 323 | flip = self.flip |
|
|
260 | 324 | profileList = range(self.dataOut.nProfiles) |
|
|
261 | 325 | |
|
|
262 | 326 | if not channelList: |
|
|
263 | 327 | for thisProfile in profileList: |
|
|
264 | 328 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
265 | 329 | flip *= -1.0 |
|
|
266 | 330 | else: |
|
|
267 | 331 | for thisChannel in channelList: |
|
|
268 | 332 | if thisChannel not in self.dataOut.channelList: |
|
|
269 | 333 | continue |
|
|
270 | 334 | |
|
|
271 | 335 | for thisProfile in profileList: |
|
|
272 | 336 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
273 | 337 | flip *= -1.0 |
|
|
274 | 338 | |
|
|
275 | 339 | self.flip = flip |
|
|
276 | 340 | |
|
|
277 | 341 | else: |
|
|
278 | 342 | if not channelList: |
|
|
279 | 343 | data[:,:] = data[:,:]*self.flip |
|
|
280 | 344 | else: |
|
|
281 | 345 | for thisChannel in channelList: |
|
|
282 | 346 | if thisChannel not in self.dataOut.channelList: |
|
|
283 | 347 | continue |
|
|
284 | 348 | |
|
|
285 | 349 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
286 | 350 | |
|
|
287 | 351 | self.flip *= -1. |
|
|
288 | 352 | |
|
|
289 | 353 | self.dataOut.data = data |
|
|
290 | 354 | |
|
|
291 | 355 | def setRadarFrequency(self, frequency=None): |
|
|
292 | 356 | |
|
|
293 | 357 | if frequency != None: |
|
|
294 | 358 | self.dataOut.frequency = frequency |
|
|
295 | 359 | |
|
|
296 | 360 | return 1 |
|
|
297 | 361 | |
|
|
298 | 362 | def interpolateHeights(self, topLim, botLim): |
|
|
299 | 363 | #69 al 72 para julia |
|
|
300 | 364 | #82-84 para meteoros |
|
|
301 | 365 | if len(numpy.shape(self.dataOut.data))==2: |
|
|
302 | 366 | sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2 |
|
|
303 | 367 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
304 | 368 | #self.dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
305 | 369 | self.dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
306 | 370 | else: |
|
|
307 | 371 | nHeights = self.dataOut.data.shape[2] |
|
|
308 | 372 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
309 | 373 | y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)] |
|
|
310 | 374 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
311 | 375 | xnew = numpy.arange(botLim,topLim+1) |
|
|
312 | 376 | ynew = f(xnew) |
|
|
313 | 377 | |
|
|
314 | 378 | self.dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
315 | 379 | |
|
|
316 | 380 | # import collections |
|
|
317 | 381 | |
|
|
318 | 382 | class CohInt(Operation): |
|
|
319 | 383 | |
|
|
320 | 384 | isConfig = False |
|
|
321 | 385 | __profIndex = 0 |
|
|
322 | 386 | __byTime = False |
|
|
323 | 387 | __initime = None |
|
|
324 | 388 | __lastdatatime = None |
|
|
325 | 389 | __integrationtime = None |
|
|
326 | 390 | __buffer = None |
|
|
327 | 391 | __bufferStride = [] |
|
|
328 | 392 | __dataReady = False |
|
|
329 | 393 | __profIndexStride = 0 |
|
|
330 | 394 | __dataToPutStride = False |
|
|
331 | 395 | n = None |
|
|
332 | 396 | |
|
|
333 | 397 | def __init__(self, **kwargs): |
|
|
334 | 398 | |
|
|
335 | 399 | Operation.__init__(self, **kwargs) |
|
|
336 | 400 | |
|
|
337 | 401 | # self.isConfig = False |
|
|
338 | 402 | |
|
|
339 | 403 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
340 | 404 | """ |
|
|
341 | 405 | Set the parameters of the integration class. |
|
|
342 | 406 | |
|
|
343 | 407 | Inputs: |
|
|
344 | 408 | |
|
|
345 | 409 | n : Number of coherent integrations |
|
|
346 | 410 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
347 | 411 | overlapping : |
|
|
348 | 412 | """ |
|
|
349 | 413 | |
|
|
350 | 414 | self.__initime = None |
|
|
351 | 415 | self.__lastdatatime = 0 |
|
|
352 | 416 | self.__buffer = None |
|
|
353 | 417 | self.__dataReady = False |
|
|
354 | 418 | self.byblock = byblock |
|
|
355 | 419 | self.stride = stride |
|
|
356 | 420 | |
|
|
357 | 421 | if n == None and timeInterval == None: |
|
|
358 | 422 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
359 | 423 | |
|
|
360 | 424 | if n != None: |
|
|
361 | 425 | self.n = n |
|
|
362 | 426 | self.__byTime = False |
|
|
363 | 427 | else: |
|
|
364 | 428 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
365 | 429 | self.n = 9999 |
|
|
366 | 430 | self.__byTime = True |
|
|
367 | 431 | |
|
|
368 | 432 | if overlapping: |
|
|
369 | 433 | self.__withOverlapping = True |
|
|
370 | 434 | self.__buffer = None |
|
|
371 | 435 | else: |
|
|
372 | 436 | self.__withOverlapping = False |
|
|
373 | 437 | self.__buffer = 0 |
|
|
374 | 438 | |
|
|
375 | 439 | self.__profIndex = 0 |
|
|
376 | 440 | |
|
|
377 | 441 | def putData(self, data): |
|
|
378 | 442 | |
|
|
379 | 443 | """ |
|
|
380 | 444 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
381 | 445 | |
|
|
382 | 446 | """ |
|
|
383 | 447 | |
|
|
384 | 448 | if not self.__withOverlapping: |
|
|
385 | 449 | self.__buffer += data.copy() |
|
|
386 | 450 | self.__profIndex += 1 |
|
|
387 | 451 | return |
|
|
388 | 452 | |
|
|
389 | 453 | #Overlapping data |
|
|
390 | 454 | nChannels, nHeis = data.shape |
|
|
391 | 455 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
392 | 456 | |
|
|
393 | 457 | #If the buffer is empty then it takes the data value |
|
|
394 | 458 | if self.__buffer is None: |
|
|
395 | 459 | self.__buffer = data |
|
|
396 | 460 | self.__profIndex += 1 |
|
|
397 | 461 | return |
|
|
398 | 462 | |
|
|
399 | 463 | #If the buffer length is lower than n then stakcing the data value |
|
|
400 | 464 | if self.__profIndex < self.n: |
|
|
401 | 465 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
402 | 466 | self.__profIndex += 1 |
|
|
403 | 467 | return |
|
|
404 | 468 | |
|
|
405 | 469 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
406 | 470 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
407 | 471 | self.__buffer[self.n-1] = data |
|
|
408 | 472 | self.__profIndex = self.n |
|
|
409 | 473 | return |
|
|
410 | 474 | |
|
|
411 | 475 | |
|
|
412 | 476 | def pushData(self): |
|
|
413 | 477 | """ |
|
|
414 | 478 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
415 | 479 | |
|
|
416 | 480 | Affected: |
|
|
417 | 481 | |
|
|
418 | 482 | self.__profileIndex |
|
|
419 | 483 | |
|
|
420 | 484 | """ |
|
|
421 | 485 | |
|
|
422 | 486 | if not self.__withOverlapping: |
|
|
423 | 487 | data = self.__buffer |
|
|
424 | 488 | n = self.__profIndex |
|
|
425 | 489 | |
|
|
426 | 490 | self.__buffer = 0 |
|
|
427 | 491 | self.__profIndex = 0 |
|
|
428 | 492 | |
|
|
429 | 493 | return data, n |
|
|
430 | 494 | |
|
|
431 | 495 | #Integration with Overlapping |
|
|
432 | 496 | data = numpy.sum(self.__buffer, axis=0) |
|
|
433 | 497 | # print data |
|
|
434 | 498 | # raise |
|
|
435 | 499 | n = self.__profIndex |
|
|
436 | 500 | |
|
|
437 | 501 | return data, n |
|
|
438 | 502 | |
|
|
439 | 503 | def byProfiles(self, data): |
|
|
440 | 504 | |
|
|
441 | 505 | self.__dataReady = False |
|
|
442 | 506 | avgdata = None |
|
|
443 | 507 | # n = None |
|
|
444 | 508 | # print data |
|
|
445 | 509 | # raise |
|
|
446 | 510 | self.putData(data) |
|
|
447 | 511 | |
|
|
448 | 512 | if self.__profIndex == self.n: |
|
|
449 | 513 | avgdata, n = self.pushData() |
|
|
450 | 514 | self.__dataReady = True |
|
|
451 | 515 | |
|
|
452 | 516 | return avgdata |
|
|
453 | 517 | |
|
|
454 | 518 | def byTime(self, data, datatime): |
|
|
455 | 519 | |
|
|
456 | 520 | self.__dataReady = False |
|
|
457 | 521 | avgdata = None |
|
|
458 | 522 | n = None |
|
|
459 | 523 | |
|
|
460 | 524 | self.putData(data) |
|
|
461 | 525 | |
|
|
462 | 526 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
463 | 527 | avgdata, n = self.pushData() |
|
|
464 | 528 | self.n = n |
|
|
465 | 529 | self.__dataReady = True |
|
|
466 | 530 | |
|
|
467 | 531 | return avgdata |
|
|
468 | 532 | |
|
|
469 | 533 | def integrateByStride(self, data, datatime): |
|
|
470 | 534 | # print data |
|
|
471 | 535 | if self.__profIndex == 0: |
|
|
472 | 536 | self.__buffer = [[data.copy(), datatime]] |
|
|
473 | 537 | else: |
|
|
474 | 538 | self.__buffer.append([data.copy(),datatime]) |
|
|
475 | 539 | self.__profIndex += 1 |
|
|
476 | 540 | self.__dataReady = False |
|
|
477 | 541 | |
|
|
478 | 542 | if self.__profIndex == self.n * self.stride : |
|
|
479 | 543 | self.__dataToPutStride = True |
|
|
480 | 544 | self.__profIndexStride = 0 |
|
|
481 | 545 | self.__profIndex = 0 |
|
|
482 | 546 | self.__bufferStride = [] |
|
|
483 | 547 | for i in range(self.stride): |
|
|
484 | 548 | current = self.__buffer[i::self.stride] |
|
|
485 | 549 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
486 | 550 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
487 | 551 | # print data |
|
|
488 | 552 | self.__bufferStride.append((data, avgdatatime)) |
|
|
489 | 553 | |
|
|
490 | 554 | if self.__dataToPutStride: |
|
|
491 | 555 | self.__dataReady = True |
|
|
492 | 556 | self.__profIndexStride += 1 |
|
|
493 | 557 | if self.__profIndexStride == self.stride: |
|
|
494 | 558 | self.__dataToPutStride = False |
|
|
495 | 559 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
496 | 560 | # raise |
|
|
497 | 561 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
498 | 562 | |
|
|
499 | 563 | |
|
|
500 | 564 | return None, None |
|
|
501 | 565 | |
|
|
502 | 566 | def integrate(self, data, datatime=None): |
|
|
503 | 567 | |
|
|
504 | 568 | if self.__initime == None: |
|
|
505 | 569 | self.__initime = datatime |
|
|
506 | 570 | |
|
|
507 | 571 | if self.__byTime: |
|
|
508 | 572 | avgdata = self.byTime(data, datatime) |
|
|
509 | 573 | else: |
|
|
510 | 574 | avgdata = self.byProfiles(data) |
|
|
511 | 575 | |
|
|
512 | 576 | |
|
|
513 | 577 | self.__lastdatatime = datatime |
|
|
514 | 578 | |
|
|
515 | 579 | if avgdata is None: |
|
|
516 | 580 | return None, None |
|
|
517 | 581 | |
|
|
518 | 582 | avgdatatime = self.__initime |
|
|
519 | 583 | |
|
|
520 | 584 | deltatime = datatime - self.__lastdatatime |
|
|
521 | 585 | |
|
|
522 | 586 | if not self.__withOverlapping: |
|
|
523 | 587 | self.__initime = datatime |
|
|
524 | 588 | else: |
|
|
525 | 589 | self.__initime += deltatime |
|
|
526 | 590 | |
|
|
527 | 591 | return avgdata, avgdatatime |
|
|
528 | 592 | |
|
|
529 | 593 | def integrateByBlock(self, dataOut): |
|
|
530 | 594 | |
|
|
531 | 595 | times = int(dataOut.data.shape[1]/self.n) |
|
|
532 | 596 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
533 | 597 | |
|
|
534 | 598 | id_min = 0 |
|
|
535 | 599 | id_max = self.n |
|
|
536 | 600 | |
|
|
537 | 601 | for i in range(times): |
|
|
538 | 602 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
539 | 603 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
540 | 604 | id_min += self.n |
|
|
541 | 605 | id_max += self.n |
|
|
542 | 606 | |
|
|
543 | 607 | timeInterval = dataOut.ippSeconds*self.n |
|
|
544 | 608 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
545 | 609 | self.__dataReady = True |
|
|
546 | 610 | return avgdata, avgdatatime |
|
|
547 | 611 | |
|
|
548 | 612 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
549 | 613 | if not self.isConfig: |
|
|
550 | 614 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
551 | 615 | self.isConfig = True |
|
|
552 | 616 | |
|
|
553 | 617 | if dataOut.flagDataAsBlock: |
|
|
554 | 618 | """ |
|
|
555 | 619 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
556 | 620 | """ |
|
|
557 | 621 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
558 | 622 | dataOut.nProfiles /= self.n |
|
|
559 | 623 | else: |
|
|
560 | 624 | if stride is None: |
|
|
561 | 625 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
562 | 626 | else: |
|
|
563 | 627 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
564 | 628 | |
|
|
565 | 629 | |
|
|
566 | 630 | # dataOut.timeInterval *= n |
|
|
567 | 631 | dataOut.flagNoData = True |
|
|
568 | 632 | |
|
|
569 | 633 | if self.__dataReady: |
|
|
570 | 634 | dataOut.data = avgdata |
|
|
571 | 635 | dataOut.nCohInt *= self.n |
|
|
572 | 636 | dataOut.utctime = avgdatatime |
|
|
573 | 637 | # print avgdata, avgdatatime |
|
|
574 | 638 | # raise |
|
|
575 | 639 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
576 | 640 | dataOut.flagNoData = False |
|
|
577 | 641 | |
|
|
578 | 642 | class Decoder(Operation): |
|
|
579 | 643 | |
|
|
580 | 644 | isConfig = False |
|
|
581 | 645 | __profIndex = 0 |
|
|
582 | 646 | |
|
|
583 | 647 | code = None |
|
|
584 | 648 | |
|
|
585 | 649 | nCode = None |
|
|
586 | 650 | nBaud = None |
|
|
587 | 651 | |
|
|
588 | 652 | def __init__(self, **kwargs): |
|
|
589 | 653 | |
|
|
590 | 654 | Operation.__init__(self, **kwargs) |
|
|
591 | 655 | |
|
|
592 | 656 | self.times = None |
|
|
593 | 657 | self.osamp = None |
|
|
594 | 658 | # self.__setValues = False |
|
|
595 | 659 | self.isConfig = False |
|
|
596 | 660 | |
|
|
597 | 661 | def setup(self, code, osamp, dataOut): |
|
|
598 | 662 | |
|
|
599 | 663 | self.__profIndex = 0 |
|
|
600 | 664 | |
|
|
601 | 665 | self.code = code |
|
|
602 | 666 | |
|
|
603 | 667 | self.nCode = len(code) |
|
|
604 | 668 | self.nBaud = len(code[0]) |
|
|
605 | 669 | |
|
|
606 | 670 | if (osamp != None) and (osamp >1): |
|
|
607 | 671 | self.osamp = osamp |
|
|
608 | 672 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
609 | 673 | self.nBaud = self.nBaud*self.osamp |
|
|
610 | 674 | |
|
|
611 | 675 | self.__nChannels = dataOut.nChannels |
|
|
612 | 676 | self.__nProfiles = dataOut.nProfiles |
|
|
613 | 677 | self.__nHeis = dataOut.nHeights |
|
|
614 | 678 | |
|
|
615 | 679 | if self.__nHeis < self.nBaud: |
|
|
616 | 680 | raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud) |
|
|
617 | 681 | |
|
|
618 | 682 | #Frequency |
|
|
619 | 683 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
620 | 684 | |
|
|
621 | 685 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
622 | 686 | |
|
|
623 | 687 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
624 | 688 | |
|
|
625 | 689 | if dataOut.flagDataAsBlock: |
|
|
626 | 690 | |
|
|
627 | 691 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
628 | 692 | |
|
|
629 | 693 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
630 | 694 | |
|
|
631 | 695 | else: |
|
|
632 | 696 | |
|
|
633 | 697 | #Time |
|
|
634 | 698 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
635 | 699 | |
|
|
636 | 700 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
637 | 701 | |
|
|
638 | 702 | def __convolutionInFreq(self, data): |
|
|
639 | 703 | |
|
|
640 | 704 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
641 | 705 | |
|
|
642 | 706 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
643 | 707 | |
|
|
644 | 708 | conv = fft_data*fft_code |
|
|
645 | 709 | |
|
|
646 | 710 | data = numpy.fft.ifft(conv,axis=1) |
|
|
647 | 711 | |
|
|
648 | 712 | return data |
|
|
649 | 713 | |
|
|
650 | 714 | def __convolutionInFreqOpt(self, data): |
|
|
651 | 715 | |
|
|
652 | 716 | raise NotImplementedError |
|
|
653 | 717 | |
|
|
654 | 718 | def __convolutionInTime(self, data): |
|
|
655 | 719 | |
|
|
656 | 720 | code = self.code[self.__profIndex] |
|
|
657 | 721 | for i in range(self.__nChannels): |
|
|
658 | 722 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
659 | 723 | |
|
|
660 | 724 | return self.datadecTime |
|
|
661 | 725 | |
|
|
662 | 726 | def __convolutionByBlockInTime(self, data): |
|
|
663 | 727 | |
|
|
664 | 728 | repetitions = self.__nProfiles / self.nCode |
|
|
665 | 729 | |
|
|
666 | 730 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
667 | 731 | junk = junk.flatten() |
|
|
668 | 732 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
669 | 733 | profilesList = xrange(self.__nProfiles) |
|
|
670 | 734 | |
|
|
671 | 735 | for i in range(self.__nChannels): |
|
|
672 | 736 | for j in profilesList: |
|
|
673 | 737 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
674 | 738 | return self.datadecTime |
|
|
675 | 739 | |
|
|
676 | 740 | def __convolutionByBlockInFreq(self, data): |
|
|
677 | 741 | |
|
|
678 | 742 | raise NotImplementedError, "Decoder by frequency fro Blocks not implemented" |
|
|
679 | 743 | |
|
|
680 | 744 | |
|
|
681 | 745 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
682 | 746 | |
|
|
683 | 747 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
684 | 748 | |
|
|
685 | 749 | conv = fft_data*fft_code |
|
|
686 | 750 | |
|
|
687 | 751 | data = numpy.fft.ifft(conv,axis=2) |
|
|
688 | 752 | |
|
|
689 | 753 | return data |
|
|
690 | 754 | |
|
|
691 | 755 | |
|
|
692 | 756 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
693 | 757 | |
|
|
694 | 758 | if dataOut.flagDecodeData: |
|
|
695 | 759 | print "This data is already decoded, recoding again ..." |
|
|
696 | 760 | |
|
|
697 | 761 | if not self.isConfig: |
|
|
698 | 762 | |
|
|
699 | 763 | if code is None: |
|
|
700 | 764 | if dataOut.code is None: |
|
|
701 | 765 | raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type |
|
|
702 | 766 | |
|
|
703 | 767 | code = dataOut.code |
|
|
704 | 768 | else: |
|
|
705 | 769 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
706 | 770 | self.setup(code, osamp, dataOut) |
|
|
707 | 771 | |
|
|
708 | 772 | self.isConfig = True |
|
|
709 | 773 | |
|
|
710 | 774 | if mode == 3: |
|
|
711 | 775 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
712 | 776 | |
|
|
713 | 777 | if times != None: |
|
|
714 | 778 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
715 | 779 | |
|
|
716 | 780 | if self.code is None: |
|
|
717 | 781 | print "Fail decoding: Code is not defined." |
|
|
718 | 782 | return |
|
|
719 | 783 | |
|
|
720 | 784 | self.__nProfiles = dataOut.nProfiles |
|
|
721 | 785 | datadec = None |
|
|
722 | 786 | |
|
|
723 | 787 | if mode == 3: |
|
|
724 | 788 | mode = 0 |
|
|
725 | 789 | |
|
|
726 | 790 | if dataOut.flagDataAsBlock: |
|
|
727 | 791 | """ |
|
|
728 | 792 | Decoding when data have been read as block, |
|
|
729 | 793 | """ |
|
|
730 | 794 | |
|
|
731 | 795 | if mode == 0: |
|
|
732 | 796 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
733 | 797 | if mode == 1: |
|
|
734 | 798 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
735 | 799 | else: |
|
|
736 | 800 | """ |
|
|
737 | 801 | Decoding when data have been read profile by profile |
|
|
738 | 802 | """ |
|
|
739 | 803 | if mode == 0: |
|
|
740 | 804 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
741 | 805 | |
|
|
742 | 806 | if mode == 1: |
|
|
743 | 807 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
744 | 808 | |
|
|
745 | 809 | if mode == 2: |
|
|
746 | 810 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
747 | 811 | |
|
|
748 | 812 | if datadec is None: |
|
|
749 | 813 | raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode |
|
|
750 | 814 | |
|
|
751 | 815 | dataOut.code = self.code |
|
|
752 | 816 | dataOut.nCode = self.nCode |
|
|
753 | 817 | dataOut.nBaud = self.nBaud |
|
|
754 | 818 | |
|
|
755 | 819 | dataOut.data = datadec |
|
|
756 | 820 | |
|
|
757 | 821 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
758 | 822 | |
|
|
759 | 823 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
760 | 824 | |
|
|
761 | 825 | if self.__profIndex == self.nCode-1: |
|
|
762 | 826 | self.__profIndex = 0 |
|
|
763 | 827 | return 1 |
|
|
764 | 828 | |
|
|
765 | 829 | self.__profIndex += 1 |
|
|
766 | 830 | |
|
|
767 | 831 | return 1 |
|
|
768 | 832 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
769 | 833 | |
|
|
770 | 834 | |
|
|
771 | 835 | class ProfileConcat(Operation): |
|
|
772 | 836 | |
|
|
773 | 837 | isConfig = False |
|
|
774 | 838 | buffer = None |
|
|
775 | 839 | |
|
|
776 | 840 | def __init__(self, **kwargs): |
|
|
777 | 841 | |
|
|
778 | 842 | Operation.__init__(self, **kwargs) |
|
|
779 | 843 | self.profileIndex = 0 |
|
|
780 | 844 | |
|
|
781 | 845 | def reset(self): |
|
|
782 | 846 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
783 | 847 | self.start_index = 0 |
|
|
784 | 848 | self.times = 1 |
|
|
785 | 849 | |
|
|
786 | 850 | def setup(self, data, m, n=1): |
|
|
787 | 851 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
788 | 852 | self.nHeights = data.shape[1]#.nHeights |
|
|
789 | 853 | self.start_index = 0 |
|
|
790 | 854 | self.times = 1 |
|
|
791 | 855 | |
|
|
792 | 856 | def concat(self, data): |
|
|
793 | 857 | |
|
|
794 | 858 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
795 | 859 | self.start_index = self.start_index + self.nHeights |
|
|
796 | 860 | |
|
|
797 | 861 | def run(self, dataOut, m): |
|
|
798 | 862 | |
|
|
799 | 863 | dataOut.flagNoData = True |
|
|
800 | 864 | |
|
|
801 | 865 | if not self.isConfig: |
|
|
802 | 866 | self.setup(dataOut.data, m, 1) |
|
|
803 | 867 | self.isConfig = True |
|
|
804 | 868 | |
|
|
805 | 869 | if dataOut.flagDataAsBlock: |
|
|
806 | 870 | raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False" |
|
|
807 | 871 | |
|
|
808 | 872 | else: |
|
|
809 | 873 | self.concat(dataOut.data) |
|
|
810 | 874 | self.times += 1 |
|
|
811 | 875 | if self.times > m: |
|
|
812 | 876 | dataOut.data = self.buffer |
|
|
813 | 877 | self.reset() |
|
|
814 | 878 | dataOut.flagNoData = False |
|
|
815 | 879 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
816 | 880 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
817 | 881 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
818 | 882 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
819 | 883 | dataOut.ippSeconds *= m |
|
|
820 | 884 | |
|
|
821 | 885 | class ProfileSelector(Operation): |
|
|
822 | 886 | |
|
|
823 | 887 | profileIndex = None |
|
|
824 | 888 | # Tamanho total de los perfiles |
|
|
825 | 889 | nProfiles = None |
|
|
826 | 890 | |
|
|
827 | 891 | def __init__(self, **kwargs): |
|
|
828 | 892 | |
|
|
829 | 893 | Operation.__init__(self, **kwargs) |
|
|
830 | 894 | self.profileIndex = 0 |
|
|
831 | 895 | |
|
|
832 | 896 | def incProfileIndex(self): |
|
|
833 | 897 | |
|
|
834 | 898 | self.profileIndex += 1 |
|
|
835 | 899 | |
|
|
836 | 900 | if self.profileIndex >= self.nProfiles: |
|
|
837 | 901 | self.profileIndex = 0 |
|
|
838 | 902 | |
|
|
839 | 903 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
840 | 904 | |
|
|
841 | 905 | if profileIndex < minIndex: |
|
|
842 | 906 | return False |
|
|
843 | 907 | |
|
|
844 | 908 | if profileIndex > maxIndex: |
|
|
845 | 909 | return False |
|
|
846 | 910 | |
|
|
847 | 911 | return True |
|
|
848 | 912 | |
|
|
849 | 913 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
850 | 914 | |
|
|
851 | 915 | if profileIndex not in profileList: |
|
|
852 | 916 | return False |
|
|
853 | 917 | |
|
|
854 | 918 | return True |
|
|
855 | 919 | |
|
|
856 | 920 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
857 | 921 | |
|
|
858 | 922 | """ |
|
|
859 | 923 | ProfileSelector: |
|
|
860 | 924 | |
|
|
861 | 925 | Inputs: |
|
|
862 | 926 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
863 | 927 | |
|
|
864 | 928 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
865 | 929 | |
|
|
866 | 930 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
867 | 931 | |
|
|
868 | 932 | """ |
|
|
869 | 933 | |
|
|
870 | 934 | if rangeList is not None: |
|
|
871 | 935 | if type(rangeList[0]) not in (tuple, list): |
|
|
872 | 936 | rangeList = [rangeList] |
|
|
873 | 937 | |
|
|
874 | 938 | dataOut.flagNoData = True |
|
|
875 | 939 | |
|
|
876 | 940 | if dataOut.flagDataAsBlock: |
|
|
877 | 941 | """ |
|
|
878 | 942 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
879 | 943 | """ |
|
|
880 | 944 | if profileList != None: |
|
|
881 | 945 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
882 | 946 | |
|
|
883 | 947 | if profileRangeList != None: |
|
|
884 | 948 | minIndex = profileRangeList[0] |
|
|
885 | 949 | maxIndex = profileRangeList[1] |
|
|
886 | 950 | profileList = range(minIndex, maxIndex+1) |
|
|
887 | 951 | |
|
|
888 | 952 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
889 | 953 | |
|
|
890 | 954 | if rangeList != None: |
|
|
891 | 955 | |
|
|
892 | 956 | profileList = [] |
|
|
893 | 957 | |
|
|
894 | 958 | for thisRange in rangeList: |
|
|
895 | 959 | minIndex = thisRange[0] |
|
|
896 | 960 | maxIndex = thisRange[1] |
|
|
897 | 961 | |
|
|
898 | 962 | profileList.extend(range(minIndex, maxIndex+1)) |
|
|
899 | 963 | |
|
|
900 | 964 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
901 | 965 | |
|
|
902 | 966 | dataOut.nProfiles = len(profileList) |
|
|
903 | 967 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
904 | 968 | dataOut.flagNoData = False |
|
|
905 | 969 | |
|
|
906 | 970 | return True |
|
|
907 | 971 | |
|
|
908 | 972 | """ |
|
|
909 | 973 | data dimension = [nChannels, nHeis] |
|
|
910 | 974 | """ |
|
|
911 | 975 | |
|
|
912 | 976 | if profileList != None: |
|
|
913 | 977 | |
|
|
914 | 978 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
915 | 979 | |
|
|
916 | 980 | self.nProfiles = len(profileList) |
|
|
917 | 981 | dataOut.nProfiles = self.nProfiles |
|
|
918 | 982 | dataOut.profileIndex = self.profileIndex |
|
|
919 | 983 | dataOut.flagNoData = False |
|
|
920 | 984 | |
|
|
921 | 985 | self.incProfileIndex() |
|
|
922 | 986 | return True |
|
|
923 | 987 | |
|
|
924 | 988 | if profileRangeList != None: |
|
|
925 | 989 | |
|
|
926 | 990 | minIndex = profileRangeList[0] |
|
|
927 | 991 | maxIndex = profileRangeList[1] |
|
|
928 | 992 | |
|
|
929 | 993 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
930 | 994 | |
|
|
931 | 995 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
932 | 996 | dataOut.nProfiles = self.nProfiles |
|
|
933 | 997 | dataOut.profileIndex = self.profileIndex |
|
|
934 | 998 | dataOut.flagNoData = False |
|
|
935 | 999 | |
|
|
936 | 1000 | self.incProfileIndex() |
|
|
937 | 1001 | return True |
|
|
938 | 1002 | |
|
|
939 | 1003 | if rangeList != None: |
|
|
940 | 1004 | |
|
|
941 | 1005 | nProfiles = 0 |
|
|
942 | 1006 | |
|
|
943 | 1007 | for thisRange in rangeList: |
|
|
944 | 1008 | minIndex = thisRange[0] |
|
|
945 | 1009 | maxIndex = thisRange[1] |
|
|
946 | 1010 | |
|
|
947 | 1011 | nProfiles += maxIndex - minIndex + 1 |
|
|
948 | 1012 | |
|
|
949 | 1013 | for thisRange in rangeList: |
|
|
950 | 1014 | |
|
|
951 | 1015 | minIndex = thisRange[0] |
|
|
952 | 1016 | maxIndex = thisRange[1] |
|
|
953 | 1017 | |
|
|
954 | 1018 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
955 | 1019 | |
|
|
956 | 1020 | self.nProfiles = nProfiles |
|
|
957 | 1021 | dataOut.nProfiles = self.nProfiles |
|
|
958 | 1022 | dataOut.profileIndex = self.profileIndex |
|
|
959 | 1023 | dataOut.flagNoData = False |
|
|
960 | 1024 | |
|
|
961 | 1025 | self.incProfileIndex() |
|
|
962 | 1026 | |
|
|
963 | 1027 | break |
|
|
964 | 1028 | |
|
|
965 | 1029 | return True |
|
|
966 | 1030 | |
|
|
967 | 1031 | |
|
|
968 | 1032 | if beam != None: #beam is only for AMISR data |
|
|
969 | 1033 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
970 | 1034 | dataOut.flagNoData = False |
|
|
971 | 1035 | dataOut.profileIndex = self.profileIndex |
|
|
972 | 1036 | |
|
|
973 | 1037 | self.incProfileIndex() |
|
|
974 | 1038 | |
|
|
975 | 1039 | return True |
|
|
976 | 1040 | |
|
|
977 | 1041 | raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter" |
|
|
978 | 1042 | |
|
|
979 | 1043 | return False |
|
|
980 | 1044 | |
|
|
981 | 1045 | class Reshaper(Operation): |
|
|
982 | 1046 | |
|
|
983 | 1047 | def __init__(self, **kwargs): |
|
|
984 | 1048 | |
|
|
985 | 1049 | Operation.__init__(self, **kwargs) |
|
|
986 | 1050 | |
|
|
987 | 1051 | self.__buffer = None |
|
|
988 | 1052 | self.__nitems = 0 |
|
|
989 | 1053 | |
|
|
990 | 1054 | def __appendProfile(self, dataOut, nTxs): |
|
|
991 | 1055 | |
|
|
992 | 1056 | if self.__buffer is None: |
|
|
993 | 1057 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
994 | 1058 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
995 | 1059 | |
|
|
996 | 1060 | ini = dataOut.nHeights * self.__nitems |
|
|
997 | 1061 | end = ini + dataOut.nHeights |
|
|
998 | 1062 | |
|
|
999 | 1063 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1000 | 1064 | |
|
|
1001 | 1065 | self.__nitems += 1 |
|
|
1002 | 1066 | |
|
|
1003 | 1067 | return int(self.__nitems*nTxs) |
|
|
1004 | 1068 | |
|
|
1005 | 1069 | def __getBuffer(self): |
|
|
1006 | 1070 | |
|
|
1007 | 1071 | if self.__nitems == int(1./self.__nTxs): |
|
|
1008 | 1072 | |
|
|
1009 | 1073 | self.__nitems = 0 |
|
|
1010 | 1074 | |
|
|
1011 | 1075 | return self.__buffer.copy() |
|
|
1012 | 1076 | |
|
|
1013 | 1077 | return None |
|
|
1014 | 1078 | |
|
|
1015 | 1079 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1016 | 1080 | |
|
|
1017 | 1081 | if shape is None and nTxs is None: |
|
|
1018 | 1082 | raise ValueError, "Reshaper: shape of factor should be defined" |
|
|
1019 | 1083 | |
|
|
1020 | 1084 | if nTxs: |
|
|
1021 | 1085 | if nTxs < 0: |
|
|
1022 | 1086 | raise ValueError, "nTxs should be greater than 0" |
|
|
1023 | 1087 | |
|
|
1024 | 1088 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1025 | 1089 | raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)) |
|
|
1026 | 1090 | |
|
|
1027 | 1091 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1028 | 1092 | |
|
|
1029 | 1093 | return shape, nTxs |
|
|
1030 | 1094 | |
|
|
1031 | 1095 | if len(shape) != 2 and len(shape) != 3: |
|
|
1032 | 1096 | 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) |
|
|
1033 | 1097 | |
|
|
1034 | 1098 | if len(shape) == 2: |
|
|
1035 | 1099 | shape_tuple = [dataOut.nChannels] |
|
|
1036 | 1100 | shape_tuple.extend(shape) |
|
|
1037 | 1101 | else: |
|
|
1038 | 1102 | shape_tuple = list(shape) |
|
|
1039 | 1103 | |
|
|
1040 | 1104 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1041 | 1105 | |
|
|
1042 | 1106 | return shape_tuple, nTxs |
|
|
1043 | 1107 | |
|
|
1044 | 1108 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1045 | 1109 | |
|
|
1046 | 1110 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1047 | 1111 | |
|
|
1048 | 1112 | dataOut.flagNoData = True |
|
|
1049 | 1113 | profileIndex = None |
|
|
1050 | 1114 | |
|
|
1051 | 1115 | if dataOut.flagDataAsBlock: |
|
|
1052 | 1116 | |
|
|
1053 | 1117 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1054 | 1118 | dataOut.flagNoData = False |
|
|
1055 | 1119 | |
|
|
1056 | 1120 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1057 | 1121 | |
|
|
1058 | 1122 | else: |
|
|
1059 | 1123 | |
|
|
1060 | 1124 | if self.__nTxs < 1: |
|
|
1061 | 1125 | |
|
|
1062 | 1126 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1063 | 1127 | new_data = self.__getBuffer() |
|
|
1064 | 1128 | |
|
|
1065 | 1129 | if new_data is not None: |
|
|
1066 | 1130 | dataOut.data = new_data |
|
|
1067 | 1131 | dataOut.flagNoData = False |
|
|
1068 | 1132 | |
|
|
1069 | 1133 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1070 | 1134 | |
|
|
1071 | 1135 | else: |
|
|
1072 | 1136 | raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)" |
|
|
1073 | 1137 | |
|
|
1074 | 1138 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1075 | 1139 | |
|
|
1076 | 1140 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1077 | 1141 | |
|
|
1078 | 1142 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1079 | 1143 | |
|
|
1080 | 1144 | dataOut.profileIndex = profileIndex |
|
|
1081 | 1145 | |
|
|
1082 | 1146 | dataOut.ippSeconds /= self.__nTxs |
|
|
1083 | 1147 | |
|
|
1084 | 1148 | class SplitProfiles(Operation): |
|
|
1085 | 1149 | |
|
|
1086 | 1150 | def __init__(self, **kwargs): |
|
|
1087 | 1151 | |
|
|
1088 | 1152 | Operation.__init__(self, **kwargs) |
|
|
1089 | 1153 | |
|
|
1090 | 1154 | def run(self, dataOut, n): |
|
|
1091 | 1155 | |
|
|
1092 | 1156 | dataOut.flagNoData = True |
|
|
1093 | 1157 | profileIndex = None |
|
|
1094 | 1158 | |
|
|
1095 | 1159 | if dataOut.flagDataAsBlock: |
|
|
1096 | 1160 | |
|
|
1097 | 1161 | #nchannels, nprofiles, nsamples |
|
|
1098 | 1162 | shape = dataOut.data.shape |
|
|
1099 | 1163 | |
|
|
1100 | 1164 | if shape[2] % n != 0: |
|
|
1101 | 1165 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]) |
|
|
1102 | 1166 | |
|
|
1103 | 1167 | new_shape = shape[0], shape[1]*n, shape[2]/n |
|
|
1104 | 1168 | |
|
|
1105 | 1169 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1106 | 1170 | dataOut.flagNoData = False |
|
|
1107 | 1171 | |
|
|
1108 | 1172 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1109 | 1173 | |
|
|
1110 | 1174 | else: |
|
|
1111 | 1175 | |
|
|
1112 | 1176 | raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)" |
|
|
1113 | 1177 | |
|
|
1114 | 1178 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1115 | 1179 | |
|
|
1116 | 1180 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1117 | 1181 | |
|
|
1118 | 1182 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1119 | 1183 | |
|
|
1120 | 1184 | dataOut.profileIndex = profileIndex |
|
|
1121 | 1185 | |
|
|
1122 | 1186 | dataOut.ippSeconds /= n |
|
|
1123 | 1187 | |
|
|
1124 | 1188 | class CombineProfiles(Operation): |
|
|
1125 | 1189 | |
|
|
1126 | 1190 | def __init__(self, **kwargs): |
|
|
1127 | 1191 | |
|
|
1128 | 1192 | Operation.__init__(self, **kwargs) |
|
|
1129 | 1193 | |
|
|
1130 | 1194 | self.__remData = None |
|
|
1131 | 1195 | self.__profileIndex = 0 |
|
|
1132 | 1196 | |
|
|
1133 | 1197 | def run(self, dataOut, n): |
|
|
1134 | 1198 | |
|
|
1135 | 1199 | dataOut.flagNoData = True |
|
|
1136 | 1200 | profileIndex = None |
|
|
1137 | 1201 | |
|
|
1138 | 1202 | if dataOut.flagDataAsBlock: |
|
|
1139 | 1203 | |
|
|
1140 | 1204 | #nchannels, nprofiles, nsamples |
|
|
1141 | 1205 | shape = dataOut.data.shape |
|
|
1142 | 1206 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1143 | 1207 | |
|
|
1144 | 1208 | if shape[1] % n != 0: |
|
|
1145 | 1209 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]) |
|
|
1146 | 1210 | |
|
|
1147 | 1211 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1148 | 1212 | dataOut.flagNoData = False |
|
|
1149 | 1213 | |
|
|
1150 | 1214 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1151 | 1215 | |
|
|
1152 | 1216 | else: |
|
|
1153 | 1217 | |
|
|
1154 | 1218 | #nchannels, nsamples |
|
|
1155 | 1219 | if self.__remData is None: |
|
|
1156 | 1220 | newData = dataOut.data |
|
|
1157 | 1221 | else: |
|
|
1158 | 1222 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1159 | 1223 | |
|
|
1160 | 1224 | self.__profileIndex += 1 |
|
|
1161 | 1225 | |
|
|
1162 | 1226 | if self.__profileIndex < n: |
|
|
1163 | 1227 | self.__remData = newData |
|
|
1164 | 1228 | #continue |
|
|
1165 | 1229 | return |
|
|
1166 | 1230 | |
|
|
1167 | 1231 | self.__profileIndex = 0 |
|
|
1168 | 1232 | self.__remData = None |
|
|
1169 | 1233 | |
|
|
1170 | 1234 | dataOut.data = newData |
|
|
1171 | 1235 | dataOut.flagNoData = False |
|
|
1172 | 1236 | |
|
|
1173 | 1237 | profileIndex = dataOut.profileIndex/n |
|
|
1174 | 1238 | |
|
|
1175 | 1239 | |
|
|
1176 | 1240 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1177 | 1241 | |
|
|
1178 | 1242 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1179 | 1243 | |
|
|
1180 | 1244 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1181 | 1245 | |
|
|
1182 | 1246 | dataOut.profileIndex = profileIndex |
|
|
1183 | 1247 | |
|
|
1184 | 1248 | dataOut.ippSeconds *= n |
|
|
1185 | 1249 | |
|
|
1186 | 1250 | |
|
|
1187 | 1251 | class SSheightProfiles(Operation): |
|
|
1188 | 1252 | |
|
|
1189 | 1253 | step = None |
|
|
1190 | 1254 | nsamples = None |
|
|
1191 | 1255 | bufferShape = None |
|
|
1192 | 1256 | profileShape = None |
|
|
1193 | 1257 | sshProfiles = None |
|
|
1194 | 1258 | profileIndex = None |
|
|
1195 | 1259 | |
|
|
1196 | 1260 | def __init__(self, **kwargs): |
|
|
1197 | 1261 | |
|
|
1198 | 1262 | Operation.__init__(self, **kwargs) |
|
|
1199 | 1263 | self.isConfig = False |
|
|
1200 | 1264 | |
|
|
1201 | 1265 | def setup(self,dataOut ,step = None , nsamples = None): |
|
|
1202 | 1266 | |
|
|
1203 | 1267 | if step == None and nsamples == None: |
|
|
1204 | 1268 | raise ValueError, "step or nheights should be specified ..." |
|
|
1205 | 1269 | |
|
|
1206 | 1270 | self.step = step |
|
|
1207 | 1271 | self.nsamples = nsamples |
|
|
1208 | 1272 | self.__nChannels = dataOut.nChannels |
|
|
1209 | 1273 | self.__nProfiles = dataOut.nProfiles |
|
|
1210 | 1274 | self.__nHeis = dataOut.nHeights |
|
|
1211 | 1275 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
|
|
1212 | 1276 | |
|
|
1213 | 1277 | |
|
|
1214 | 1278 | residue = (shape[1] - self.nsamples) % self.step |
|
|
1215 | 1279 | if residue != 0: |
|
|
1216 | 1280 | print "The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue) |
|
|
1217 | 1281 | |
|
|
1218 | 1282 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1219 | 1283 | numberProfile = self.nsamples |
|
|
1220 | 1284 | numberSamples = (shape[1] - self.nsamples)/self.step |
|
|
1221 | 1285 | |
|
|
1222 | 1286 | print "New number of profile: %d, number of height: %d, Resolution %d Km"%(numberProfile,numberSamples,deltaHeight*self.step) |
|
|
1223 | 1287 | |
|
|
1224 | 1288 | self.bufferShape = shape[0], numberSamples, numberProfile # nchannels, nsamples , nprofiles |
|
|
1225 | 1289 | self.profileShape = shape[0], numberProfile, numberSamples # nchannels, nprofiles, nsamples |
|
|
1226 | 1290 | |
|
|
1227 | 1291 | self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex) |
|
|
1228 | 1292 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex) |
|
|
1229 | 1293 | |
|
|
1230 | 1294 | def run(self, dataOut, step, nsamples): |
|
|
1231 | 1295 | |
|
|
1232 | 1296 | dataOut.flagNoData = True |
|
|
1233 | 1297 | dataOut.flagDataAsBlock = False |
|
|
1234 | 1298 | profileIndex = None |
|
|
1235 | 1299 | |
|
|
1236 | 1300 | if not self.isConfig: |
|
|
1237 | 1301 | self.setup(dataOut, step=step , nsamples=nsamples) |
|
|
1238 | 1302 | self.isConfig = True |
|
|
1239 | 1303 | |
|
|
1240 | 1304 | for i in range(self.buffer.shape[1]): |
|
|
1241 | 1305 | self.buffer[:,i] = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples]) |
|
|
1242 | 1306 | #self.buffer[:,j,self.__nHeis-j*self.step - self.nheights:self.__nHeis-j*self.step] = numpy.flip(dataOut.data[:,j*self.step:j*self.step + self.nheights]) |
|
|
1243 | 1307 | |
|
|
1244 | 1308 | for j in range(self.buffer.shape[0]): |
|
|
1245 | 1309 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) |
|
|
1246 | 1310 | |
|
|
1247 | 1311 | profileIndex = self.nsamples |
|
|
1248 | 1312 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1249 | 1313 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
|
|
1250 | 1314 | |
|
|
1251 | 1315 | dataOut.data = self.sshProfiles |
|
|
1252 | 1316 | dataOut.flagNoData = False |
|
|
1253 | 1317 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
|
|
1254 | 1318 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) |
|
|
1255 | 1319 | dataOut.profileIndex = profileIndex |
|
|
1256 | 1320 | dataOut.flagDataAsBlock = True |
|
|
1257 | 1321 | dataOut.ippSeconds = ippSeconds |
|
|
1258 | 1322 | dataOut.step = self.step |
|
|
1259 | 1323 | |
|
|
1260 | 1324 | |
|
|
1325 | import time | |
|
|
1326 | ################################################# | |
|
|
1327 | ||
|
|
1328 | class decoPseudorandom(Operation): | |
|
|
1329 | ||
|
|
1330 | nProfiles= 0 | |
|
|
1331 | buffer= None | |
|
|
1332 | isConfig = False | |
|
|
1333 | ||
|
|
1334 | def setup(self, clen= 10000,seed= 0,Nranges= 1000,oversample=1): | |
|
|
1335 | #code = create_pseudo_random_code(clen=clen, seed=seed) | |
|
|
1336 | code= rep_seq(create_pseudo_random_code(clen=clen, seed=seed),rep=oversample) | |
|
|
1337 | #print ("code_rx", code.shape) | |
|
|
1338 | #N = int(an_len/clen) # 100 | |
|
|
1339 | B_cache = 0 | |
|
|
1340 | r_cache = 0 | |
|
|
1341 | B_cached = False | |
|
|
1342 | r = create_estimation_matrix(code=code, cache=True, rmax=Nranges) | |
|
|
1343 | #print ("code shape", code.shape) | |
|
|
1344 | #print ("seed",seed) | |
|
|
1345 | #print ("Code", code[0:10]) | |
|
|
1346 | self.B = r['B'] | |
|
|
1347 | ||
|
|
1348 | ||
|
|
1349 | def run (self,dataOut,length_code= 10000,seed= 0,Nranges= 1000,oversample=1): | |
|
|
1350 | #print((dataOut.data.shape)) | |
|
|
1351 | if not self.isConfig: | |
|
|
1352 | self.setup(clen= length_code,seed= seed,Nranges= Nranges,oversample=oversample) | |
|
|
1353 | self.isConfig = True | |
|
|
1354 | ||
|
|
1355 | dataOut.flagNoData = True | |
|
|
1356 | data =dataOut.data | |
|
|
1357 | #print "length_CODE",length_code | |
|
|
1358 | data_shape = (data.shape[1]) | |
|
|
1359 | #print "data_shape",data_shape | |
|
|
1360 | n = (length_code /data_shape) | |
|
|
1361 | #print "we need this number of sample",n | |
|
|
1362 | ||
|
|
1363 | if n>0 and self.buffer is None: | |
|
|
1364 | self.buffer = numpy.zeros([1, length_code], dtype=numpy.complex64) | |
|
|
1365 | self.buffer[0][0:data_shape] = data[0] | |
|
|
1366 | #print "FIRST CREATION",self.buffer.shape | |
|
|
1367 | ||
|
|
1368 | else: | |
|
|
1369 | self.buffer[0][self.nProfiles*data_shape:(self.nProfiles+1)*data_shape]=data[0] | |
|
|
1370 | ||
|
|
1371 | #print "buffer_shape",(self.buffer.shape) | |
|
|
1372 | self.nProfiles += 1 | |
|
|
1373 | #print "count",self.nProfiles | |
|
|
1374 | ||
|
|
1375 | if self.nProfiles== n: | |
|
|
1376 | temporal = numpy.dot(self.B, numpy.transpose(self.buffer)) | |
|
|
1377 | #print temporal.shape | |
|
|
1378 | #import time | |
|
|
1379 | #time.sleep(40) | |
|
|
1380 | dataOut.data=numpy.transpose(temporal) | |
|
|
1381 | ||
|
|
1382 | dataOut.flagNoData = False | |
|
|
1383 | self.buffer= None | |
|
|
1384 | self.nProfiles = 0 | |
|
|
1385 | ||
|
|
1261 | 1386 | # import collections |
|
|
1262 | 1387 | # from scipy.stats import mode |
|
|
1263 | 1388 | # |
|
|
1264 | 1389 | # class Synchronize(Operation): |
|
|
1265 | 1390 | # |
|
|
1266 | 1391 | # isConfig = False |
|
|
1267 | 1392 | # __profIndex = 0 |
|
|
1268 | 1393 | # |
|
|
1269 | 1394 | # def __init__(self, **kwargs): |
|
|
1270 | 1395 | # |
|
|
1271 | 1396 | # Operation.__init__(self, **kwargs) |
|
|
1272 | 1397 | # # self.isConfig = False |
|
|
1273 | 1398 | # self.__powBuffer = None |
|
|
1274 | 1399 | # self.__startIndex = 0 |
|
|
1275 | 1400 | # self.__pulseFound = False |
|
|
1276 | 1401 | # |
|
|
1277 | 1402 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1278 | 1403 | # |
|
|
1279 | 1404 | # #Read data |
|
|
1280 | 1405 | # |
|
|
1281 | 1406 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1282 | 1407 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1283 | 1408 | # |
|
|
1284 | 1409 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1285 | 1410 | # |
|
|
1286 | 1411 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1287 | 1412 | # |
|
|
1288 | 1413 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1289 | 1414 | # |
|
|
1290 | 1415 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1291 | 1416 | # |
|
|
1292 | 1417 | # if maxValue < noisedB + 10: |
|
|
1293 | 1418 | # #No se encuentra ningun pulso de transmision |
|
|
1294 | 1419 | # return None |
|
|
1295 | 1420 | # |
|
|
1296 | 1421 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1297 | 1422 | # |
|
|
1298 | 1423 | # if len(maxValuesIndex) < 2: |
|
|
1299 | 1424 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1300 | 1425 | # return None |
|
|
1301 | 1426 | # |
|
|
1302 | 1427 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1303 | 1428 | # |
|
|
1304 | 1429 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1305 | 1430 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1306 | 1431 | # |
|
|
1307 | 1432 | # if len(pulseIndex) < 2: |
|
|
1308 | 1433 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1309 | 1434 | # return None |
|
|
1310 | 1435 | # |
|
|
1311 | 1436 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1312 | 1437 | # |
|
|
1313 | 1438 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1314 | 1439 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1315 | 1440 | # |
|
|
1316 | 1441 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1317 | 1442 | # |
|
|
1318 | 1443 | # if len(realIndex) < 2: |
|
|
1319 | 1444 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1320 | 1445 | # return None |
|
|
1321 | 1446 | # |
|
|
1322 | 1447 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1323 | 1448 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1324 | 1449 | # |
|
|
1325 | 1450 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1326 | 1451 | # |
|
|
1327 | 1452 | # print "IPP = %d samples" %period |
|
|
1328 | 1453 | # |
|
|
1329 | 1454 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1330 | 1455 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1331 | 1456 | # |
|
|
1332 | 1457 | # return 1 |
|
|
1333 | 1458 | # |
|
|
1334 | 1459 | # |
|
|
1335 | 1460 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1336 | 1461 | # |
|
|
1337 | 1462 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1338 | 1463 | # maxlen = buffer_size*nSamples) |
|
|
1339 | 1464 | # |
|
|
1340 | 1465 | # bufferList = [] |
|
|
1341 | 1466 | # |
|
|
1342 | 1467 | # for i in range(nChannels): |
|
|
1343 | 1468 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1344 | 1469 | # maxlen = buffer_size*nSamples) |
|
|
1345 | 1470 | # |
|
|
1346 | 1471 | # bufferList.append(bufferByChannel) |
|
|
1347 | 1472 | # |
|
|
1348 | 1473 | # self.__nSamples = nSamples |
|
|
1349 | 1474 | # self.__nChannels = nChannels |
|
|
1350 | 1475 | # self.__bufferList = bufferList |
|
|
1351 | 1476 | # |
|
|
1352 | 1477 | # def run(self, dataOut, channel = 0): |
|
|
1353 | 1478 | # |
|
|
1354 | 1479 | # if not self.isConfig: |
|
|
1355 | 1480 | # nSamples = dataOut.nHeights |
|
|
1356 | 1481 | # nChannels = dataOut.nChannels |
|
|
1357 | 1482 | # self.setup(nSamples, nChannels) |
|
|
1358 | 1483 | # self.isConfig = True |
|
|
1359 | 1484 | # |
|
|
1360 | 1485 | # #Append new data to internal buffer |
|
|
1361 | 1486 | # for thisChannel in range(self.__nChannels): |
|
|
1362 | 1487 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1363 | 1488 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1364 | 1489 | # |
|
|
1365 | 1490 | # if self.__pulseFound: |
|
|
1366 | 1491 | # self.__startIndex -= self.__nSamples |
|
|
1367 | 1492 | # |
|
|
1368 | 1493 | # #Finding Tx Pulse |
|
|
1369 | 1494 | # if not self.__pulseFound: |
|
|
1370 | 1495 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1371 | 1496 | # |
|
|
1372 | 1497 | # if indexFound == None: |
|
|
1373 | 1498 | # dataOut.flagNoData = True |
|
|
1374 | 1499 | # return |
|
|
1375 | 1500 | # |
|
|
1376 | 1501 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1377 | 1502 | # self.__pulseFound = True |
|
|
1378 | 1503 | # self.__startIndex = indexFound |
|
|
1379 | 1504 | # |
|
|
1380 | 1505 | # #If pulse was found ... |
|
|
1381 | 1506 | # for thisChannel in range(self.__nChannels): |
|
|
1382 | 1507 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1383 | 1508 | # #print self.__startIndex |
|
|
1384 | 1509 | # x = numpy.array(bufferByChannel) |
|
|
1385 | 1510 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1386 | 1511 | # |
|
|
1387 | 1512 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1388 | 1513 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1389 | 1514 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1390 | 1515 | # |
|
|
1391 | 1516 | # dataOut.data = self.__arrayBuffer |
|
|
1392 | 1517 | # |
|
|
1393 | 1518 | # self.__startIndex += self.__newNSamples |
|
|
1394 | 1519 | # |
|
|
1395 | 1520 | # return |
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