| @@ -1,2161 +1,2393 | |||
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1 | 1 | import os |
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2 | 2 | import datetime |
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3 | 3 | import numpy |
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4 | 4 | import inspect |
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5 | 5 | from figure import Figure, isRealtime, isTimeInHourRange |
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6 | 6 | from plotting_codes import * |
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7 | 7 | |
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8 | class ParamLine(Figure): | |
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9 | ||
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10 | isConfig = None | |
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11 | ||
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12 | def __init__(self): | |
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13 | ||
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14 | self.isConfig = False | |
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15 | self.WIDTH = 300 | |
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16 | self.HEIGHT = 200 | |
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17 | self.counter_imagwr = 0 | |
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18 | ||
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19 | def getSubplots(self): | |
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20 | ||
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21 | nrow = self.nplots | |
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22 | ncol = 3 | |
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23 | return nrow, ncol | |
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24 | ||
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25 | def setup(self, id, nplots, wintitle, show): | |
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26 | ||
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27 | self.nplots = nplots | |
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28 | ||
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29 | self.createFigure(id=id, | |
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30 | wintitle=wintitle, | |
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31 | show=show) | |
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32 | ||
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33 | nrow,ncol = self.getSubplots() | |
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34 | colspan = 3 | |
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35 | rowspan = 1 | |
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36 | ||
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37 | for i in range(nplots): | |
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38 | self.addAxes(nrow, ncol, i, 0, colspan, rowspan) | |
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39 | ||
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40 | def plot_iq(self, x, y, id, channelIndexList, thisDatetime, wintitle, show, xmin, xmax, ymin, ymax): | |
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41 | yreal = y[channelIndexList,:].real | |
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42 | yimag = y[channelIndexList,:].imag | |
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43 | ||
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44 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
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45 | xlabel = "Range (Km)" | |
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46 | ylabel = "Intensity - IQ" | |
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47 | ||
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48 | if not self.isConfig: | |
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49 | nplots = len(channelIndexList) | |
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50 | ||
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51 | self.setup(id=id, | |
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52 | nplots=nplots, | |
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53 | wintitle='', | |
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54 | show=show) | |
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55 | ||
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56 | if xmin == None: xmin = numpy.nanmin(x) | |
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57 | if xmax == None: xmax = numpy.nanmax(x) | |
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58 | if ymin == None: ymin = min(numpy.nanmin(yreal),numpy.nanmin(yimag)) | |
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59 | if ymax == None: ymax = max(numpy.nanmax(yreal),numpy.nanmax(yimag)) | |
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60 | ||
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61 | self.isConfig = True | |
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62 | ||
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63 | self.setWinTitle(title) | |
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64 | ||
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65 | for i in range(len(self.axesList)): | |
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66 | title = "Channel %d" %(i) | |
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67 | axes = self.axesList[i] | |
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68 | ||
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69 | axes.pline(x, yreal[i,:], | |
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70 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
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71 | xlabel=xlabel, ylabel=ylabel, title=title) | |
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72 | ||
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73 | axes.addpline(x, yimag[i,:], idline=1, color="red", linestyle="solid", lw=2) | |
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74 | ||
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75 | def plot_power(self, x, y, id, channelIndexList, thisDatetime, wintitle, show, xmin, xmax, ymin, ymax): | |
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76 | y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:]) | |
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77 | yreal = y.real | |
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78 | ||
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79 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
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80 | xlabel = "Range (Km)" | |
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81 | ylabel = "Intensity" | |
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82 | ||
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83 | if not self.isConfig: | |
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84 | nplots = len(channelIndexList) | |
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85 | ||
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86 | self.setup(id=id, | |
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87 | nplots=nplots, | |
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88 | wintitle='', | |
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89 | show=show) | |
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90 | ||
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91 | if xmin == None: xmin = numpy.nanmin(x) | |
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92 | if xmax == None: xmax = numpy.nanmax(x) | |
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93 | if ymin == None: ymin = numpy.nanmin(yreal) | |
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94 | if ymax == None: ymax = numpy.nanmax(yreal) | |
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95 | ||
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96 | self.isConfig = True | |
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97 | ||
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98 | self.setWinTitle(title) | |
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99 | ||
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100 | for i in range(len(self.axesList)): | |
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101 | title = "Channel %d" %(i) | |
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102 | axes = self.axesList[i] | |
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103 | ychannel = yreal[i,:] | |
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104 | axes.pline(x, ychannel, | |
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105 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
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106 | xlabel=xlabel, ylabel=ylabel, title=title) | |
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107 | ||
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108 | ||
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109 | def run(self, dataOut, id, wintitle="", channelList=None, | |
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110 | xmin=None, xmax=None, ymin=None, ymax=None, save=False, | |
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111 | figpath='./', figfile=None, show=True, wr_period=1, | |
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112 | ftp=False, server=None, folder=None, username=None, password=None): | |
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113 | ||
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114 | """ | |
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115 | ||
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116 | Input: | |
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117 | dataOut : | |
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118 | id : | |
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119 | wintitle : | |
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120 | channelList : | |
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121 | xmin : None, | |
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122 | xmax : None, | |
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123 | ymin : None, | |
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124 | ymax : None, | |
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125 | """ | |
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126 | ||
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127 | if channelList == None: | |
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128 | channelIndexList = dataOut.channelIndexList | |
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129 | else: | |
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130 | channelIndexList = [] | |
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131 | for channel in channelList: | |
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132 | if channel not in dataOut.channelList: | |
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133 | raise ValueError, "Channel %d is not in dataOut.channelList" | |
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134 | channelIndexList.append(dataOut.channelList.index(channel)) | |
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135 | ||
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136 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) | |
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137 | ||
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138 | y = dataOut.RR | |
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139 | ||
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140 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
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141 | xlabel = "Range (Km)" | |
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142 | ylabel = "Intensity" | |
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143 | ||
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144 | if not self.isConfig: | |
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145 | nplots = len(channelIndexList) | |
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146 | ||
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147 | self.setup(id=id, | |
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148 | nplots=nplots, | |
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149 | wintitle='', | |
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150 | show=show) | |
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151 | ||
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152 | if xmin == None: xmin = numpy.nanmin(x) | |
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153 | if xmax == None: xmax = numpy.nanmax(x) | |
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154 | if ymin == None: ymin = numpy.nanmin(y) | |
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155 | if ymax == None: ymax = numpy.nanmax(y) | |
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156 | ||
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157 | self.isConfig = True | |
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158 | ||
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159 | self.setWinTitle(title) | |
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160 | ||
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161 | for i in range(len(self.axesList)): | |
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162 | title = "Channel %d" %(i) | |
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163 | axes = self.axesList[i] | |
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164 | ychannel = y[i,:] | |
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165 | axes.pline(x, ychannel, | |
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166 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
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167 | xlabel=xlabel, ylabel=ylabel, title=title) | |
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168 | ||
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169 | ||
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170 | self.draw() | |
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171 | ||
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172 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") + "_" + str(dataOut.profileIndex) | |
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173 | figfile = self.getFilename(name = str_datetime) | |
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174 | ||
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175 | self.save(figpath=figpath, | |
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176 | figfile=figfile, | |
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177 | save=save, | |
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178 | ftp=ftp, | |
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179 | wr_period=wr_period, | |
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180 | thisDatetime=thisDatetime) | |
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181 | ||
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182 | ||
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8 | 183 | |
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9 | 184 | class SpcParamPlot(Figure): |
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10 | 185 | |
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11 | 186 | isConfig = None |
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12 | 187 | __nsubplots = None |
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13 | 188 | |
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14 | 189 | WIDTHPROF = None |
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15 | 190 | HEIGHTPROF = None |
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16 | 191 | PREFIX = 'SpcParam' |
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17 | 192 | |
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18 | 193 | def __init__(self, **kwargs): |
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19 | 194 | Figure.__init__(self, **kwargs) |
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20 | 195 | self.isConfig = False |
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21 | 196 | self.__nsubplots = 1 |
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22 | 197 | |
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23 | 198 | self.WIDTH = 250 |
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24 | 199 | self.HEIGHT = 250 |
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25 | 200 | self.WIDTHPROF = 120 |
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26 | 201 | self.HEIGHTPROF = 0 |
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27 | 202 | self.counter_imagwr = 0 |
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28 | 203 | |
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29 | 204 | self.PLOT_CODE = SPEC_CODE |
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30 | 205 | |
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31 | 206 | self.FTP_WEI = None |
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32 | 207 | self.EXP_CODE = None |
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33 | 208 | self.SUB_EXP_CODE = None |
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34 | 209 | self.PLOT_POS = None |
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35 | 210 | |
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36 | 211 | self.__xfilter_ena = False |
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37 | 212 | self.__yfilter_ena = False |
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38 | 213 | |
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39 | 214 | def getSubplots(self): |
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40 | 215 | |
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41 | 216 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
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42 | 217 | nrow = int(self.nplots*1./ncol + 0.9) |
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43 | 218 | |
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44 | 219 | return nrow, ncol |
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45 | 220 | |
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46 | 221 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
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47 | 222 | |
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48 | 223 | self.__showprofile = showprofile |
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49 | 224 | self.nplots = nplots |
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50 | 225 | |
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51 | 226 | ncolspan = 1 |
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52 | 227 | colspan = 1 |
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53 | 228 | if showprofile: |
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54 | 229 | ncolspan = 3 |
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55 | 230 | colspan = 2 |
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56 | 231 | self.__nsubplots = 2 |
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57 | 232 | |
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58 | 233 | self.createFigure(id = id, |
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59 | 234 | wintitle = wintitle, |
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60 | 235 | widthplot = self.WIDTH + self.WIDTHPROF, |
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61 | 236 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
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62 | 237 | show=show) |
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63 | 238 | |
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64 | 239 | nrow, ncol = self.getSubplots() |
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65 | 240 | |
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66 | 241 | counter = 0 |
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67 | 242 | for y in range(nrow): |
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68 | 243 | for x in range(ncol): |
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69 | 244 | |
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70 | 245 | if counter >= self.nplots: |
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71 | 246 | break |
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72 | 247 | |
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73 | 248 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
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74 | 249 | |
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75 | 250 | if showprofile: |
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76 | 251 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
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77 | 252 | |
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78 | 253 | counter += 1 |
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79 | 254 | |
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80 | 255 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
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81 | 256 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
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82 | 257 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
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83 | 258 | server=None, folder=None, username=None, password=None, |
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84 | 259 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
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85 | 260 | xaxis="frequency", colormap='jet', normFactor=None , Selector = 0): |
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86 | 261 | |
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87 | 262 | """ |
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88 | 263 | |
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89 | 264 | Input: |
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90 | 265 | dataOut : |
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91 | 266 | id : |
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92 | 267 | wintitle : |
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93 | 268 | channelList : |
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94 | 269 | showProfile : |
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95 | 270 | xmin : None, |
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96 | 271 | xmax : None, |
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97 | 272 | ymin : None, |
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98 | 273 | ymax : None, |
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99 | 274 | zmin : None, |
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100 | 275 | zmax : None |
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101 | 276 | """ |
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102 | 277 | if realtime: |
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103 | 278 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
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104 | 279 | print 'Skipping this plot function' |
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105 | 280 | return |
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106 | 281 | |
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107 | 282 | if channelList == None: |
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108 | 283 | channelIndexList = dataOut.channelIndexList |
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109 | 284 | else: |
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110 | 285 | channelIndexList = [] |
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111 | 286 | for channel in channelList: |
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112 | 287 | if channel not in dataOut.channelList: |
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113 | 288 | raise ValueError, "Channel %d is not in dataOut.channelList" %channel |
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114 | 289 | channelIndexList.append(dataOut.channelList.index(channel)) |
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115 | 290 | |
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116 | 291 | # if normFactor is None: |
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117 | 292 | # factor = dataOut.normFactor |
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118 | 293 | # else: |
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119 | 294 | # factor = normFactor |
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120 | 295 | if xaxis == "frequency": |
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121 | 296 | x = dataOut.spcparam_range[0] |
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122 | 297 | xlabel = "Frequency (kHz)" |
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123 | 298 | |
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124 | 299 | elif xaxis == "time": |
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125 | 300 | x = dataOut.spcparam_range[1] |
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126 | 301 | xlabel = "Time (ms)" |
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127 | 302 | |
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128 | 303 | else: |
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129 | 304 | x = dataOut.spcparam_range[2] |
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130 | 305 | xlabel = "Velocity (m/s)" |
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131 | 306 | print "Vmax=",x[-1] |
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132 | 307 | |
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133 | 308 | ylabel = "Range (km)" |
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134 | 309 | |
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135 | 310 | y = dataOut.getHeiRange() |
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136 | 311 | |
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137 | 312 | z = dataOut.SPCparam[Selector] /1966080.0#/ dataOut.normFactor#GauSelector] #dataOut.data_spc/factor |
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138 | 313 | #print 'GausSPC', z[0,32,10:40] |
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139 | 314 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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140 | 315 | zdB = 10*numpy.log10(z) |
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141 | 316 | |
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142 | 317 | avg = numpy.average(z, axis=1) |
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143 | 318 | avgdB = 10*numpy.log10(avg) |
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144 | 319 | |
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145 | 320 | noise = dataOut.spc_noise |
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146 | 321 | noisedB = 10*numpy.log10(noise) |
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147 | 322 | |
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148 | 323 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
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149 | 324 | title = wintitle + " Spectra" |
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150 | 325 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
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151 | 326 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
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152 | 327 | |
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153 | 328 | if not self.isConfig: |
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154 | 329 | |
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155 | 330 | nplots = len(channelIndexList) |
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156 | 331 | |
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157 | 332 | self.setup(id=id, |
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158 | 333 | nplots=nplots, |
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159 | 334 | wintitle=wintitle, |
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160 | 335 | showprofile=showprofile, |
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161 | 336 | show=show) |
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162 | 337 | |
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163 | 338 | if xmin == None: xmin = numpy.nanmin(x) |
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164 | 339 | if xmax == None: xmax = numpy.nanmax(x) |
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165 | 340 | if ymin == None: ymin = numpy.nanmin(y) |
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166 | 341 | if ymax == None: ymax = numpy.nanmax(y) |
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167 | 342 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
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168 | 343 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
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169 | 344 | |
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170 | 345 | self.FTP_WEI = ftp_wei |
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171 | 346 | self.EXP_CODE = exp_code |
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172 | 347 | self.SUB_EXP_CODE = sub_exp_code |
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173 | 348 | self.PLOT_POS = plot_pos |
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174 | 349 | |
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175 | 350 | self.isConfig = True |
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176 | 351 | |
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177 | 352 | self.setWinTitle(title) |
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178 | 353 | |
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179 | 354 | for i in range(self.nplots): |
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180 | 355 | index = channelIndexList[i] |
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181 | 356 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
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182 | 357 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[index], noisedB[index], str_datetime) |
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183 | 358 | if len(dataOut.beam.codeList) != 0: |
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184 | 359 | 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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185 | 360 | |
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186 | 361 | axes = self.axesList[i*self.__nsubplots] |
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187 | 362 | axes.pcolor(x, y, zdB[index,:,:], |
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188 | 363 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
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189 | 364 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=colormap, |
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190 | 365 | ticksize=9, cblabel='') |
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191 | 366 | |
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192 | 367 | if self.__showprofile: |
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193 | 368 | axes = self.axesList[i*self.__nsubplots +1] |
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194 | 369 | axes.pline(avgdB[index,:], y, |
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195 | 370 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
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196 | 371 | xlabel='dB', ylabel='', title='', |
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197 | 372 | ytick_visible=False, |
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198 | 373 | grid='x') |
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199 | 374 | |
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200 | 375 | noiseline = numpy.repeat(noisedB[index], len(y)) |
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201 | 376 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
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202 | 377 | |
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203 | 378 | self.draw() |
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204 | 379 | |
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205 | 380 | if figfile == None: |
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206 | 381 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
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207 | 382 | name = str_datetime |
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208 | 383 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
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209 | 384 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
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210 | 385 | figfile = self.getFilename(name) |
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211 | 386 | |
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212 | 387 | self.save(figpath=figpath, |
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213 | 388 | figfile=figfile, |
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214 | 389 | save=save, |
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215 | 390 | ftp=ftp, |
|
|
216 | 391 | wr_period=wr_period, |
|
|
217 | 392 | thisDatetime=thisDatetime) |
|
|
218 | 393 | |
|
|
219 | 394 | |
|
|
220 | 395 | |
|
|
221 | 396 | class MomentsPlot(Figure): |
|
|
222 | 397 | |
|
|
223 | 398 | isConfig = None |
|
|
224 | 399 | __nsubplots = None |
|
|
225 | 400 | |
|
|
226 | 401 | WIDTHPROF = None |
|
|
227 | 402 | HEIGHTPROF = None |
|
|
228 | 403 | PREFIX = 'prm' |
|
|
229 | 404 | |
|
|
230 | 405 | def __init__(self, **kwargs): |
|
|
231 | 406 | Figure.__init__(self, **kwargs) |
|
|
232 | 407 | self.isConfig = False |
|
|
233 | 408 | self.__nsubplots = 1 |
|
|
234 | 409 | |
|
|
235 | 410 | self.WIDTH = 280 |
|
|
236 | 411 | self.HEIGHT = 250 |
|
|
237 | 412 | self.WIDTHPROF = 120 |
|
|
238 | 413 | self.HEIGHTPROF = 0 |
|
|
239 | 414 | self.counter_imagwr = 0 |
|
|
240 | 415 | |
|
|
241 | 416 | self.PLOT_CODE = MOMENTS_CODE |
|
|
242 | 417 | |
|
|
243 | 418 | self.FTP_WEI = None |
|
|
244 | 419 | self.EXP_CODE = None |
|
|
245 | 420 | self.SUB_EXP_CODE = None |
|
|
246 | 421 | self.PLOT_POS = None |
|
|
247 | 422 | |
|
|
248 | 423 | def getSubplots(self): |
|
|
249 | 424 | |
|
|
250 | 425 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
251 | 426 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
252 | 427 | |
|
|
253 | 428 | return nrow, ncol |
|
|
254 | 429 | |
|
|
255 | 430 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
256 | 431 | |
|
|
257 | 432 | self.__showprofile = showprofile |
|
|
258 | 433 | self.nplots = nplots |
|
|
259 | 434 | |
|
|
260 | 435 | ncolspan = 1 |
|
|
261 | 436 | colspan = 1 |
|
|
262 | 437 | if showprofile: |
|
|
263 | 438 | ncolspan = 3 |
|
|
264 | 439 | colspan = 2 |
|
|
265 | 440 | self.__nsubplots = 2 |
|
|
266 | 441 | |
|
|
267 | 442 | self.createFigure(id = id, |
|
|
268 | 443 | wintitle = wintitle, |
|
|
269 | 444 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
270 | 445 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
271 | 446 | show=show) |
|
|
272 | 447 | |
|
|
273 | 448 | nrow, ncol = self.getSubplots() |
|
|
274 | 449 | |
|
|
275 | 450 | counter = 0 |
|
|
276 | 451 | for y in range(nrow): |
|
|
277 | 452 | for x in range(ncol): |
|
|
278 | 453 | |
|
|
279 | 454 | if counter >= self.nplots: |
|
|
280 | 455 | break |
|
|
281 | 456 | |
|
|
282 | 457 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
283 | 458 | |
|
|
284 | 459 | if showprofile: |
|
|
285 | 460 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
286 | 461 | |
|
|
287 | 462 | counter += 1 |
|
|
288 | 463 | |
|
|
289 | 464 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
290 | 465 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
291 | 466 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
292 | 467 | server=None, folder=None, username=None, password=None, |
|
|
293 | 468 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
294 | 469 | |
|
|
295 | 470 | """ |
|
|
296 | 471 | |
|
|
297 | 472 | Input: |
|
|
298 | 473 | dataOut : |
|
|
299 | 474 | id : |
|
|
300 | 475 | wintitle : |
|
|
301 | 476 | channelList : |
|
|
302 | 477 | showProfile : |
|
|
303 | 478 | xmin : None, |
|
|
304 | 479 | xmax : None, |
|
|
305 | 480 | ymin : None, |
|
|
306 | 481 | ymax : None, |
|
|
307 | 482 | zmin : None, |
|
|
308 | 483 | zmax : None |
|
|
309 | 484 | """ |
|
|
310 | 485 | |
|
|
311 | 486 | if dataOut.flagNoData: |
|
|
312 | 487 | return None |
|
|
313 | 488 | |
|
|
314 | 489 | if realtime: |
|
|
315 | 490 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
316 | 491 | print 'Skipping this plot function' |
|
|
317 | 492 | return |
|
|
318 | 493 | |
|
|
319 | 494 | if channelList == None: |
|
|
320 | 495 | channelIndexList = dataOut.channelIndexList |
|
|
321 | 496 | else: |
|
|
322 | 497 | channelIndexList = [] |
|
|
323 | 498 | for channel in channelList: |
|
|
324 | 499 | if channel not in dataOut.channelList: |
|
|
325 | 500 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
326 | 501 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
327 | 502 | |
|
|
328 | 503 | factor = dataOut.normFactor |
|
|
329 | 504 | x = dataOut.abscissaList |
|
|
330 | 505 | y = dataOut.heightList |
|
|
331 | 506 | |
|
|
332 | 507 | z = dataOut.data_pre[channelIndexList,:,:]/factor |
|
|
333 | 508 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
334 | 509 | avg = numpy.average(z, axis=1) |
|
|
335 | 510 | noise = dataOut.noise/factor |
|
|
336 | 511 | |
|
|
337 | 512 | zdB = 10*numpy.log10(z) |
|
|
338 | 513 | avgdB = 10*numpy.log10(avg) |
|
|
339 | 514 | noisedB = 10*numpy.log10(noise) |
|
|
340 | 515 | |
|
|
341 | 516 | #thisDatetime = dataOut.datatime |
|
|
342 | 517 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
343 | 518 | title = wintitle + " Parameters" |
|
|
344 | 519 | xlabel = "Velocity (m/s)" |
|
|
345 | 520 | ylabel = "Range (Km)" |
|
|
346 | 521 | |
|
|
347 | 522 | update_figfile = False |
|
|
348 | 523 | |
|
|
349 | 524 | if not self.isConfig: |
|
|
350 | 525 | |
|
|
351 | 526 | nplots = len(channelIndexList) |
|
|
352 | 527 | |
|
|
353 | 528 | self.setup(id=id, |
|
|
354 | 529 | nplots=nplots, |
|
|
355 | 530 | wintitle=wintitle, |
|
|
356 | 531 | showprofile=showprofile, |
|
|
357 | 532 | show=show) |
|
|
358 | 533 | |
|
|
359 | 534 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
360 | 535 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
361 | 536 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
362 | 537 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
363 | 538 | if zmin == None: zmin = numpy.nanmin(avgdB)*0.9 |
|
|
364 | 539 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
|
|
365 | 540 | |
|
|
366 | 541 | self.FTP_WEI = ftp_wei |
|
|
367 | 542 | self.EXP_CODE = exp_code |
|
|
368 | 543 | self.SUB_EXP_CODE = sub_exp_code |
|
|
369 | 544 | self.PLOT_POS = plot_pos |
|
|
370 | 545 | |
|
|
371 | 546 | self.isConfig = True |
|
|
372 | 547 | update_figfile = True |
|
|
373 | 548 | |
|
|
374 | 549 | self.setWinTitle(title) |
|
|
375 | 550 | |
|
|
376 | 551 | for i in range(self.nplots): |
|
|
377 | 552 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
378 | 553 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[i], noisedB[i], str_datetime) |
|
|
379 | 554 | axes = self.axesList[i*self.__nsubplots] |
|
|
380 | 555 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
381 | 556 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
382 | 557 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
383 | 558 | ticksize=9, cblabel='') |
|
|
384 | 559 | #Mean Line |
|
|
385 | 560 | mean = dataOut.data_param[i, 1, :] |
|
|
386 | 561 | axes.addpline(mean, y, idline=0, color="black", linestyle="solid", lw=1) |
|
|
387 | 562 | |
|
|
388 | 563 | if self.__showprofile: |
|
|
389 | 564 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
390 | 565 | axes.pline(avgdB[i], y, |
|
|
391 | 566 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
392 | 567 | xlabel='dB', ylabel='', title='', |
|
|
393 | 568 | ytick_visible=False, |
|
|
394 | 569 | grid='x') |
|
|
395 | 570 | |
|
|
396 | 571 | noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
397 | 572 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
398 | 573 | |
|
|
399 | 574 | self.draw() |
|
|
400 | 575 | |
|
|
401 | 576 | self.save(figpath=figpath, |
|
|
402 | 577 | figfile=figfile, |
|
|
403 | 578 | save=save, |
|
|
404 | 579 | ftp=ftp, |
|
|
405 | 580 | wr_period=wr_period, |
|
|
406 | 581 | thisDatetime=thisDatetime) |
|
|
407 | 582 | |
|
|
408 | 583 | |
|
|
409 | 584 | |
|
|
410 | 585 | class SkyMapPlot(Figure): |
|
|
411 | 586 | |
|
|
412 | 587 | __isConfig = None |
|
|
413 | 588 | __nsubplots = None |
|
|
414 | 589 | |
|
|
415 | 590 | WIDTHPROF = None |
|
|
416 | 591 | HEIGHTPROF = None |
|
|
417 | 592 | PREFIX = 'mmap' |
|
|
418 | 593 | |
|
|
419 | 594 | def __init__(self, **kwargs): |
|
|
420 | 595 | Figure.__init__(self, **kwargs) |
|
|
421 | 596 | self.isConfig = False |
|
|
422 | 597 | self.__nsubplots = 1 |
|
|
423 | 598 | |
|
|
424 | 599 | # self.WIDTH = 280 |
|
|
425 | 600 | # self.HEIGHT = 250 |
|
|
426 | 601 | self.WIDTH = 600 |
|
|
427 | 602 | self.HEIGHT = 600 |
|
|
428 | 603 | self.WIDTHPROF = 120 |
|
|
429 | 604 | self.HEIGHTPROF = 0 |
|
|
430 | 605 | self.counter_imagwr = 0 |
|
|
431 | 606 | |
|
|
432 | 607 | self.PLOT_CODE = MSKYMAP_CODE |
|
|
433 | 608 | |
|
|
434 | 609 | self.FTP_WEI = None |
|
|
435 | 610 | self.EXP_CODE = None |
|
|
436 | 611 | self.SUB_EXP_CODE = None |
|
|
437 | 612 | self.PLOT_POS = None |
|
|
438 | 613 | |
|
|
439 | 614 | def getSubplots(self): |
|
|
440 | 615 | |
|
|
441 | 616 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
442 | 617 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
443 | 618 | |
|
|
444 | 619 | return nrow, ncol |
|
|
445 | 620 | |
|
|
446 | 621 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
447 | 622 | |
|
|
448 | 623 | self.__showprofile = showprofile |
|
|
449 | 624 | self.nplots = nplots |
|
|
450 | 625 | |
|
|
451 | 626 | ncolspan = 1 |
|
|
452 | 627 | colspan = 1 |
|
|
453 | 628 | |
|
|
454 | 629 | self.createFigure(id = id, |
|
|
455 | 630 | wintitle = wintitle, |
|
|
456 | 631 | widthplot = self.WIDTH, #+ self.WIDTHPROF, |
|
|
457 | 632 | heightplot = self.HEIGHT,# + self.HEIGHTPROF, |
|
|
458 | 633 | show=show) |
|
|
459 | 634 | |
|
|
460 | 635 | nrow, ncol = 1,1 |
|
|
461 | 636 | counter = 0 |
|
|
462 | 637 | x = 0 |
|
|
463 | 638 | y = 0 |
|
|
464 | 639 | self.addAxes(1, 1, 0, 0, 1, 1, True) |
|
|
465 | 640 | |
|
|
466 | 641 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
467 | 642 | tmin=0, tmax=24, timerange=None, |
|
|
468 | 643 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
469 | 644 | server=None, folder=None, username=None, password=None, |
|
|
470 | 645 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
471 | 646 | |
|
|
472 | 647 | """ |
|
|
473 | 648 | |
|
|
474 | 649 | Input: |
|
|
475 | 650 | dataOut : |
|
|
476 | 651 | id : |
|
|
477 | 652 | wintitle : |
|
|
478 | 653 | channelList : |
|
|
479 | 654 | showProfile : |
|
|
480 | 655 | xmin : None, |
|
|
481 | 656 | xmax : None, |
|
|
482 | 657 | ymin : None, |
|
|
483 | 658 | ymax : None, |
|
|
484 | 659 | zmin : None, |
|
|
485 | 660 | zmax : None |
|
|
486 | 661 | """ |
|
|
487 | 662 | |
|
|
488 | 663 | arrayParameters = dataOut.data_param |
|
|
489 | 664 | error = arrayParameters[:,-1] |
|
|
490 | 665 | indValid = numpy.where(error == 0)[0] |
|
|
491 | 666 | finalMeteor = arrayParameters[indValid,:] |
|
|
492 | 667 | finalAzimuth = finalMeteor[:,3] |
|
|
493 | 668 | finalZenith = finalMeteor[:,4] |
|
|
494 | 669 | |
|
|
495 | 670 | x = finalAzimuth*numpy.pi/180 |
|
|
496 | 671 | y = finalZenith |
|
|
497 | 672 | x1 = [dataOut.ltctime, dataOut.ltctime] |
|
|
498 | 673 | |
|
|
499 | 674 | #thisDatetime = dataOut.datatime |
|
|
500 | 675 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
501 | 676 | title = wintitle + " Parameters" |
|
|
502 | 677 | xlabel = "Zonal Zenith Angle (deg) " |
|
|
503 | 678 | ylabel = "Meridional Zenith Angle (deg)" |
|
|
504 | 679 | update_figfile = False |
|
|
505 | 680 | |
|
|
506 | 681 | if not self.isConfig: |
|
|
507 | 682 | |
|
|
508 | 683 | nplots = 1 |
|
|
509 | 684 | |
|
|
510 | 685 | self.setup(id=id, |
|
|
511 | 686 | nplots=nplots, |
|
|
512 | 687 | wintitle=wintitle, |
|
|
513 | 688 | showprofile=showprofile, |
|
|
514 | 689 | show=show) |
|
|
515 | 690 | |
|
|
516 | 691 | if self.xmin is None and self.xmax is None: |
|
|
517 | 692 | self.xmin, self.xmax = self.getTimeLim(x1, tmin, tmax, timerange) |
|
|
518 | 693 | |
|
|
519 | 694 | if timerange != None: |
|
|
520 | 695 | self.timerange = timerange |
|
|
521 | 696 | else: |
|
|
522 | 697 | self.timerange = self.xmax - self.xmin |
|
|
523 | 698 | |
|
|
524 | 699 | self.FTP_WEI = ftp_wei |
|
|
525 | 700 | self.EXP_CODE = exp_code |
|
|
526 | 701 | self.SUB_EXP_CODE = sub_exp_code |
|
|
527 | 702 | self.PLOT_POS = plot_pos |
|
|
528 | 703 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
529 | 704 | self.firstdate = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
530 | 705 | self.isConfig = True |
|
|
531 | 706 | update_figfile = True |
|
|
532 | 707 | |
|
|
533 | 708 | self.setWinTitle(title) |
|
|
534 | 709 | |
|
|
535 | 710 | i = 0 |
|
|
536 | 711 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
537 | 712 | |
|
|
538 | 713 | axes = self.axesList[i*self.__nsubplots] |
|
|
539 | 714 | nevents = axes.x_buffer.shape[0] + x.shape[0] |
|
|
540 | 715 | title = "Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n" %(self.firstdate,str_datetime,nevents) |
|
|
541 | 716 | axes.polar(x, y, |
|
|
542 | 717 | title=title, xlabel=xlabel, ylabel=ylabel, |
|
|
543 | 718 | ticksize=9, cblabel='') |
|
|
544 | 719 | |
|
|
545 | 720 | self.draw() |
|
|
546 | 721 | |
|
|
547 | 722 | self.save(figpath=figpath, |
|
|
548 | 723 | figfile=figfile, |
|
|
549 | 724 | save=save, |
|
|
550 | 725 | ftp=ftp, |
|
|
551 | 726 | wr_period=wr_period, |
|
|
552 | 727 | thisDatetime=thisDatetime, |
|
|
553 | 728 | update_figfile=update_figfile) |
|
|
554 | 729 | |
|
|
555 | 730 | if dataOut.ltctime >= self.xmax: |
|
|
556 | 731 | self.isConfigmagwr = wr_period |
|
|
557 | 732 | self.isConfig = False |
|
|
558 | 733 | update_figfile = True |
|
|
559 | 734 | axes.__firsttime = True |
|
|
560 | 735 | self.xmin += self.timerange |
|
|
561 | 736 | self.xmax += self.timerange |
|
|
562 | 737 | |
|
|
563 | 738 | |
|
|
564 | 739 | |
|
|
565 | 740 | |
|
|
566 | 741 | class WindProfilerPlot(Figure): |
|
|
567 | 742 | |
|
|
568 | 743 | __isConfig = None |
|
|
569 | 744 | __nsubplots = None |
|
|
570 | 745 | |
|
|
571 | 746 | WIDTHPROF = None |
|
|
572 | 747 | HEIGHTPROF = None |
|
|
573 | 748 | PREFIX = 'wind' |
|
|
574 | 749 | |
|
|
575 | 750 | def __init__(self, **kwargs): |
|
|
576 | 751 | Figure.__init__(self, **kwargs) |
|
|
577 | 752 | self.timerange = None |
|
|
578 | 753 | self.isConfig = False |
|
|
579 | 754 | self.__nsubplots = 1 |
|
|
580 | 755 | |
|
|
581 | 756 | self.WIDTH = 800 |
|
|
582 | 757 | self.HEIGHT = 300 |
|
|
583 | 758 | self.WIDTHPROF = 120 |
|
|
584 | 759 | self.HEIGHTPROF = 0 |
|
|
585 | 760 | self.counter_imagwr = 0 |
|
|
586 | 761 | |
|
|
587 | 762 | self.PLOT_CODE = WIND_CODE |
|
|
588 | 763 | |
|
|
589 | 764 | self.FTP_WEI = None |
|
|
590 | 765 | self.EXP_CODE = None |
|
|
591 | 766 | self.SUB_EXP_CODE = None |
|
|
592 | 767 | self.PLOT_POS = None |
|
|
593 | 768 | self.tmin = None |
|
|
594 | 769 | self.tmax = None |
|
|
595 | 770 | |
|
|
596 | 771 | self.xmin = None |
|
|
597 | 772 | self.xmax = None |
|
|
598 | 773 | |
|
|
599 | 774 | self.figfile = None |
|
|
600 | 775 | |
|
|
601 | 776 | def getSubplots(self): |
|
|
602 | 777 | |
|
|
603 | 778 | ncol = 1 |
|
|
604 | 779 | nrow = self.nplots |
|
|
605 | 780 | |
|
|
606 | 781 | return nrow, ncol |
|
|
607 | 782 | |
|
|
608 | 783 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
609 | 784 | |
|
|
610 | 785 | self.__showprofile = showprofile |
|
|
611 | 786 | self.nplots = nplots |
|
|
612 | 787 | |
|
|
613 | 788 | ncolspan = 1 |
|
|
614 | 789 | colspan = 1 |
|
|
615 | 790 | |
|
|
616 | 791 | self.createFigure(id = id, |
|
|
617 | 792 | wintitle = wintitle, |
|
|
618 | 793 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
619 | 794 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
620 | 795 | show=show) |
|
|
621 | 796 | |
|
|
622 | 797 | nrow, ncol = self.getSubplots() |
|
|
623 | 798 | |
|
|
624 | 799 | counter = 0 |
|
|
625 | 800 | for y in range(nrow): |
|
|
626 | 801 | if counter >= self.nplots: |
|
|
627 | 802 | break |
|
|
628 | 803 | |
|
|
629 | 804 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
630 | 805 | counter += 1 |
|
|
631 | 806 | |
|
|
632 | 807 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='False', |
|
|
633 | 808 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
634 | 809 | zmax_ver = None, zmin_ver = None, SNRmin = None, SNRmax = None, |
|
|
635 | 810 | timerange=None, SNRthresh = None, |
|
|
636 | 811 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
637 | 812 | server=None, folder=None, username=None, password=None, |
|
|
638 | 813 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
639 | 814 | """ |
|
|
640 | 815 | |
|
|
641 | 816 | Input: |
|
|
642 | 817 | dataOut : |
|
|
643 | 818 | id : |
|
|
644 | 819 | wintitle : |
|
|
645 | 820 | channelList : |
|
|
646 | 821 | showProfile : |
|
|
647 | 822 | xmin : None, |
|
|
648 | 823 | xmax : None, |
|
|
649 | 824 | ymin : None, |
|
|
650 | 825 | ymax : None, |
|
|
651 | 826 | zmin : None, |
|
|
652 | 827 | zmax : None |
|
|
653 | 828 | """ |
|
|
654 | 829 | |
|
|
655 | 830 | # if timerange is not None: |
|
|
656 | 831 | # self.timerange = timerange |
|
|
657 | 832 | # |
|
|
658 | 833 | # tmin = None |
|
|
659 | 834 | # tmax = None |
|
|
660 | 835 | |
|
|
661 | 836 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
662 | 837 | y = dataOut.heightList |
|
|
663 | 838 | z = dataOut.data_output.copy() |
|
|
664 | 839 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
665 | 840 | nplotsw = nplots |
|
|
666 | 841 | |
|
|
667 | 842 | |
|
|
668 | 843 | #If there is a SNR function defined |
|
|
669 | 844 | if dataOut.data_SNR is not None: |
|
|
670 | 845 | nplots += 1 |
|
|
671 | 846 | SNR = dataOut.data_SNR[0] |
|
|
672 | 847 | SNRavg = SNR#numpy.average(SNR, axis=0) |
|
|
673 | 848 | |
|
|
674 | 849 | SNRdB = 10*numpy.log10(SNR) |
|
|
675 | 850 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
676 | 851 | |
|
|
677 | 852 | if SNRthresh == None: |
|
|
678 | 853 | SNRthresh = -5.0 |
|
|
679 | 854 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
680 | 855 | |
|
|
681 | 856 | for i in range(nplotsw): |
|
|
682 | 857 | z[i,ind] = numpy.nan |
|
|
683 | 858 | |
|
|
684 | 859 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
685 | 860 | #thisDatetime = datetime.datetime.now() |
|
|
686 | 861 | title = wintitle + "Wind" |
|
|
687 | 862 | xlabel = "" |
|
|
688 | 863 | ylabel = "Height (km)" |
|
|
689 | 864 | update_figfile = False |
|
|
690 | 865 | |
|
|
691 | 866 | if not self.isConfig: |
|
|
692 | 867 | |
|
|
693 | 868 | self.setup(id=id, |
|
|
694 | 869 | nplots=nplots, |
|
|
695 | 870 | wintitle=wintitle, |
|
|
696 | 871 | showprofile=showprofile, |
|
|
697 | 872 | show=show) |
|
|
698 | 873 | |
|
|
699 | 874 | if timerange is not None: |
|
|
700 | 875 | self.timerange = timerange |
|
|
701 | 876 | |
|
|
702 | 877 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
703 | 878 | |
|
|
704 | 879 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
705 | 880 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
706 | 881 | |
|
|
707 | 882 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
|
|
708 | 883 | #if numpy.isnan(zmax): zmax = 50 |
|
|
709 | 884 | if zmin == None: zmin = -zmax |
|
|
710 | 885 | |
|
|
711 | 886 | if nplotsw == 3: |
|
|
712 | 887 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
|
|
713 | 888 | if zmin_ver == None: zmin_ver = -zmax_ver |
|
|
714 | 889 | |
|
|
715 | 890 | if dataOut.data_SNR is not None: |
|
|
716 | 891 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
717 | 892 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
718 | 893 | |
|
|
719 | 894 | |
|
|
720 | 895 | self.FTP_WEI = ftp_wei |
|
|
721 | 896 | self.EXP_CODE = exp_code |
|
|
722 | 897 | self.SUB_EXP_CODE = sub_exp_code |
|
|
723 | 898 | self.PLOT_POS = plot_pos |
|
|
724 | 899 | |
|
|
725 | 900 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
726 | 901 | self.isConfig = True |
|
|
727 | 902 | self.figfile = figfile |
|
|
728 | 903 | update_figfile = True |
|
|
729 | 904 | |
|
|
730 | 905 | self.setWinTitle(title) |
|
|
731 | 906 | |
|
|
732 | 907 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
733 | 908 | x[1] = self.xmax |
|
|
734 | 909 | |
|
|
735 | 910 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
|
|
736 | 911 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
|
|
737 | 912 | zmaxVector = [zmax, zmax, zmax_ver] |
|
|
738 | 913 | zminVector = [zmin, zmin, zmin_ver] |
|
|
739 | 914 | windFactor = [1,1,100] |
|
|
740 | 915 | |
|
|
741 | 916 | for i in range(nplotsw): |
|
|
742 | 917 | |
|
|
743 | 918 | title = "%s Wind: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
744 | 919 | axes = self.axesList[i*self.__nsubplots] |
|
|
745 | 920 | |
|
|
746 | 921 | z1 = z[i,:].reshape((1,-1))*windFactor[i] |
|
|
747 | 922 | |
|
|
748 | 923 | print 'x', x |
|
|
749 | 924 | print datetime.datetime.utcfromtimestamp(x[0]) |
|
|
750 | 925 | print datetime.datetime.utcfromtimestamp(x[1]) |
|
|
751 | 926 | |
|
|
752 | 927 | #z1=numpy.ma.masked_where(z1==0.,z1) |
|
|
753 | 928 | |
|
|
754 | 929 | axes.pcolorbuffer(x, y, z1, |
|
|
755 | 930 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
756 | 931 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
757 | 932 | ticksize=9, cblabel=strCb[i], cbsize="1%", colormap="seismic" ) |
|
|
758 | 933 | |
|
|
759 | 934 | if dataOut.data_SNR is not None: |
|
|
760 | 935 | i += 1 |
|
|
761 | 936 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
762 | 937 | axes = self.axesList[i*self.__nsubplots] |
|
|
763 | 938 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
764 | 939 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
765 | 940 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
766 | 941 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
767 | 942 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
768 | 943 | |
|
|
769 | 944 | self.draw() |
|
|
770 | 945 | |
|
|
771 | 946 | self.save(figpath=figpath, |
|
|
772 | 947 | figfile=figfile, |
|
|
773 | 948 | save=save, |
|
|
774 | 949 | ftp=ftp, |
|
|
775 | 950 | wr_period=wr_period, |
|
|
776 | 951 | thisDatetime=thisDatetime, |
|
|
777 | 952 | update_figfile=update_figfile) |
|
|
778 | 953 | |
|
|
779 | 954 | if dataOut.ltctime + dataOut.paramInterval >= self.xmax: |
|
|
780 | 955 | self.counter_imagwr = wr_period |
|
|
781 | 956 | self.isConfig = False |
|
|
782 | 957 | update_figfile = True |
|
|
783 | 958 | |
|
|
784 | 959 | |
|
|
785 | 960 | class ParametersPlot(Figure): |
|
|
786 | 961 | |
|
|
787 | 962 | __isConfig = None |
|
|
788 | 963 | __nsubplots = None |
|
|
789 | 964 | |
|
|
790 | 965 | WIDTHPROF = None |
|
|
791 | 966 | HEIGHTPROF = None |
|
|
792 | 967 | PREFIX = 'param' |
|
|
793 | 968 | |
|
|
794 | 969 | nplots = None |
|
|
795 | 970 | nchan = None |
|
|
796 | 971 | |
|
|
797 | 972 | def __init__(self, **kwargs): |
|
|
798 | 973 | Figure.__init__(self, **kwargs) |
|
|
799 | 974 | self.timerange = None |
|
|
800 | 975 | self.isConfig = False |
|
|
801 | 976 | self.__nsubplots = 1 |
|
|
802 | 977 | |
|
|
803 |
self.WIDTH = |
|
|
|
804 |
self.HEIGHT = |
|
|
|
978 | self.WIDTH = 300 | |
|
|
979 | self.HEIGHT = 550 | |
|
|
805 | 980 | self.WIDTHPROF = 120 |
|
|
806 | 981 | self.HEIGHTPROF = 0 |
|
|
807 | 982 | self.counter_imagwr = 0 |
|
|
808 | 983 | |
|
|
809 | 984 | self.PLOT_CODE = RTI_CODE |
|
|
810 | 985 | |
|
|
811 | 986 | self.FTP_WEI = None |
|
|
812 | 987 | self.EXP_CODE = None |
|
|
813 | 988 | self.SUB_EXP_CODE = None |
|
|
814 | 989 | self.PLOT_POS = None |
|
|
815 | 990 | self.tmin = None |
|
|
816 | 991 | self.tmax = None |
|
|
817 | 992 | |
|
|
818 | 993 | self.xmin = None |
|
|
819 | 994 | self.xmax = None |
|
|
820 | 995 | |
|
|
821 | 996 | self.figfile = None |
|
|
822 | 997 | |
|
|
823 | 998 | def getSubplots(self): |
|
|
824 | 999 | |
|
|
825 | 1000 | ncol = 1 |
|
|
826 | 1001 | nrow = self.nplots |
|
|
827 | 1002 | |
|
|
828 | 1003 | return nrow, ncol |
|
|
829 | 1004 | |
|
|
830 | 1005 | def setup(self, id, nplots, wintitle, show=True): |
|
|
831 | 1006 | |
|
|
832 | 1007 | self.nplots = nplots |
|
|
833 | 1008 | |
|
|
834 | 1009 | ncolspan = 1 |
|
|
835 | 1010 | colspan = 1 |
|
|
836 | 1011 | |
|
|
837 | 1012 | self.createFigure(id = id, |
|
|
838 | 1013 | wintitle = wintitle, |
|
|
839 | 1014 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
840 | 1015 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
841 | 1016 | show=show) |
|
|
842 | 1017 | |
|
|
843 | 1018 | nrow, ncol = self.getSubplots() |
|
|
844 | 1019 | |
|
|
845 | 1020 | counter = 0 |
|
|
846 | 1021 | for y in range(nrow): |
|
|
847 | 1022 | for x in range(ncol): |
|
|
848 | 1023 | |
|
|
849 | 1024 | if counter >= self.nplots: |
|
|
850 | 1025 | break |
|
|
851 | 1026 | |
|
|
852 | 1027 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
853 | 1028 | |
|
|
854 | 1029 | counter += 1 |
|
|
855 | 1030 | |
|
|
856 | 1031 | def run(self, dataOut, id, wintitle="", channelList=None, paramIndex = 0, colormap="jet", |
|
|
857 | 1032 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, timerange=None, |
|
|
858 | 1033 | showSNR=False, SNRthresh = -numpy.inf, SNRmin=None, SNRmax=None, |
|
|
859 | 1034 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
860 | 1035 | server=None, folder=None, username=None, password=None, |
|
|
861 | 1036 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, HEIGHT=None): |
|
|
862 | 1037 | """ |
|
|
863 | 1038 | |
|
|
864 | 1039 | Input: |
|
|
865 | 1040 | dataOut : |
|
|
866 | 1041 | id : |
|
|
867 | 1042 | wintitle : |
|
|
868 | 1043 | channelList : |
|
|
869 | 1044 | showProfile : |
|
|
870 | 1045 | xmin : None, |
|
|
871 | 1046 | xmax : None, |
|
|
872 | 1047 | ymin : None, |
|
|
873 | 1048 | ymax : None, |
|
|
874 | 1049 | zmin : None, |
|
|
875 | 1050 | zmax : None |
|
|
876 | 1051 | """ |
|
|
877 | 1052 | |
|
|
878 | 1053 | if HEIGHT is not None: |
|
|
879 | 1054 | self.HEIGHT = HEIGHT |
|
|
880 | 1055 | |
|
|
881 | 1056 | |
|
|
882 | 1057 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
883 | 1058 | return |
|
|
884 | 1059 | |
|
|
885 | 1060 | if channelList == None: |
|
|
886 | 1061 | channelIndexList = range(dataOut.data_param.shape[0]) |
|
|
887 | 1062 | else: |
|
|
888 | 1063 | channelIndexList = [] |
|
|
889 | 1064 | for channel in channelList: |
|
|
890 | 1065 | if channel not in dataOut.channelList: |
|
|
891 | 1066 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
892 | 1067 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
893 | 1068 | |
|
|
894 | 1069 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
895 | 1070 | y = dataOut.getHeiRange() |
|
|
896 | 1071 | |
|
|
897 | 1072 | if dataOut.data_param.ndim == 3: |
|
|
898 | 1073 | z = dataOut.data_param[channelIndexList,paramIndex,:] |
|
|
899 | 1074 | else: |
|
|
900 | 1075 | z = dataOut.data_param[channelIndexList,:] |
|
|
901 | 1076 | |
|
|
902 | 1077 | if showSNR: |
|
|
903 | 1078 | #SNR data |
|
|
904 | 1079 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
905 | 1080 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
906 | 1081 | ind = numpy.where(SNRdB < SNRthresh) |
|
|
907 | 1082 | z[ind] = numpy.nan |
|
|
908 | 1083 | |
|
|
909 | 1084 | thisDatetime = dataOut.datatime |
|
|
910 | 1085 | # thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
911 | 1086 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
912 | 1087 | xlabel = "" |
|
|
913 |
ylabel = "Range ( |
|
|
|
1088 | ylabel = "Range (km)" | |
|
|
914 | 1089 | |
|
|
915 | 1090 | update_figfile = False |
|
|
916 | 1091 | |
|
|
917 | 1092 | if not self.isConfig: |
|
|
918 | 1093 | |
|
|
919 | 1094 | nchan = len(channelIndexList) |
|
|
920 | 1095 | self.nchan = nchan |
|
|
921 | 1096 | self.plotFact = 1 |
|
|
922 | 1097 | nplots = nchan |
|
|
923 | 1098 | |
|
|
924 | 1099 | if showSNR: |
|
|
925 | 1100 | nplots = nchan*2 |
|
|
926 | 1101 | self.plotFact = 2 |
|
|
927 | 1102 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
928 | 1103 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
929 | 1104 | |
|
|
930 | 1105 | self.setup(id=id, |
|
|
931 | 1106 | nplots=nplots, |
|
|
932 | 1107 | wintitle=wintitle, |
|
|
933 | 1108 | show=show) |
|
|
934 | 1109 | |
|
|
935 | 1110 | if timerange != None: |
|
|
936 | 1111 | self.timerange = timerange |
|
|
937 | 1112 | |
|
|
938 | 1113 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
939 | 1114 | |
|
|
940 | 1115 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
941 | 1116 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
942 | 1117 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
943 | 1118 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
944 | 1119 | |
|
|
945 | 1120 | self.FTP_WEI = ftp_wei |
|
|
946 | 1121 | self.EXP_CODE = exp_code |
|
|
947 | 1122 | self.SUB_EXP_CODE = sub_exp_code |
|
|
948 | 1123 | self.PLOT_POS = plot_pos |
|
|
949 | 1124 | |
|
|
950 | 1125 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
951 | 1126 | self.isConfig = True |
|
|
952 | 1127 | self.figfile = figfile |
|
|
953 | 1128 | update_figfile = True |
|
|
954 | 1129 | |
|
|
955 | 1130 | self.setWinTitle(title) |
|
|
956 | 1131 | |
|
|
957 | for i in range(self.nchan): | |
|
|
958 | index = channelIndexList[i] | |
|
|
959 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
960 | axes = self.axesList[i*self.plotFact] | |
|
|
961 | z1 = z[i,:].reshape((1,-1)) | |
|
|
962 | axes.pcolorbuffer(x, y, z1, | |
|
|
963 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, | |
|
|
964 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
965 | ticksize=9, cblabel='', cbsize="1%",colormap=colormap) | |
|
|
966 | ||
|
|
967 | if showSNR: | |
|
|
968 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
969 | axes = self.axesList[i*self.plotFact + 1] | |
|
|
970 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
971 | axes.pcolorbuffer(x, y, SNRdB1, | |
|
|
972 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
973 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
974 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') | |
|
|
1132 | # for i in range(self.nchan): | |
|
|
1133 | # index = channelIndexList[i] | |
|
|
1134 | # title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1135 | # axes = self.axesList[i*self.plotFact] | |
|
|
1136 | # z1 = z[i,:].reshape((1,-1)) | |
|
|
1137 | # axes.pcolorbuffer(x, y, z1, | |
|
|
1138 | # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, | |
|
|
1139 | # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1140 | # ticksize=9, cblabel='', cbsize="1%",colormap=colormap) | |
|
|
1141 | # | |
|
|
1142 | # if showSNR: | |
|
|
1143 | # title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1144 | # axes = self.axesList[i*self.plotFact + 1] | |
|
|
1145 | # SNRdB1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
1146 | # axes.pcolorbuffer(x, y, SNRdB1, | |
|
|
1147 | # xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1148 | # xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1149 | # ticksize=9, cblabel='', cbsize="1%",colormap='jet') | |
|
|
1150 | ||
|
|
1151 | i=0 | |
|
|
1152 | index = channelIndexList[i] | |
|
|
1153 | title = "Factor de reflectividad Z [dBZ]" | |
|
|
1154 | axes = self.axesList[i*self.plotFact] | |
|
|
1155 | z1 = z[i,:].reshape((1,-1)) | |
|
|
1156 | axes.pcolorbuffer(x, y, z1, | |
|
|
1157 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, | |
|
|
1158 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1159 | ticksize=9, cblabel='', cbsize="1%",colormap=colormap) | |
|
|
1160 | ||
|
|
1161 | if showSNR: | |
|
|
1162 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1163 | axes = self.axesList[i*self.plotFact + 1] | |
|
|
1164 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
1165 | axes.pcolorbuffer(x, y, SNRdB1, | |
|
|
1166 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1167 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1168 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') | |
|
|
1169 | ||
|
|
1170 | i=1 | |
|
|
1171 | index = channelIndexList[i] | |
|
|
1172 | title = "Velocidad vertical Doppler [m/s]" | |
|
|
1173 | axes = self.axesList[i*self.plotFact] | |
|
|
1174 | z1 = z[i,:].reshape((1,-1)) | |
|
|
1175 | axes.pcolorbuffer(x, y, z1, | |
|
|
1176 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=-10, zmax=10, | |
|
|
1177 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1178 | ticksize=9, cblabel='', cbsize="1%",colormap='seismic_r') | |
|
|
1179 | ||
|
|
1180 | if showSNR: | |
|
|
1181 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1182 | axes = self.axesList[i*self.plotFact + 1] | |
|
|
1183 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
1184 | axes.pcolorbuffer(x, y, SNRdB1, | |
|
|
1185 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1186 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1187 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') | |
|
|
1188 | ||
|
|
1189 | i=2 | |
|
|
1190 | index = channelIndexList[i] | |
|
|
1191 | title = "Intensidad de lluvia [mm/h]" | |
|
|
1192 | axes = self.axesList[i*self.plotFact] | |
|
|
1193 | z1 = z[i,:].reshape((1,-1)) | |
|
|
1194 | axes.pcolorbuffer(x, y, z1, | |
|
|
1195 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=0, zmax=40, | |
|
|
1196 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1197 | ticksize=9, cblabel='', cbsize="1%",colormap='ocean_r') | |
|
|
1198 | ||
|
|
1199 | if showSNR: | |
|
|
1200 | title = "Channel %d SNR: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1201 | axes = self.axesList[i*self.plotFact + 1] | |
|
|
1202 | SNRdB1 = SNRdB[i,:].reshape((1,-1)) | |
|
|
1203 | axes.pcolorbuffer(x, y, SNRdB1, | |
|
|
1204 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1205 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1206 | ticksize=9, cblabel='', cbsize="1%",colormap='jet') | |
|
|
975 | 1207 | |
|
|
976 | 1208 | |
|
|
977 | 1209 | self.draw() |
|
|
978 | 1210 | |
|
|
979 | 1211 | if dataOut.ltctime >= self.xmax: |
|
|
980 | 1212 | self.counter_imagwr = wr_period |
|
|
981 | 1213 | self.isConfig = False |
|
|
982 | 1214 | update_figfile = True |
|
|
983 | 1215 | |
|
|
984 | 1216 | self.save(figpath=figpath, |
|
|
985 | 1217 | figfile=figfile, |
|
|
986 | 1218 | save=save, |
|
|
987 | 1219 | ftp=ftp, |
|
|
988 | 1220 | wr_period=wr_period, |
|
|
989 | 1221 | thisDatetime=thisDatetime, |
|
|
990 | 1222 | update_figfile=update_figfile) |
|
|
991 | 1223 | |
|
|
992 | 1224 | |
|
|
993 | 1225 | |
|
|
994 | 1226 | class Parameters1Plot(Figure): |
|
|
995 | 1227 | |
|
|
996 | 1228 | __isConfig = None |
|
|
997 | 1229 | __nsubplots = None |
|
|
998 | 1230 | |
|
|
999 | 1231 | WIDTHPROF = None |
|
|
1000 | 1232 | HEIGHTPROF = None |
|
|
1001 | 1233 | PREFIX = 'prm' |
|
|
1002 | 1234 | |
|
|
1003 | 1235 | def __init__(self, **kwargs): |
|
|
1004 | 1236 | Figure.__init__(self, **kwargs) |
|
|
1005 | 1237 | self.timerange = 2*60*60 |
|
|
1006 | 1238 | self.isConfig = False |
|
|
1007 | 1239 | self.__nsubplots = 1 |
|
|
1008 | 1240 | |
|
|
1009 | 1241 | self.WIDTH = 800 |
|
|
1010 | 1242 | self.HEIGHT = 180 |
|
|
1011 | 1243 | self.WIDTHPROF = 120 |
|
|
1012 | 1244 | self.HEIGHTPROF = 0 |
|
|
1013 | 1245 | self.counter_imagwr = 0 |
|
|
1014 | 1246 | |
|
|
1015 | 1247 | self.PLOT_CODE = PARMS_CODE |
|
|
1016 | 1248 | |
|
|
1017 | 1249 | self.FTP_WEI = None |
|
|
1018 | 1250 | self.EXP_CODE = None |
|
|
1019 | 1251 | self.SUB_EXP_CODE = None |
|
|
1020 | 1252 | self.PLOT_POS = None |
|
|
1021 | 1253 | self.tmin = None |
|
|
1022 | 1254 | self.tmax = None |
|
|
1023 | 1255 | |
|
|
1024 | 1256 | self.xmin = None |
|
|
1025 | 1257 | self.xmax = None |
|
|
1026 | 1258 | |
|
|
1027 | 1259 | self.figfile = None |
|
|
1028 | 1260 | |
|
|
1029 | 1261 | def getSubplots(self): |
|
|
1030 | 1262 | |
|
|
1031 | 1263 | ncol = 1 |
|
|
1032 | 1264 | nrow = self.nplots |
|
|
1033 | 1265 | |
|
|
1034 | 1266 | return nrow, ncol |
|
|
1035 | 1267 | |
|
|
1036 | 1268 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1037 | 1269 | |
|
|
1038 | 1270 | self.__showprofile = showprofile |
|
|
1039 | 1271 | self.nplots = nplots |
|
|
1040 | 1272 | |
|
|
1041 | 1273 | ncolspan = 1 |
|
|
1042 | 1274 | colspan = 1 |
|
|
1043 | 1275 | |
|
|
1044 | 1276 | self.createFigure(id = id, |
|
|
1045 | 1277 | wintitle = wintitle, |
|
|
1046 | 1278 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1047 | 1279 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1048 | 1280 | show=show) |
|
|
1049 | 1281 | |
|
|
1050 | 1282 | nrow, ncol = self.getSubplots() |
|
|
1051 | 1283 | |
|
|
1052 | 1284 | counter = 0 |
|
|
1053 | 1285 | for y in range(nrow): |
|
|
1054 | 1286 | for x in range(ncol): |
|
|
1055 | 1287 | |
|
|
1056 | 1288 | if counter >= self.nplots: |
|
|
1057 | 1289 | break |
|
|
1058 | 1290 | |
|
|
1059 | 1291 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1060 | 1292 | |
|
|
1061 | 1293 | if showprofile: |
|
|
1062 | 1294 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
1063 | 1295 | |
|
|
1064 | 1296 | counter += 1 |
|
|
1065 | 1297 | |
|
|
1066 | 1298 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
1067 | 1299 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,timerange=None, |
|
|
1068 | 1300 | parameterIndex = None, onlyPositive = False, |
|
|
1069 | 1301 | SNRthresh = -numpy.inf, SNR = True, SNRmin = None, SNRmax = None, onlySNR = False, |
|
|
1070 | 1302 | DOP = True, |
|
|
1071 | 1303 | zlabel = "", parameterName = "", parameterObject = "data_param", |
|
|
1072 | 1304 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1073 | 1305 | server=None, folder=None, username=None, password=None, |
|
|
1074 | 1306 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1075 | 1307 | #print inspect.getargspec(self.run).args |
|
|
1076 | 1308 | """ |
|
|
1077 | 1309 | |
|
|
1078 | 1310 | Input: |
|
|
1079 | 1311 | dataOut : |
|
|
1080 | 1312 | id : |
|
|
1081 | 1313 | wintitle : |
|
|
1082 | 1314 | channelList : |
|
|
1083 | 1315 | showProfile : |
|
|
1084 | 1316 | xmin : None, |
|
|
1085 | 1317 | xmax : None, |
|
|
1086 | 1318 | ymin : None, |
|
|
1087 | 1319 | ymax : None, |
|
|
1088 | 1320 | zmin : None, |
|
|
1089 | 1321 | zmax : None |
|
|
1090 | 1322 | """ |
|
|
1091 | 1323 | |
|
|
1092 | 1324 | data_param = getattr(dataOut, parameterObject) |
|
|
1093 | 1325 | |
|
|
1094 | 1326 | if channelList == None: |
|
|
1095 | 1327 | channelIndexList = numpy.arange(data_param.shape[0]) |
|
|
1096 | 1328 | else: |
|
|
1097 | 1329 | channelIndexList = numpy.array(channelList) |
|
|
1098 | 1330 | |
|
|
1099 | 1331 | nchan = len(channelIndexList) #Number of channels being plotted |
|
|
1100 | 1332 | |
|
|
1101 | 1333 | if nchan < 1: |
|
|
1102 | 1334 | return |
|
|
1103 | 1335 | |
|
|
1104 | 1336 | nGraphsByChannel = 0 |
|
|
1105 | 1337 | |
|
|
1106 | 1338 | if SNR: |
|
|
1107 | 1339 | nGraphsByChannel += 1 |
|
|
1108 | 1340 | if DOP: |
|
|
1109 | 1341 | nGraphsByChannel += 1 |
|
|
1110 | 1342 | |
|
|
1111 | 1343 | if nGraphsByChannel < 1: |
|
|
1112 | 1344 | return |
|
|
1113 | 1345 | |
|
|
1114 | 1346 | nplots = nGraphsByChannel*nchan |
|
|
1115 | 1347 | |
|
|
1116 | 1348 | if timerange is not None: |
|
|
1117 | 1349 | self.timerange = timerange |
|
|
1118 | 1350 | |
|
|
1119 | 1351 | #tmin = None |
|
|
1120 | 1352 | #tmax = None |
|
|
1121 | 1353 | if parameterIndex == None: |
|
|
1122 | 1354 | parameterIndex = 1 |
|
|
1123 | 1355 | |
|
|
1124 | 1356 | x = dataOut.getTimeRange1(dataOut.paramInterval) |
|
|
1125 | 1357 | y = dataOut.heightList |
|
|
1126 | 1358 | z = data_param[channelIndexList,parameterIndex,:].copy() |
|
|
1127 | 1359 | |
|
|
1128 | 1360 | zRange = dataOut.abscissaList |
|
|
1129 | 1361 | # nChannels = z.shape[0] #Number of wind dimensions estimated |
|
|
1130 | 1362 | # thisDatetime = dataOut.datatime |
|
|
1131 | 1363 | |
|
|
1132 | 1364 | if dataOut.data_SNR is not None: |
|
|
1133 | 1365 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
1134 | 1366 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
1135 | 1367 | # SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
1136 | 1368 | ind = numpy.where(SNRdB < 10**(SNRthresh/10)) |
|
|
1137 | 1369 | z[ind] = numpy.nan |
|
|
1138 | 1370 | |
|
|
1139 | 1371 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1140 | 1372 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1141 | 1373 | xlabel = "" |
|
|
1142 | 1374 | ylabel = "Range (Km)" |
|
|
1143 | 1375 | |
|
|
1144 | 1376 | if (SNR and not onlySNR): nplots = 2*nplots |
|
|
1145 | 1377 | |
|
|
1146 | 1378 | if onlyPositive: |
|
|
1147 | 1379 | colormap = "jet" |
|
|
1148 | 1380 | zmin = 0 |
|
|
1149 | 1381 | else: colormap = "RdBu_r" |
|
|
1150 | 1382 | |
|
|
1151 | 1383 | if not self.isConfig: |
|
|
1152 | 1384 | |
|
|
1153 | 1385 | self.setup(id=id, |
|
|
1154 | 1386 | nplots=nplots, |
|
|
1155 | 1387 | wintitle=wintitle, |
|
|
1156 | 1388 | showprofile=showprofile, |
|
|
1157 | 1389 | show=show) |
|
|
1158 | 1390 | |
|
|
1159 | 1391 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1160 | 1392 | |
|
|
1161 | 1393 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1162 | 1394 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1163 | 1395 | if zmin == None: zmin = numpy.nanmin(zRange) |
|
|
1164 | 1396 | if zmax == None: zmax = numpy.nanmax(zRange) |
|
|
1165 | 1397 | |
|
|
1166 | 1398 | if SNR: |
|
|
1167 | 1399 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
1168 | 1400 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
1169 | 1401 | |
|
|
1170 | 1402 | self.FTP_WEI = ftp_wei |
|
|
1171 | 1403 | self.EXP_CODE = exp_code |
|
|
1172 | 1404 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1173 | 1405 | self.PLOT_POS = plot_pos |
|
|
1174 | 1406 | |
|
|
1175 | 1407 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1176 | 1408 | self.isConfig = True |
|
|
1177 | 1409 | self.figfile = figfile |
|
|
1178 | 1410 | |
|
|
1179 | 1411 | self.setWinTitle(title) |
|
|
1180 | 1412 | |
|
|
1181 | 1413 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1182 | 1414 | x[1] = self.xmax |
|
|
1183 | 1415 | |
|
|
1184 | 1416 | for i in range(nchan): |
|
|
1185 | 1417 | |
|
|
1186 | 1418 | if (SNR and not onlySNR): j = 2*i |
|
|
1187 | 1419 | else: j = i |
|
|
1188 | 1420 | |
|
|
1189 | 1421 | j = nGraphsByChannel*i |
|
|
1190 | 1422 | |
|
|
1191 | 1423 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1192 | 1424 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
1193 | 1425 | |
|
|
1194 | 1426 | if not onlySNR: |
|
|
1195 | 1427 | axes = self.axesList[j*self.__nsubplots] |
|
|
1196 | 1428 | z1 = z[i,:].reshape((1,-1)) |
|
|
1197 | 1429 | axes.pcolorbuffer(x, y, z1, |
|
|
1198 | 1430 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
1199 | 1431 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
1200 | 1432 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1201 | 1433 | |
|
|
1202 | 1434 | if DOP: |
|
|
1203 | 1435 | title = "%s Channel %d: %s" %(parameterName, channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1204 | 1436 | |
|
|
1205 | 1437 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1206 | 1438 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
1207 | 1439 | axes = self.axesList[j] |
|
|
1208 | 1440 | z1 = z[i,:].reshape((1,-1)) |
|
|
1209 | 1441 | axes.pcolorbuffer(x, y, z1, |
|
|
1210 | 1442 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
1211 | 1443 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
1212 | 1444 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1213 | 1445 | |
|
|
1214 | 1446 | if SNR: |
|
|
1215 | 1447 | title = "Channel %d Signal Noise Ratio (SNR): %s" %(channelIndexList[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1216 | 1448 | axes = self.axesList[(j)*self.__nsubplots] |
|
|
1217 | 1449 | if not onlySNR: |
|
|
1218 | 1450 | axes = self.axesList[(j + 1)*self.__nsubplots] |
|
|
1219 | 1451 | |
|
|
1220 | 1452 | axes = self.axesList[(j + nGraphsByChannel-1)] |
|
|
1221 | 1453 | |
|
|
1222 | 1454 | z1 = SNRdB[i,:].reshape((1,-1)) |
|
|
1223 | 1455 | axes.pcolorbuffer(x, y, z1, |
|
|
1224 | 1456 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1225 | 1457 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap="jet", |
|
|
1226 | 1458 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
1227 | 1459 | |
|
|
1228 | 1460 | |
|
|
1229 | 1461 | |
|
|
1230 | 1462 | self.draw() |
|
|
1231 | 1463 | |
|
|
1232 | 1464 | if x[1] >= self.axesList[0].xmax: |
|
|
1233 | 1465 | self.counter_imagwr = wr_period |
|
|
1234 | 1466 | self.isConfig = False |
|
|
1235 | 1467 | self.figfile = None |
|
|
1236 | 1468 | |
|
|
1237 | 1469 | self.save(figpath=figpath, |
|
|
1238 | 1470 | figfile=figfile, |
|
|
1239 | 1471 | save=save, |
|
|
1240 | 1472 | ftp=ftp, |
|
|
1241 | 1473 | wr_period=wr_period, |
|
|
1242 | 1474 | thisDatetime=thisDatetime, |
|
|
1243 | 1475 | update_figfile=False) |
|
|
1244 | 1476 | |
|
|
1245 | 1477 | class SpectralFittingPlot(Figure): |
|
|
1246 | 1478 | |
|
|
1247 | 1479 | __isConfig = None |
|
|
1248 | 1480 | __nsubplots = None |
|
|
1249 | 1481 | |
|
|
1250 | 1482 | WIDTHPROF = None |
|
|
1251 | 1483 | HEIGHTPROF = None |
|
|
1252 | 1484 | PREFIX = 'prm' |
|
|
1253 | 1485 | |
|
|
1254 | 1486 | |
|
|
1255 | 1487 | N = None |
|
|
1256 | 1488 | ippSeconds = None |
|
|
1257 | 1489 | |
|
|
1258 | 1490 | def __init__(self, **kwargs): |
|
|
1259 | 1491 | Figure.__init__(self, **kwargs) |
|
|
1260 | 1492 | self.isConfig = False |
|
|
1261 | 1493 | self.__nsubplots = 1 |
|
|
1262 | 1494 | |
|
|
1263 | 1495 | self.PLOT_CODE = SPECFIT_CODE |
|
|
1264 | 1496 | |
|
|
1265 | 1497 | self.WIDTH = 450 |
|
|
1266 | 1498 | self.HEIGHT = 250 |
|
|
1267 | 1499 | self.WIDTHPROF = 0 |
|
|
1268 | 1500 | self.HEIGHTPROF = 0 |
|
|
1269 | 1501 | |
|
|
1270 | 1502 | def getSubplots(self): |
|
|
1271 | 1503 | |
|
|
1272 | 1504 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
1273 | 1505 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
1274 | 1506 | |
|
|
1275 | 1507 | return nrow, ncol |
|
|
1276 | 1508 | |
|
|
1277 | 1509 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
1278 | 1510 | |
|
|
1279 | 1511 | showprofile = False |
|
|
1280 | 1512 | self.__showprofile = showprofile |
|
|
1281 | 1513 | self.nplots = nplots |
|
|
1282 | 1514 | |
|
|
1283 | 1515 | ncolspan = 5 |
|
|
1284 | 1516 | colspan = 4 |
|
|
1285 | 1517 | if showprofile: |
|
|
1286 | 1518 | ncolspan = 5 |
|
|
1287 | 1519 | colspan = 4 |
|
|
1288 | 1520 | self.__nsubplots = 2 |
|
|
1289 | 1521 | |
|
|
1290 | 1522 | self.createFigure(id = id, |
|
|
1291 | 1523 | wintitle = wintitle, |
|
|
1292 | 1524 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1293 | 1525 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1294 | 1526 | show=show) |
|
|
1295 | 1527 | |
|
|
1296 | 1528 | nrow, ncol = self.getSubplots() |
|
|
1297 | 1529 | |
|
|
1298 | 1530 | counter = 0 |
|
|
1299 | 1531 | for y in range(nrow): |
|
|
1300 | 1532 | for x in range(ncol): |
|
|
1301 | 1533 | |
|
|
1302 | 1534 | if counter >= self.nplots: |
|
|
1303 | 1535 | break |
|
|
1304 | 1536 | |
|
|
1305 | 1537 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1306 | 1538 | |
|
|
1307 | 1539 | if showprofile: |
|
|
1308 | 1540 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
1309 | 1541 | |
|
|
1310 | 1542 | counter += 1 |
|
|
1311 | 1543 | |
|
|
1312 | 1544 | def run(self, dataOut, id, cutHeight=None, fit=False, wintitle="", channelList=None, showprofile=True, |
|
|
1313 | 1545 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1314 | 1546 | save=False, figpath='./', figfile=None, show=True): |
|
|
1315 | 1547 | |
|
|
1316 | 1548 | """ |
|
|
1317 | 1549 | |
|
|
1318 | 1550 | Input: |
|
|
1319 | 1551 | dataOut : |
|
|
1320 | 1552 | id : |
|
|
1321 | 1553 | wintitle : |
|
|
1322 | 1554 | channelList : |
|
|
1323 | 1555 | showProfile : |
|
|
1324 | 1556 | xmin : None, |
|
|
1325 | 1557 | xmax : None, |
|
|
1326 | 1558 | zmin : None, |
|
|
1327 | 1559 | zmax : None |
|
|
1328 | 1560 | """ |
|
|
1329 | 1561 | |
|
|
1330 | 1562 | if cutHeight==None: |
|
|
1331 | 1563 | h=270 |
|
|
1332 | 1564 | heightindex = numpy.abs(cutHeight - dataOut.heightList).argmin() |
|
|
1333 | 1565 | cutHeight = dataOut.heightList[heightindex] |
|
|
1334 | 1566 | |
|
|
1335 | 1567 | factor = dataOut.normFactor |
|
|
1336 | 1568 | x = dataOut.abscissaList[:-1] |
|
|
1337 | 1569 | #y = dataOut.getHeiRange() |
|
|
1338 | 1570 | |
|
|
1339 | 1571 | z = dataOut.data_pre[:,:,heightindex]/factor |
|
|
1340 | 1572 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1341 | 1573 | avg = numpy.average(z, axis=1) |
|
|
1342 | 1574 | listChannels = z.shape[0] |
|
|
1343 | 1575 | |
|
|
1344 | 1576 | #Reconstruct Function |
|
|
1345 | 1577 | if fit==True: |
|
|
1346 | 1578 | groupArray = dataOut.groupList |
|
|
1347 | 1579 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1348 | 1580 | listChannels.sort() |
|
|
1349 | 1581 | spcFitLine = numpy.zeros(z.shape) |
|
|
1350 | 1582 | constants = dataOut.constants |
|
|
1351 | 1583 | |
|
|
1352 | 1584 | nGroups = groupArray.shape[0] |
|
|
1353 | 1585 | nChannels = groupArray.shape[1] |
|
|
1354 | 1586 | nProfiles = z.shape[1] |
|
|
1355 | 1587 | |
|
|
1356 | 1588 | for f in range(nGroups): |
|
|
1357 | 1589 | groupChann = groupArray[f,:] |
|
|
1358 | 1590 | p = dataOut.data_param[f,:,heightindex] |
|
|
1359 | 1591 | # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167]) |
|
|
1360 | 1592 | fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles |
|
|
1361 | 1593 | fitLineAux = fitLineAux.reshape((nChannels,nProfiles)) |
|
|
1362 | 1594 | spcFitLine[groupChann,:] = fitLineAux |
|
|
1363 | 1595 | # spcFitLine = spcFitLine/factor |
|
|
1364 | 1596 | |
|
|
1365 | 1597 | z = z[listChannels,:] |
|
|
1366 | 1598 | spcFitLine = spcFitLine[listChannels,:] |
|
|
1367 | 1599 | spcFitLinedB = 10*numpy.log10(spcFitLine) |
|
|
1368 | 1600 | |
|
|
1369 | 1601 | zdB = 10*numpy.log10(z) |
|
|
1370 | 1602 | #thisDatetime = dataOut.datatime |
|
|
1371 | 1603 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1372 | 1604 | title = wintitle + " Doppler Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1373 | 1605 | xlabel = "Velocity (m/s)" |
|
|
1374 | 1606 | ylabel = "Spectrum" |
|
|
1375 | 1607 | |
|
|
1376 | 1608 | if not self.isConfig: |
|
|
1377 | 1609 | |
|
|
1378 | 1610 | nplots = listChannels.size |
|
|
1379 | 1611 | |
|
|
1380 | 1612 | self.setup(id=id, |
|
|
1381 | 1613 | nplots=nplots, |
|
|
1382 | 1614 | wintitle=wintitle, |
|
|
1383 | 1615 | showprofile=showprofile, |
|
|
1384 | 1616 | show=show) |
|
|
1385 | 1617 | |
|
|
1386 | 1618 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
1387 | 1619 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
1388 | 1620 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1389 | 1621 | if ymax == None: ymax = numpy.nanmax(zdB)+2 |
|
|
1390 | 1622 | |
|
|
1391 | 1623 | self.isConfig = True |
|
|
1392 | 1624 | |
|
|
1393 | 1625 | self.setWinTitle(title) |
|
|
1394 | 1626 | for i in range(self.nplots): |
|
|
1395 | 1627 | # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i]) |
|
|
1396 | 1628 | title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i]) |
|
|
1397 | 1629 | axes = self.axesList[i*self.__nsubplots] |
|
|
1398 | 1630 | if fit == False: |
|
|
1399 | 1631 | axes.pline(x, zdB[i,:], |
|
|
1400 | 1632 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1401 | 1633 | xlabel=xlabel, ylabel=ylabel, title=title |
|
|
1402 | 1634 | ) |
|
|
1403 | 1635 | if fit == True: |
|
|
1404 | 1636 | fitline=spcFitLinedB[i,:] |
|
|
1405 | 1637 | y=numpy.vstack([zdB[i,:],fitline] ) |
|
|
1406 | 1638 | legendlabels=['Data','Fitting'] |
|
|
1407 | 1639 | axes.pmultilineyaxis(x, y, |
|
|
1408 | 1640 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1409 | 1641 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
1410 | 1642 | legendlabels=legendlabels, marker=None, |
|
|
1411 | 1643 | linestyle='solid', grid='both') |
|
|
1412 | 1644 | |
|
|
1413 | 1645 | self.draw() |
|
|
1414 | 1646 | |
|
|
1415 | 1647 | self.save(figpath=figpath, |
|
|
1416 | 1648 | figfile=figfile, |
|
|
1417 | 1649 | save=save, |
|
|
1418 | 1650 | ftp=ftp, |
|
|
1419 | 1651 | wr_period=wr_period, |
|
|
1420 | 1652 | thisDatetime=thisDatetime) |
|
|
1421 | 1653 | |
|
|
1422 | 1654 | |
|
|
1423 | 1655 | class EWDriftsPlot(Figure): |
|
|
1424 | 1656 | |
|
|
1425 | 1657 | __isConfig = None |
|
|
1426 | 1658 | __nsubplots = None |
|
|
1427 | 1659 | |
|
|
1428 | 1660 | WIDTHPROF = None |
|
|
1429 | 1661 | HEIGHTPROF = None |
|
|
1430 | 1662 | PREFIX = 'drift' |
|
|
1431 | 1663 | |
|
|
1432 | 1664 | def __init__(self, **kwargs): |
|
|
1433 | 1665 | Figure.__init__(self, **kwargs) |
|
|
1434 | 1666 | self.timerange = 2*60*60 |
|
|
1435 | 1667 | self.isConfig = False |
|
|
1436 | 1668 | self.__nsubplots = 1 |
|
|
1437 | 1669 | |
|
|
1438 | 1670 | self.WIDTH = 800 |
|
|
1439 | 1671 | self.HEIGHT = 150 |
|
|
1440 | 1672 | self.WIDTHPROF = 120 |
|
|
1441 | 1673 | self.HEIGHTPROF = 0 |
|
|
1442 | 1674 | self.counter_imagwr = 0 |
|
|
1443 | 1675 | |
|
|
1444 | 1676 | self.PLOT_CODE = EWDRIFT_CODE |
|
|
1445 | 1677 | |
|
|
1446 | 1678 | self.FTP_WEI = None |
|
|
1447 | 1679 | self.EXP_CODE = None |
|
|
1448 | 1680 | self.SUB_EXP_CODE = None |
|
|
1449 | 1681 | self.PLOT_POS = None |
|
|
1450 | 1682 | self.tmin = None |
|
|
1451 | 1683 | self.tmax = None |
|
|
1452 | 1684 | |
|
|
1453 | 1685 | self.xmin = None |
|
|
1454 | 1686 | self.xmax = None |
|
|
1455 | 1687 | |
|
|
1456 | 1688 | self.figfile = None |
|
|
1457 | 1689 | |
|
|
1458 | 1690 | def getSubplots(self): |
|
|
1459 | 1691 | |
|
|
1460 | 1692 | ncol = 1 |
|
|
1461 | 1693 | nrow = self.nplots |
|
|
1462 | 1694 | |
|
|
1463 | 1695 | return nrow, ncol |
|
|
1464 | 1696 | |
|
|
1465 | 1697 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1466 | 1698 | |
|
|
1467 | 1699 | self.__showprofile = showprofile |
|
|
1468 | 1700 | self.nplots = nplots |
|
|
1469 | 1701 | |
|
|
1470 | 1702 | ncolspan = 1 |
|
|
1471 | 1703 | colspan = 1 |
|
|
1472 | 1704 | |
|
|
1473 | 1705 | self.createFigure(id = id, |
|
|
1474 | 1706 | wintitle = wintitle, |
|
|
1475 | 1707 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1476 | 1708 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1477 | 1709 | show=show) |
|
|
1478 | 1710 | |
|
|
1479 | 1711 | nrow, ncol = self.getSubplots() |
|
|
1480 | 1712 | |
|
|
1481 | 1713 | counter = 0 |
|
|
1482 | 1714 | for y in range(nrow): |
|
|
1483 | 1715 | if counter >= self.nplots: |
|
|
1484 | 1716 | break |
|
|
1485 | 1717 | |
|
|
1486 | 1718 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
1487 | 1719 | counter += 1 |
|
|
1488 | 1720 | |
|
|
1489 | 1721 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1490 | 1722 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
1491 | 1723 | zmaxVertical = None, zminVertical = None, zmaxZonal = None, zminZonal = None, |
|
|
1492 | 1724 | timerange=None, SNRthresh = -numpy.inf, SNRmin = None, SNRmax = None, SNR_1 = False, |
|
|
1493 | 1725 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1494 | 1726 | server=None, folder=None, username=None, password=None, |
|
|
1495 | 1727 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1496 | 1728 | """ |
|
|
1497 | 1729 | |
|
|
1498 | 1730 | Input: |
|
|
1499 | 1731 | dataOut : |
|
|
1500 | 1732 | id : |
|
|
1501 | 1733 | wintitle : |
|
|
1502 | 1734 | channelList : |
|
|
1503 | 1735 | showProfile : |
|
|
1504 | 1736 | xmin : None, |
|
|
1505 | 1737 | xmax : None, |
|
|
1506 | 1738 | ymin : None, |
|
|
1507 | 1739 | ymax : None, |
|
|
1508 | 1740 | zmin : None, |
|
|
1509 | 1741 | zmax : None |
|
|
1510 | 1742 | """ |
|
|
1511 | 1743 | |
|
|
1512 | 1744 | if timerange is not None: |
|
|
1513 | 1745 | self.timerange = timerange |
|
|
1514 | 1746 | |
|
|
1515 | 1747 | tmin = None |
|
|
1516 | 1748 | tmax = None |
|
|
1517 | 1749 | |
|
|
1518 | 1750 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1519 | 1751 | # y = dataOut.heightList |
|
|
1520 | 1752 | y = dataOut.heightList |
|
|
1521 | 1753 | |
|
|
1522 | 1754 | z = dataOut.data_output |
|
|
1523 | 1755 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
1524 | 1756 | nplotsw = nplots |
|
|
1525 | 1757 | |
|
|
1526 | 1758 | #If there is a SNR function defined |
|
|
1527 | 1759 | if dataOut.data_SNR is not None: |
|
|
1528 | 1760 | nplots += 1 |
|
|
1529 | 1761 | SNR = dataOut.data_SNR |
|
|
1530 | 1762 | |
|
|
1531 | 1763 | if SNR_1: |
|
|
1532 | 1764 | SNR += 1 |
|
|
1533 | 1765 | |
|
|
1534 | 1766 | SNRavg = numpy.average(SNR, axis=0) |
|
|
1535 | 1767 | |
|
|
1536 | 1768 | SNRdB = 10*numpy.log10(SNR) |
|
|
1537 | 1769 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
1538 | 1770 | |
|
|
1539 | 1771 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
1540 | 1772 | |
|
|
1541 | 1773 | for i in range(nplotsw): |
|
|
1542 | 1774 | z[i,ind] = numpy.nan |
|
|
1543 | 1775 | |
|
|
1544 | 1776 | |
|
|
1545 | 1777 | showprofile = False |
|
|
1546 | 1778 | # thisDatetime = dataOut.datatime |
|
|
1547 | 1779 | thisDatetime = datetime.datetime.utcfromtimestamp(x[1]) |
|
|
1548 | 1780 | title = wintitle + " EW Drifts" |
|
|
1549 | 1781 | xlabel = "" |
|
|
1550 | 1782 | ylabel = "Height (Km)" |
|
|
1551 | 1783 | |
|
|
1552 | 1784 | if not self.isConfig: |
|
|
1553 | 1785 | |
|
|
1554 | 1786 | self.setup(id=id, |
|
|
1555 | 1787 | nplots=nplots, |
|
|
1556 | 1788 | wintitle=wintitle, |
|
|
1557 | 1789 | showprofile=showprofile, |
|
|
1558 | 1790 | show=show) |
|
|
1559 | 1791 | |
|
|
1560 | 1792 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1561 | 1793 | |
|
|
1562 | 1794 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1563 | 1795 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1564 | 1796 | |
|
|
1565 | 1797 | if zmaxZonal == None: zmaxZonal = numpy.nanmax(abs(z[0,:])) |
|
|
1566 | 1798 | if zminZonal == None: zminZonal = -zmaxZonal |
|
|
1567 | 1799 | if zmaxVertical == None: zmaxVertical = numpy.nanmax(abs(z[1,:])) |
|
|
1568 | 1800 | if zminVertical == None: zminVertical = -zmaxVertical |
|
|
1569 | 1801 | |
|
|
1570 | 1802 | if dataOut.data_SNR is not None: |
|
|
1571 | 1803 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
1572 | 1804 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
1573 | 1805 | |
|
|
1574 | 1806 | self.FTP_WEI = ftp_wei |
|
|
1575 | 1807 | self.EXP_CODE = exp_code |
|
|
1576 | 1808 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1577 | 1809 | self.PLOT_POS = plot_pos |
|
|
1578 | 1810 | |
|
|
1579 | 1811 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1580 | 1812 | self.isConfig = True |
|
|
1581 | 1813 | |
|
|
1582 | 1814 | |
|
|
1583 | 1815 | self.setWinTitle(title) |
|
|
1584 | 1816 | |
|
|
1585 | 1817 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1586 | 1818 | x[1] = self.xmax |
|
|
1587 | 1819 | |
|
|
1588 | 1820 | strWind = ['Zonal','Vertical'] |
|
|
1589 | 1821 | strCb = 'Velocity (m/s)' |
|
|
1590 | 1822 | zmaxVector = [zmaxZonal, zmaxVertical] |
|
|
1591 | 1823 | zminVector = [zminZonal, zminVertical] |
|
|
1592 | 1824 | |
|
|
1593 | 1825 | for i in range(nplotsw): |
|
|
1594 | 1826 | |
|
|
1595 | 1827 | title = "%s Drifts: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1596 | 1828 | axes = self.axesList[i*self.__nsubplots] |
|
|
1597 | 1829 | |
|
|
1598 | 1830 | z1 = z[i,:].reshape((1,-1)) |
|
|
1599 | 1831 | |
|
|
1600 | 1832 | axes.pcolorbuffer(x, y, z1, |
|
|
1601 | 1833 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
1602 | 1834 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1603 | 1835 | ticksize=9, cblabel=strCb, cbsize="1%", colormap="RdBu_r") |
|
|
1604 | 1836 | |
|
|
1605 | 1837 | if dataOut.data_SNR is not None: |
|
|
1606 | 1838 | i += 1 |
|
|
1607 | 1839 | if SNR_1: |
|
|
1608 | 1840 | title = "Signal Noise Ratio + 1 (SNR+1): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1609 | 1841 | else: |
|
|
1610 | 1842 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1611 | 1843 | axes = self.axesList[i*self.__nsubplots] |
|
|
1612 | 1844 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
1613 | 1845 | |
|
|
1614 | 1846 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
1615 | 1847 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1616 | 1848 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1617 | 1849 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
1618 | 1850 | |
|
|
1619 | 1851 | self.draw() |
|
|
1620 | 1852 | |
|
|
1621 | 1853 | if x[1] >= self.axesList[0].xmax: |
|
|
1622 | 1854 | self.counter_imagwr = wr_period |
|
|
1623 | 1855 | self.isConfig = False |
|
|
1624 | 1856 | self.figfile = None |
|
|
1625 | 1857 | |
|
|
1626 | 1858 | |
|
|
1627 | 1859 | |
|
|
1628 | 1860 | |
|
|
1629 | 1861 | class PhasePlot(Figure): |
|
|
1630 | 1862 | |
|
|
1631 | 1863 | __isConfig = None |
|
|
1632 | 1864 | __nsubplots = None |
|
|
1633 | 1865 | |
|
|
1634 | 1866 | PREFIX = 'mphase' |
|
|
1635 | 1867 | |
|
|
1636 | 1868 | def __init__(self, **kwargs): |
|
|
1637 | 1869 | Figure.__init__(self, **kwargs) |
|
|
1638 | 1870 | self.timerange = 24*60*60 |
|
|
1639 | 1871 | self.isConfig = False |
|
|
1640 | 1872 | self.__nsubplots = 1 |
|
|
1641 | 1873 | self.counter_imagwr = 0 |
|
|
1642 | 1874 | self.WIDTH = 600 |
|
|
1643 | 1875 | self.HEIGHT = 300 |
|
|
1644 | 1876 | self.WIDTHPROF = 120 |
|
|
1645 | 1877 | self.HEIGHTPROF = 0 |
|
|
1646 | 1878 | self.xdata = None |
|
|
1647 | 1879 | self.ydata = None |
|
|
1648 | 1880 | |
|
|
1649 | 1881 | self.PLOT_CODE = MPHASE_CODE |
|
|
1650 | 1882 | |
|
|
1651 | 1883 | self.FTP_WEI = None |
|
|
1652 | 1884 | self.EXP_CODE = None |
|
|
1653 | 1885 | self.SUB_EXP_CODE = None |
|
|
1654 | 1886 | self.PLOT_POS = None |
|
|
1655 | 1887 | |
|
|
1656 | 1888 | |
|
|
1657 | 1889 | self.filename_phase = None |
|
|
1658 | 1890 | |
|
|
1659 | 1891 | self.figfile = None |
|
|
1660 | 1892 | |
|
|
1661 | 1893 | def getSubplots(self): |
|
|
1662 | 1894 | |
|
|
1663 | 1895 | ncol = 1 |
|
|
1664 | 1896 | nrow = 1 |
|
|
1665 | 1897 | |
|
|
1666 | 1898 | return nrow, ncol |
|
|
1667 | 1899 | |
|
|
1668 | 1900 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1669 | 1901 | |
|
|
1670 | 1902 | self.__showprofile = showprofile |
|
|
1671 | 1903 | self.nplots = nplots |
|
|
1672 | 1904 | |
|
|
1673 | 1905 | ncolspan = 7 |
|
|
1674 | 1906 | colspan = 6 |
|
|
1675 | 1907 | self.__nsubplots = 2 |
|
|
1676 | 1908 | |
|
|
1677 | 1909 | self.createFigure(id = id, |
|
|
1678 | 1910 | wintitle = wintitle, |
|
|
1679 | 1911 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1680 | 1912 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1681 | 1913 | show=show) |
|
|
1682 | 1914 | |
|
|
1683 | 1915 | nrow, ncol = self.getSubplots() |
|
|
1684 | 1916 | |
|
|
1685 | 1917 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1686 | 1918 | |
|
|
1687 | 1919 | |
|
|
1688 | 1920 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1689 | 1921 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1690 | 1922 | timerange=None, |
|
|
1691 | 1923 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1692 | 1924 | server=None, folder=None, username=None, password=None, |
|
|
1693 | 1925 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1694 | 1926 | |
|
|
1695 | 1927 | |
|
|
1696 | 1928 | tmin = None |
|
|
1697 | 1929 | tmax = None |
|
|
1698 | 1930 | x = dataOut.getTimeRange1(dataOut.outputInterval) |
|
|
1699 | 1931 | y = dataOut.getHeiRange() |
|
|
1700 | 1932 | |
|
|
1701 | 1933 | |
|
|
1702 | 1934 | #thisDatetime = dataOut.datatime |
|
|
1703 | 1935 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1704 | 1936 | title = wintitle + " Phase of Beacon Signal" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1705 | 1937 | xlabel = "Local Time" |
|
|
1706 | 1938 | ylabel = "Phase" |
|
|
1707 | 1939 | |
|
|
1708 | 1940 | |
|
|
1709 | 1941 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1710 | 1942 | phase_beacon = dataOut.data_output |
|
|
1711 | 1943 | update_figfile = False |
|
|
1712 | 1944 | |
|
|
1713 | 1945 | if not self.isConfig: |
|
|
1714 | 1946 | |
|
|
1715 | 1947 | self.nplots = phase_beacon.size |
|
|
1716 | 1948 | |
|
|
1717 | 1949 | self.setup(id=id, |
|
|
1718 | 1950 | nplots=self.nplots, |
|
|
1719 | 1951 | wintitle=wintitle, |
|
|
1720 | 1952 | showprofile=showprofile, |
|
|
1721 | 1953 | show=show) |
|
|
1722 | 1954 | |
|
|
1723 | 1955 | if timerange is not None: |
|
|
1724 | 1956 | self.timerange = timerange |
|
|
1725 | 1957 | |
|
|
1726 | 1958 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1727 | 1959 | |
|
|
1728 | 1960 | if ymin == None: ymin = numpy.nanmin(phase_beacon) - 10.0 |
|
|
1729 | 1961 | if ymax == None: ymax = numpy.nanmax(phase_beacon) + 10.0 |
|
|
1730 | 1962 | |
|
|
1731 | 1963 | self.FTP_WEI = ftp_wei |
|
|
1732 | 1964 | self.EXP_CODE = exp_code |
|
|
1733 | 1965 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1734 | 1966 | self.PLOT_POS = plot_pos |
|
|
1735 | 1967 | |
|
|
1736 | 1968 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1737 | 1969 | self.isConfig = True |
|
|
1738 | 1970 | self.figfile = figfile |
|
|
1739 | 1971 | self.xdata = numpy.array([]) |
|
|
1740 | 1972 | self.ydata = numpy.array([]) |
|
|
1741 | 1973 | |
|
|
1742 | 1974 | #open file beacon phase |
|
|
1743 | 1975 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1744 | 1976 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1745 | 1977 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1746 | 1978 | update_figfile = True |
|
|
1747 | 1979 | |
|
|
1748 | 1980 | |
|
|
1749 | 1981 | #store data beacon phase |
|
|
1750 | 1982 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1751 | 1983 | |
|
|
1752 | 1984 | self.setWinTitle(title) |
|
|
1753 | 1985 | |
|
|
1754 | 1986 | |
|
|
1755 | 1987 | title = "Phase Offset %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1756 | 1988 | |
|
|
1757 | 1989 | legendlabels = ["phase %d"%(chan) for chan in numpy.arange(self.nplots)] |
|
|
1758 | 1990 | |
|
|
1759 | 1991 | axes = self.axesList[0] |
|
|
1760 | 1992 | |
|
|
1761 | 1993 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1762 | 1994 | |
|
|
1763 | 1995 | if len(self.ydata)==0: |
|
|
1764 | 1996 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1765 | 1997 | else: |
|
|
1766 | 1998 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1767 | 1999 | |
|
|
1768 | 2000 | |
|
|
1769 | 2001 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1770 | 2002 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1771 | 2003 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1772 | 2004 | XAxisAsTime=True, grid='both' |
|
|
1773 | 2005 | ) |
|
|
1774 | 2006 | |
|
|
1775 | 2007 | self.draw() |
|
|
1776 | 2008 | |
|
|
1777 | 2009 | self.save(figpath=figpath, |
|
|
1778 | 2010 | figfile=figfile, |
|
|
1779 | 2011 | save=save, |
|
|
1780 | 2012 | ftp=ftp, |
|
|
1781 | 2013 | wr_period=wr_period, |
|
|
1782 | 2014 | thisDatetime=thisDatetime, |
|
|
1783 | 2015 | update_figfile=update_figfile) |
|
|
1784 | 2016 | |
|
|
1785 | 2017 | if dataOut.ltctime + dataOut.outputInterval >= self.xmax: |
|
|
1786 | 2018 | self.counter_imagwr = wr_period |
|
|
1787 | 2019 | self.isConfig = False |
|
|
1788 | 2020 | update_figfile = True |
|
|
1789 | 2021 | |
|
|
1790 | 2022 | |
|
|
1791 | 2023 | |
|
|
1792 | 2024 | class NSMeteorDetection1Plot(Figure): |
|
|
1793 | 2025 | |
|
|
1794 | 2026 | isConfig = None |
|
|
1795 | 2027 | __nsubplots = None |
|
|
1796 | 2028 | |
|
|
1797 | 2029 | WIDTHPROF = None |
|
|
1798 | 2030 | HEIGHTPROF = None |
|
|
1799 | 2031 | PREFIX = 'nsm' |
|
|
1800 | 2032 | |
|
|
1801 | 2033 | zminList = None |
|
|
1802 | 2034 | zmaxList = None |
|
|
1803 | 2035 | cmapList = None |
|
|
1804 | 2036 | titleList = None |
|
|
1805 | 2037 | nPairs = None |
|
|
1806 | 2038 | nChannels = None |
|
|
1807 | 2039 | nParam = None |
|
|
1808 | 2040 | |
|
|
1809 | 2041 | def __init__(self, **kwargs): |
|
|
1810 | 2042 | Figure.__init__(self, **kwargs) |
|
|
1811 | 2043 | self.isConfig = False |
|
|
1812 | 2044 | self.__nsubplots = 1 |
|
|
1813 | 2045 | |
|
|
1814 | 2046 | self.WIDTH = 750 |
|
|
1815 | 2047 | self.HEIGHT = 250 |
|
|
1816 | 2048 | self.WIDTHPROF = 120 |
|
|
1817 | 2049 | self.HEIGHTPROF = 0 |
|
|
1818 | 2050 | self.counter_imagwr = 0 |
|
|
1819 | 2051 | |
|
|
1820 | 2052 | self.PLOT_CODE = SPEC_CODE |
|
|
1821 | 2053 | |
|
|
1822 | 2054 | self.FTP_WEI = None |
|
|
1823 | 2055 | self.EXP_CODE = None |
|
|
1824 | 2056 | self.SUB_EXP_CODE = None |
|
|
1825 | 2057 | self.PLOT_POS = None |
|
|
1826 | 2058 | |
|
|
1827 | 2059 | self.__xfilter_ena = False |
|
|
1828 | 2060 | self.__yfilter_ena = False |
|
|
1829 | 2061 | |
|
|
1830 | 2062 | def getSubplots(self): |
|
|
1831 | 2063 | |
|
|
1832 | 2064 | ncol = 3 |
|
|
1833 | 2065 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
1834 | 2066 | |
|
|
1835 | 2067 | return nrow, ncol |
|
|
1836 | 2068 | |
|
|
1837 | 2069 | def setup(self, id, nplots, wintitle, show=True): |
|
|
1838 | 2070 | |
|
|
1839 | 2071 | self.nplots = nplots |
|
|
1840 | 2072 | |
|
|
1841 | 2073 | ncolspan = 1 |
|
|
1842 | 2074 | colspan = 1 |
|
|
1843 | 2075 | |
|
|
1844 | 2076 | self.createFigure(id = id, |
|
|
1845 | 2077 | wintitle = wintitle, |
|
|
1846 | 2078 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1847 | 2079 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1848 | 2080 | show=show) |
|
|
1849 | 2081 | |
|
|
1850 | 2082 | nrow, ncol = self.getSubplots() |
|
|
1851 | 2083 | |
|
|
1852 | 2084 | counter = 0 |
|
|
1853 | 2085 | for y in range(nrow): |
|
|
1854 | 2086 | for x in range(ncol): |
|
|
1855 | 2087 | |
|
|
1856 | 2088 | if counter >= self.nplots: |
|
|
1857 | 2089 | break |
|
|
1858 | 2090 | |
|
|
1859 | 2091 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1860 | 2092 | |
|
|
1861 | 2093 | counter += 1 |
|
|
1862 | 2094 | |
|
|
1863 | 2095 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
1864 | 2096 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
1865 | 2097 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
1866 | 2098 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1867 | 2099 | server=None, folder=None, username=None, password=None, |
|
|
1868 | 2100 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
1869 | 2101 | xaxis="frequency"): |
|
|
1870 | 2102 | |
|
|
1871 | 2103 | """ |
|
|
1872 | 2104 | |
|
|
1873 | 2105 | Input: |
|
|
1874 | 2106 | dataOut : |
|
|
1875 | 2107 | id : |
|
|
1876 | 2108 | wintitle : |
|
|
1877 | 2109 | channelList : |
|
|
1878 | 2110 | showProfile : |
|
|
1879 | 2111 | xmin : None, |
|
|
1880 | 2112 | xmax : None, |
|
|
1881 | 2113 | ymin : None, |
|
|
1882 | 2114 | ymax : None, |
|
|
1883 | 2115 | zmin : None, |
|
|
1884 | 2116 | zmax : None |
|
|
1885 | 2117 | """ |
|
|
1886 | 2118 | #SEPARAR EN DOS PLOTS |
|
|
1887 | 2119 | nParam = dataOut.data_param.shape[1] - 3 |
|
|
1888 | 2120 | |
|
|
1889 | 2121 | utctime = dataOut.data_param[0,0] |
|
|
1890 | 2122 | tmet = dataOut.data_param[:,1].astype(int) |
|
|
1891 | 2123 | hmet = dataOut.data_param[:,2].astype(int) |
|
|
1892 | 2124 | |
|
|
1893 | 2125 | x = dataOut.abscissaList |
|
|
1894 | 2126 | y = dataOut.heightList |
|
|
1895 | 2127 | |
|
|
1896 | 2128 | z = numpy.zeros((nParam, y.size, x.size - 1)) |
|
|
1897 | 2129 | z[:,:] = numpy.nan |
|
|
1898 | 2130 | z[:,hmet,tmet] = dataOut.data_param[:,3:].T |
|
|
1899 | 2131 | z[0,:,:] = 10*numpy.log10(z[0,:,:]) |
|
|
1900 | 2132 | |
|
|
1901 | 2133 | xlabel = "Time (s)" |
|
|
1902 | 2134 | ylabel = "Range (km)" |
|
|
1903 | 2135 | |
|
|
1904 | 2136 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
1905 | 2137 | |
|
|
1906 | 2138 | if not self.isConfig: |
|
|
1907 | 2139 | |
|
|
1908 | 2140 | nplots = nParam |
|
|
1909 | 2141 | |
|
|
1910 | 2142 | self.setup(id=id, |
|
|
1911 | 2143 | nplots=nplots, |
|
|
1912 | 2144 | wintitle=wintitle, |
|
|
1913 | 2145 | show=show) |
|
|
1914 | 2146 | |
|
|
1915 | 2147 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
1916 | 2148 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
1917 | 2149 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
1918 | 2150 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
1919 | 2151 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
1920 | 2152 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
1921 | 2153 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
1922 | 2154 | if vmin is None: vmin = -vmax |
|
|
1923 | 2155 | if wmin is None: wmin = 0 |
|
|
1924 | 2156 | if wmax is None: wmax = 50 |
|
|
1925 | 2157 | |
|
|
1926 | 2158 | pairsList = dataOut.groupList |
|
|
1927 | 2159 | self.nPairs = len(dataOut.groupList) |
|
|
1928 | 2160 | |
|
|
1929 | 2161 | zminList = [SNRmin, vmin, cmin] + [pmin]*self.nPairs |
|
|
1930 | 2162 | zmaxList = [SNRmax, vmax, cmax] + [pmax]*self.nPairs |
|
|
1931 | 2163 | titleList = ["SNR","Radial Velocity","Coherence"] |
|
|
1932 | 2164 | cmapList = ["jet","RdBu_r","jet"] |
|
|
1933 | 2165 | |
|
|
1934 | 2166 | for i in range(self.nPairs): |
|
|
1935 | 2167 | strAux1 = "Phase Difference "+ str(pairsList[i][0]) + str(pairsList[i][1]) |
|
|
1936 | 2168 | titleList = titleList + [strAux1] |
|
|
1937 | 2169 | cmapList = cmapList + ["RdBu_r"] |
|
|
1938 | 2170 | |
|
|
1939 | 2171 | self.zminList = zminList |
|
|
1940 | 2172 | self.zmaxList = zmaxList |
|
|
1941 | 2173 | self.cmapList = cmapList |
|
|
1942 | 2174 | self.titleList = titleList |
|
|
1943 | 2175 | |
|
|
1944 | 2176 | self.FTP_WEI = ftp_wei |
|
|
1945 | 2177 | self.EXP_CODE = exp_code |
|
|
1946 | 2178 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1947 | 2179 | self.PLOT_POS = plot_pos |
|
|
1948 | 2180 | |
|
|
1949 | 2181 | self.isConfig = True |
|
|
1950 | 2182 | |
|
|
1951 | 2183 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
1952 | 2184 | |
|
|
1953 | 2185 | for i in range(nParam): |
|
|
1954 | 2186 | title = self.titleList[i] + ": " +str_datetime |
|
|
1955 | 2187 | axes = self.axesList[i] |
|
|
1956 | 2188 | axes.pcolor(x, y, z[i,:].T, |
|
|
1957 | 2189 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
1958 | 2190 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
1959 | 2191 | self.draw() |
|
|
1960 | 2192 | |
|
|
1961 | 2193 | if figfile == None: |
|
|
1962 | 2194 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1963 | 2195 | name = str_datetime |
|
|
1964 | 2196 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
1965 | 2197 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
1966 | 2198 | figfile = self.getFilename(name) |
|
|
1967 | 2199 | |
|
|
1968 | 2200 | self.save(figpath=figpath, |
|
|
1969 | 2201 | figfile=figfile, |
|
|
1970 | 2202 | save=save, |
|
|
1971 | 2203 | ftp=ftp, |
|
|
1972 | 2204 | wr_period=wr_period, |
|
|
1973 | 2205 | thisDatetime=thisDatetime) |
|
|
1974 | 2206 | |
|
|
1975 | 2207 | |
|
|
1976 | 2208 | class NSMeteorDetection2Plot(Figure): |
|
|
1977 | 2209 | |
|
|
1978 | 2210 | isConfig = None |
|
|
1979 | 2211 | __nsubplots = None |
|
|
1980 | 2212 | |
|
|
1981 | 2213 | WIDTHPROF = None |
|
|
1982 | 2214 | HEIGHTPROF = None |
|
|
1983 | 2215 | PREFIX = 'nsm' |
|
|
1984 | 2216 | |
|
|
1985 | 2217 | zminList = None |
|
|
1986 | 2218 | zmaxList = None |
|
|
1987 | 2219 | cmapList = None |
|
|
1988 | 2220 | titleList = None |
|
|
1989 | 2221 | nPairs = None |
|
|
1990 | 2222 | nChannels = None |
|
|
1991 | 2223 | nParam = None |
|
|
1992 | 2224 | |
|
|
1993 | 2225 | def __init__(self, **kwargs): |
|
|
1994 | 2226 | Figure.__init__(self, **kwargs) |
|
|
1995 | 2227 | self.isConfig = False |
|
|
1996 | 2228 | self.__nsubplots = 1 |
|
|
1997 | 2229 | |
|
|
1998 | 2230 | self.WIDTH = 750 |
|
|
1999 | 2231 | self.HEIGHT = 250 |
|
|
2000 | 2232 | self.WIDTHPROF = 120 |
|
|
2001 | 2233 | self.HEIGHTPROF = 0 |
|
|
2002 | 2234 | self.counter_imagwr = 0 |
|
|
2003 | 2235 | |
|
|
2004 | 2236 | self.PLOT_CODE = SPEC_CODE |
|
|
2005 | 2237 | |
|
|
2006 | 2238 | self.FTP_WEI = None |
|
|
2007 | 2239 | self.EXP_CODE = None |
|
|
2008 | 2240 | self.SUB_EXP_CODE = None |
|
|
2009 | 2241 | self.PLOT_POS = None |
|
|
2010 | 2242 | |
|
|
2011 | 2243 | self.__xfilter_ena = False |
|
|
2012 | 2244 | self.__yfilter_ena = False |
|
|
2013 | 2245 | |
|
|
2014 | 2246 | def getSubplots(self): |
|
|
2015 | 2247 | |
|
|
2016 | 2248 | ncol = 3 |
|
|
2017 | 2249 | nrow = int(numpy.ceil(self.nplots/3.0)) |
|
|
2018 | 2250 | |
|
|
2019 | 2251 | return nrow, ncol |
|
|
2020 | 2252 | |
|
|
2021 | 2253 | def setup(self, id, nplots, wintitle, show=True): |
|
|
2022 | 2254 | |
|
|
2023 | 2255 | self.nplots = nplots |
|
|
2024 | 2256 | |
|
|
2025 | 2257 | ncolspan = 1 |
|
|
2026 | 2258 | colspan = 1 |
|
|
2027 | 2259 | |
|
|
2028 | 2260 | self.createFigure(id = id, |
|
|
2029 | 2261 | wintitle = wintitle, |
|
|
2030 | 2262 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
2031 | 2263 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
2032 | 2264 | show=show) |
|
|
2033 | 2265 | |
|
|
2034 | 2266 | nrow, ncol = self.getSubplots() |
|
|
2035 | 2267 | |
|
|
2036 | 2268 | counter = 0 |
|
|
2037 | 2269 | for y in range(nrow): |
|
|
2038 | 2270 | for x in range(ncol): |
|
|
2039 | 2271 | |
|
|
2040 | 2272 | if counter >= self.nplots: |
|
|
2041 | 2273 | break |
|
|
2042 | 2274 | |
|
|
2043 | 2275 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
2044 | 2276 | |
|
|
2045 | 2277 | counter += 1 |
|
|
2046 | 2278 | |
|
|
2047 | 2279 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
2048 | 2280 | xmin=None, xmax=None, ymin=None, ymax=None, SNRmin=None, SNRmax=None, |
|
|
2049 | 2281 | vmin=None, vmax=None, wmin=None, wmax=None, mode = 'SA', |
|
|
2050 | 2282 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
2051 | 2283 | server=None, folder=None, username=None, password=None, |
|
|
2052 | 2284 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
2053 | 2285 | xaxis="frequency"): |
|
|
2054 | 2286 | |
|
|
2055 | 2287 | """ |
|
|
2056 | 2288 | |
|
|
2057 | 2289 | Input: |
|
|
2058 | 2290 | dataOut : |
|
|
2059 | 2291 | id : |
|
|
2060 | 2292 | wintitle : |
|
|
2061 | 2293 | channelList : |
|
|
2062 | 2294 | showProfile : |
|
|
2063 | 2295 | xmin : None, |
|
|
2064 | 2296 | xmax : None, |
|
|
2065 | 2297 | ymin : None, |
|
|
2066 | 2298 | ymax : None, |
|
|
2067 | 2299 | zmin : None, |
|
|
2068 | 2300 | zmax : None |
|
|
2069 | 2301 | """ |
|
|
2070 | 2302 | #Rebuild matrix |
|
|
2071 | 2303 | utctime = dataOut.data_param[0,0] |
|
|
2072 | 2304 | cmet = dataOut.data_param[:,1].astype(int) |
|
|
2073 | 2305 | tmet = dataOut.data_param[:,2].astype(int) |
|
|
2074 | 2306 | hmet = dataOut.data_param[:,3].astype(int) |
|
|
2075 | 2307 | |
|
|
2076 | 2308 | nParam = 3 |
|
|
2077 | 2309 | nChan = len(dataOut.groupList) |
|
|
2078 | 2310 | x = dataOut.abscissaList |
|
|
2079 | 2311 | y = dataOut.heightList |
|
|
2080 | 2312 | |
|
|
2081 | 2313 | z = numpy.full((nChan, nParam, y.size, x.size - 1),numpy.nan) |
|
|
2082 | 2314 | z[cmet,:,hmet,tmet] = dataOut.data_param[:,4:] |
|
|
2083 | 2315 | z[:,0,:,:] = 10*numpy.log10(z[:,0,:,:]) #logarithmic scale |
|
|
2084 | 2316 | z = numpy.reshape(z, (nChan*nParam, y.size, x.size-1)) |
|
|
2085 | 2317 | |
|
|
2086 | 2318 | xlabel = "Time (s)" |
|
|
2087 | 2319 | ylabel = "Range (km)" |
|
|
2088 | 2320 | |
|
|
2089 | 2321 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.ltctime) |
|
|
2090 | 2322 | |
|
|
2091 | 2323 | if not self.isConfig: |
|
|
2092 | 2324 | |
|
|
2093 | 2325 | nplots = nParam*nChan |
|
|
2094 | 2326 | |
|
|
2095 | 2327 | self.setup(id=id, |
|
|
2096 | 2328 | nplots=nplots, |
|
|
2097 | 2329 | wintitle=wintitle, |
|
|
2098 | 2330 | show=show) |
|
|
2099 | 2331 | |
|
|
2100 | 2332 | if xmin is None: xmin = numpy.nanmin(x) |
|
|
2101 | 2333 | if xmax is None: xmax = numpy.nanmax(x) |
|
|
2102 | 2334 | if ymin is None: ymin = numpy.nanmin(y) |
|
|
2103 | 2335 | if ymax is None: ymax = numpy.nanmax(y) |
|
|
2104 | 2336 | if SNRmin is None: SNRmin = numpy.nanmin(z[0,:]) |
|
|
2105 | 2337 | if SNRmax is None: SNRmax = numpy.nanmax(z[0,:]) |
|
|
2106 | 2338 | if vmax is None: vmax = numpy.nanmax(numpy.abs(z[1,:])) |
|
|
2107 | 2339 | if vmin is None: vmin = -vmax |
|
|
2108 | 2340 | if wmin is None: wmin = 0 |
|
|
2109 | 2341 | if wmax is None: wmax = 50 |
|
|
2110 | 2342 | |
|
|
2111 | 2343 | self.nChannels = nChan |
|
|
2112 | 2344 | |
|
|
2113 | 2345 | zminList = [] |
|
|
2114 | 2346 | zmaxList = [] |
|
|
2115 | 2347 | titleList = [] |
|
|
2116 | 2348 | cmapList = [] |
|
|
2117 | 2349 | for i in range(self.nChannels): |
|
|
2118 | 2350 | strAux1 = "SNR Channel "+ str(i) |
|
|
2119 | 2351 | strAux2 = "Radial Velocity Channel "+ str(i) |
|
|
2120 | 2352 | strAux3 = "Spectral Width Channel "+ str(i) |
|
|
2121 | 2353 | |
|
|
2122 | 2354 | titleList = titleList + [strAux1,strAux2,strAux3] |
|
|
2123 | 2355 | cmapList = cmapList + ["jet","RdBu_r","jet"] |
|
|
2124 | 2356 | zminList = zminList + [SNRmin,vmin,wmin] |
|
|
2125 | 2357 | zmaxList = zmaxList + [SNRmax,vmax,wmax] |
|
|
2126 | 2358 | |
|
|
2127 | 2359 | self.zminList = zminList |
|
|
2128 | 2360 | self.zmaxList = zmaxList |
|
|
2129 | 2361 | self.cmapList = cmapList |
|
|
2130 | 2362 | self.titleList = titleList |
|
|
2131 | 2363 | |
|
|
2132 | 2364 | self.FTP_WEI = ftp_wei |
|
|
2133 | 2365 | self.EXP_CODE = exp_code |
|
|
2134 | 2366 | self.SUB_EXP_CODE = sub_exp_code |
|
|
2135 | 2367 | self.PLOT_POS = plot_pos |
|
|
2136 | 2368 | |
|
|
2137 | 2369 | self.isConfig = True |
|
|
2138 | 2370 | |
|
|
2139 | 2371 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
2140 | 2372 | |
|
|
2141 | 2373 | for i in range(self.nplots): |
|
|
2142 | 2374 | title = self.titleList[i] + ": " +str_datetime |
|
|
2143 | 2375 | axes = self.axesList[i] |
|
|
2144 | 2376 | axes.pcolor(x, y, z[i,:].T, |
|
|
2145 | 2377 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=self.zminList[i], zmax=self.zmaxList[i], |
|
|
2146 | 2378 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=self.cmapList[i],ticksize=9, cblabel='') |
|
|
2147 | 2379 | self.draw() |
|
|
2148 | 2380 | |
|
|
2149 | 2381 | if figfile == None: |
|
|
2150 | 2382 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
2151 | 2383 | name = str_datetime |
|
|
2152 | 2384 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
2153 | 2385 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
2154 | 2386 | figfile = self.getFilename(name) |
|
|
2155 | 2387 | |
|
|
2156 | 2388 | self.save(figpath=figpath, |
|
|
2157 | 2389 | figfile=figfile, |
|
|
2158 | 2390 | save=save, |
|
|
2159 | 2391 | ftp=ftp, |
|
|
2160 | 2392 | wr_period=wr_period, |
|
|
2161 | 2393 | thisDatetime=thisDatetime) |
| @@ -1,1155 +1,795 | |||
|
|
1 | 1 | import os, sys |
|
|
2 | 2 | import glob |
|
|
3 | 3 | import fnmatch |
|
|
4 | 4 | import datetime |
|
|
5 | 5 | import time |
|
|
6 | 6 | import re |
|
|
7 | 7 | import h5py |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | import matplotlib.pyplot as plt |
|
|
10 | 10 | |
|
|
11 | 11 | import pylab as plb |
|
|
12 | 12 | from scipy.optimize import curve_fit |
|
|
13 | 13 | from scipy import asarray as ar, exp |
|
|
14 | 14 | from scipy import stats |
|
|
15 | 15 | |
|
|
16 | 16 | from numpy.ma.core import getdata |
|
|
17 | 17 | |
|
|
18 | 18 | SPEED_OF_LIGHT = 299792458 |
|
|
19 | 19 | SPEED_OF_LIGHT = 3e8 |
|
|
20 | 20 | |
|
|
21 | 21 | try: |
|
|
22 | 22 | from gevent import sleep |
|
|
23 | 23 | except: |
|
|
24 | 24 | from time import sleep |
|
|
25 | 25 | |
|
|
26 | 26 | from schainpy.model.data.jrodata import Spectra |
|
|
27 | 27 | #from schainpy.model.data.BLTRheaderIO import FileHeader, RecordHeader |
|
|
28 | 28 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation |
|
|
29 | 29 | #from schainpy.model.io.jroIO_bltr import BLTRReader |
|
|
30 | 30 | from numpy import imag, shape, NaN |
|
|
31 | 31 | |
|
|
32 | 32 | from jroIO_base import JRODataReader |
|
|
33 | 33 | |
|
|
34 | 34 | |
|
|
35 | 35 | class Header(object): |
|
|
36 | 36 | |
|
|
37 | 37 | def __init__(self): |
|
|
38 | 38 | raise NotImplementedError |
|
|
39 | 39 | |
|
|
40 | 40 | |
|
|
41 | 41 | def read(self): |
|
|
42 | 42 | |
|
|
43 | 43 | raise NotImplementedError |
|
|
44 | 44 | |
|
|
45 | 45 | def write(self): |
|
|
46 | 46 | |
|
|
47 | 47 | raise NotImplementedError |
|
|
48 | 48 | |
|
|
49 | 49 | def printInfo(self): |
|
|
50 | 50 | |
|
|
51 | 51 | message = "#"*50 + "\n" |
|
|
52 | 52 | message += self.__class__.__name__.upper() + "\n" |
|
|
53 | 53 | message += "#"*50 + "\n" |
|
|
54 | 54 | |
|
|
55 | 55 | keyList = self.__dict__.keys() |
|
|
56 | 56 | keyList.sort() |
|
|
57 | 57 | |
|
|
58 | 58 | for key in keyList: |
|
|
59 | 59 | message += "%s = %s" %(key, self.__dict__[key]) + "\n" |
|
|
60 | 60 | |
|
|
61 | 61 | if "size" not in keyList: |
|
|
62 | 62 | attr = getattr(self, "size") |
|
|
63 | 63 | |
|
|
64 | 64 | if attr: |
|
|
65 | 65 | message += "%s = %s" %("size", attr) + "\n" |
|
|
66 | 66 | |
|
|
67 | 67 | #print message |
|
|
68 | 68 | |
|
|
69 | 69 | |
|
|
70 | 70 | |
|
|
71 | 71 | |
|
|
72 | 72 | |
|
|
73 | 73 | FILE_STRUCTURE = numpy.dtype([ #HEADER 48bytes |
|
|
74 | 74 | ('FileMgcNumber','<u4'), #0x23020100 |
|
|
75 | 75 | ('nFDTdataRecors','<u4'), #No Of FDT data records in this file (0 or more) |
|
|
76 | 76 | ('OffsetStartHeader','<u4'), |
|
|
77 | 77 | ('RadarUnitId','<u4'), |
|
|
78 | 78 | ('SiteName',numpy.str_,32), #Null terminated |
|
|
79 | 79 | ]) |
|
|
80 | 80 | |
|
|
81 | 81 | class FileHeaderBLTR(Header): |
|
|
82 | 82 | |
|
|
83 | 83 | def __init__(self): |
|
|
84 | 84 | |
|
|
85 | 85 | self.FileMgcNumber= 0 #0x23020100 |
|
|
86 | 86 | self.nFDTdataRecors=0 #No Of FDT data records in this file (0 or more) |
|
|
87 | 87 | self.RadarUnitId= 0 |
|
|
88 | 88 | self.OffsetStartHeader=0 |
|
|
89 | 89 | self.SiteName= "" |
|
|
90 | 90 | self.size = 48 |
|
|
91 | 91 | |
|
|
92 | 92 | def FHread(self, fp): |
|
|
93 | 93 | #try: |
|
|
94 | 94 | startFp = open(fp,"rb") |
|
|
95 | 95 | |
|
|
96 | 96 | header = numpy.fromfile(startFp, FILE_STRUCTURE,1) |
|
|
97 | 97 | |
|
|
98 | 98 | print ' ' |
|
|
99 | 99 | print 'puntero file header', startFp.tell() |
|
|
100 | 100 | print ' ' |
|
|
101 | 101 | |
|
|
102 | 102 | |
|
|
103 | 103 | ''' numpy.fromfile(file, dtype, count, sep='') |
|
|
104 | 104 | file : file or str |
|
|
105 | 105 | Open file object or filename. |
|
|
106 | 106 | |
|
|
107 | 107 | dtype : data-type |
|
|
108 | 108 | Data type of the returned array. For binary files, it is used to determine |
|
|
109 | 109 | the size and byte-order of the items in the file. |
|
|
110 | 110 | |
|
|
111 | 111 | count : int |
|
|
112 | 112 | Number of items to read. -1 means all items (i.e., the complete file). |
|
|
113 | 113 | |
|
|
114 | 114 | sep : str |
|
|
115 | 115 | Separator between items if file is a text file. Empty ("") separator means |
|
|
116 | 116 | the file should be treated as binary. Spaces (" ") in the separator match zero |
|
|
117 | 117 | or more whitespace characters. A separator consisting only of spaces must match |
|
|
118 | 118 | at least one whitespace. |
|
|
119 | 119 | |
|
|
120 | 120 | ''' |
|
|
121 | 121 | |
|
|
122 | 122 | |
|
|
123 | 123 | |
|
|
124 | 124 | self.FileMgcNumber= hex(header['FileMgcNumber'][0]) |
|
|
125 | 125 | self.nFDTdataRecors=int(header['nFDTdataRecors'][0]) #No Of FDT data records in this file (0 or more) |
|
|
126 | 126 | self.RadarUnitId= int(header['RadarUnitId'][0]) |
|
|
127 | 127 | self.OffsetStartHeader= int(header['OffsetStartHeader'][0]) |
|
|
128 | 128 | self.SiteName= str(header['SiteName'][0]) |
|
|
129 | 129 | |
|
|
130 | 130 | #print 'Numero de bloques', self.nFDTdataRecors |
|
|
131 | 131 | |
|
|
132 | 132 | |
|
|
133 | 133 | if self.size <48: |
|
|
134 | 134 | return 0 |
|
|
135 | 135 | |
|
|
136 | 136 | return 1 |
|
|
137 | 137 | |
|
|
138 | 138 | |
|
|
139 | 139 | def write(self, fp): |
|
|
140 | 140 | |
|
|
141 | 141 | headerTuple = (self.FileMgcNumber, |
|
|
142 | 142 | self.nFDTdataRecors, |
|
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143 | 143 | self.RadarUnitId, |
|
|
144 | 144 | self.SiteName, |
|
|
145 | 145 | self.size) |
|
|
146 | 146 | |
|
|
147 | 147 | |
|
|
148 | 148 | header = numpy.array(headerTuple, FILE_STRUCTURE) |
|
|
149 | 149 | # numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0) |
|
|
150 | 150 | header.tofile(fp) |
|
|
151 | 151 | ''' ndarray.tofile(fid, sep, format) Write array to a file as text or binary (default). |
|
|
152 | 152 | |
|
|
153 | 153 | fid : file or str |
|
|
154 | 154 | An open file object, or a string containing a filename. |
|
|
155 | 155 | |
|
|
156 | 156 | sep : str |
|
|
157 | 157 | Separator between array items for text output. If "" (empty), a binary file is written, |
|
|
158 | 158 | equivalent to file.write(a.tobytes()). |
|
|
159 | 159 | |
|
|
160 | 160 | format : str |
|
|
161 | 161 | Format string for text file output. Each entry in the array is formatted to text by |
|
|
162 | 162 | first converting it to the closest Python type, and then using "format" % item. |
|
|
163 | 163 | |
|
|
164 | 164 | ''' |
|
|
165 | 165 | |
|
|
166 | 166 | return 1 |
|
|
167 | 167 | |
|
|
168 | 168 | |
|
|
169 | 169 | |
|
|
170 | 170 | |
|
|
171 | 171 | |
|
|
172 | 172 | RECORD_STRUCTURE = numpy.dtype([ #RECORD HEADER 180+20N bytes |
|
|
173 | 173 | ('RecMgcNumber','<u4'), #0x23030001 |
|
|
174 | 174 | ('RecCounter','<u4'), #Record counter(0,1, ...) |
|
|
175 | 175 | ('Off2StartNxtRec','<u4'), #Offset to start of next record form start of this record |
|
|
176 | 176 | ('Off2StartData','<u4'), #Offset to start of data from start of this record |
|
|
177 | 177 | ('nUtime','<i4'), #Epoch time stamp of start of acquisition (seconds) |
|
|
178 | 178 | ('nMilisec','<u4'), #Millisecond component of time stamp (0,...,999) |
|
|
179 | 179 | ('ExpTagName',numpy.str_,32), #Experiment tag name (null terminated) |
|
|
180 | 180 | ('ExpComment',numpy.str_,32), #Experiment comment (null terminated) |
|
|
181 | 181 | ('SiteLatDegrees','<f4'), #Site latitude (from GPS) in degrees (positive implies North) |
|
|
182 | 182 | ('SiteLongDegrees','<f4'), #Site longitude (from GPS) in degrees (positive implies East) |
|
|
183 | 183 | ('RTCgpsStatus','<u4'), #RTC GPS engine status (0=SEEK, 1=LOCK, 2=NOT FITTED, 3=UNAVAILABLE) |
|
|
184 | 184 | ('TransmitFrec','<u4'), #Transmit frequency (Hz) |
|
|
185 | 185 | ('ReceiveFrec','<u4'), #Receive frequency |
|
|
186 | 186 | ('FirstOsciFrec','<u4'), #First local oscillator frequency (Hz) |
|
|
187 | 187 | ('Polarisation','<u4'), #(0="O", 1="E", 2="linear 1", 3="linear2") |
|
|
188 | 188 | ('ReceiverFiltSett','<u4'), #Receiver filter settings (0,1,2,3) |
|
|
189 | 189 | ('nModesInUse','<u4'), #Number of modes in use (1 or 2) |
|
|
190 | 190 | ('DualModeIndex','<u4'), #Dual Mode index number for these data (0 or 1) |
|
|
191 | 191 | ('DualModeRange','<u4'), #Dual Mode range correction for these data (m) |
|
|
192 | 192 | ('nDigChannels','<u4'), #Number of digital channels acquired (2*N) |
|
|
193 | 193 | ('SampResolution','<u4'), #Sampling resolution (meters) |
|
|
194 | 194 | ('nHeights','<u4'), #Number of range gates sampled |
|
|
195 | 195 | ('StartRangeSamp','<u4'), #Start range of sampling (meters) |
|
|
196 | 196 | ('PRFhz','<u4'), #PRF (Hz) |
|
|
197 | 197 | ('nCohInt','<u4'), #Integrations |
|
|
198 | 198 | ('nProfiles','<u4'), #Number of data points transformed |
|
|
199 | 199 | ('nChannels','<u4'), #Number of receive beams stored in file (1 or N) |
|
|
200 | 200 | ('nIncohInt','<u4'), #Number of spectral averages |
|
|
201 | 201 | ('FFTwindowingInd','<u4'), #FFT windowing index (0 = no window) |
|
|
202 | 202 | ('BeamAngleAzim','<f4'), #Beam steer angle (azimuth) in degrees (clockwise from true North) |
|
|
203 | 203 | ('BeamAngleZen','<f4'), #Beam steer angle (zenith) in degrees (0=> vertical) |
|
|
204 | 204 | ('AntennaCoord0','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
205 | 205 | ('AntennaAngl0','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
206 | 206 | ('AntennaCoord1','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
207 | 207 | ('AntennaAngl1','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
208 | 208 | ('AntennaCoord2','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
209 | 209 | ('AntennaAngl2','<f4'), #Antenna coordinates (Range(meters), Bearing(degrees)) - N pairs |
|
|
210 | 210 | ('RecPhaseCalibr0','<f4'), #Receiver phase calibration (degrees) - N values |
|
|
211 | 211 | ('RecPhaseCalibr1','<f4'), #Receiver phase calibration (degrees) - N values |
|
|
212 | 212 | ('RecPhaseCalibr2','<f4'), #Receiver phase calibration (degrees) - N values |
|
|
213 | 213 | ('RecAmpCalibr0','<f4'), #Receiver amplitude calibration (ratio relative to receiver one) - N values |
|
|
214 | 214 | ('RecAmpCalibr1','<f4'), #Receiver amplitude calibration (ratio relative to receiver one) - N values |
|
|
215 | 215 | ('RecAmpCalibr2','<f4'), #Receiver amplitude calibration (ratio relative to receiver one) - N values |
|
|
216 | 216 | ('ReceiverGaindB0','<i4'), #Receiver gains in dB - N values |
|
|
217 | 217 | ('ReceiverGaindB1','<i4'), #Receiver gains in dB - N values |
|
|
218 | 218 | ('ReceiverGaindB2','<i4'), #Receiver gains in dB - N values |
|
|
219 | 219 | ]) |
|
|
220 | 220 | |
|
|
221 | 221 | |
|
|
222 | 222 | class RecordHeaderBLTR(Header): |
|
|
223 | 223 | |
|
|
224 | 224 | def __init__(self, RecMgcNumber=None, RecCounter= 0, Off2StartNxtRec= 811248, |
|
|
225 | 225 | nUtime= 0, nMilisec= 0, ExpTagName= None, |
|
|
226 | 226 | ExpComment=None, SiteLatDegrees=0, SiteLongDegrees= 0, |
|
|
227 | 227 | RTCgpsStatus= 0, TransmitFrec= 0, ReceiveFrec= 0, |
|
|
228 | 228 | FirstOsciFrec= 0, Polarisation= 0, ReceiverFiltSett= 0, |
|
|
229 | 229 | nModesInUse= 0, DualModeIndex= 0, DualModeRange= 0, |
|
|
230 | 230 | nDigChannels= 0, SampResolution= 0, nHeights= 0, |
|
|
231 | 231 | StartRangeSamp= 0, PRFhz= 0, nCohInt= 0, |
|
|
232 | 232 | nProfiles= 0, nChannels= 0, nIncohInt= 0, |
|
|
233 | 233 | FFTwindowingInd= 0, BeamAngleAzim= 0, BeamAngleZen= 0, |
|
|
234 | 234 | AntennaCoord0= 0, AntennaCoord1= 0, AntennaCoord2= 0, |
|
|
235 | 235 | RecPhaseCalibr0= 0, RecPhaseCalibr1= 0, RecPhaseCalibr2= 0, |
|
|
236 | 236 | RecAmpCalibr0= 0, RecAmpCalibr1= 0, RecAmpCalibr2= 0, |
|
|
237 | 237 | AntennaAngl0=0, AntennaAngl1=0, AntennaAngl2=0, |
|
|
238 | 238 | ReceiverGaindB0= 0, ReceiverGaindB1= 0, ReceiverGaindB2= 0, Off2StartData=0, OffsetStartHeader=0): |
|
|
239 | 239 | |
|
|
240 | 240 | self.RecMgcNumber = RecMgcNumber #0x23030001 |
|
|
241 | 241 | self.RecCounter = RecCounter |
|
|
242 | 242 | self.Off2StartNxtRec = Off2StartNxtRec |
|
|
243 | 243 | self.Off2StartData = Off2StartData |
|
|
244 | 244 | self.nUtime = nUtime |
|
|
245 | 245 | self.nMilisec = nMilisec |
|
|
246 | 246 | self.ExpTagName = ExpTagName |
|
|
247 | 247 | self.ExpComment = ExpComment |
|
|
248 | 248 | self.SiteLatDegrees = SiteLatDegrees |
|
|
249 | 249 | self.SiteLongDegrees = SiteLongDegrees |
|
|
250 | 250 | self.RTCgpsStatus = RTCgpsStatus |
|
|
251 | 251 | self.TransmitFrec = TransmitFrec |
|
|
252 | 252 | self.ReceiveFrec = ReceiveFrec |
|
|
253 | 253 | self.FirstOsciFrec = FirstOsciFrec |
|
|
254 | 254 | self.Polarisation = Polarisation |
|
|
255 | 255 | self.ReceiverFiltSett = ReceiverFiltSett |
|
|
256 | 256 | self.nModesInUse = nModesInUse |
|
|
257 | 257 | self.DualModeIndex = DualModeIndex |
|
|
258 | 258 | self.DualModeRange = DualModeRange |
|
|
259 | 259 | self.nDigChannels = nDigChannels |
|
|
260 | 260 | self.SampResolution = SampResolution |
|
|
261 | 261 | self.nHeights = nHeights |
|
|
262 | 262 | self.StartRangeSamp = StartRangeSamp |
|
|
263 | 263 | self.PRFhz = PRFhz |
|
|
264 | 264 | self.nCohInt = nCohInt |
|
|
265 | 265 | self.nProfiles = nProfiles |
|
|
266 | 266 | self.nChannels = nChannels |
|
|
267 | 267 | self.nIncohInt = nIncohInt |
|
|
268 | 268 | self.FFTwindowingInd = FFTwindowingInd |
|
|
269 | 269 | self.BeamAngleAzim = BeamAngleAzim |
|
|
270 | 270 | self.BeamAngleZen = BeamAngleZen |
|
|
271 | 271 | self.AntennaCoord0 = AntennaCoord0 |
|
|
272 | 272 | self.AntennaAngl0 = AntennaAngl0 |
|
|
273 | 273 | self.AntennaAngl1 = AntennaAngl1 |
|
|
274 | 274 | self.AntennaAngl2 = AntennaAngl2 |
|
|
275 | 275 | self.AntennaCoord1 = AntennaCoord1 |
|
|
276 | 276 | self.AntennaCoord2 = AntennaCoord2 |
|
|
277 | 277 | self.RecPhaseCalibr0 = RecPhaseCalibr0 |
|
|
278 | 278 | self.RecPhaseCalibr1 = RecPhaseCalibr1 |
|
|
279 | 279 | self.RecPhaseCalibr2 = RecPhaseCalibr2 |
|
|
280 | 280 | self.RecAmpCalibr0 = RecAmpCalibr0 |
|
|
281 | 281 | self.RecAmpCalibr1 = RecAmpCalibr1 |
|
|
282 | 282 | self.RecAmpCalibr2 = RecAmpCalibr2 |
|
|
283 | 283 | self.ReceiverGaindB0 = ReceiverGaindB0 |
|
|
284 | 284 | self.ReceiverGaindB1 = ReceiverGaindB1 |
|
|
285 | 285 | self.ReceiverGaindB2 = ReceiverGaindB2 |
|
|
286 | 286 | self.OffsetStartHeader = 48 |
|
|
287 | 287 | |
|
|
288 | 288 | |
|
|
289 | 289 | |
|
|
290 | 290 | def RHread(self, fp): |
|
|
291 | 291 | #print fp |
|
|
292 | 292 | #startFp = open('/home/erick/Documents/Data/huancayo.20161019.22.fdt',"rb") #The method tell() returns the current position of the file read/write pointer within the file. |
|
|
293 | 293 | startFp = open(fp,"rb") #The method tell() returns the current position of the file read/write pointer within the file. |
|
|
294 | 294 | #RecCounter=0 |
|
|
295 | 295 | #Off2StartNxtRec=811248 |
|
|
296 | 296 | OffRHeader= self.OffsetStartHeader + self.RecCounter*self.Off2StartNxtRec |
|
|
297 | 297 | print ' ' |
|
|
298 | 298 | print 'puntero Record Header', startFp.tell() |
|
|
299 | 299 | print ' ' |
|
|
300 | 300 | |
|
|
301 | 301 | |
|
|
302 | 302 | startFp.seek(OffRHeader, os.SEEK_SET) |
|
|
303 | 303 | |
|
|
304 | 304 | print ' ' |
|
|
305 | 305 | print 'puntero Record Header con seek', startFp.tell() |
|
|
306 | 306 | print ' ' |
|
|
307 | 307 | |
|
|
308 | 308 | #print 'Posicion del bloque: ',OffRHeader |
|
|
309 | 309 | |
|
|
310 | 310 | header = numpy.fromfile(startFp,RECORD_STRUCTURE,1) |
|
|
311 | 311 | |
|
|
312 | 312 | print ' ' |
|
|
313 | 313 | print 'puntero Record Header con seek', startFp.tell() |
|
|
314 | 314 | print ' ' |
|
|
315 | 315 | |
|
|
316 | 316 | print ' ' |
|
|
317 | 317 | # |
|
|
318 | 318 | #print 'puntero Record Header despues de seek', header.tell() |
|
|
319 | 319 | print ' ' |
|
|
320 | 320 | |
|
|
321 | 321 | self.RecMgcNumber = hex(header['RecMgcNumber'][0]) #0x23030001 |
|
|
322 | 322 | self.RecCounter = int(header['RecCounter'][0]) |
|
|
323 | 323 | self.Off2StartNxtRec = int(header['Off2StartNxtRec'][0]) |
|
|
324 | 324 | self.Off2StartData = int(header['Off2StartData'][0]) |
|
|
325 | 325 | self.nUtime = header['nUtime'][0] |
|
|
326 | 326 | self.nMilisec = header['nMilisec'][0] |
|
|
327 | 327 | self.ExpTagName = str(header['ExpTagName'][0]) |
|
|
328 | 328 | self.ExpComment = str(header['ExpComment'][0]) |
|
|
329 | 329 | self.SiteLatDegrees = header['SiteLatDegrees'][0] |
|
|
330 | 330 | self.SiteLongDegrees = header['SiteLongDegrees'][0] |
|
|
331 | 331 | self.RTCgpsStatus = header['RTCgpsStatus'][0] |
|
|
332 | 332 | self.TransmitFrec = header['TransmitFrec'][0] |
|
|
333 | 333 | self.ReceiveFrec = header['ReceiveFrec'][0] |
|
|
334 | 334 | self.FirstOsciFrec = header['FirstOsciFrec'][0] |
|
|
335 | 335 | self.Polarisation = header['Polarisation'][0] |
|
|
336 | 336 | self.ReceiverFiltSett = header['ReceiverFiltSett'][0] |
|
|
337 | 337 | self.nModesInUse = header['nModesInUse'][0] |
|
|
338 | 338 | self.DualModeIndex = header['DualModeIndex'][0] |
|
|
339 | 339 | self.DualModeRange = header['DualModeRange'][0] |
|
|
340 | 340 | self.nDigChannels = header['nDigChannels'][0] |
|
|
341 | 341 | self.SampResolution = header['SampResolution'][0] |
|
|
342 | 342 | self.nHeights = header['nHeights'][0] |
|
|
343 | 343 | self.StartRangeSamp = header['StartRangeSamp'][0] |
|
|
344 | 344 | self.PRFhz = header['PRFhz'][0] |
|
|
345 | 345 | self.nCohInt = header['nCohInt'][0] |
|
|
346 | 346 | self.nProfiles = header['nProfiles'][0] |
|
|
347 | 347 | self.nChannels = header['nChannels'][0] |
|
|
348 | 348 | self.nIncohInt = header['nIncohInt'][0] |
|
|
349 | 349 | self.FFTwindowingInd = header['FFTwindowingInd'][0] |
|
|
350 | 350 | self.BeamAngleAzim = header['BeamAngleAzim'][0] |
|
|
351 | 351 | self.BeamAngleZen = header['BeamAngleZen'][0] |
|
|
352 | 352 | self.AntennaCoord0 = header['AntennaCoord0'][0] |
|
|
353 | 353 | self.AntennaAngl0 = header['AntennaAngl0'][0] |
|
|
354 | 354 | self.AntennaCoord1 = header['AntennaCoord1'][0] |
|
|
355 | 355 | self.AntennaAngl1 = header['AntennaAngl1'][0] |
|
|
356 | 356 | self.AntennaCoord2 = header['AntennaCoord2'][0] |
|
|
357 | 357 | self.AntennaAngl2 = header['AntennaAngl2'][0] |
|
|
358 | 358 | self.RecPhaseCalibr0 = header['RecPhaseCalibr0'][0] |
|
|
359 | 359 | self.RecPhaseCalibr1 = header['RecPhaseCalibr1'][0] |
|
|
360 | 360 | self.RecPhaseCalibr2 = header['RecPhaseCalibr2'][0] |
|
|
361 | 361 | self.RecAmpCalibr0 = header['RecAmpCalibr0'][0] |
|
|
362 | 362 | self.RecAmpCalibr1 = header['RecAmpCalibr1'][0] |
|
|
363 | 363 | self.RecAmpCalibr2 = header['RecAmpCalibr2'][0] |
|
|
364 | 364 | self.ReceiverGaindB0 = header['ReceiverGaindB0'][0] |
|
|
365 | 365 | self.ReceiverGaindB1 = header['ReceiverGaindB1'][0] |
|
|
366 | 366 | self.ReceiverGaindB2 = header['ReceiverGaindB2'][0] |
|
|
367 | 367 | |
|
|
368 | 368 | self.ipp= 0.5*(SPEED_OF_LIGHT/self.PRFhz) |
|
|
369 | 369 | |
|
|
370 | 370 | self.RHsize = 180+20*self.nChannels |
|
|
371 | 371 | self.Datasize= self.nProfiles*self.nChannels*self.nHeights*2*4 |
|
|
372 | 372 | #print 'Datasize',self.Datasize |
|
|
373 | 373 | endFp = self.OffsetStartHeader + self.RecCounter*self.Off2StartNxtRec |
|
|
374 | 374 | |
|
|
375 | 375 | print '==============================================' |
|
|
376 | 376 | print 'RecMgcNumber ',self.RecMgcNumber |
|
|
377 | 377 | print 'RecCounter ',self.RecCounter |
|
|
378 | 378 | print 'Off2StartNxtRec ',self.Off2StartNxtRec |
|
|
379 | 379 | print 'Off2StartData ',self.Off2StartData |
|
|
380 | 380 | print 'Range Resolution ',self.SampResolution |
|
|
381 | 381 | print 'First Height ',self.StartRangeSamp |
|
|
382 | 382 | print 'PRF (Hz) ',self.PRFhz |
|
|
383 | 383 | print 'Heights (K) ',self.nHeights |
|
|
384 | 384 | print 'Channels (N) ',self.nChannels |
|
|
385 | 385 | print 'Profiles (J) ',self.nProfiles |
|
|
386 | 386 | print 'iCoh ',self.nCohInt |
|
|
387 | 387 | print 'iInCoh ',self.nIncohInt |
|
|
388 | 388 | print 'BeamAngleAzim ',self.BeamAngleAzim |
|
|
389 | 389 | print 'BeamAngleZen ',self.BeamAngleZen |
|
|
390 | 390 | |
|
|
391 | 391 | #print 'ModoEnUso ',self.DualModeIndex |
|
|
392 | 392 | #print 'UtcTime ',self.nUtime |
|
|
393 | 393 | #print 'MiliSec ',self.nMilisec |
|
|
394 | 394 | #print 'Exp TagName ',self.ExpTagName |
|
|
395 | 395 | #print 'Exp Comment ',self.ExpComment |
|
|
396 | 396 | #print 'FFT Window Index ',self.FFTwindowingInd |
|
|
397 | 397 | #print 'N Dig. Channels ',self.nDigChannels |
|
|
398 | 398 | print 'Size de bloque ',self.RHsize |
|
|
399 | 399 | print 'DataSize ',self.Datasize |
|
|
400 | 400 | print 'BeamAngleAzim ',self.BeamAngleAzim |
|
|
401 | 401 | #print 'AntennaCoord0 ',self.AntennaCoord0 |
|
|
402 | 402 | #print 'AntennaAngl0 ',self.AntennaAngl0 |
|
|
403 | 403 | #print 'AntennaCoord1 ',self.AntennaCoord1 |
|
|
404 | 404 | #print 'AntennaAngl1 ',self.AntennaAngl1 |
|
|
405 | 405 | #print 'AntennaCoord2 ',self.AntennaCoord2 |
|
|
406 | 406 | #print 'AntennaAngl2 ',self.AntennaAngl2 |
|
|
407 | 407 | print 'RecPhaseCalibr0 ',self.RecPhaseCalibr0 |
|
|
408 | 408 | print 'RecPhaseCalibr1 ',self.RecPhaseCalibr1 |
|
|
409 | 409 | print 'RecPhaseCalibr2 ',self.RecPhaseCalibr2 |
|
|
410 | 410 | print 'RecAmpCalibr0 ',self.RecAmpCalibr0 |
|
|
411 | 411 | print 'RecAmpCalibr1 ',self.RecAmpCalibr1 |
|
|
412 | 412 | print 'RecAmpCalibr2 ',self.RecAmpCalibr2 |
|
|
413 | 413 | print 'ReceiverGaindB0 ',self.ReceiverGaindB0 |
|
|
414 | 414 | print 'ReceiverGaindB1 ',self.ReceiverGaindB1 |
|
|
415 | 415 | print 'ReceiverGaindB2 ',self.ReceiverGaindB2 |
|
|
416 | 416 | print '==============================================' |
|
|
417 | 417 | |
|
|
418 | 418 | if OffRHeader > endFp: |
|
|
419 | 419 | sys.stderr.write("Warning %s: Size value read from System Header is lower than it has to be\n" %fp) |
|
|
420 | 420 | return 0 |
|
|
421 | 421 | |
|
|
422 | 422 | if OffRHeader < endFp: |
|
|
423 | 423 | sys.stderr.write("Warning %s: Size value read from System Header size is greater than it has to be\n" %fp) |
|
|
424 | 424 | return 0 |
|
|
425 | 425 | |
|
|
426 | 426 | return 1 |
|
|
427 | 427 | |
|
|
428 | 428 | |
|
|
429 | 429 | class BLTRSpectraReader (ProcessingUnit, FileHeaderBLTR, RecordHeaderBLTR, JRODataReader): |
|
|
430 | 430 | |
|
|
431 | 431 | path = None |
|
|
432 | 432 | startDate = None |
|
|
433 | 433 | endDate = None |
|
|
434 | 434 | startTime = None |
|
|
435 | 435 | endTime = None |
|
|
436 | 436 | walk = None |
|
|
437 | 437 | isConfig = False |
|
|
438 | 438 | |
|
|
439 | 439 | |
|
|
440 | 440 | fileList= None |
|
|
441 | 441 | |
|
|
442 | 442 | #metadata |
|
|
443 | 443 | TimeZone= None |
|
|
444 | 444 | Interval= None |
|
|
445 | 445 | heightList= None |
|
|
446 | 446 | |
|
|
447 | 447 | #data |
|
|
448 | 448 | data= None |
|
|
449 | 449 | utctime= None |
|
|
450 | 450 | |
|
|
451 | 451 | |
|
|
452 | 452 | |
|
|
453 | 453 | def __init__(self, **kwargs): |
|
|
454 | 454 | |
|
|
455 | 455 | #Eliminar de la base la herencia |
|
|
456 | 456 | ProcessingUnit.__init__(self, **kwargs) |
|
|
457 | 457 | |
|
|
458 | 458 | #self.isConfig = False |
|
|
459 | 459 | |
|
|
460 | 460 | #self.pts2read_SelfSpectra = 0 |
|
|
461 | 461 | #self.pts2read_CrossSpectra = 0 |
|
|
462 | 462 | #self.pts2read_DCchannels = 0 |
|
|
463 | 463 | #self.datablock = None |
|
|
464 | 464 | self.utc = None |
|
|
465 | 465 | self.ext = ".fdt" |
|
|
466 | 466 | self.optchar = "P" |
|
|
467 | 467 | self.fpFile=None |
|
|
468 | 468 | self.fp = None |
|
|
469 | 469 | self.BlockCounter=0 |
|
|
470 | 470 | self.dtype = None |
|
|
471 | 471 | self.fileSizeByHeader = None |
|
|
472 | 472 | self.filenameList = [] |
|
|
473 | 473 | self.fileSelector = 0 |
|
|
474 | 474 | self.Off2StartNxtRec=0 |
|
|
475 | 475 | self.RecCounter=0 |
|
|
476 | 476 | self.flagNoMoreFiles = 0 |
|
|
477 | 477 | self.data_spc=None |
|
|
478 | 478 | self.data_cspc=None |
|
|
479 | 479 | self.data_output=None |
|
|
480 | 480 | self.path = None |
|
|
481 | 481 | self.OffsetStartHeader=0 |
|
|
482 | 482 | self.Off2StartData=0 |
|
|
483 | 483 | self.ipp = 0 |
|
|
484 | 484 | self.nFDTdataRecors=0 |
|
|
485 | 485 | self.blocksize = 0 |
|
|
486 | 486 | self.dataOut = Spectra() |
|
|
487 | 487 | self.profileIndex = 1 #Always |
|
|
488 | 488 | self.dataOut.flagNoData=False |
|
|
489 | 489 | self.dataOut.nRdPairs = 0 |
|
|
490 | 490 | self.dataOut.pairsList = [] |
|
|
491 | 491 | self.dataOut.data_spc=None |
|
|
492 | 492 | self.dataOut.noise=[] |
|
|
493 | 493 | self.dataOut.velocityX=[] |
|
|
494 | 494 | self.dataOut.velocityY=[] |
|
|
495 | 495 | self.dataOut.velocityV=[] |
|
|
496 | 496 | |
|
|
497 | 497 | |
|
|
498 | 498 | |
|
|
499 | 499 | def Files2Read(self, fp): |
|
|
500 | 500 | ''' |
|
|
501 | 501 | Function that indicates the number of .fdt files that exist in the folder to be read. |
|
|
502 | 502 | It also creates an organized list with the names of the files to read. |
|
|
503 | 503 | ''' |
|
|
504 | 504 | #self.__checkPath() |
|
|
505 | 505 | |
|
|
506 | 506 | ListaData=os.listdir(fp) #Gets the list of files within the fp address |
|
|
507 | 507 | ListaData=sorted(ListaData) #Sort the list of files from least to largest by names |
|
|
508 | 508 | nFiles=0 #File Counter |
|
|
509 | 509 | FileList=[] #A list is created that will contain the .fdt files |
|
|
510 | 510 | for IndexFile in ListaData : |
|
|
511 | 511 | if '.fdt' in IndexFile: |
|
|
512 | 512 | FileList.append(IndexFile) |
|
|
513 | 513 | nFiles+=1 |
|
|
514 | 514 | |
|
|
515 | 515 | #print 'Files2Read' |
|
|
516 | 516 | #print 'Existen '+str(nFiles)+' archivos .fdt' |
|
|
517 | 517 | |
|
|
518 | 518 | self.filenameList=FileList #List of files from least to largest by names |
|
|
519 | 519 | |
|
|
520 | 520 | |
|
|
521 | 521 | def run(self, **kwargs): |
|
|
522 | 522 | ''' |
|
|
523 | 523 | This method will be the one that will initiate the data entry, will be called constantly. |
|
|
524 | 524 | You should first verify that your Setup () is set up and then continue to acquire |
|
|
525 | 525 | the data to be processed with getData (). |
|
|
526 | 526 | ''' |
|
|
527 | 527 | if not self.isConfig: |
|
|
528 | 528 | self.setup(**kwargs) |
|
|
529 | 529 | self.isConfig = True |
|
|
530 | 530 | |
|
|
531 | 531 | self.getData() |
|
|
532 | 532 | #print 'running' |
|
|
533 | 533 | |
|
|
534 | 534 | |
|
|
535 | 535 | def setup(self, path=None, |
|
|
536 | 536 | startDate=None, |
|
|
537 | 537 | endDate=None, |
|
|
538 | 538 | startTime=None, |
|
|
539 | 539 | endTime=None, |
|
|
540 | 540 | walk=True, |
|
|
541 | 541 | timezone='utc', |
|
|
542 | 542 | code = None, |
|
|
543 | 543 | online=False, |
|
|
544 | 544 | ReadMode=None, |
|
|
545 | 545 | **kwargs): |
|
|
546 | 546 | |
|
|
547 | 547 | self.isConfig = True |
|
|
548 | 548 | |
|
|
549 | 549 | self.path=path |
|
|
550 | 550 | self.startDate=startDate |
|
|
551 | 551 | self.endDate=endDate |
|
|
552 | 552 | self.startTime=startTime |
|
|
553 | 553 | self.endTime=endTime |
|
|
554 | 554 | self.walk=walk |
|
|
555 | 555 | self.ReadMode=int(ReadMode) |
|
|
556 | 556 | |
|
|
557 | 557 | pass |
|
|
558 | 558 | |
|
|
559 | 559 | |
|
|
560 | 560 | def getData(self): |
|
|
561 | 561 | ''' |
|
|
562 | 562 | Before starting this function, you should check that there is still an unread file, |
|
|
563 | 563 | If there are still blocks to read or if the data block is empty. |
|
|
564 | 564 | |
|
|
565 | 565 | You should call the file "read". |
|
|
566 | 566 | |
|
|
567 | 567 | ''' |
|
|
568 | 568 | |
|
|
569 | 569 | if self.flagNoMoreFiles: |
|
|
570 | 570 | self.dataOut.flagNoData = True |
|
|
571 | print 'NoData se vuelve true' | |
|
|
571 | #print 'NoData se vuelve true' | |
|
|
572 | 572 | return 0 |
|
|
573 | 573 | |
|
|
574 | 574 | self.fp=self.path |
|
|
575 | 575 | self.Files2Read(self.fp) |
|
|
576 | 576 | self.readFile(self.fp) |
|
|
577 | 577 | self.dataOut.data_spc = self.data_spc |
|
|
578 | 578 | self.dataOut.data_cspc =self.data_cspc |
|
|
579 | 579 | self.dataOut.data_output=self.data_output |
|
|
580 | 580 | |
|
|
581 | print 'self.dataOut.data_output', shape(self.dataOut.data_output) | |
|
|
581 | #print 'self.dataOut.data_output', shape(self.dataOut.data_output) | |
|
|
582 | 582 | |
|
|
583 | 583 | #self.removeDC() |
|
|
584 | 584 | return self.dataOut.data_spc |
|
|
585 | 585 | |
|
|
586 | 586 | |
|
|
587 | 587 | def readFile(self,fp): |
|
|
588 | 588 | ''' |
|
|
589 | 589 | You must indicate if you are reading in Online or Offline mode and load the |
|
|
590 | 590 | The parameters for this file reading mode. |
|
|
591 | 591 | |
|
|
592 | 592 | Then you must do 2 actions: |
|
|
593 | 593 | |
|
|
594 | 594 | 1. Get the BLTR FileHeader. |
|
|
595 | 595 | 2. Start reading the first block. |
|
|
596 | 596 | ''' |
|
|
597 | 597 | |
|
|
598 | 598 | #The address of the folder is generated the name of the .fdt file that will be read |
|
|
599 | print "File: ",self.fileSelector+1 | |
|
|
599 | #print "File: ",self.fileSelector+1 | |
|
|
600 | 600 | |
|
|
601 | 601 | if self.fileSelector < len(self.filenameList): |
|
|
602 | 602 | |
|
|
603 | 603 | self.fpFile=str(fp)+'/'+str(self.filenameList[self.fileSelector]) |
|
|
604 | 604 | #print self.fpFile |
|
|
605 | 605 | fheader = FileHeaderBLTR() |
|
|
606 | 606 | fheader.FHread(self.fpFile) #Bltr FileHeader Reading |
|
|
607 | 607 | self.nFDTdataRecors=fheader.nFDTdataRecors |
|
|
608 | 608 | |
|
|
609 | 609 | self.readBlock() #Block reading |
|
|
610 | 610 | else: |
|
|
611 | print 'readFile FlagNoData becomes true' | |
|
|
611 | #print 'readFile FlagNoData becomes true' | |
|
|
612 | 612 | self.flagNoMoreFiles=True |
|
|
613 | 613 | self.dataOut.flagNoData = True |
|
|
614 | 614 | return 0 |
|
|
615 | 615 | |
|
|
616 | 616 | def getVelRange(self, extrapoints=0): |
|
|
617 | 617 | Lambda= SPEED_OF_LIGHT/50000000 |
|
|
618 | 618 | PRF = self.dataOut.PRF#1./(self.dataOut.ippSeconds * self.dataOut.nCohInt) |
|
|
619 | 619 | Vmax=-Lambda/(4.*(1./PRF)*self.dataOut.nCohInt*2.) |
|
|
620 | 620 | deltafreq = PRF / (self.nProfiles) |
|
|
621 | 621 | deltavel = (Vmax*2) / (self.nProfiles) |
|
|
622 | 622 | freqrange = deltafreq*(numpy.arange(self.nProfiles)-self.nProfiles/2.) - deltafreq/2 |
|
|
623 | 623 | velrange = deltavel*(numpy.arange(self.nProfiles)-self.nProfiles/2.) |
|
|
624 | 624 | return velrange |
|
|
625 | 625 | |
|
|
626 | 626 | def readBlock(self): |
|
|
627 | 627 | ''' |
|
|
628 | 628 | It should be checked if the block has data, if it is not passed to the next file. |
|
|
629 | 629 | |
|
|
630 | 630 | Then the following is done: |
|
|
631 | 631 | |
|
|
632 | 632 | 1. Read the RecordHeader |
|
|
633 | 633 | 2. Fill the buffer with the current block number. |
|
|
634 | 634 | |
|
|
635 | 635 | ''' |
|
|
636 | 636 | |
|
|
637 |
if self.BlockCounter < self.nFDTdataRecors- |
|
|
|
638 |
print self.nFDTdataRecors, 'CONDICION |
|
|
|
637 | if self.BlockCounter < self.nFDTdataRecors-1: | |
|
|
638 | #print self.nFDTdataRecors, 'CONDICION' | |
|
|
639 | 639 | if self.ReadMode==1: |
|
|
640 | 640 | rheader = RecordHeaderBLTR(RecCounter=self.BlockCounter+1) |
|
|
641 | 641 | elif self.ReadMode==0: |
|
|
642 | 642 | rheader = RecordHeaderBLTR(RecCounter=self.BlockCounter) |
|
|
643 | 643 | |
|
|
644 | 644 | rheader.RHread(self.fpFile) #Bltr FileHeader Reading |
|
|
645 | 645 | |
|
|
646 | 646 | self.OffsetStartHeader=rheader.OffsetStartHeader |
|
|
647 | 647 | self.RecCounter=rheader.RecCounter |
|
|
648 | 648 | self.Off2StartNxtRec=rheader.Off2StartNxtRec |
|
|
649 | 649 | self.Off2StartData=rheader.Off2StartData |
|
|
650 | 650 | self.nProfiles=rheader.nProfiles |
|
|
651 | 651 | self.nChannels=rheader.nChannels |
|
|
652 | 652 | self.nHeights=rheader.nHeights |
|
|
653 | 653 | self.frequency=rheader.TransmitFrec |
|
|
654 | 654 | self.DualModeIndex=rheader.DualModeIndex |
|
|
655 | 655 | |
|
|
656 | 656 | self.pairsList =[(0,1),(0,2),(1,2)] |
|
|
657 | 657 | self.dataOut.pairsList = self.pairsList |
|
|
658 | 658 | |
|
|
659 | 659 | self.nRdPairs=len(self.dataOut.pairsList) |
|
|
660 | 660 | self.dataOut.nRdPairs = self.nRdPairs |
|
|
661 | 661 | |
|
|
662 | 662 | self.__firstHeigth=rheader.StartRangeSamp |
|
|
663 | 663 | self.__deltaHeigth=rheader.SampResolution |
|
|
664 | 664 | self.dataOut.heightList= self.__firstHeigth + numpy.array(range(self.nHeights))*self.__deltaHeigth |
|
|
665 | 665 | self.dataOut.channelList = range(self.nChannels) |
|
|
666 | 666 | self.dataOut.nProfiles=rheader.nProfiles |
|
|
667 | 667 | self.dataOut.nIncohInt=rheader.nIncohInt |
|
|
668 | 668 | self.dataOut.nCohInt=rheader.nCohInt |
|
|
669 | 669 | self.dataOut.ippSeconds= 1/float(rheader.PRFhz) |
|
|
670 | 670 | self.dataOut.PRF=rheader.PRFhz |
|
|
671 | 671 | self.dataOut.nFFTPoints=rheader.nProfiles |
|
|
672 | 672 | self.dataOut.utctime=rheader.nUtime |
|
|
673 | 673 | self.dataOut.timeZone=0 |
|
|
674 | 674 | self.dataOut.normFactor= self.dataOut.nProfiles*self.dataOut.nIncohInt*self.dataOut.nCohInt |
|
|
675 | 675 | self.dataOut.outputInterval= self.dataOut.ippSeconds * self.dataOut.nCohInt * self.dataOut.nIncohInt * self.nProfiles |
|
|
676 | 676 | |
|
|
677 | 677 | self.data_output=numpy.ones([3,rheader.nHeights])*numpy.NaN |
|
|
678 | print 'self.data_output', shape(self.data_output) | |
|
|
678 | #print 'self.data_output', shape(self.data_output) | |
|
|
679 | 679 | self.dataOut.velocityX=[] |
|
|
680 | 680 | self.dataOut.velocityY=[] |
|
|
681 | 681 | self.dataOut.velocityV=[] |
|
|
682 | 682 | |
|
|
683 | 683 | '''Block Reading, the Block Data is received and Reshape is used to give it |
|
|
684 | 684 | shape. |
|
|
685 | 685 | ''' |
|
|
686 | 686 | |
|
|
687 | 687 | #Procedure to take the pointer to where the date block starts |
|
|
688 | 688 | startDATA = open(self.fpFile,"rb") |
|
|
689 | 689 | OffDATA= self.OffsetStartHeader + self.RecCounter*self.Off2StartNxtRec+self.Off2StartData |
|
|
690 | 690 | startDATA.seek(OffDATA, os.SEEK_SET) |
|
|
691 | 691 | |
|
|
692 | 692 | def moving_average(x, N=2): |
|
|
693 | 693 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
694 | 694 | |
|
|
695 | 695 | def gaus(xSamples,a,x0,sigma): |
|
|
696 | 696 | return a*exp(-(xSamples-x0)**2/(2*sigma**2)) |
|
|
697 | 697 | |
|
|
698 | 698 | def Find(x,value): |
|
|
699 | 699 | for index in range(len(x)): |
|
|
700 | 700 | if x[index]==value: |
|
|
701 | 701 | return index |
|
|
702 | 702 | |
|
|
703 | 703 | def pol2cart(rho, phi): |
|
|
704 | 704 | x = rho * numpy.cos(phi) |
|
|
705 | 705 | y = rho * numpy.sin(phi) |
|
|
706 | 706 | return(x, y) |
|
|
707 | 707 | |
|
|
708 | 708 | |
|
|
709 | 709 | |
|
|
710 | 710 | |
|
|
711 | 711 | if self.DualModeIndex==self.ReadMode: |
|
|
712 | 712 | |
|
|
713 | 713 | self.data_fft = numpy.fromfile( startDATA, [('complex','<c8')],self.nProfiles*self.nChannels*self.nHeights ) |
|
|
714 | # | |
|
|
715 | # if len(self.data_fft) is not 101376: | |
|
|
716 | # | |
|
|
717 | # self.data_fft = numpy.empty(101376) | |
|
|
714 | 718 | |
|
|
715 | 719 | self.data_fft=self.data_fft.astype(numpy.dtype('complex')) |
|
|
716 | 720 | |
|
|
721 | ||
|
|
722 | ||
|
|
723 | ||
|
|
724 | ||
|
|
717 | 725 | self.data_block=numpy.reshape(self.data_fft,(self.nHeights, self.nChannels, self.nProfiles )) |
|
|
718 | 726 | |
|
|
719 | 727 | self.data_block = numpy.transpose(self.data_block, (1,2,0)) |
|
|
720 | 728 | |
|
|
721 | 729 | copy = self.data_block.copy() |
|
|
722 | 730 | spc = copy * numpy.conjugate(copy) |
|
|
723 | 731 | |
|
|
724 | 732 | self.data_spc = numpy.absolute(spc) # valor absoluto o magnitud |
|
|
725 | 733 | |
|
|
726 | 734 | factor = self.dataOut.normFactor |
|
|
727 | 735 | |
|
|
728 | 736 | |
|
|
729 | 737 | z = self.data_spc.copy()#/factor |
|
|
730 | 738 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
731 | 739 | #zdB = 10*numpy.log10(z) |
|
|
732 |
|
|
|
|
733 | print 'Z: ' | |
|
|
734 | print shape(z) | |
|
|
735 | print ' ' | |
|
|
736 | print ' ' | |
|
|
740 | ||
|
|
737 | 741 | |
|
|
738 | 742 | self.dataOut.data_spc=self.data_spc |
|
|
739 | 743 | |
|
|
740 | 744 | self.noise = self.dataOut.getNoise(ymin_index=80, ymax_index=132)#/factor |
|
|
741 | 745 | #noisedB = 10*numpy.log10(self.noise) |
|
|
742 | 746 | |
|
|
743 | 747 | |
|
|
744 | 748 | ySamples=numpy.ones([3,self.nProfiles]) |
|
|
745 | 749 | phase=numpy.ones([3,self.nProfiles]) |
|
|
746 | 750 | CSPCSamples=numpy.ones([3,self.nProfiles],dtype=numpy.complex_) |
|
|
747 | 751 | coherence=numpy.ones([3,self.nProfiles]) |
|
|
748 | 752 | PhaseSlope=numpy.ones(3) |
|
|
749 | 753 | PhaseInter=numpy.ones(3) |
|
|
750 | 754 | |
|
|
751 | 755 | '''****** Getting CrossSpectra ******''' |
|
|
752 | 756 | cspc=self.data_block.copy() |
|
|
753 | 757 | self.data_cspc=self.data_block.copy() |
|
|
754 | 758 | |
|
|
755 | 759 | xFrec=self.getVelRange(1) |
|
|
756 | 760 | VelRange=self.getVelRange(1) |
|
|
757 | 761 | self.dataOut.VelRange=VelRange |
|
|
758 | 762 | #print ' ' |
|
|
759 | 763 | #print ' ' |
|
|
760 | 764 | #print 'xFrec',xFrec |
|
|
761 | 765 | #print ' ' |
|
|
762 | 766 | #print ' ' |
|
|
763 | 767 | #Height=35 |
|
|
764 | 768 | |
|
|
765 | 769 | for i in range(self.nRdPairs): |
|
|
766 | 770 | |
|
|
767 | 771 | chan_index0 = self.dataOut.pairsList[i][0] |
|
|
768 | 772 | chan_index1 = self.dataOut.pairsList[i][1] |
|
|
769 | 773 | |
|
|
770 | 774 | self.data_cspc[i,:,:]=cspc[chan_index0,:,:] * numpy.conjugate(cspc[chan_index1,:,:]) |
|
|
771 | 775 | |
|
|
772 | 776 | |
|
|
773 | 777 | '''Getting Eij and Nij''' |
|
|
774 | 778 | (AntennaX0,AntennaY0)=pol2cart(rheader.AntennaCoord0, rheader.AntennaAngl0*numpy.pi/180) |
|
|
775 | 779 | (AntennaX1,AntennaY1)=pol2cart(rheader.AntennaCoord1, rheader.AntennaAngl1*numpy.pi/180) |
|
|
776 | 780 | (AntennaX2,AntennaY2)=pol2cart(rheader.AntennaCoord2, rheader.AntennaAngl2*numpy.pi/180) |
|
|
777 | 781 | |
|
|
778 | 782 | E01=AntennaX0-AntennaX1 |
|
|
779 | 783 | N01=AntennaY0-AntennaY1 |
|
|
780 | 784 | |
|
|
781 | 785 | E02=AntennaX0-AntennaX2 |
|
|
782 | 786 | N02=AntennaY0-AntennaY2 |
|
|
783 | 787 | |
|
|
784 | 788 | E12=AntennaX1-AntennaX2 |
|
|
785 | 789 | N12=AntennaY1-AntennaY2 |
|
|
786 | 790 | |
|
|
787 | 791 | self.ChanDist= numpy.array([[E01, N01],[E02,N02],[E12,N12]]) |
|
|
788 | 792 | |
|
|
789 | 793 | self.dataOut.ChanDist = self.ChanDist |
|
|
790 | 794 | |
|
|
791 | 795 | |
|
|
792 | # for Height in range(self.nHeights): | |
|
|
793 | # | |
|
|
794 | # for i in range(self.nRdPairs): | |
|
|
795 | # | |
|
|
796 | # '''****** Line of Data SPC ******''' | |
|
|
797 | # zline=z[i,:,Height] | |
|
|
798 | # | |
|
|
799 | # '''****** DC is removed ******''' | |
|
|
800 | # DC=Find(zline,numpy.amax(zline)) | |
|
|
801 | # zline[DC]=(zline[DC-1]+zline[DC+1])/2 | |
|
|
802 | # | |
|
|
803 | # | |
|
|
804 | # '''****** SPC is normalized ******''' | |
|
|
805 | # FactNorm= zline.copy() / numpy.sum(zline.copy()) | |
|
|
806 | # FactNorm= FactNorm/numpy.sum(FactNorm) | |
|
|
807 | # | |
|
|
808 | # SmoothSPC=moving_average(FactNorm,N=3) | |
|
|
809 | # | |
|
|
810 | # xSamples = ar(range(len(SmoothSPC))) | |
|
|
811 | # ySamples[i] = SmoothSPC-self.noise[i] | |
|
|
812 | # | |
|
|
813 | # for i in range(self.nRdPairs): | |
|
|
814 | # | |
|
|
815 | # '''****** Line of Data CSPC ******''' | |
|
|
816 | # cspcLine=self.data_cspc[i,:,Height].copy() | |
|
|
817 | # | |
|
|
818 | # | |
|
|
819 | # | |
|
|
820 | # '''****** CSPC is normalized ******''' | |
|
|
821 | # chan_index0 = self.dataOut.pairsList[i][0] | |
|
|
822 | # chan_index1 = self.dataOut.pairsList[i][1] | |
|
|
823 | # CSPCFactor= numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1]) | |
|
|
824 | # | |
|
|
825 | # | |
|
|
826 | # CSPCNorm= cspcLine.copy() / numpy.sqrt(CSPCFactor) | |
|
|
827 | # | |
|
|
828 | # | |
|
|
829 | # CSPCSamples[i] = CSPCNorm-self.noise[i] | |
|
|
830 | # coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor) | |
|
|
831 | # | |
|
|
832 | # '''****** DC is removed ******''' | |
|
|
833 | # DC=Find(coherence[i],numpy.amax(coherence[i])) | |
|
|
834 | # coherence[i][DC]=(coherence[i][DC-1]+coherence[i][DC+1])/2 | |
|
|
835 | # coherence[i]= moving_average(coherence[i],N=2) | |
|
|
836 | # | |
|
|
837 | # phase[i] = moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi | |
|
|
838 | # | |
|
|
839 | # | |
|
|
840 | # '''****** Getting fij width ******''' | |
|
|
841 | # | |
|
|
842 | # yMean=[] | |
|
|
843 | # yMean2=[] | |
|
|
844 | # | |
|
|
845 | # for j in range(len(ySamples[1])): | |
|
|
846 | # yMean=numpy.append(yMean,numpy.average([ySamples[0,j],ySamples[1,j],ySamples[2,j]])) | |
|
|
847 | # | |
|
|
848 | # '''******* Getting fitting Gaussian ******''' | |
|
|
849 | # meanGauss=sum(xSamples*yMean) / len(xSamples) | |
|
|
850 | # sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples) | |
|
|
851 | # #print 'Height',Height,'SNR', meanGauss/sigma**2 | |
|
|
852 | # | |
|
|
853 | # if (abs(meanGauss/sigma**2) > 0.0001) : | |
|
|
854 | # | |
|
|
855 | # try: | |
|
|
856 | # popt,pcov = curve_fit(gaus,xSamples,yMean,p0=[1,meanGauss,sigma]) | |
|
|
857 | # | |
|
|
858 | # if numpy.amax(popt)>numpy.amax(yMean)*0.3: | |
|
|
859 | # FitGauss=gaus(xSamples,*popt) | |
|
|
860 | # | |
|
|
861 | # else: | |
|
|
862 | # FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |
|
|
863 | # print 'Verificador: Dentro', Height | |
|
|
864 | # except RuntimeError: | |
|
|
865 | # | |
|
|
866 | # try: | |
|
|
867 | # for j in range(len(ySamples[1])): | |
|
|
868 | # yMean2=numpy.append(yMean2,numpy.average([ySamples[1,j],ySamples[2,j]])) | |
|
|
869 | # popt,pcov = curve_fit(gaus,xSamples,yMean2,p0=[1,meanGauss,sigma]) | |
|
|
870 | # FitGauss=gaus(xSamples,*popt) | |
|
|
871 | # print 'Verificador: Exepcion1', Height | |
|
|
872 | # except RuntimeError: | |
|
|
873 | # | |
|
|
874 | # try: | |
|
|
875 | # popt,pcov = curve_fit(gaus,xSamples,ySamples[1],p0=[1,meanGauss,sigma]) | |
|
|
876 | # FitGauss=gaus(xSamples,*popt) | |
|
|
877 | # print 'Verificador: Exepcion2', Height | |
|
|
878 | # except RuntimeError: | |
|
|
879 | # FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |
|
|
880 | # print 'Verificador: Exepcion3', Height | |
|
|
881 | # else: | |
|
|
882 | # FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) | |
|
|
883 | # #print 'Verificador: Fuera', Height | |
|
|
884 | # | |
|
|
885 | # | |
|
|
886 | # | |
|
|
887 | # Maximun=numpy.amax(yMean) | |
|
|
888 | # eMinus1=Maximun*numpy.exp(-1) | |
|
|
889 | # | |
|
|
890 | # HWpos=Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1))) | |
|
|
891 | # HalfWidth= xFrec[HWpos] | |
|
|
892 | # GCpos=Find(FitGauss, numpy.amax(FitGauss)) | |
|
|
893 | # Vpos=Find(FactNorm, numpy.amax(FactNorm)) | |
|
|
894 | # #Vpos=numpy.sum(FactNorm)/len(FactNorm) | |
|
|
895 | # #Vpos=Find(FactNorm, min(FactNorm, key=lambda value:abs(value- numpy.mean(FactNorm) ))) | |
|
|
896 | # #print 'GCpos',GCpos, numpy.amax(FitGauss), 'HWpos',HWpos | |
|
|
897 | # '''****** Getting Fij ******''' | |
|
|
898 | # | |
|
|
899 | # GaussCenter=xFrec[GCpos] | |
|
|
900 | # if (GaussCenter<0 and HalfWidth>0) or (GaussCenter>0 and HalfWidth<0): | |
|
|
901 | # Fij=abs(GaussCenter)+abs(HalfWidth)+0.0000001 | |
|
|
902 | # else: | |
|
|
903 | # Fij=abs(GaussCenter-HalfWidth)+0.0000001 | |
|
|
904 | # | |
|
|
905 | # '''****** Getting Frecuency range of significant data ******''' | |
|
|
906 | # | |
|
|
907 | # Rangpos=Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.10))) | |
|
|
908 | # | |
|
|
909 | # if Rangpos<GCpos: | |
|
|
910 | # Range=numpy.array([Rangpos,2*GCpos-Rangpos]) | |
|
|
911 | # else: | |
|
|
912 | # Range=numpy.array([2*GCpos-Rangpos,Rangpos]) | |
|
|
913 | # | |
|
|
914 | # FrecRange=xFrec[Range[0]:Range[1]] | |
|
|
915 | # | |
|
|
916 | # #print 'FrecRange', FrecRange | |
|
|
917 | # '''****** Getting SCPC Slope ******''' | |
|
|
918 | # | |
|
|
919 | # for i in range(self.nRdPairs): | |
|
|
920 | # | |
|
|
921 | # if len(FrecRange)>5 and len(FrecRange)<self.nProfiles*0.5: | |
|
|
922 | # PhaseRange=moving_average(phase[i,Range[0]:Range[1]],N=3) | |
|
|
923 | # | |
|
|
924 | # slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange,PhaseRange) | |
|
|
925 | # PhaseSlope[i]=slope | |
|
|
926 | # PhaseInter[i]=intercept | |
|
|
927 | # else: | |
|
|
928 | # PhaseSlope[i]=0 | |
|
|
929 | # PhaseInter[i]=0 | |
|
|
930 | # | |
|
|
931 | # # plt.figure(i+15) | |
|
|
932 | # # plt.title('FASE ( CH%s*CH%s )' %(self.dataOut.pairsList[i][0],self.dataOut.pairsList[i][1])) | |
|
|
933 | # # plt.xlabel('Frecuencia (KHz)') | |
|
|
934 | # # plt.ylabel('Magnitud') | |
|
|
935 | # # #plt.subplot(311+i) | |
|
|
936 | # # plt.plot(FrecRange,PhaseRange,'b') | |
|
|
937 | # # plt.plot(FrecRange,FrecRange*PhaseSlope[i]+PhaseInter[i],'r') | |
|
|
938 | # | |
|
|
939 | # #plt.axis([-0.6, 0.2, -3.2, 3.2]) | |
|
|
940 | # | |
|
|
941 | # | |
|
|
942 | # '''Getting constant C''' | |
|
|
943 | # cC=(Fij*numpy.pi)**2 | |
|
|
944 | # | |
|
|
945 | # # '''Getting Eij and Nij''' | |
|
|
946 | # # (AntennaX0,AntennaY0)=pol2cart(rheader.AntennaCoord0, rheader.AntennaAngl0*numpy.pi/180) | |
|
|
947 | # # (AntennaX1,AntennaY1)=pol2cart(rheader.AntennaCoord1, rheader.AntennaAngl1*numpy.pi/180) | |
|
|
948 | # # (AntennaX2,AntennaY2)=pol2cart(rheader.AntennaCoord2, rheader.AntennaAngl2*numpy.pi/180) | |
|
|
949 | # # | |
|
|
950 | # # E01=AntennaX0-AntennaX1 | |
|
|
951 | # # N01=AntennaY0-AntennaY1 | |
|
|
952 | # # | |
|
|
953 | # # E02=AntennaX0-AntennaX2 | |
|
|
954 | # # N02=AntennaY0-AntennaY2 | |
|
|
955 | # # | |
|
|
956 | # # E12=AntennaX1-AntennaX2 | |
|
|
957 | # # N12=AntennaY1-AntennaY2 | |
|
|
958 | # | |
|
|
959 | # '''****** Getting constants F and G ******''' | |
|
|
960 | # MijEijNij=numpy.array([[E02,N02], [E12,N12]]) | |
|
|
961 | # MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi) | |
|
|
962 | # MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi) | |
|
|
963 | # MijResults=numpy.array([MijResult0,MijResult1]) | |
|
|
964 | # (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
|
|
965 | # | |
|
|
966 | # '''****** Getting constants A, B and H ******''' | |
|
|
967 | # W01=numpy.amax(coherence[0]) | |
|
|
968 | # W02=numpy.amax(coherence[1]) | |
|
|
969 | # W12=numpy.amax(coherence[2]) | |
|
|
970 | # | |
|
|
971 | # WijResult0=((cF*E01+cG*N01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC)) | |
|
|
972 | # WijResult1=((cF*E02+cG*N02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC)) | |
|
|
973 | # WijResult2=((cF*E12+cG*N12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC)) | |
|
|
974 | # | |
|
|
975 | # WijResults=numpy.array([WijResult0, WijResult1, WijResult2]) | |
|
|
976 | # | |
|
|
977 | # WijEijNij=numpy.array([ [E01**2, N01**2, 2*E01*N01] , [E02**2, N02**2, 2*E02*N02] , [E12**2, N12**2, 2*E12*N12] ]) | |
|
|
978 | # (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |
|
|
979 | # | |
|
|
980 | # VxVy=numpy.array([[cA,cH],[cH,cB]]) | |
|
|
981 | # | |
|
|
982 | # VxVyResults=numpy.array([-cF,-cG]) | |
|
|
983 | # (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) | |
|
|
984 | # Vzon = Vy | |
|
|
985 | # Vmer = Vx | |
|
|
986 | # Vmag=numpy.sqrt(Vzon**2+Vmer**2) | |
|
|
987 | # Vang=numpy.arctan2(Vmer,Vzon) | |
|
|
988 | # | |
|
|
989 | # if abs(Vy)<100 and abs(Vy)> 0.: | |
|
|
990 | # self.dataOut.velocityX=numpy.append(self.dataOut.velocityX, Vzon) #Vmag | |
|
|
991 | # #print 'Vmag',Vmag | |
|
|
992 | # else: | |
|
|
993 | # self.dataOut.velocityX=numpy.append(self.dataOut.velocityX, NaN) | |
|
|
994 | # | |
|
|
995 | # if abs(Vx)<100 and abs(Vx) > 0.: | |
|
|
996 | # self.dataOut.velocityY=numpy.append(self.dataOut.velocityY, Vmer) #Vang | |
|
|
997 | # #print 'Vang',Vang | |
|
|
998 | # else: | |
|
|
999 | # self.dataOut.velocityY=numpy.append(self.dataOut.velocityY, NaN) | |
|
|
1000 | # | |
|
|
1001 | # if abs(GaussCenter)<2: | |
|
|
1002 | # self.dataOut.velocityV=numpy.append(self.dataOut.velocityV, xFrec[Vpos]) | |
|
|
1003 | # | |
|
|
1004 | # else: | |
|
|
1005 | # self.dataOut.velocityV=numpy.append(self.dataOut.velocityV, NaN) | |
|
|
1006 | # | |
|
|
1007 | # | |
|
|
1008 | # # print '********************************************' | |
|
|
1009 | # # print 'HalfWidth ', HalfWidth | |
|
|
1010 | # # print 'Maximun ', Maximun | |
|
|
1011 | # # print 'eMinus1 ', eMinus1 | |
|
|
1012 | # # print 'Rangpos ', Rangpos | |
|
|
1013 | # # print 'GaussCenter ',GaussCenter | |
|
|
1014 | # # print 'E01 ',E01 | |
|
|
1015 | # # print 'N01 ',N01 | |
|
|
1016 | # # print 'E02 ',E02 | |
|
|
1017 | # # print 'N02 ',N02 | |
|
|
1018 | # # print 'E12 ',E12 | |
|
|
1019 | # # print 'N12 ',N12 | |
|
|
1020 | # #print 'self.dataOut.velocityX ', self.dataOut.velocityX | |
|
|
1021 | # # print 'Fij ', Fij | |
|
|
1022 | # # print 'cC ', cC | |
|
|
1023 | # # print 'cF ', cF | |
|
|
1024 | # # print 'cG ', cG | |
|
|
1025 | # # print 'cA ', cA | |
|
|
1026 | # # print 'cB ', cB | |
|
|
1027 | # # print 'cH ', cH | |
|
|
1028 | # # print 'Vx ', Vx | |
|
|
1029 | # # print 'Vy ', Vy | |
|
|
1030 | # # print 'Vmag ', Vmag | |
|
|
1031 | # # print 'Vang ', Vang*180/numpy.pi | |
|
|
1032 | # # print 'PhaseSlope ',PhaseSlope[0] | |
|
|
1033 | # # print 'PhaseSlope ',PhaseSlope[1] | |
|
|
1034 | # # print 'PhaseSlope ',PhaseSlope[2] | |
|
|
1035 | # # print '********************************************' | |
|
|
1036 | # #print 'data_output',shape(self.dataOut.velocityX), shape(self.dataOut.velocityY) | |
|
|
1037 | # | |
|
|
1038 | # #print 'self.dataOut.velocityX', len(self.dataOut.velocityX) | |
|
|
1039 | # #print 'self.dataOut.velocityY', len(self.dataOut.velocityY) | |
|
|
1040 | # #print 'self.dataOut.velocityV', self.dataOut.velocityV | |
|
|
1041 | # | |
|
|
1042 | # self.data_output[0]=numpy.array(self.dataOut.velocityX) | |
|
|
1043 | # self.data_output[1]=numpy.array(self.dataOut.velocityY) | |
|
|
1044 | # self.data_output[2]=numpy.array(self.dataOut.velocityV) | |
|
|
1045 | # | |
|
|
1046 | # prin= self.data_output[0][~numpy.isnan(self.data_output[0])] | |
|
|
1047 | # print ' ' | |
|
|
1048 | # print 'VmagAverage',numpy.mean(prin) | |
|
|
1049 | # print ' ' | |
|
|
1050 | # # plt.figure(5) | |
|
|
1051 | # # plt.subplot(211) | |
|
|
1052 | # # plt.plot(self.dataOut.velocityX,'yo:') | |
|
|
1053 | # # plt.subplot(212) | |
|
|
1054 | # # plt.plot(self.dataOut.velocityY,'yo:') | |
|
|
1055 | # | |
|
|
1056 | # # plt.figure(1) | |
|
|
1057 | # # # plt.subplot(121) | |
|
|
1058 | # # # plt.plot(xFrec,ySamples[0],'k',label='Ch0') | |
|
|
1059 | # # # plt.plot(xFrec,ySamples[1],'g',label='Ch1') | |
|
|
1060 | # # # plt.plot(xFrec,ySamples[2],'r',label='Ch2') | |
|
|
1061 | # # # plt.plot(xFrec,FitGauss,'yo:',label='fit') | |
|
|
1062 | # # # plt.legend() | |
|
|
1063 | # # plt.title('DATOS A ALTURA DE 2850 METROS') | |
|
|
1064 | # # | |
|
|
1065 | # # plt.xlabel('Frecuencia (KHz)') | |
|
|
1066 | # # plt.ylabel('Magnitud') | |
|
|
1067 | # # # plt.subplot(122) | |
|
|
1068 | # # # plt.title('Fit for Time Constant') | |
|
|
1069 | # # #plt.plot(xFrec,zline) | |
|
|
1070 | # # #plt.plot(xFrec,SmoothSPC,'g') | |
|
|
1071 | # # plt.plot(xFrec,FactNorm) | |
|
|
1072 | # # plt.axis([-4, 4, 0, 0.15]) | |
|
|
1073 | # # # plt.xlabel('SelfSpectra KHz') | |
|
|
1074 | # # | |
|
|
1075 | # # plt.figure(10) | |
|
|
1076 | # # # plt.subplot(121) | |
|
|
1077 | # # plt.plot(xFrec,ySamples[0],'b',label='Ch0') | |
|
|
1078 | # # plt.plot(xFrec,ySamples[1],'y',label='Ch1') | |
|
|
1079 | # # plt.plot(xFrec,ySamples[2],'r',label='Ch2') | |
|
|
1080 | # # # plt.plot(xFrec,FitGauss,'yo:',label='fit') | |
|
|
1081 | # # plt.legend() | |
|
|
1082 | # # plt.title('SELFSPECTRA EN CANALES') | |
|
|
1083 | # # | |
|
|
1084 | # # plt.xlabel('Frecuencia (KHz)') | |
|
|
1085 | # # plt.ylabel('Magnitud') | |
|
|
1086 | # # # plt.subplot(122) | |
|
|
1087 | # # # plt.title('Fit for Time Constant') | |
|
|
1088 | # # #plt.plot(xFrec,zline) | |
|
|
1089 | # # #plt.plot(xFrec,SmoothSPC,'g') | |
|
|
1090 | # # # plt.plot(xFrec,FactNorm) | |
|
|
1091 | # # # plt.axis([-4, 4, 0, 0.15]) | |
|
|
1092 | # # # plt.xlabel('SelfSpectra KHz') | |
|
|
1093 | # # | |
|
|
1094 | # # plt.figure(9) | |
|
|
1095 | # # | |
|
|
1096 | # # | |
|
|
1097 | # # plt.title('DATOS SUAVIZADOS') | |
|
|
1098 | # # plt.xlabel('Frecuencia (KHz)') | |
|
|
1099 | # # plt.ylabel('Magnitud') | |
|
|
1100 | # # plt.plot(xFrec,SmoothSPC,'g') | |
|
|
1101 | # # | |
|
|
1102 | # # #plt.plot(xFrec,FactNorm) | |
|
|
1103 | # # plt.axis([-4, 4, 0, 0.15]) | |
|
|
1104 | # # # plt.xlabel('SelfSpectra KHz') | |
|
|
1105 | # # # | |
|
|
1106 | # # plt.figure(2) | |
|
|
1107 | # # # #plt.subplot(121) | |
|
|
1108 | # # plt.plot(xFrec,yMean,'r',label='Mean SelfSpectra') | |
|
|
1109 | # # plt.plot(xFrec,FitGauss,'yo:',label='Ajuste Gaussiano') | |
|
|
1110 | # # # plt.plot(xFrec[Rangpos],FitGauss[Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.1)))],'bo') | |
|
|
1111 | # # # #plt.plot(xFrec,phase) | |
|
|
1112 | # # # plt.xlabel('Suavizado, promediado KHz') | |
|
|
1113 | # # plt.title('SELFSPECTRA PROMEDIADO') | |
|
|
1114 | # # # #plt.subplot(122) | |
|
|
1115 | # # # #plt.plot(xSamples,zline) | |
|
|
1116 | # # plt.xlabel('Frecuencia (KHz)') | |
|
|
1117 | # # plt.ylabel('Magnitud') | |
|
|
1118 | # # plt.legend() | |
|
|
1119 | # # # | |
|
|
1120 | # # # plt.figure(3) | |
|
|
1121 | # # # plt.subplot(311) | |
|
|
1122 | # # # #plt.plot(xFrec,phase[0]) | |
|
|
1123 | # # # plt.plot(xFrec,phase[0],'g') | |
|
|
1124 | # # # plt.subplot(312) | |
|
|
1125 | # # # plt.plot(xFrec,phase[1],'g') | |
|
|
1126 | # # # plt.subplot(313) | |
|
|
1127 | # # # plt.plot(xFrec,phase[2],'g') | |
|
|
1128 | # # # #plt.plot(xFrec,phase[2]) | |
|
|
1129 | # # # | |
|
|
1130 | # # # plt.figure(4) | |
|
|
1131 | # # # | |
|
|
1132 | # # # plt.plot(xSamples,coherence[0],'b') | |
|
|
1133 | # # # plt.plot(xSamples,coherence[1],'r') | |
|
|
1134 | # # # plt.plot(xSamples,coherence[2],'g') | |
|
|
1135 | # # plt.show() | |
|
|
1136 | # # # | |
|
|
1137 | # # # plt.clf() | |
|
|
1138 | # # # plt.cla() | |
|
|
1139 | # # # plt.close() | |
|
|
1140 | # | |
|
|
1141 | # print ' ' | |
|
|
1142 | ||
|
|
1143 | ||
|
|
1144 | ||
|
|
1145 | self.BlockCounter+=2 | |
|
|
1146 | ||
|
|
1147 | else: | |
|
|
1148 | self.fileSelector+=1 | |
|
|
1149 | self.BlockCounter=0 | |
|
|
1150 | print "Next File" | |
|
|
1151 | ||
|
|
1152 | ||
|
|
1153 | ||
|
|
1154 | ||
|
|
1155 | ||
| @@ -1,1091 +1,1095 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import time |
|
|
3 | 3 | import os |
|
|
4 | 4 | import h5py |
|
|
5 | 5 | import re |
|
|
6 | 6 | import datetime |
|
|
7 | 7 | |
|
|
8 | 8 | from schainpy.model.data.jrodata import * |
|
|
9 | 9 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation |
|
|
10 | 10 | # from jroIO_base import * |
|
|
11 | 11 | from schainpy.model.io.jroIO_base import * |
|
|
12 | 12 | import schainpy |
|
|
13 | 13 | |
|
|
14 | 14 | |
|
|
15 | 15 | class ParamReader(ProcessingUnit): |
|
|
16 | 16 | ''' |
|
|
17 | 17 | Reads HDF5 format files |
|
|
18 | 18 | |
|
|
19 | 19 | path |
|
|
20 | 20 | |
|
|
21 | 21 | startDate |
|
|
22 | 22 | |
|
|
23 | 23 | endDate |
|
|
24 | 24 | |
|
|
25 | 25 | startTime |
|
|
26 | 26 | |
|
|
27 | 27 | endTime |
|
|
28 | 28 | ''' |
|
|
29 | 29 | |
|
|
30 | 30 | ext = ".hdf5" |
|
|
31 | 31 | |
|
|
32 | 32 | optchar = "D" |
|
|
33 | 33 | |
|
|
34 | 34 | timezone = None |
|
|
35 | 35 | |
|
|
36 | 36 | startTime = None |
|
|
37 | 37 | |
|
|
38 | 38 | endTime = None |
|
|
39 | 39 | |
|
|
40 | 40 | fileIndex = None |
|
|
41 | 41 | |
|
|
42 | 42 | utcList = None #To select data in the utctime list |
|
|
43 | 43 | |
|
|
44 | 44 | blockList = None #List to blocks to be read from the file |
|
|
45 | 45 | |
|
|
46 | 46 | blocksPerFile = None #Number of blocks to be read |
|
|
47 | 47 | |
|
|
48 | 48 | blockIndex = None |
|
|
49 | 49 | |
|
|
50 | 50 | path = None |
|
|
51 | 51 | |
|
|
52 | 52 | #List of Files |
|
|
53 | 53 | |
|
|
54 | 54 | filenameList = None |
|
|
55 | 55 | |
|
|
56 | 56 | datetimeList = None |
|
|
57 | 57 | |
|
|
58 | 58 | #Hdf5 File |
|
|
59 | 59 | |
|
|
60 | 60 | listMetaname = None |
|
|
61 | 61 | |
|
|
62 | 62 | listMeta = None |
|
|
63 | 63 | |
|
|
64 | 64 | listDataname = None |
|
|
65 | 65 | |
|
|
66 | 66 | listData = None |
|
|
67 | 67 | |
|
|
68 | 68 | listShapes = None |
|
|
69 | 69 | |
|
|
70 | 70 | fp = None |
|
|
71 | 71 | |
|
|
72 | 72 | #dataOut reconstruction |
|
|
73 | 73 | |
|
|
74 | 74 | dataOut = None |
|
|
75 | 75 | |
|
|
76 | 76 | |
|
|
77 | 77 | def __init__(self, **kwargs): |
|
|
78 | 78 | ProcessingUnit.__init__(self, **kwargs) |
|
|
79 | 79 | self.dataOut = Parameters() |
|
|
80 | 80 | return |
|
|
81 | 81 | |
|
|
82 | 82 | def setup(self, **kwargs): |
|
|
83 | 83 | |
|
|
84 | 84 | path = kwargs['path'] |
|
|
85 | 85 | startDate = kwargs['startDate'] |
|
|
86 | 86 | endDate = kwargs['endDate'] |
|
|
87 | 87 | startTime = kwargs['startTime'] |
|
|
88 | 88 | endTime = kwargs['endTime'] |
|
|
89 | 89 | walk = kwargs['walk'] |
|
|
90 | 90 | if kwargs.has_key('ext'): |
|
|
91 | 91 | ext = kwargs['ext'] |
|
|
92 | 92 | else: |
|
|
93 | 93 | ext = '.hdf5' |
|
|
94 | 94 | if kwargs.has_key('timezone'): |
|
|
95 | 95 | self.timezone = kwargs['timezone'] |
|
|
96 | 96 | else: |
|
|
97 | 97 | self.timezone = 'lt' |
|
|
98 | 98 | |
|
|
99 | 99 | print "[Reading] Searching files in offline mode ..." |
|
|
100 | 100 | pathList, filenameList = self.__searchFilesOffLine(path, startDate=startDate, endDate=endDate, |
|
|
101 | 101 | startTime=startTime, endTime=endTime, |
|
|
102 | 102 | ext=ext, walk=walk) |
|
|
103 | 103 | |
|
|
104 | 104 | if not(filenameList): |
|
|
105 | 105 | print "There is no files into the folder: %s"%(path) |
|
|
106 | 106 | sys.exit(-1) |
|
|
107 | 107 | |
|
|
108 | 108 | self.fileIndex = -1 |
|
|
109 | 109 | self.startTime = startTime |
|
|
110 | 110 | self.endTime = endTime |
|
|
111 | 111 | |
|
|
112 | 112 | self.__readMetadata() |
|
|
113 | 113 | |
|
|
114 | 114 | self.__setNextFileOffline() |
|
|
115 | 115 | |
|
|
116 | 116 | return |
|
|
117 | 117 | |
|
|
118 | 118 | def __searchFilesOffLine(self, |
|
|
119 | 119 | path, |
|
|
120 | 120 | startDate=None, |
|
|
121 | 121 | endDate=None, |
|
|
122 | 122 | startTime=datetime.time(0,0,0), |
|
|
123 | 123 | endTime=datetime.time(23,59,59), |
|
|
124 | 124 | ext='.hdf5', |
|
|
125 | 125 | walk=True): |
|
|
126 | 126 | |
|
|
127 | 127 | expLabel = '' |
|
|
128 | 128 | self.filenameList = [] |
|
|
129 | 129 | self.datetimeList = [] |
|
|
130 | 130 | |
|
|
131 | 131 | pathList = [] |
|
|
132 | 132 | |
|
|
133 | 133 | JRODataObj = JRODataReader() |
|
|
134 | 134 | dateList, pathList = JRODataObj.findDatafiles(path, startDate, endDate, expLabel, ext, walk, include_path=True) |
|
|
135 | 135 | |
|
|
136 | 136 | if dateList == []: |
|
|
137 | 137 | print "[Reading] No *%s files in %s from %s to %s)"%(ext, path, |
|
|
138 | 138 | datetime.datetime.combine(startDate,startTime).ctime(), |
|
|
139 | 139 | datetime.datetime.combine(endDate,endTime).ctime()) |
|
|
140 | 140 | |
|
|
141 | 141 | return None, None |
|
|
142 | 142 | |
|
|
143 | 143 | if len(dateList) > 1: |
|
|
144 | 144 | print "[Reading] %d days were found in date range: %s - %s" %(len(dateList), startDate, endDate) |
|
|
145 | 145 | else: |
|
|
146 | 146 | print "[Reading] data was found for the date %s" %(dateList[0]) |
|
|
147 | 147 | |
|
|
148 | 148 | filenameList = [] |
|
|
149 | 149 | datetimeList = [] |
|
|
150 | 150 | |
|
|
151 | 151 | #---------------------------------------------------------------------------------- |
|
|
152 | 152 | |
|
|
153 | 153 | for thisPath in pathList: |
|
|
154 | 154 | # thisPath = pathList[pathDict[file]] |
|
|
155 | 155 | |
|
|
156 | 156 | fileList = glob.glob1(thisPath, "*%s" %ext) |
|
|
157 | 157 | fileList.sort() |
|
|
158 | 158 | |
|
|
159 | 159 | for file in fileList: |
|
|
160 | 160 | |
|
|
161 | 161 | filename = os.path.join(thisPath,file) |
|
|
162 | 162 | |
|
|
163 | 163 | if not isFileInDateRange(filename, startDate, endDate): |
|
|
164 | 164 | continue |
|
|
165 | 165 | |
|
|
166 | 166 | thisDatetime = self.__isFileInTimeRange(filename, startDate, endDate, startTime, endTime) |
|
|
167 | 167 | |
|
|
168 | 168 | if not(thisDatetime): |
|
|
169 | 169 | continue |
|
|
170 | 170 | |
|
|
171 | 171 | filenameList.append(filename) |
|
|
172 | 172 | datetimeList.append(thisDatetime) |
|
|
173 | 173 | |
|
|
174 | 174 | if not(filenameList): |
|
|
175 | 175 | print "[Reading] Any file was found int time range %s - %s" %(datetime.datetime.combine(startDate,startTime).ctime(), datetime.datetime.combine(endDate,endTime).ctime()) |
|
|
176 | 176 | return None, None |
|
|
177 | 177 | |
|
|
178 | 178 | print "[Reading] %d file(s) was(were) found in time range: %s - %s" %(len(filenameList), startTime, endTime) |
|
|
179 | 179 | |
|
|
180 | 180 | |
|
|
181 | 181 | # for i in range(len(filenameList)): |
|
|
182 | 182 | # print "[Reading] %s -> [%s]" %(filenameList[i], datetimeList[i].ctime()) |
|
|
183 | 183 | |
|
|
184 | 184 | self.filenameList = filenameList |
|
|
185 | 185 | self.datetimeList = datetimeList |
|
|
186 | 186 | |
|
|
187 | 187 | return pathList, filenameList |
|
|
188 | 188 | |
|
|
189 | 189 | def __isFileInTimeRange(self,filename, startDate, endDate, startTime, endTime): |
|
|
190 | 190 | |
|
|
191 | 191 | """ |
|
|
192 | 192 | Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado. |
|
|
193 | 193 | |
|
|
194 | 194 | Inputs: |
|
|
195 | 195 | filename : nombre completo del archivo de datos en formato Jicamarca (.r) |
|
|
196 | 196 | |
|
|
197 | 197 | startDate : fecha inicial del rango seleccionado en formato datetime.date |
|
|
198 | 198 | |
|
|
199 | 199 | endDate : fecha final del rango seleccionado en formato datetime.date |
|
|
200 | 200 | |
|
|
201 | 201 | startTime : tiempo inicial del rango seleccionado en formato datetime.time |
|
|
202 | 202 | |
|
|
203 | 203 | endTime : tiempo final del rango seleccionado en formato datetime.time |
|
|
204 | 204 | |
|
|
205 | 205 | Return: |
|
|
206 | 206 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
207 | 207 | fecha especificado, de lo contrario retorna False. |
|
|
208 | 208 | |
|
|
209 | 209 | Excepciones: |
|
|
210 | 210 | Si el archivo no existe o no puede ser abierto |
|
|
211 | 211 | Si la cabecera no puede ser leida. |
|
|
212 | 212 | |
|
|
213 | 213 | """ |
|
|
214 | 214 | |
|
|
215 | 215 | try: |
|
|
216 | 216 | fp = h5py.File(filename,'r') |
|
|
217 | 217 | grp1 = fp['Data'] |
|
|
218 | 218 | |
|
|
219 | 219 | except IOError: |
|
|
220 | 220 | traceback.print_exc() |
|
|
221 | 221 | raise IOError, "The file %s can't be opened" %(filename) |
|
|
222 | 222 | #chino rata |
|
|
223 | 223 | #In case has utctime attribute |
|
|
224 | 224 | grp2 = grp1['utctime'] |
|
|
225 | 225 | # thisUtcTime = grp2.value[0] - 5*3600 #To convert to local time |
|
|
226 | 226 | thisUtcTime = grp2.value[0] |
|
|
227 | 227 | |
|
|
228 | 228 | fp.close() |
|
|
229 | 229 | |
|
|
230 | 230 | if self.timezone == 'lt': |
|
|
231 | 231 | thisUtcTime -= 5*3600 |
|
|
232 | 232 | |
|
|
233 | 233 | thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0] + 5*3600) |
|
|
234 | 234 | # thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0]) |
|
|
235 | 235 | thisDate = thisDatetime.date() |
|
|
236 | 236 | thisTime = thisDatetime.time() |
|
|
237 | 237 | |
|
|
238 | 238 | startUtcTime = (datetime.datetime.combine(thisDate,startTime)- datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
239 | 239 | endUtcTime = (datetime.datetime.combine(thisDate,endTime)- datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
240 | 240 | |
|
|
241 | 241 | #General case |
|
|
242 | 242 | # o>>>>>>>>>>>>>><<<<<<<<<<<<<<o |
|
|
243 | 243 | #-----------o----------------------------o----------- |
|
|
244 | 244 | # startTime endTime |
|
|
245 | 245 | |
|
|
246 | 246 | if endTime >= startTime: |
|
|
247 | 247 | thisUtcLog = numpy.logical_and(thisUtcTime > startUtcTime, thisUtcTime < endUtcTime) |
|
|
248 | 248 | if numpy.any(thisUtcLog): #If there is one block between the hours mentioned |
|
|
249 | 249 | return thisDatetime |
|
|
250 | 250 | return None |
|
|
251 | 251 | |
|
|
252 | 252 | #If endTime < startTime then endTime belongs to the next day |
|
|
253 | 253 | #<<<<<<<<<<<o o>>>>>>>>>>> |
|
|
254 | 254 | #-----------o----------------------------o----------- |
|
|
255 | 255 | # endTime startTime |
|
|
256 | 256 | |
|
|
257 | 257 | if (thisDate == startDate) and numpy.all(thisUtcTime < startUtcTime): |
|
|
258 | 258 | return None |
|
|
259 | 259 | |
|
|
260 | 260 | if (thisDate == endDate) and numpy.all(thisUtcTime > endUtcTime): |
|
|
261 | 261 | return None |
|
|
262 | 262 | |
|
|
263 | 263 | if numpy.all(thisUtcTime < startUtcTime) and numpy.all(thisUtcTime > endUtcTime): |
|
|
264 | 264 | return None |
|
|
265 | 265 | |
|
|
266 | 266 | return thisDatetime |
|
|
267 | 267 | |
|
|
268 | 268 | def __setNextFileOffline(self): |
|
|
269 | 269 | |
|
|
270 | 270 | self.fileIndex += 1 |
|
|
271 | 271 | idFile = self.fileIndex |
|
|
272 | 272 | |
|
|
273 | 273 | if not(idFile < len(self.filenameList)): |
|
|
274 | 274 | print "No more Files" |
|
|
275 | 275 | return 0 |
|
|
276 | 276 | |
|
|
277 | 277 | filename = self.filenameList[idFile] |
|
|
278 | 278 | |
|
|
279 | 279 | filePointer = h5py.File(filename,'r') |
|
|
280 | 280 | |
|
|
281 | 281 | self.filename = filename |
|
|
282 | 282 | |
|
|
283 | 283 | self.fp = filePointer |
|
|
284 | 284 | |
|
|
285 | 285 | print "Setting the file: %s"%self.filename |
|
|
286 | 286 | |
|
|
287 | 287 | # self.__readMetadata() |
|
|
288 | 288 | self.__setBlockList() |
|
|
289 | 289 | self.__readData() |
|
|
290 | 290 | # self.nRecords = self.fp['Data'].attrs['blocksPerFile'] |
|
|
291 | 291 | # self.nRecords = self.fp['Data'].attrs['nRecords'] |
|
|
292 | 292 | self.blockIndex = 0 |
|
|
293 | 293 | return 1 |
|
|
294 | 294 | |
|
|
295 | 295 | def __setBlockList(self): |
|
|
296 | 296 | ''' |
|
|
297 | 297 | Selects the data within the times defined |
|
|
298 | 298 | |
|
|
299 | 299 | self.fp |
|
|
300 | 300 | self.startTime |
|
|
301 | 301 | self.endTime |
|
|
302 | 302 | |
|
|
303 | 303 | self.blockList |
|
|
304 | 304 | self.blocksPerFile |
|
|
305 | 305 | |
|
|
306 | 306 | ''' |
|
|
307 | 307 | fp = self.fp |
|
|
308 | 308 | startTime = self.startTime |
|
|
309 | 309 | endTime = self.endTime |
|
|
310 | 310 | |
|
|
311 | 311 | grp = fp['Data'] |
|
|
312 | 312 | thisUtcTime = grp['utctime'].value.astype(numpy.float)[0] |
|
|
313 | 313 | |
|
|
314 | 314 | #ERROOOOR |
|
|
315 | 315 | if self.timezone == 'lt': |
|
|
316 | 316 | thisUtcTime -= 5*3600 |
|
|
317 | 317 | |
|
|
318 | 318 | thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0] + 5*3600) |
|
|
319 | 319 | |
|
|
320 | 320 | thisDate = thisDatetime.date() |
|
|
321 | 321 | thisTime = thisDatetime.time() |
|
|
322 | 322 | |
|
|
323 | 323 | startUtcTime = (datetime.datetime.combine(thisDate,startTime) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
324 | 324 | endUtcTime = (datetime.datetime.combine(thisDate,endTime) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
325 | 325 | |
|
|
326 | 326 | ind = numpy.where(numpy.logical_and(thisUtcTime >= startUtcTime, thisUtcTime < endUtcTime))[0] |
|
|
327 | 327 | |
|
|
328 | 328 | self.blockList = ind |
|
|
329 | 329 | self.blocksPerFile = len(ind) |
|
|
330 | 330 | |
|
|
331 | 331 | return |
|
|
332 | 332 | |
|
|
333 | 333 | def __readMetadata(self): |
|
|
334 | 334 | ''' |
|
|
335 | 335 | Reads Metadata |
|
|
336 | 336 | |
|
|
337 | 337 | self.pathMeta |
|
|
338 | 338 | |
|
|
339 | 339 | self.listShapes |
|
|
340 | 340 | self.listMetaname |
|
|
341 | 341 | self.listMeta |
|
|
342 | 342 | |
|
|
343 | 343 | ''' |
|
|
344 | 344 | |
|
|
345 | 345 | # grp = self.fp['Data'] |
|
|
346 | 346 | # pathMeta = os.path.join(self.path, grp.attrs['metadata']) |
|
|
347 | 347 | # |
|
|
348 | 348 | # if pathMeta == self.pathMeta: |
|
|
349 | 349 | # return |
|
|
350 | 350 | # else: |
|
|
351 | 351 | # self.pathMeta = pathMeta |
|
|
352 | 352 | # |
|
|
353 | 353 | # filePointer = h5py.File(self.pathMeta,'r') |
|
|
354 | 354 | # groupPointer = filePointer['Metadata'] |
|
|
355 | 355 | |
|
|
356 | 356 | filename = self.filenameList[0] |
|
|
357 | 357 | |
|
|
358 | 358 | fp = h5py.File(filename,'r') |
|
|
359 | 359 | |
|
|
360 | 360 | gp = fp['Metadata'] |
|
|
361 | 361 | |
|
|
362 | 362 | listMetaname = [] |
|
|
363 | 363 | listMetadata = [] |
|
|
364 | 364 | for item in gp.items(): |
|
|
365 | 365 | name = item[0] |
|
|
366 | 366 | |
|
|
367 | 367 | if name=='array dimensions': |
|
|
368 | 368 | table = gp[name][:] |
|
|
369 | 369 | listShapes = {} |
|
|
370 | 370 | for shapes in table: |
|
|
371 | 371 | listShapes[shapes[0]] = numpy.array([shapes[1],shapes[2],shapes[3],shapes[4],shapes[5]]) |
|
|
372 | 372 | else: |
|
|
373 | 373 | data = gp[name].value |
|
|
374 | 374 | listMetaname.append(name) |
|
|
375 | 375 | listMetadata.append(data) |
|
|
376 | 376 | |
|
|
377 | 377 | # if name=='type': |
|
|
378 | 378 | # self.__initDataOut(data) |
|
|
379 | 379 | |
|
|
380 | 380 | self.listShapes = listShapes |
|
|
381 | 381 | self.listMetaname = listMetaname |
|
|
382 | 382 | self.listMeta = listMetadata |
|
|
383 | 383 | |
|
|
384 | 384 | fp.close() |
|
|
385 | 385 | return |
|
|
386 | 386 | |
|
|
387 | 387 | def __readData(self): |
|
|
388 | 388 | grp = self.fp['Data'] |
|
|
389 | 389 | listdataname = [] |
|
|
390 | 390 | listdata = [] |
|
|
391 | 391 | |
|
|
392 | 392 | for item in grp.items(): |
|
|
393 | 393 | name = item[0] |
|
|
394 | 394 | listdataname.append(name) |
|
|
395 | 395 | |
|
|
396 | 396 | array = self.__setDataArray(grp[name],self.listShapes[name]) |
|
|
397 | 397 | listdata.append(array) |
|
|
398 | 398 | |
|
|
399 | 399 | self.listDataname = listdataname |
|
|
400 | 400 | self.listData = listdata |
|
|
401 | 401 | return |
|
|
402 | 402 | |
|
|
403 | 403 | def __setDataArray(self, dataset, shapes): |
|
|
404 | 404 | |
|
|
405 | 405 | nDims = shapes[0] |
|
|
406 | 406 | |
|
|
407 | 407 | nDim2 = shapes[1] #Dimension 0 |
|
|
408 | 408 | |
|
|
409 | 409 | nDim1 = shapes[2] #Dimension 1, number of Points or Parameters |
|
|
410 | 410 | |
|
|
411 | 411 | nDim0 = shapes[3] #Dimension 2, number of samples or ranges |
|
|
412 | 412 | |
|
|
413 | 413 | mode = shapes[4] #Mode of storing |
|
|
414 | 414 | |
|
|
415 | 415 | blockList = self.blockList |
|
|
416 | 416 | |
|
|
417 | 417 | blocksPerFile = self.blocksPerFile |
|
|
418 | 418 | |
|
|
419 | 419 | #Depending on what mode the data was stored |
|
|
420 | 420 | if mode == 0: #Divided in channels |
|
|
421 | 421 | arrayData = dataset.value.astype(numpy.float)[0][blockList] |
|
|
422 | 422 | if mode == 1: #Divided in parameter |
|
|
423 | 423 | strds = 'table' |
|
|
424 | 424 | nDatas = nDim1 |
|
|
425 | 425 | newShapes = (blocksPerFile,nDim2,nDim0) |
|
|
426 | 426 | elif mode==2: #Concatenated in a table |
|
|
427 | 427 | strds = 'table0' |
|
|
428 | 428 | arrayData = dataset[strds].value |
|
|
429 | 429 | #Selecting part of the dataset |
|
|
430 | 430 | utctime = arrayData[:,0] |
|
|
431 | 431 | u, indices = numpy.unique(utctime, return_index=True) |
|
|
432 | 432 | |
|
|
433 | 433 | if blockList.size != indices.size: |
|
|
434 | 434 | indMin = indices[blockList[0]] |
|
|
435 | 435 | if blockList[1] + 1 >= indices.size: |
|
|
436 | 436 | arrayData = arrayData[indMin:,:] |
|
|
437 | 437 | else: |
|
|
438 | 438 | indMax = indices[blockList[1] + 1] |
|
|
439 | 439 | arrayData = arrayData[indMin:indMax,:] |
|
|
440 | 440 | return arrayData |
|
|
441 | 441 | |
|
|
442 | 442 | # One dimension |
|
|
443 | 443 | if nDims == 0: |
|
|
444 | 444 | arrayData = dataset.value.astype(numpy.float)[0][blockList] |
|
|
445 | 445 | |
|
|
446 | 446 | # Two dimensions |
|
|
447 | 447 | elif nDims == 2: |
|
|
448 | 448 | arrayData = numpy.zeros((blocksPerFile,nDim1,nDim0)) |
|
|
449 | 449 | newShapes = (blocksPerFile,nDim0) |
|
|
450 | 450 | nDatas = nDim1 |
|
|
451 | 451 | |
|
|
452 | 452 | for i in range(nDatas): |
|
|
453 | 453 | data = dataset[strds + str(i)].value |
|
|
454 | 454 | arrayData[:,i,:] = data[blockList,:] |
|
|
455 | 455 | |
|
|
456 | 456 | # Three dimensions |
|
|
457 | 457 | else: |
|
|
458 | 458 | arrayData = numpy.zeros((blocksPerFile,nDim2,nDim1,nDim0)) |
|
|
459 | 459 | for i in range(nDatas): |
|
|
460 | 460 | |
|
|
461 | 461 | data = dataset[strds + str(i)].value |
|
|
462 | 462 | |
|
|
463 | 463 | for b in range(blockList.size): |
|
|
464 | 464 | arrayData[b,:,i,:] = data[:,:,blockList[b]] |
|
|
465 | 465 | |
|
|
466 | 466 | return arrayData |
|
|
467 | 467 | |
|
|
468 | 468 | def __setDataOut(self): |
|
|
469 | 469 | listMeta = self.listMeta |
|
|
470 | 470 | listMetaname = self.listMetaname |
|
|
471 | 471 | listDataname = self.listDataname |
|
|
472 | 472 | listData = self.listData |
|
|
473 | 473 | listShapes = self.listShapes |
|
|
474 | 474 | |
|
|
475 | 475 | blockIndex = self.blockIndex |
|
|
476 | 476 | # blockList = self.blockList |
|
|
477 | 477 | |
|
|
478 | 478 | for i in range(len(listMeta)): |
|
|
479 | 479 | setattr(self.dataOut,listMetaname[i],listMeta[i]) |
|
|
480 | 480 | |
|
|
481 | 481 | for j in range(len(listData)): |
|
|
482 | 482 | nShapes = listShapes[listDataname[j]][0] |
|
|
483 | 483 | mode = listShapes[listDataname[j]][4] |
|
|
484 | 484 | if nShapes == 1: |
|
|
485 | 485 | setattr(self.dataOut,listDataname[j],listData[j][blockIndex]) |
|
|
486 | 486 | elif nShapes > 1: |
|
|
487 | 487 | setattr(self.dataOut,listDataname[j],listData[j][blockIndex,:]) |
|
|
488 | 488 | elif mode==0: |
|
|
489 | 489 | setattr(self.dataOut,listDataname[j],listData[j][blockIndex]) |
|
|
490 | 490 | #Mode Meteors |
|
|
491 | 491 | elif mode ==2: |
|
|
492 | 492 | selectedData = self.__selectDataMode2(listData[j], blockIndex) |
|
|
493 | 493 | setattr(self.dataOut, listDataname[j], selectedData) |
|
|
494 | 494 | return |
|
|
495 | 495 | |
|
|
496 | 496 | def __selectDataMode2(self, data, blockIndex): |
|
|
497 | 497 | utctime = data[:,0] |
|
|
498 | 498 | aux, indices = numpy.unique(utctime, return_inverse=True) |
|
|
499 | 499 | selInd = numpy.where(indices == blockIndex)[0] |
|
|
500 | 500 | selData = data[selInd,:] |
|
|
501 | 501 | |
|
|
502 | 502 | return selData |
|
|
503 | 503 | |
|
|
504 | 504 | def getData(self): |
|
|
505 | 505 | |
|
|
506 | 506 | # if self.flagNoMoreFiles: |
|
|
507 | 507 | # self.dataOut.flagNoData = True |
|
|
508 | 508 | # print 'Process finished' |
|
|
509 | 509 | # return 0 |
|
|
510 | 510 | # |
|
|
511 | 511 | if self.blockIndex==self.blocksPerFile: |
|
|
512 | 512 | if not( self.__setNextFileOffline() ): |
|
|
513 | 513 | self.dataOut.flagNoData = True |
|
|
514 | 514 | return 0 |
|
|
515 | 515 | |
|
|
516 | 516 | # if self.datablock == None: # setear esta condicion cuando no hayan datos por leers |
|
|
517 | 517 | # self.dataOut.flagNoData = True |
|
|
518 | 518 | # return 0 |
|
|
519 | 519 | # self.__readData() |
|
|
520 | 520 | self.__setDataOut() |
|
|
521 | 521 | self.dataOut.flagNoData = False |
|
|
522 | 522 | |
|
|
523 | 523 | self.blockIndex += 1 |
|
|
524 | 524 | |
|
|
525 | 525 | return |
|
|
526 | 526 | |
|
|
527 | 527 | def run(self, **kwargs): |
|
|
528 | 528 | |
|
|
529 | 529 | if not(self.isConfig): |
|
|
530 | 530 | self.setup(**kwargs) |
|
|
531 | 531 | # self.setObjProperties() |
|
|
532 | 532 | self.isConfig = True |
|
|
533 | 533 | |
|
|
534 | 534 | self.getData() |
|
|
535 | 535 | |
|
|
536 | 536 | return |
|
|
537 | 537 | |
|
|
538 | 538 | class ParamWriter(Operation): |
|
|
539 | 539 | ''' |
|
|
540 | 540 | HDF5 Writer, stores parameters data in HDF5 format files |
|
|
541 | 541 | |
|
|
542 | 542 | path: path where the files will be stored |
|
|
543 | 543 | |
|
|
544 | 544 | blocksPerFile: number of blocks that will be saved in per HDF5 format file |
|
|
545 | 545 | |
|
|
546 | 546 | mode: selects the data stacking mode: '0' channels, '1' parameters, '3' table (for meteors) |
|
|
547 | 547 | |
|
|
548 | 548 | metadataList: list of attributes that will be stored as metadata |
|
|
549 | 549 | |
|
|
550 | 550 | dataList: list of attributes that will be stores as data |
|
|
551 | 551 | |
|
|
552 | 552 | ''' |
|
|
553 | 553 | |
|
|
554 | 554 | |
|
|
555 | 555 | ext = ".hdf5" |
|
|
556 | 556 | |
|
|
557 | 557 | optchar = "D" |
|
|
558 | 558 | |
|
|
559 | 559 | metaoptchar = "M" |
|
|
560 | 560 | |
|
|
561 | 561 | metaFile = None |
|
|
562 | 562 | |
|
|
563 | 563 | filename = None |
|
|
564 | 564 | |
|
|
565 | 565 | path = None |
|
|
566 | 566 | |
|
|
567 | 567 | setFile = None |
|
|
568 | 568 | |
|
|
569 | 569 | fp = None |
|
|
570 | 570 | |
|
|
571 | 571 | grp = None |
|
|
572 | 572 | |
|
|
573 | 573 | ds = None |
|
|
574 | 574 | |
|
|
575 | 575 | firsttime = True |
|
|
576 | 576 | |
|
|
577 | 577 | #Configurations |
|
|
578 | 578 | |
|
|
579 | 579 | blocksPerFile = None |
|
|
580 | 580 | |
|
|
581 | 581 | blockIndex = None |
|
|
582 | 582 | |
|
|
583 | 583 | dataOut = None |
|
|
584 | 584 | |
|
|
585 | 585 | #Data Arrays |
|
|
586 | 586 | |
|
|
587 | 587 | dataList = None |
|
|
588 | 588 | |
|
|
589 | 589 | metadataList = None |
|
|
590 | 590 | |
|
|
591 | 591 | # arrayDim = None |
|
|
592 | 592 | |
|
|
593 | 593 | dsList = None #List of dictionaries with dataset properties |
|
|
594 | 594 | |
|
|
595 | 595 | tableDim = None |
|
|
596 | 596 | |
|
|
597 | 597 | # dtype = [('arrayName', 'S20'),('nChannels', 'i'), ('nPoints', 'i'), ('nSamples', 'i'),('mode', 'b')] |
|
|
598 | 598 | |
|
|
599 | 599 | dtype = [('arrayName', 'S20'),('nDimensions', 'i'), ('dim2', 'i'), ('dim1', 'i'),('dim0', 'i'),('mode', 'b')] |
|
|
600 | 600 | |
|
|
601 | 601 | currentDay = None |
|
|
602 | 602 | |
|
|
603 | 603 | lastTime = None |
|
|
604 | 604 | |
|
|
605 | 605 | def __init__(self, **kwargs): |
|
|
606 | 606 | Operation.__init__(self, **kwargs) |
|
|
607 | 607 | self.isConfig = False |
|
|
608 | 608 | return |
|
|
609 | 609 | |
|
|
610 | 610 | def setup(self, dataOut, **kwargs): |
|
|
611 | 611 | |
|
|
612 | 612 | self.path = kwargs['path'] |
|
|
613 | 613 | |
|
|
614 | 614 | if kwargs.has_key('blocksPerFile'): |
|
|
615 | 615 | self.blocksPerFile = kwargs['blocksPerFile'] |
|
|
616 | 616 | else: |
|
|
617 | 617 | self.blocksPerFile = 10 |
|
|
618 | 618 | |
|
|
619 | 619 | self.metadataList = kwargs['metadataList'] |
|
|
620 | 620 | self.dataList = kwargs['dataList'] |
|
|
621 | 621 | self.dataOut = dataOut |
|
|
622 | 622 | |
|
|
623 | 623 | if kwargs.has_key('mode'): |
|
|
624 | 624 | mode = kwargs['mode'] |
|
|
625 | 625 | |
|
|
626 | 626 | if type(mode) == int: |
|
|
627 | 627 | mode = numpy.zeros(len(self.dataList)) + mode |
|
|
628 | 628 | else: |
|
|
629 | 629 | mode = numpy.ones(len(self.dataList)) |
|
|
630 | 630 | |
|
|
631 | 631 | self.mode = mode |
|
|
632 | 632 | |
|
|
633 | 633 | arrayDim = numpy.zeros((len(self.dataList),5)) |
|
|
634 | 634 | |
|
|
635 | 635 | #Table dimensions |
|
|
636 | 636 | dtype0 = self.dtype |
|
|
637 | 637 | tableList = [] |
|
|
638 | 638 | |
|
|
639 | 639 | #Dictionary and list of tables |
|
|
640 | 640 | dsList = [] |
|
|
641 | 641 | |
|
|
642 | 642 | for i in range(len(self.dataList)): |
|
|
643 | 643 | dsDict = {} |
|
|
644 | 644 | dataAux = getattr(self.dataOut, self.dataList[i]) |
|
|
645 | 645 | dsDict['variable'] = self.dataList[i] |
|
|
646 | 646 | #--------------------- Conditionals ------------------------ |
|
|
647 | 647 | #There is no data |
|
|
648 | ||
|
|
649 | ||
|
|
648 | 650 | if dataAux is None: |
|
|
651 | ||
|
|
649 | 652 | return 0 |
|
|
650 | 653 | |
|
|
651 | 654 | #Not array, just a number |
|
|
652 | 655 | #Mode 0 |
|
|
653 | 656 | if type(dataAux)==float or type(dataAux)==int: |
|
|
654 | 657 | dsDict['mode'] = 0 |
|
|
655 | 658 | dsDict['nDim'] = 0 |
|
|
656 | 659 | arrayDim[i,0] = 0 |
|
|
657 | 660 | dsList.append(dsDict) |
|
|
658 | 661 | |
|
|
659 | 662 | #Mode 2: meteors |
|
|
660 | 663 | elif mode[i] == 2: |
|
|
661 | 664 | # dsDict['nDim'] = 0 |
|
|
662 | 665 | dsDict['dsName'] = 'table0' |
|
|
663 | 666 | dsDict['mode'] = 2 # Mode meteors |
|
|
664 | 667 | dsDict['shape'] = dataAux.shape[-1] |
|
|
665 | 668 | dsDict['nDim'] = 0 |
|
|
666 | 669 | dsDict['dsNumber'] = 1 |
|
|
667 | 670 | |
|
|
668 | 671 | arrayDim[i,3] = dataAux.shape[-1] |
|
|
669 | 672 | arrayDim[i,4] = mode[i] #Mode the data was stored |
|
|
670 | 673 | |
|
|
671 | 674 | dsList.append(dsDict) |
|
|
672 | 675 | |
|
|
673 | 676 | #Mode 1 |
|
|
674 | 677 | else: |
|
|
675 | 678 | arrayDim0 = dataAux.shape #Data dimensions |
|
|
676 | 679 | arrayDim[i,0] = len(arrayDim0) #Number of array dimensions |
|
|
677 | 680 | arrayDim[i,4] = mode[i] #Mode the data was stored |
|
|
678 | 681 | |
|
|
679 | 682 | strtable = 'table' |
|
|
680 | 683 | dsDict['mode'] = 1 # Mode parameters |
|
|
681 | 684 | |
|
|
682 | 685 | # Three-dimension arrays |
|
|
683 | 686 | if len(arrayDim0) == 3: |
|
|
684 | 687 | arrayDim[i,1:-1] = numpy.array(arrayDim0) |
|
|
685 | 688 | nTables = int(arrayDim[i,2]) |
|
|
686 | 689 | dsDict['dsNumber'] = nTables |
|
|
687 | 690 | dsDict['shape'] = arrayDim[i,2:4] |
|
|
688 | 691 | dsDict['nDim'] = 3 |
|
|
689 | 692 | |
|
|
690 | 693 | for j in range(nTables): |
|
|
691 | 694 | dsDict = dsDict.copy() |
|
|
692 | 695 | dsDict['dsName'] = strtable + str(j) |
|
|
693 | 696 | dsList.append(dsDict) |
|
|
694 | 697 | |
|
|
695 | 698 | # Two-dimension arrays |
|
|
696 | 699 | elif len(arrayDim0) == 2: |
|
|
697 | 700 | arrayDim[i,2:-1] = numpy.array(arrayDim0) |
|
|
698 | 701 | nTables = int(arrayDim[i,2]) |
|
|
699 | 702 | dsDict['dsNumber'] = nTables |
|
|
700 | 703 | dsDict['shape'] = arrayDim[i,3] |
|
|
701 | 704 | dsDict['nDim'] = 2 |
|
|
702 | 705 | |
|
|
703 | 706 | for j in range(nTables): |
|
|
704 | 707 | dsDict = dsDict.copy() |
|
|
705 | 708 | dsDict['dsName'] = strtable + str(j) |
|
|
706 | 709 | dsList.append(dsDict) |
|
|
707 | 710 | |
|
|
708 | 711 | # One-dimension arrays |
|
|
709 | 712 | elif len(arrayDim0) == 1: |
|
|
710 | 713 | arrayDim[i,3] = arrayDim0[0] |
|
|
711 | 714 | dsDict['shape'] = arrayDim0[0] |
|
|
712 | 715 | dsDict['dsNumber'] = 1 |
|
|
713 | 716 | dsDict['dsName'] = strtable + str(0) |
|
|
714 | 717 | dsDict['nDim'] = 1 |
|
|
715 | 718 | dsList.append(dsDict) |
|
|
716 | 719 | |
|
|
717 | 720 | table = numpy.array((self.dataList[i],) + tuple(arrayDim[i,:]),dtype = dtype0) |
|
|
718 | 721 | tableList.append(table) |
|
|
719 | 722 | |
|
|
720 | 723 | # self.arrayDim = arrayDim |
|
|
721 | 724 | self.dsList = dsList |
|
|
722 | 725 | self.tableDim = numpy.array(tableList, dtype = dtype0) |
|
|
723 | 726 | self.blockIndex = 0 |
|
|
724 | 727 | |
|
|
725 | 728 | timeTuple = time.localtime(dataOut.utctime) |
|
|
726 | 729 | self.currentDay = timeTuple.tm_yday |
|
|
727 | 730 | return 1 |
|
|
728 | 731 | |
|
|
729 | 732 | def putMetadata(self): |
|
|
730 | 733 | |
|
|
731 | 734 | fp = self.createMetadataFile() |
|
|
732 | 735 | self.writeMetadata(fp) |
|
|
733 | 736 | fp.close() |
|
|
734 | 737 | return |
|
|
735 | 738 | |
|
|
736 | 739 | def createMetadataFile(self): |
|
|
737 | 740 | ext = self.ext |
|
|
738 | 741 | path = self.path |
|
|
739 | 742 | setFile = self.setFile |
|
|
740 | 743 | |
|
|
741 | 744 | timeTuple = time.localtime(self.dataOut.utctime) |
|
|
742 | 745 | |
|
|
743 | 746 | subfolder = '' |
|
|
744 | 747 | fullpath = os.path.join( path, subfolder ) |
|
|
745 | 748 | |
|
|
746 | 749 | if not( os.path.exists(fullpath) ): |
|
|
747 | 750 | os.mkdir(fullpath) |
|
|
748 | 751 | setFile = -1 #inicializo mi contador de seteo |
|
|
749 | 752 | |
|
|
750 | 753 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) |
|
|
751 | 754 | fullpath = os.path.join( path, subfolder ) |
|
|
752 | 755 | |
|
|
753 | 756 | if not( os.path.exists(fullpath) ): |
|
|
754 | 757 | os.mkdir(fullpath) |
|
|
755 | 758 | setFile = -1 #inicializo mi contador de seteo |
|
|
756 | 759 | |
|
|
757 | 760 | else: |
|
|
758 | 761 | filesList = os.listdir( fullpath ) |
|
|
759 | 762 | filesList = sorted( filesList, key=str.lower ) |
|
|
760 | 763 | if len( filesList ) > 0: |
|
|
761 | 764 | filesList = [k for k in filesList if 'M' in k] |
|
|
762 | 765 | filen = filesList[-1] |
|
|
763 | 766 | # el filename debera tener el siguiente formato |
|
|
764 | 767 | # 0 1234 567 89A BCDE (hex) |
|
|
765 | 768 | # x YYYY DDD SSS .ext |
|
|
766 | 769 | if isNumber( filen[8:11] ): |
|
|
767 | 770 | setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
768 | 771 | else: |
|
|
769 | 772 | setFile = -1 |
|
|
770 | 773 | else: |
|
|
771 | 774 | setFile = -1 #inicializo mi contador de seteo |
|
|
772 | 775 | |
|
|
773 | 776 | setFile += 1 |
|
|
774 | 777 | |
|
|
775 | 778 | file = '%s%4.4d%3.3d%3.3d%s' % (self.metaoptchar, |
|
|
776 | 779 | timeTuple.tm_year, |
|
|
777 | 780 | timeTuple.tm_yday, |
|
|
778 | 781 | setFile, |
|
|
779 | 782 | ext ) |
|
|
780 | 783 | |
|
|
781 | 784 | filename = os.path.join( path, subfolder, file ) |
|
|
782 | 785 | self.metaFile = file |
|
|
783 | 786 | #Setting HDF5 File |
|
|
784 | 787 | fp = h5py.File(filename,'w') |
|
|
785 | 788 | |
|
|
786 | 789 | return fp |
|
|
787 | 790 | |
|
|
788 | 791 | def writeMetadata(self, fp): |
|
|
789 | 792 | |
|
|
790 | 793 | grp = fp.create_group("Metadata") |
|
|
791 | 794 | grp.create_dataset('array dimensions', data = self.tableDim, dtype = self.dtype) |
|
|
792 | 795 | |
|
|
793 | 796 | for i in range(len(self.metadataList)): |
|
|
794 | 797 | print '#####',self.metadataList[i], getattr(self.dataOut, self.metadataList[i]) |
|
|
795 | 798 | grp.create_dataset(self.metadataList[i], data=getattr(self.dataOut, self.metadataList[i])) |
|
|
796 | 799 | return |
|
|
797 | 800 | |
|
|
798 | 801 | def timeFlag(self): |
|
|
799 | 802 | currentTime = self.dataOut.utctime |
|
|
800 | 803 | |
|
|
801 | 804 | if self.lastTime is None: |
|
|
802 | 805 | self.lastTime = currentTime |
|
|
803 | 806 | |
|
|
804 | 807 | #Day |
|
|
805 | 808 | timeTuple = time.localtime(currentTime) |
|
|
806 | 809 | dataDay = timeTuple.tm_yday |
|
|
807 | 810 | |
|
|
808 | 811 | #Time |
|
|
809 | 812 | timeDiff = currentTime - self.lastTime |
|
|
810 | 813 | |
|
|
811 | 814 | #Si el dia es diferente o si la diferencia entre un dato y otro supera la hora |
|
|
812 | 815 | if dataDay != self.currentDay: |
|
|
813 | 816 | self.currentDay = dataDay |
|
|
814 | 817 | return True |
|
|
815 | 818 | elif timeDiff > 3*60*60: |
|
|
816 | 819 | self.lastTime = currentTime |
|
|
817 | 820 | return True |
|
|
818 | 821 | else: |
|
|
819 | 822 | self.lastTime = currentTime |
|
|
820 | 823 | return False |
|
|
821 | 824 | |
|
|
822 | 825 | def setNextFile(self): |
|
|
823 | ||
|
|
826 | ||
|
|
824 | 827 | ext = self.ext |
|
|
825 | 828 | path = self.path |
|
|
826 | 829 | setFile = self.setFile |
|
|
827 | 830 | mode = self.mode |
|
|
828 | 831 | |
|
|
829 | 832 | timeTuple = time.localtime(self.dataOut.utctime) |
|
|
830 | 833 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) |
|
|
831 | 834 | |
|
|
832 | 835 | fullpath = os.path.join( path, subfolder ) |
|
|
833 | 836 | |
|
|
834 | 837 | if os.path.exists(fullpath): |
|
|
835 | 838 | filesList = os.listdir( fullpath ) |
|
|
836 | 839 | filesList = [k for k in filesList if 'D' in k] |
|
|
837 | 840 | if len( filesList ) > 0: |
|
|
838 | 841 | filesList = sorted( filesList, key=str.lower ) |
|
|
839 | 842 | filen = filesList[-1] |
|
|
840 | 843 | # el filename debera tener el siguiente formato |
|
|
841 | 844 | # 0 1234 567 89A BCDE (hex) |
|
|
842 | 845 | # x YYYY DDD SSS .ext |
|
|
843 | 846 | if isNumber( filen[8:11] ): |
|
|
844 | 847 | setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
845 | 848 | else: |
|
|
846 | 849 | setFile = -1 |
|
|
847 | 850 | else: |
|
|
848 | 851 | setFile = -1 #inicializo mi contador de seteo |
|
|
849 | 852 | else: |
|
|
850 | 853 | os.makedirs(fullpath) |
|
|
851 | 854 | setFile = -1 #inicializo mi contador de seteo |
|
|
852 | 855 | |
|
|
853 | 856 | setFile += 1 |
|
|
854 | 857 | |
|
|
855 | 858 | file = '%s%4.4d%3.3d%3.3d%s' % (self.optchar, |
|
|
856 | 859 | timeTuple.tm_year, |
|
|
857 | 860 | timeTuple.tm_yday, |
|
|
858 | 861 | setFile, |
|
|
859 | 862 | ext ) |
|
|
860 | 863 | |
|
|
861 | 864 | filename = os.path.join( path, subfolder, file ) |
|
|
862 | 865 | |
|
|
863 | 866 | #Setting HDF5 File |
|
|
864 | 867 | fp = h5py.File(filename,'w') |
|
|
865 | 868 | #write metadata |
|
|
866 | 869 | self.writeMetadata(fp) |
|
|
867 | 870 | #Write data |
|
|
868 | 871 | grp = fp.create_group("Data") |
|
|
869 | 872 | # grp.attrs['metadata'] = self.metaFile |
|
|
870 | 873 | |
|
|
871 | 874 | # grp.attrs['blocksPerFile'] = 0 |
|
|
872 | 875 | ds = [] |
|
|
873 | 876 | data = [] |
|
|
874 | 877 | dsList = self.dsList |
|
|
875 | 878 | i = 0 |
|
|
876 | 879 | while i < len(dsList): |
|
|
877 | 880 | dsInfo = dsList[i] |
|
|
878 | 881 | #One-dimension data |
|
|
879 | 882 | if dsInfo['mode'] == 0: |
|
|
880 | 883 | # ds0 = grp.create_dataset(self.dataList[i], (1,1), maxshape=(1,self.blocksPerFile) , chunks = True, dtype='S20') |
|
|
881 | 884 | ds0 = grp.create_dataset(dsInfo['variable'], (1,1), maxshape=(1,self.blocksPerFile) , chunks = True, dtype=numpy.float64) |
|
|
882 | 885 | ds.append(ds0) |
|
|
883 | 886 | data.append([]) |
|
|
884 | 887 | i += 1 |
|
|
885 | 888 | continue |
|
|
886 | 889 | # nDimsForDs.append(nDims[i]) |
|
|
887 | 890 | |
|
|
888 | 891 | elif dsInfo['mode'] == 2: |
|
|
889 | 892 | grp0 = grp.create_group(dsInfo['variable']) |
|
|
890 | 893 | ds0 = grp0.create_dataset(dsInfo['dsName'], (1,dsInfo['shape']), data = numpy.zeros((1,dsInfo['shape'])) , maxshape=(None,dsInfo['shape']), chunks=True) |
|
|
891 | 894 | ds.append(ds0) |
|
|
892 | 895 | data.append([]) |
|
|
893 | 896 | i += 1 |
|
|
894 | 897 | continue |
|
|
895 | 898 | |
|
|
896 | 899 | elif dsInfo['mode'] == 1: |
|
|
897 | 900 | grp0 = grp.create_group(dsInfo['variable']) |
|
|
898 | 901 | |
|
|
899 | 902 | for j in range(dsInfo['dsNumber']): |
|
|
900 | 903 | dsInfo = dsList[i] |
|
|
901 | 904 | tableName = dsInfo['dsName'] |
|
|
902 | 905 | shape = int(dsInfo['shape']) |
|
|
903 | 906 | |
|
|
904 | 907 | if dsInfo['nDim'] == 3: |
|
|
905 | 908 | ds0 = grp0.create_dataset(tableName, (shape[0],shape[1],1) , data = numpy.zeros((shape[0],shape[1],1)), maxshape = (None,shape[1],None), chunks=True) |
|
|
906 | 909 | else: |
|
|
907 | 910 | ds0 = grp0.create_dataset(tableName, (1,shape), data = numpy.zeros((1,shape)) , maxshape=(None,shape), chunks=True) |
|
|
908 | 911 | |
|
|
909 | 912 | ds.append(ds0) |
|
|
910 | 913 | data.append([]) |
|
|
911 | 914 | i += 1 |
|
|
912 | 915 | # nDimsForDs.append(nDims[i]) |
|
|
913 | 916 | |
|
|
914 | 917 | fp.flush() |
|
|
915 | 918 | fp.close() |
|
|
916 | 919 | |
|
|
917 | 920 | # self.nDatas = nDatas |
|
|
918 | 921 | # self.nDims = nDims |
|
|
919 | 922 | # self.nDimsForDs = nDimsForDs |
|
|
920 | 923 | #Saving variables |
|
|
921 | 924 | print 'Writing the file: %s'%filename |
|
|
922 | 925 | self.filename = filename |
|
|
923 | 926 | # self.fp = fp |
|
|
924 | 927 | # self.grp = grp |
|
|
925 | 928 | # self.grp.attrs.modify('nRecords', 1) |
|
|
926 | 929 | self.ds = ds |
|
|
927 | 930 | self.data = data |
|
|
928 | 931 | # self.setFile = setFile |
|
|
929 | 932 | self.firsttime = True |
|
|
930 | 933 | self.blockIndex = 0 |
|
|
931 | 934 | return |
|
|
932 | 935 | |
|
|
933 | 936 | def putData(self): |
|
|
934 | 937 | |
|
|
935 | 938 | if self.blockIndex == self.blocksPerFile or self.timeFlag(): |
|
|
936 | 939 | self.setNextFile() |
|
|
937 | 940 | |
|
|
938 | 941 | # if not self.firsttime: |
|
|
939 | 942 | self.readBlock() |
|
|
940 | 943 | self.setBlock() #Prepare data to be written |
|
|
941 | 944 | self.writeBlock() #Write data |
|
|
942 | 945 | |
|
|
943 | 946 | return |
|
|
944 | 947 | |
|
|
945 | 948 | def readBlock(self): |
|
|
946 | 949 | |
|
|
947 | 950 | ''' |
|
|
948 | 951 | data Array configured |
|
|
949 | 952 | |
|
|
950 | 953 | |
|
|
951 | 954 | self.data |
|
|
952 | 955 | ''' |
|
|
953 | 956 | dsList = self.dsList |
|
|
954 | 957 | ds = self.ds |
|
|
955 | 958 | #Setting HDF5 File |
|
|
956 | 959 | fp = h5py.File(self.filename,'r+') |
|
|
957 | 960 | grp = fp["Data"] |
|
|
958 | 961 | ind = 0 |
|
|
959 | 962 | |
|
|
960 | 963 | # grp.attrs['blocksPerFile'] = 0 |
|
|
961 | 964 | while ind < len(dsList): |
|
|
962 | 965 | dsInfo = dsList[ind] |
|
|
963 | 966 | |
|
|
964 | 967 | if dsInfo['mode'] == 0: |
|
|
965 | 968 | ds0 = grp[dsInfo['variable']] |
|
|
966 | 969 | ds[ind] = ds0 |
|
|
967 | 970 | ind += 1 |
|
|
968 | 971 | else: |
|
|
969 | 972 | |
|
|
970 | 973 | grp0 = grp[dsInfo['variable']] |
|
|
971 | 974 | |
|
|
972 | 975 | for j in range(dsInfo['dsNumber']): |
|
|
973 | 976 | dsInfo = dsList[ind] |
|
|
974 | 977 | ds0 = grp0[dsInfo['dsName']] |
|
|
975 | 978 | ds[ind] = ds0 |
|
|
976 | 979 | ind += 1 |
|
|
977 | 980 | |
|
|
978 | 981 | self.fp = fp |
|
|
979 | 982 | self.grp = grp |
|
|
980 | 983 | self.ds = ds |
|
|
981 | 984 | |
|
|
982 | 985 | return |
|
|
983 | 986 | |
|
|
984 | 987 | def setBlock(self): |
|
|
985 | 988 | ''' |
|
|
986 | 989 | data Array configured |
|
|
987 | 990 | |
|
|
988 | 991 | |
|
|
989 | 992 | self.data |
|
|
990 | 993 | ''' |
|
|
991 | 994 | #Creating Arrays |
|
|
992 | 995 | dsList = self.dsList |
|
|
993 | 996 | data = self.data |
|
|
994 | 997 | ind = 0 |
|
|
995 | 998 | |
|
|
996 | 999 | while ind < len(dsList): |
|
|
997 | 1000 | dsInfo = dsList[ind] |
|
|
998 | 1001 | dataAux = getattr(self.dataOut, dsInfo['variable']) |
|
|
999 | 1002 | |
|
|
1000 | 1003 | mode = dsInfo['mode'] |
|
|
1001 | 1004 | nDim = dsInfo['nDim'] |
|
|
1002 | 1005 | |
|
|
1003 | 1006 | if mode == 0 or mode == 2 or nDim == 1: |
|
|
1004 | 1007 | data[ind] = dataAux |
|
|
1005 | 1008 | ind += 1 |
|
|
1006 | 1009 | # elif nDim == 1: |
|
|
1007 | 1010 | # data[ind] = numpy.reshape(dataAux,(numpy.size(dataAux),1)) |
|
|
1008 | 1011 | # ind += 1 |
|
|
1009 | 1012 | elif nDim == 2: |
|
|
1010 | 1013 | for j in range(dsInfo['dsNumber']): |
|
|
1011 | 1014 | data[ind] = dataAux[j,:] |
|
|
1012 | 1015 | ind += 1 |
|
|
1013 | 1016 | elif nDim == 3: |
|
|
1014 | 1017 | for j in range(dsInfo['dsNumber']): |
|
|
1015 | 1018 | data[ind] = dataAux[:,j,:] |
|
|
1016 | 1019 | ind += 1 |
|
|
1017 | 1020 | |
|
|
1018 | 1021 | self.data = data |
|
|
1019 | 1022 | return |
|
|
1020 | 1023 | |
|
|
1021 | 1024 | def writeBlock(self): |
|
|
1022 | 1025 | ''' |
|
|
1023 | 1026 | Saves the block in the HDF5 file |
|
|
1024 | 1027 | ''' |
|
|
1025 | 1028 | dsList = self.dsList |
|
|
1026 | 1029 | |
|
|
1027 | 1030 | for i in range(len(self.ds)): |
|
|
1028 | 1031 | dsInfo = dsList[i] |
|
|
1029 | 1032 | nDim = dsInfo['nDim'] |
|
|
1030 | 1033 | mode = dsInfo['mode'] |
|
|
1031 | 1034 | |
|
|
1032 | 1035 | # First time |
|
|
1033 | 1036 | if self.firsttime: |
|
|
1034 | 1037 | # self.ds[i].resize(self.data[i].shape) |
|
|
1035 | 1038 | # self.ds[i][self.blockIndex,:] = self.data[i] |
|
|
1036 | 1039 | if type(self.data[i]) == numpy.ndarray: |
|
|
1037 | 1040 | |
|
|
1038 | 1041 | if nDim == 3: |
|
|
1039 | 1042 | self.data[i] = self.data[i].reshape((self.data[i].shape[0],self.data[i].shape[1],1)) |
|
|
1040 | 1043 | self.ds[i].resize(self.data[i].shape) |
|
|
1041 | 1044 | if mode == 2: |
|
|
1042 | 1045 | self.ds[i].resize(self.data[i].shape) |
|
|
1043 | 1046 | self.ds[i][:] = self.data[i] |
|
|
1044 | 1047 | else: |
|
|
1045 | 1048 | |
|
|
1046 | 1049 | # From second time |
|
|
1047 | 1050 | # Meteors! |
|
|
1048 | 1051 | if mode == 2: |
|
|
1049 | 1052 | dataShape = self.data[i].shape |
|
|
1050 | 1053 | dsShape = self.ds[i].shape |
|
|
1051 | 1054 | self.ds[i].resize((self.ds[i].shape[0] + dataShape[0],self.ds[i].shape[1])) |
|
|
1052 | 1055 | self.ds[i][dsShape[0]:,:] = self.data[i] |
|
|
1053 | 1056 | # No dimension |
|
|
1054 | 1057 | elif mode == 0: |
|
|
1055 | 1058 | self.ds[i].resize((self.ds[i].shape[0], self.ds[i].shape[1] + 1)) |
|
|
1056 | 1059 | self.ds[i][0,-1] = self.data[i] |
|
|
1057 | 1060 | # One dimension |
|
|
1058 | 1061 | elif nDim == 1: |
|
|
1059 | 1062 | self.ds[i].resize((self.ds[i].shape[0] + 1, self.ds[i].shape[1])) |
|
|
1060 | 1063 | self.ds[i][-1,:] = self.data[i] |
|
|
1061 | 1064 | # Two dimension |
|
|
1062 | 1065 | elif nDim == 2: |
|
|
1063 | 1066 | self.ds[i].resize((self.ds[i].shape[0] + 1,self.ds[i].shape[1])) |
|
|
1064 | 1067 | self.ds[i][self.blockIndex,:] = self.data[i] |
|
|
1065 | 1068 | # Three dimensions |
|
|
1066 | 1069 | elif nDim == 3: |
|
|
1067 | 1070 | self.ds[i].resize((self.ds[i].shape[0],self.ds[i].shape[1],self.ds[i].shape[2]+1)) |
|
|
1068 | 1071 | self.ds[i][:,:,-1] = self.data[i] |
|
|
1069 | 1072 | |
|
|
1070 | 1073 | self.firsttime = False |
|
|
1071 | 1074 | self.blockIndex += 1 |
|
|
1072 | 1075 | |
|
|
1073 | 1076 | #Close to save changes |
|
|
1074 | 1077 | self.fp.flush() |
|
|
1075 | 1078 | self.fp.close() |
|
|
1076 | 1079 | return |
|
|
1077 | 1080 | |
|
|
1078 | 1081 | def run(self, dataOut, **kwargs): |
|
|
1079 | ||
|
|
1082 | ||
|
|
1080 | 1083 | if not(self.isConfig): |
|
|
1084 | ||
|
|
1081 | 1085 | flagdata = self.setup(dataOut, **kwargs) |
|
|
1082 | ||
|
|
1086 | ||
|
|
1083 | 1087 | if not(flagdata): |
|
|
1084 | 1088 | return |
|
|
1085 | 1089 | |
|
|
1086 | 1090 | self.isConfig = True |
|
|
1087 | 1091 | # self.putMetadata() |
|
|
1088 | 1092 | self.setNextFile() |
|
|
1089 | 1093 | |
|
|
1090 | 1094 | self.putData() |
|
|
1091 | 1095 | return |
| @@ -1,4027 +1,3809 | |||
|
|
1 | 1 |
import numpy |
|
|
2 | 2 |
import math |
|
|
3 | 3 |
from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 |
import scipy |
|
|
5 | 5 |
import re |
|
|
6 | 6 |
import datetime |
|
|
7 | 7 |
import copy |
|
|
8 | 8 |
import sys |
|
|
9 | 9 |
import importlib |
|
|
10 | 10 |
import itertools |
|
|
11 | 11 |
from multiprocessing import Pool, TimeoutError |
|
|
12 | 12 |
from multiprocessing.pool import ThreadPool |
|
|
13 | 13 | |
|
|
14 | 14 | |
|
|
15 | 15 | |
|
|
16 | 16 |
import time |
|
|
17 | 17 |
#from sklearn.cluster import KMeans |
|
|
18 | 18 | |
|
|
19 | 19 |
import matplotlib.pyplot as plt |
|
|
20 | 20 | |
|
|
21 | 21 |
from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
22 | 22 |
from jroproc_base import ProcessingUnit, Operation |
|
|
23 | 23 |
from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
24 | 24 |
from scipy import asarray as ar,exp |
|
|
25 | 25 |
from scipy.optimize import curve_fit |
|
|
26 | 26 | |
|
|
27 | 27 |
import warnings |
|
|
28 | 28 |
from numpy import NaN |
|
|
29 | 29 |
from scipy.optimize.optimize import OptimizeWarning |
|
|
30 | 30 |
warnings.filterwarnings('ignore') |
|
|
31 | 31 | |
|
|
32 | 32 | |
|
|
33 | 33 |
SPEED_OF_LIGHT = 299792458 |
|
|
34 | 34 | |
|
|
35 | 35 | |
|
|
36 | 36 |
'''solving pickling issue''' |
|
|
37 | 37 | |
|
|
38 | 38 |
def _pickle_method(method): |
|
|
39 | 39 |
func_name = method.im_func.__name__ |
|
|
40 | 40 |
obj = method.im_self |
|
|
41 | 41 |
cls = method.im_class |
|
|
42 | 42 |
return _unpickle_method, (func_name, obj, cls) |
|
|
43 | 43 | |
|
|
44 | 44 |
def _unpickle_method(func_name, obj, cls): |
|
|
45 | 45 |
for cls in cls.mro(): |
|
|
46 | 46 |
try: |
|
|
47 | 47 |
func = cls.__dict__[func_name] |
|
|
48 | 48 |
except KeyError: |
|
|
49 | 49 |
pass |
|
|
50 | 50 |
else: |
|
|
51 | 51 |
break |
|
|
52 | 52 |
return func.__get__(obj, cls) |
|
|
53 | 53 | |
|
|
54 | 54 |
class ParametersProc(ProcessingUnit): |
|
|
55 | 55 |
|
|
|
56 | 56 |
nSeconds = None |
|
|
57 | 57 | |
|
|
58 | 58 |
def __init__(self): |
|
|
59 | 59 |
ProcessingUnit.__init__(self) |
|
|
60 | 60 |
|
|
|
61 | 61 |
# self.objectDict = {} |
|
|
62 | 62 |
self.buffer = None |
|
|
63 | 63 |
self.firstdatatime = None |
|
|
64 | 64 |
self.profIndex = 0 |
|
|
65 | 65 |
self.dataOut = Parameters() |
|
|
66 | 66 |
|
|
|
67 | 67 |
def __updateObjFromInput(self): |
|
|
68 | 68 |
|
|
|
69 | 69 |
self.dataOut.inputUnit = self.dataIn.type |
|
|
70 | 70 |
|
|
|
71 | 71 |
self.dataOut.timeZone = self.dataIn.timeZone |
|
|
72 | 72 |
self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
73 | 73 |
self.dataOut.errorCount = self.dataIn.errorCount |
|
|
74 | 74 |
self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
75 | 75 |
|
|
|
76 | 76 |
self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
77 | 77 |
self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
78 | 78 |
self.dataOut.channelList = self.dataIn.channelList |
|
|
79 | 79 |
self.dataOut.heightList = self.dataIn.heightList |
|
|
80 | 80 |
self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
81 | 81 |
# self.dataOut.nHeights = self.dataIn.nHeights |
|
|
82 | 82 |
# self.dataOut.nChannels = self.dataIn.nChannels |
|
|
83 | 83 |
self.dataOut.nBaud = self.dataIn.nBaud |
|
|
84 | 84 |
self.dataOut.nCode = self.dataIn.nCode |
|
|
85 | 85 |
self.dataOut.code = self.dataIn.code |
|
|
86 | 86 |
# self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
87 | 87 |
self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
88 | 88 |
# self.dataOut.utctime = self.firstdatatime |
|
|
89 | 89 |
self.dataOut.utctime = self.dataIn.utctime |
|
|
90 | 90 |
self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
91 | 91 |
self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
92 | 92 |
self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
93 | 93 |
# self.dataOut.nIncohInt = 1 |
|
|
94 | 94 |
self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
95 | 95 |
# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
96 | 96 |
self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
97 | 97 |
self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
98 | 98 |
self.dataOut.frequency = self.dataIn.frequency |
|
|
99 | 99 |
self.dataOut.noise = self.dataIn.noise |
|
|
100 | 100 |
|
|
|
101 | 101 |
|
|
|
102 | 102 |
|
|
|
103 | 103 |
def run(self): |
|
|
104 | 104 |
|
|
|
105 | 105 |
#---------------------- Voltage Data --------------------------- |
|
|
106 | 106 |
|
|
|
107 | 107 |
if self.dataIn.type == "Voltage": |
|
|
108 | 108 | |
|
|
109 | 109 |
self.__updateObjFromInput() |
|
|
110 | 110 |
self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
111 | 111 |
self.dataOut.flagNoData = False |
|
|
112 | 112 |
self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
113 | 113 |
self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
114 | 114 |
return |
|
|
115 | 115 |
|
|
|
116 | 116 |
#---------------------- Spectra Data --------------------------- |
|
|
117 | 117 |
|
|
|
118 | 118 |
if self.dataIn.type == "Spectra": |
|
|
119 | 119 | |
|
|
120 | 120 |
self.dataOut.data_pre = (self.dataIn.data_spc , self.dataIn.data_cspc) |
|
|
121 | 121 |
print 'self.dataIn.data_spc', self.dataIn.data_spc.shape |
|
|
122 | 122 |
self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
123 | 123 |
self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
124 | 124 |
self.dataOut.spc_range = numpy.asanyarray((self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1) )) |
|
|
125 | 125 |
|
|
|
126 | 126 |
self.dataOut.normFactor = self.dataIn.normFactor |
|
|
127 | 127 |
#self.dataOut.outputInterval = self.dataIn.outputInterval |
|
|
128 | 128 |
self.dataOut.groupList = self.dataIn.pairsList |
|
|
129 | 129 |
self.dataOut.flagNoData = False |
|
|
130 | 130 |
#print 'datain chandist ',self.dataIn.ChanDist |
|
|
131 | 131 |
if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
132 | 132 |
self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
133 | 133 |
else: self.dataOut.ChanDist = None |
|
|
134 | 134 |
|
|
|
135 | 135 |
print 'datain chandist ',self.dataOut.ChanDist |
|
|
136 | 136 |
|
|
|
137 | 137 |
#if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
138 | 138 |
# self.dataOut.VelRange = self.dataIn.VelRange |
|
|
139 | 139 |
#else: self.dataOut.VelRange = None |
|
|
140 | 140 |
|
|
|
141 | 141 |
if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
142 | 142 |
self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
143 | 143 |
|
|
|
144 | 144 |
if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
145 | 145 |
self.dataOut.NPW = self.dataIn.NPW |
|
|
146 | 146 |
|
|
|
147 | 147 |
if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
148 | 148 |
self.dataOut.COFA = self.dataIn.COFA |
|
|
149 | 149 |
|
|
|
150 | 150 |
|
|
|
151 | 151 |
|
|
|
152 | 152 |
#---------------------- Correlation Data --------------------------- |
|
|
153 | 153 |
|
|
|
154 | 154 |
if self.dataIn.type == "Correlation": |
|
|
155 | 155 |
acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
156 | 156 |
|
|
|
157 | 157 |
self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
158 | 158 |
self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
159 | 159 |
self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
160 | 160 |
|
|
|
161 | 161 |
self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
162 | 162 |
self.dataOut.noise = self.dataIn.noise |
|
|
163 | 163 |
self.dataOut.data_SNR = self.dataIn.SNR |
|
|
164 | 164 |
self.dataOut.flagNoData = False |
|
|
165 | 165 |
self.dataOut.nAvg = self.dataIn.nAvg |
|
|
166 | 166 |
|
|
|
167 | 167 |
#---------------------- Parameters Data --------------------------- |
|
|
168 | 168 |
|
|
|
169 | 169 |
if self.dataIn.type == "Parameters": |
|
|
170 | 170 |
self.dataOut.copy(self.dataIn) |
|
|
171 | 171 |
self.dataOut.flagNoData = False |
|
|
172 | 172 |
|
|
|
173 | 173 |
return True |
|
|
174 | 174 |
|
|
|
175 | 175 |
self.__updateObjFromInput() |
|
|
176 | 176 |
self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
177 | 177 |
self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
178 | 178 |
|
|
|
179 | 179 |
return |
|
|
180 | 180 | |
|
|
181 | 181 | |
|
|
182 | 182 |
def target(tups): |
|
|
183 | 183 |
|
|
|
184 | 184 |
obj, args = tups |
|
|
185 | #print 'TARGETTT', obj, args | |
|
|
185 | ||
|
|
186 | 186 |
return obj.FitGau(args) |
|
|
187 | 187 |
|
|
|
188 | 188 |
|
|
|
189 | 189 |
class SpectralFilters(Operation): |
|
|
190 | 190 |
|
|
|
191 | 191 |
'''This class allows the Rainfall / Wind Selection for CLAIRE RADAR |
|
|
192 | 192 |
|
|
|
193 | 193 |
LimitR : It is the limit in m/s of Rainfall |
|
|
194 | 194 |
LimitW : It is the limit in m/s for Winds |
|
|
195 | 195 |
|
|
|
196 | 196 |
Input: |
|
|
197 | 197 |
|
|
|
198 | 198 |
self.dataOut.data_pre : SPC and CSPC |
|
|
199 | 199 |
self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
200 | 200 |
|
|
|
201 | 201 |
Affected: |
|
|
202 | 202 |
|
|
|
203 | 203 |
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
204 | 204 |
self.dataOut.spcparam_range : Used in SpcParamPlot |
|
|
205 | 205 |
self.dataOut.SPCparam : Used in PrecipitationProc |
|
|
206 | 206 |
|
|
|
207 | 207 |
|
|
|
208 | 208 |
''' |
|
|
209 | 209 |
|
|
|
210 | 210 |
def __init__(self, **kwargs): |
|
|
211 | 211 |
Operation.__init__(self, **kwargs) |
|
|
212 | 212 |
self.i=0 |
|
|
213 | 213 |
|
|
|
214 | 214 |
def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5): |
|
|
215 | 215 |
|
|
|
216 | 216 |
|
|
|
217 | 217 |
#Limite de vientos |
|
|
218 | 218 |
LimitR = PositiveLimit |
|
|
219 | 219 |
LimitN = NegativeLimit |
|
|
220 | 220 |
|
|
|
221 | 221 |
self.spc = dataOut.data_pre[0].copy() |
|
|
222 | 222 |
self.cspc = dataOut.data_pre[1].copy() |
|
|
223 | 223 |
|
|
|
224 | 224 |
self.Num_Hei = self.spc.shape[2] |
|
|
225 | 225 |
self.Num_Bin = self.spc.shape[1] |
|
|
226 | 226 |
self.Num_Chn = self.spc.shape[0] |
|
|
227 | 227 |
|
|
|
228 | 228 |
VelRange = dataOut.spc_range[2] |
|
|
229 | 229 |
TimeRange = dataOut.spc_range[1] |
|
|
230 | 230 |
FrecRange = dataOut.spc_range[0] |
|
|
231 | 231 |
|
|
|
232 | 232 |
Vmax= 2*numpy.max(dataOut.spc_range[2]) |
|
|
233 | 233 |
Tmax= 2*numpy.max(dataOut.spc_range[1]) |
|
|
234 | 234 |
Fmax= 2*numpy.max(dataOut.spc_range[0]) |
|
|
235 | 235 |
|
|
|
236 | 236 |
Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()] |
|
|
237 | 237 |
Breaker1R=numpy.where(VelRange == Breaker1R) |
|
|
238 | 238 |
|
|
|
239 | 239 |
Delta = self.Num_Bin/2 - Breaker1R[0] |
|
|
240 | 240 |
|
|
|
241 | #Breaker1W=VelRange[numpy.abs(VelRange-(-LimitW)).argmin()] | |
|
|
242 | #Breaker1W=numpy.where(VelRange == Breaker1W) | |
|
|
243 | ||
|
|
244 | #Breaker2W=VelRange[numpy.abs(VelRange-(LimitW)).argmin()] | |
|
|
245 | #Breaker2W=numpy.where(VelRange == Breaker2W) | |
|
|
246 | ||
|
|
247 | 241 |
|
|
|
248 | 242 |
'''Reacomodando SPCrange''' |
|
|
249 | 243 |
|
|
|
250 | 244 |
VelRange=numpy.roll(VelRange,-(self.Num_Bin/2) ,axis=0) |
|
|
251 | 245 |
|
|
|
252 | 246 |
VelRange[-(self.Num_Bin/2):]+= Vmax |
|
|
253 | 247 |
|
|
|
254 | 248 |
FrecRange=numpy.roll(FrecRange,-(self.Num_Bin/2),axis=0) |
|
|
255 | 249 |
|
|
|
256 | 250 |
FrecRange[-(self.Num_Bin/2):]+= Fmax |
|
|
257 | 251 |
|
|
|
258 | 252 |
TimeRange=numpy.roll(TimeRange,-(self.Num_Bin/2),axis=0) |
|
|
259 | 253 |
|
|
|
260 | 254 |
TimeRange[-(self.Num_Bin/2):]+= Tmax |
|
|
261 | 255 |
|
|
|
262 | 256 |
''' ------------------ ''' |
|
|
263 | 257 |
|
|
|
264 | 258 |
Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()] |
|
|
265 | 259 |
Breaker2R=numpy.where(VelRange == Breaker2R) |
|
|
266 | 260 |
|
|
|
267 | 261 |
|
|
|
268 | 262 |
SPCroll = numpy.roll(self.spc,-(self.Num_Bin/2) ,axis=1) |
|
|
269 | 263 |
|
|
|
270 | 264 |
SPCcut = SPCroll.copy() |
|
|
271 | 265 |
for i in range(self.Num_Chn): |
|
|
272 | 266 |
|
|
|
273 | 267 |
SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i] |
|
|
274 | 268 |
SPCcut[i,-int(Delta):,:] = dataOut.noise[i] |
|
|
275 | 269 |
|
|
|
276 | 270 |
SPCcut[i]=SPCcut[i]- dataOut.noise[i] |
|
|
277 | 271 |
SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20 |
|
|
278 | 272 |
|
|
|
279 | 273 |
SPCroll[i]=SPCroll[i]-dataOut.noise[i] |
|
|
280 | 274 |
SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20 |
|
|
281 | 275 |
|
|
|
282 | #self.spc[i, 0:int(Breaker1W[0]) ,:] = dataOut.noise[i] | |
|
|
283 | #self.spc[i, int(Breaker2W[0]):self.Num_Bin ,:] = dataOut.noise[i] | |
|
|
284 | ||
|
|
285 | #self.cspc[i, 0:int(Breaker1W[0]) ,:] = dataOut.noise[i] | |
|
|
286 | #self.cspc[i, int(Breaker2W[0]):self.Num_Bin ,:] = dataOut.noise[i] | |
|
|
287 | ||
|
|
288 | ||
|
|
289 | ||
|
|
290 | ||
|
|
291 | ||
|
|
292 | 276 |
SPC_ch1 = SPCroll |
|
|
293 | 277 |
|
|
|
294 | 278 |
SPC_ch2 = SPCcut |
|
|
295 | 279 |
|
|
|
296 | 280 |
SPCparam = (SPC_ch1, SPC_ch2, self.spc) |
|
|
297 | 281 |
dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
298 | 282 |
|
|
|
299 | #dataOut.data_pre= (self.spc , self.cspc) | |
|
|
300 | ||
|
|
301 | #dataOut.data_preParam = (self.spc , self.cspc) | |
|
|
302 | 283 |
|
|
|
303 | 284 |
dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1]) |
|
|
304 | 285 |
|
|
|
305 | 286 |
dataOut.spcparam_range[2]=VelRange |
|
|
306 | 287 |
dataOut.spcparam_range[1]=TimeRange |
|
|
307 | 288 |
dataOut.spcparam_range[0]=FrecRange |
|
|
308 | 289 |
|
|
|
309 | ||
|
|
310 | ||
|
|
311 | 290 |
|
|
|
312 | 291 |
class GaussianFit(Operation): |
|
|
313 | 292 |
|
|
|
314 | 293 |
''' |
|
|
315 | 294 |
Function that fit of one and two generalized gaussians (gg) based |
|
|
316 | 295 |
on the PSD shape across an "power band" identified from a cumsum of |
|
|
317 | 296 |
the measured spectrum - noise. |
|
|
318 | 297 |
|
|
|
319 | 298 |
Input: |
|
|
320 | 299 |
self.dataOut.data_pre : SelfSpectra |
|
|
321 | 300 |
|
|
|
322 | 301 |
Output: |
|
|
323 | 302 |
self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
|
324 | 303 |
|
|
|
325 | 304 |
''' |
|
|
326 | 305 |
def __init__(self, **kwargs): |
|
|
327 | 306 |
Operation.__init__(self, **kwargs) |
|
|
328 | 307 |
self.i=0 |
|
|
329 | 308 |
|
|
|
330 | 309 |
|
|
|
331 | 310 |
def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
|
332 | 311 |
"""This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
333 | 312 |
input: spc |
|
|
334 | 313 |
output: |
|
|
335 | 314 |
Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise |
|
|
336 | 315 |
""" |
|
|
337 | 316 |
|
|
|
338 | 317 |
self.spc = dataOut.data_pre[0].copy() |
|
|
339 | 318 |
|
|
|
340 | 319 |
|
|
|
341 | print 'SelfSpectra Shape', numpy.asarray(self.spc).shape | |
|
|
342 | ||
|
|
343 | ||
|
|
344 | #plt.figure(50) | |
|
|
345 | #plt.subplot(121) | |
|
|
346 | #plt.plot(self.spc,'k',label='spc(66)') | |
|
|
347 | #plt.plot(xFrec,ySamples[1],'g',label='Ch1') | |
|
|
348 | #plt.plot(xFrec,ySamples[2],'r',label='Ch2') | |
|
|
349 | #plt.plot(xFrec,FitGauss,'yo:',label='fit') | |
|
|
350 | #plt.legend() | |
|
|
351 | #plt.title('DATOS A ALTURA DE 7500 METROS') | |
|
|
352 | #plt.show() | |
|
|
353 | ||
|
|
354 | 320 |
self.Num_Hei = self.spc.shape[2] |
|
|
355 | #self.Num_Bin = len(self.spc) | |
|
|
356 | 321 |
self.Num_Bin = self.spc.shape[1] |
|
|
357 | 322 |
self.Num_Chn = self.spc.shape[0] |
|
|
358 | 323 |
Vrange = dataOut.abscissaList |
|
|
359 | 324 |
|
|
|
360 | 325 |
GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
361 | 326 |
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
362 | 327 |
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
363 | 328 |
SPC_ch1[:] = numpy.NaN |
|
|
364 | 329 |
SPC_ch2[:] = numpy.NaN |
|
|
365 | 330 | |
|
|
366 | 331 |
|
|
|
367 | 332 |
start_time = time.time() |
|
|
368 | 333 |
|
|
|
369 | 334 |
noise_ = dataOut.spc_noise[0].copy() |
|
|
370 | 335 |
|
|
|
371 | 336 |
|
|
|
372 | 337 |
pool = Pool(processes=self.Num_Chn) |
|
|
373 | 338 |
args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)] |
|
|
374 | 339 |
objs = [self for __ in range(self.Num_Chn)] |
|
|
375 | 340 |
attrs = zip(objs, args) |
|
|
376 | 341 |
gauSPC = pool.map(target, attrs) |
|
|
377 | 342 |
dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
378 | 343 | |
|
|
379 | ||
|
|
380 | ||
|
|
381 | print '========================================================' | |
|
|
382 | print 'total_time: ', time.time()-start_time | |
|
|
383 | ||
|
|
384 | 344 |
# re-normalizing spc and noise |
|
|
385 | 345 |
# This part differs from gg1 |
|
|
386 | ||
|
|
387 | ||
|
|
388 | 346 |
|
|
|
389 | 347 |
''' Parameters: |
|
|
390 | 348 |
1. Amplitude |
|
|
391 | 349 |
2. Shift |
|
|
392 | 350 |
3. Width |
|
|
393 | 351 |
4. Power |
|
|
394 | 352 |
''' |
|
|
395 | 353 |
|
|
|
396 | 354 |
|
|
|
397 | 355 |
############################################################################### |
|
|
398 | 356 |
def FitGau(self, X): |
|
|
399 | 357 |
|
|
|
400 | 358 |
Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X |
|
|
401 | #print 'VARSSSS', ch, pnoise, noise, num_intg | |
|
|
402 | ||
|
|
403 | #print 'HEIGHTS', self.Num_Hei | |
|
|
404 | ||
|
|
359 | ||
|
|
405 | 360 |
SPCparam = [] |
|
|
406 | 361 |
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
407 | 362 |
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
408 | 363 |
SPC_ch1[:] = 0#numpy.NaN |
|
|
409 | 364 |
SPC_ch2[:] = 0#numpy.NaN |
|
|
410 | 365 |
|
|
|
411 | 366 |
|
|
|
412 | 367 |
|
|
|
413 | 368 |
for ht in range(self.Num_Hei): |
|
|
414 | #print (numpy.asarray(self.spc).shape) | |
|
|
415 | ||
|
|
416 | #print 'TTTTT', ch , ht | |
|
|
417 | #print self.spc.shape | |
|
|
418 | 369 |
|
|
|
419 | 370 |
|
|
|
420 | 371 |
spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
421 | 372 |
|
|
|
422 | 373 |
############################################# |
|
|
423 | 374 |
# normalizing spc and noise |
|
|
424 | 375 |
# This part differs from gg1 |
|
|
425 | 376 |
spc_norm_max = max(spc) |
|
|
426 | 377 |
#spc = spc / spc_norm_max |
|
|
427 | 378 |
pnoise = pnoise #/ spc_norm_max |
|
|
428 | 379 |
############################################# |
|
|
429 | 380 |
|
|
|
430 | 381 |
fatspectra=1.0 |
|
|
431 | 382 |
|
|
|
432 | 383 |
wnoise = noise_ #/ spc_norm_max |
|
|
433 | 384 |
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
434 | 385 |
#if wnoise>1.1*pnoise: # to be tested later |
|
|
435 | 386 |
# wnoise=pnoise |
|
|
436 | 387 |
noisebl=wnoise*0.9; |
|
|
437 | 388 |
noisebh=wnoise*1.1 |
|
|
438 | 389 |
spc=spc-wnoise |
|
|
439 | # print 'wnoise', noise_[0], spc_norm_max, wnoise | |
|
|
390 | ||
|
|
440 | 391 |
minx=numpy.argmin(spc) |
|
|
441 | 392 |
#spcs=spc.copy() |
|
|
442 | 393 |
spcs=numpy.roll(spc,-minx) |
|
|
443 | 394 |
cum=numpy.cumsum(spcs) |
|
|
444 | 395 |
tot_noise=wnoise * self.Num_Bin #64; |
|
|
445 | #print 'spc' , spcs[5:8] , 'tot_noise', tot_noise | |
|
|
446 | #tot_signal=sum(cum[-5:])/5.; ''' How does this line work? ''' | |
|
|
447 | #snr=tot_signal/tot_noise | |
|
|
448 | #snr=cum[-1]/tot_noise | |
|
|
396 | ||
|
|
449 | 397 |
snr = sum(spcs)/tot_noise |
|
|
450 | 398 |
snrdB=10.*numpy.log10(snr) |
|
|
451 | 399 |
|
|
|
452 | 400 |
if snrdB < SNRlimit : |
|
|
453 | 401 |
snr = numpy.NaN |
|
|
454 | 402 |
SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
455 | 403 |
SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
456 | 404 |
SPCparam = (SPC_ch1,SPC_ch2) |
|
|
457 | 405 |
continue |
|
|
458 | #print 'snr',snrdB #, sum(spcs) , tot_noise | |
|
|
459 | ||
|
|
460 | 406 |
|
|
|
461 | 407 |
|
|
|
462 | 408 |
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
463 | 409 |
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
464 | 410 |
|
|
|
465 | 411 |
cummax=max(cum); |
|
|
466 | 412 |
epsi=0.08*fatspectra # cumsum to narrow down the energy region |
|
|
467 | 413 |
cumlo=cummax*epsi; |
|
|
468 | 414 |
cumhi=cummax*(1-epsi) |
|
|
469 | 415 |
powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
470 | 416 |
|
|
|
471 | 417 |
|
|
|
472 | 418 |
if len(powerindex) < 1:# case for powerindex 0 |
|
|
473 | 419 |
continue |
|
|
474 | 420 |
powerlo=powerindex[0] |
|
|
475 | 421 |
powerhi=powerindex[-1] |
|
|
476 | 422 |
powerwidth=powerhi-powerlo |
|
|
477 | 423 |
|
|
|
478 | 424 |
firstpeak=powerlo+powerwidth/10.# first gaussian energy location |
|
|
479 | 425 |
secondpeak=powerhi-powerwidth/10.#second gaussian energy location |
|
|
480 | 426 |
midpeak=(firstpeak+secondpeak)/2. |
|
|
481 | 427 |
firstamp=spcs[int(firstpeak)] |
|
|
482 | 428 |
secondamp=spcs[int(secondpeak)] |
|
|
483 | 429 |
midamp=spcs[int(midpeak)] |
|
|
484 | 430 |
|
|
|
485 | 431 |
x=numpy.arange( self.Num_Bin ) |
|
|
486 | 432 |
y_data=spc+wnoise |
|
|
487 | 433 |
|
|
|
488 | 434 |
''' single Gaussian ''' |
|
|
489 | 435 |
shift0=numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
490 | 436 |
width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
491 | 437 |
power0=2. |
|
|
492 | 438 |
amplitude0=midamp |
|
|
493 | 439 |
state0=[shift0,width0,amplitude0,power0,wnoise] |
|
|
494 | 440 |
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
495 | 441 |
lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
496 | 442 |
|
|
|
497 | 443 |
chiSq1=lsq1[1]; |
|
|
498 | 444 | |
|
|
499 | 445 |
|
|
|
500 | 446 |
if fatspectra<1.0 and powerwidth<4: |
|
|
501 | 447 |
choice=0 |
|
|
502 | 448 |
Amplitude0=lsq1[0][2] |
|
|
503 | 449 |
shift0=lsq1[0][0] |
|
|
504 | 450 |
width0=lsq1[0][1] |
|
|
505 | 451 |
p0=lsq1[0][3] |
|
|
506 | 452 |
Amplitude1=0. |
|
|
507 | 453 |
shift1=0. |
|
|
508 | 454 |
width1=0. |
|
|
509 | 455 |
p1=0. |
|
|
510 | 456 |
noise=lsq1[0][4] |
|
|
511 | 457 |
#return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
512 | 458 |
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
513 | 459 |
|
|
|
514 | 460 |
''' two gaussians ''' |
|
|
515 | 461 |
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
516 | 462 |
shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); |
|
|
517 | 463 |
shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
518 | 464 |
width0=powerwidth/6.; |
|
|
519 | 465 |
width1=width0 |
|
|
520 | 466 |
power0=2.; |
|
|
521 | 467 |
power1=power0 |
|
|
522 | 468 |
amplitude0=firstamp; |
|
|
523 | 469 |
amplitude1=secondamp |
|
|
524 | 470 |
state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
525 | 471 |
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
526 | 472 |
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
527 | 473 |
#bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) |
|
|
528 | 474 |
|
|
|
529 | 475 |
lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
|
530 | 476 |
|
|
|
531 | 477 |
|
|
|
532 | 478 |
chiSq2=lsq2[1]; |
|
|
533 | 479 |
|
|
|
534 | 480 |
|
|
|
535 | 481 |
|
|
|
536 | 482 |
oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) |
|
|
537 | 483 |
|
|
|
538 | 484 |
if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
539 | 485 |
if oneG: |
|
|
540 | 486 |
choice=0 |
|
|
541 | 487 |
else: |
|
|
542 | 488 |
w1=lsq2[0][1]; w2=lsq2[0][5] |
|
|
543 | 489 |
a1=lsq2[0][2]; a2=lsq2[0][6] |
|
|
544 | 490 |
p1=lsq2[0][3]; p2=lsq2[0][7] |
|
|
545 | 491 |
s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; |
|
|
546 | 492 |
s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; |
|
|
547 | 493 |
gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
548 | 494 |
|
|
|
549 | 495 |
if gp1>gp2: |
|
|
550 | 496 |
if a1>0.7*a2: |
|
|
551 | 497 |
choice=1 |
|
|
552 | 498 |
else: |
|
|
553 | 499 |
choice=2 |
|
|
554 | 500 |
elif gp2>gp1: |
|
|
555 | 501 |
if a2>0.7*a1: |
|
|
556 | 502 |
choice=2 |
|
|
557 | 503 |
else: |
|
|
558 | 504 |
choice=1 |
|
|
559 | 505 |
else: |
|
|
560 | 506 |
choice=numpy.argmax([a1,a2])+1 |
|
|
561 | 507 |
#else: |
|
|
562 | 508 |
#choice=argmin([std2a,std2b])+1 |
|
|
563 | 509 |
|
|
|
564 | 510 |
else: # with low SNR go to the most energetic peak |
|
|
565 | 511 |
choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
566 | 512 |
|
|
|
567 | 513 |
|
|
|
568 | 514 |
shift0=lsq2[0][0]; |
|
|
569 | 515 |
vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0]) |
|
|
570 | 516 |
shift1=lsq2[0][4]; |
|
|
571 | 517 |
vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0]) |
|
|
572 | 518 |
|
|
|
573 | 519 |
max_vel = 1.0 |
|
|
574 | 520 |
|
|
|
575 | 521 |
#first peak will be 0, second peak will be 1 |
|
|
576 | 522 |
if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range |
|
|
577 | 523 |
shift0=lsq2[0][0] |
|
|
578 | 524 |
width0=lsq2[0][1] |
|
|
579 | 525 |
Amplitude0=lsq2[0][2] |
|
|
580 | 526 |
p0=lsq2[0][3] |
|
|
581 | 527 |
|
|
|
582 | 528 |
shift1=lsq2[0][4] |
|
|
583 | 529 |
width1=lsq2[0][5] |
|
|
584 | 530 |
Amplitude1=lsq2[0][6] |
|
|
585 | 531 |
p1=lsq2[0][7] |
|
|
586 | 532 |
noise=lsq2[0][8] |
|
|
587 | 533 |
else: |
|
|
588 | 534 |
shift1=lsq2[0][0] |
|
|
589 | 535 |
width1=lsq2[0][1] |
|
|
590 | 536 |
Amplitude1=lsq2[0][2] |
|
|
591 | 537 |
p1=lsq2[0][3] |
|
|
592 | 538 |
|
|
|
593 | 539 |
shift0=lsq2[0][4] |
|
|
594 | 540 |
width0=lsq2[0][5] |
|
|
595 | 541 |
Amplitude0=lsq2[0][6] |
|
|
596 | 542 |
p0=lsq2[0][7] |
|
|
597 | 543 |
noise=lsq2[0][8] |
|
|
598 | 544 |
|
|
|
599 | 545 |
if Amplitude0<0.05: # in case the peak is noise |
|
|
600 | 546 |
shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN] |
|
|
601 | 547 |
if Amplitude1<0.05: |
|
|
602 | 548 |
shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN] |
|
|
603 | 549 |
|
|
|
604 | 550 |
|
|
|
605 | # if choice==0: # pick the single gaussian fit | |
|
|
606 | # Amplitude0=lsq1[0][2] | |
|
|
607 | # shift0=lsq1[0][0] | |
|
|
608 | # width0=lsq1[0][1] | |
|
|
609 | # p0=lsq1[0][3] | |
|
|
610 | # Amplitude1=0. | |
|
|
611 | # shift1=0. | |
|
|
612 | # width1=0. | |
|
|
613 | # p1=0. | |
|
|
614 | # noise=lsq1[0][4] | |
|
|
615 | # elif choice==1: # take the first one of the 2 gaussians fitted | |
|
|
616 | # Amplitude0 = lsq2[0][2] | |
|
|
617 | # shift0 = lsq2[0][0] | |
|
|
618 | # width0 = lsq2[0][1] | |
|
|
619 | # p0 = lsq2[0][3] | |
|
|
620 | # Amplitude1 = lsq2[0][6] # This is 0 in gg1 | |
|
|
621 | # shift1 = lsq2[0][4] # This is 0 in gg1 | |
|
|
622 | # width1 = lsq2[0][5] # This is 0 in gg1 | |
|
|
623 | # p1 = lsq2[0][7] # This is 0 in gg1 | |
|
|
624 | # noise = lsq2[0][8] | |
|
|
625 | # else: # the second one | |
|
|
626 | # Amplitude0 = lsq2[0][6] | |
|
|
627 | # shift0 = lsq2[0][4] | |
|
|
628 | # width0 = lsq2[0][5] | |
|
|
629 | # p0 = lsq2[0][7] | |
|
|
630 | # Amplitude1 = lsq2[0][2] # This is 0 in gg1 | |
|
|
631 | # shift1 = lsq2[0][0] # This is 0 in gg1 | |
|
|
632 | # width1 = lsq2[0][1] # This is 0 in gg1 | |
|
|
633 | # p1 = lsq2[0][3] # This is 0 in gg1 | |
|
|
634 | # noise = lsq2[0][8] | |
|
|
635 | ||
|
|
636 | #print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0) | |
|
|
637 | 551 |
SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 |
|
|
638 | 552 |
SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 |
|
|
639 | #print 'SPC_ch1.shape',SPC_ch1.shape | |
|
|
640 | #print 'SPC_ch2.shape',SPC_ch2.shape | |
|
|
641 | #dataOut.data_param = SPC_ch1 | |
|
|
642 | 553 |
SPCparam = (SPC_ch1,SPC_ch2) |
|
|
643 | #GauSPC[1] = SPC_ch2 | |
|
|
644 | ||
|
|
645 | # print 'shift0', shift0 | |
|
|
646 | # print 'Amplitude0', Amplitude0 | |
|
|
647 | # print 'width0', width0 | |
|
|
648 | # print 'p0', p0 | |
|
|
649 | # print '========================' | |
|
|
650 | # print 'shift1', shift1 | |
|
|
651 | # print 'Amplitude1', Amplitude1 | |
|
|
652 | # print 'width1', width1 | |
|
|
653 | # print 'p1', p1 | |
|
|
654 | # print 'noise', noise | |
|
|
655 | # print 's_noise', wnoise | |
|
|
554 | ||
|
|
656 | 555 |
|
|
|
657 | 556 |
return GauSPC |
|
|
658 | 557 |
|
|
|
659 | 558 |
def y_model1(self,x,state): |
|
|
660 | 559 |
shift0,width0,amplitude0,power0,noise=state |
|
|
661 | 560 |
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
662 | 561 |
|
|
|
663 | 562 |
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
664 | 563 |
|
|
|
665 | 564 |
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
666 | 565 |
return model0+model0u+model0d+noise |
|
|
667 | 566 |
|
|
|
668 | 567 |
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
669 | 568 |
shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state |
|
|
670 | 569 |
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
671 | 570 |
|
|
|
672 | 571 |
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
673 | 572 |
|
|
|
674 | 573 |
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
675 | 574 |
model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) |
|
|
676 | 575 |
|
|
|
677 | 576 |
model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) |
|
|
678 | 577 |
|
|
|
679 | 578 |
model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) |
|
|
680 | 579 |
return model0+model0u+model0d+model1+model1u+model1d+noise |
|
|
681 | 580 |
|
|
|
682 | 581 |
def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. |
|
|
683 | 582 | |
|
|
684 | 583 |
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
685 | 584 |
|
|
|
686 | 585 |
def misfit2(self,state,y_data,x,num_intg): |
|
|
687 | 586 |
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
688 | 587 |
|
|
|
689 | ||
|
|
588 | ||
|
|
690 | 589 | |
|
|
691 | 590 |
class PrecipitationProc(Operation): |
|
|
692 | 591 |
|
|
|
693 | 592 |
''' |
|
|
694 | 593 |
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
695 | 594 |
|
|
|
696 | 595 |
Input: |
|
|
697 | 596 |
self.dataOut.data_pre : SelfSpectra |
|
|
698 | 597 |
|
|
|
699 | 598 |
Output: |
|
|
700 | 599 |
|
|
|
701 | 600 |
self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
702 | 601 |
|
|
|
703 | 602 |
|
|
|
704 | 603 |
Parameters affected: |
|
|
705 | 604 |
''' |
|
|
605 | ||
|
|
606 | def __init__(self, **kwargs): | |
|
|
607 | Operation.__init__(self, **kwargs) | |
|
|
608 | self.i=0 | |
|
|
609 | ||
|
|
610 | ||
|
|
706 | 611 |
def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
707 | 612 |
return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
708 | 613 |
|
|
|
709 | 614 |
|
|
|
710 | 615 |
|
|
|
711 | 616 |
def Moments(self, ySamples, xSamples): |
|
|
712 | 617 |
Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|
713 | 618 |
yNorm = ySamples / Pot |
|
|
714 | 619 |
|
|
|
715 | 620 |
Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|
716 | 621 |
Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|
717 | 622 |
Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|
718 | 623 |
|
|
|
719 | 624 |
return numpy.array([Pot, Vr, Desv]) |
|
|
720 | 625 |
|
|
|
721 | 626 |
def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
|
722 | 627 |
tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km = 0.93, Altitude=3350): |
|
|
723 | 628 |
|
|
|
724 | 629 |
|
|
|
725 | 630 |
Velrange = dataOut.spcparam_range[2] |
|
|
726 | 631 |
FrecRange = dataOut.spcparam_range[0] |
|
|
727 | 632 |
|
|
|
728 | 633 |
dV= Velrange[1]-Velrange[0] |
|
|
729 | 634 |
dF= FrecRange[1]-FrecRange[0] |
|
|
730 | 635 |
|
|
|
731 | 636 |
if radar == "MIRA35C" : |
|
|
732 | 637 |
|
|
|
733 | 638 |
self.spc = dataOut.data_pre[0].copy() |
|
|
734 | 639 |
self.Num_Hei = self.spc.shape[2] |
|
|
735 | 640 |
self.Num_Bin = self.spc.shape[1] |
|
|
736 | 641 |
self.Num_Chn = self.spc.shape[0] |
|
|
737 | 642 |
Ze = self.dBZeMODE2(dataOut) |
|
|
738 | 643 |
|
|
|
739 | 644 |
else: |
|
|
740 | 645 |
|
|
|
741 | 646 |
self.spc = dataOut.SPCparam[1].copy() #dataOut.data_pre[0].copy() # |
|
|
647 | ||
|
|
648 | """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" | |
|
|
649 | ||
|
|
650 | self.spc[:,:,0:7]= numpy.NaN | |
|
|
651 | ||
|
|
652 | """##########################################""" | |
|
|
653 | ||
|
|
742 | 654 |
self.Num_Hei = self.spc.shape[2] |
|
|
743 | 655 |
self.Num_Bin = self.spc.shape[1] |
|
|
744 | 656 |
self.Num_Chn = self.spc.shape[0] |
|
|
745 | print '==================== SPC SHAPE',numpy.shape(self.spc) | |
|
|
746 | ||
|
|
747 | 657 |
|
|
|
748 | 658 |
''' Se obtiene la constante del RADAR ''' |
|
|
749 | 659 |
|
|
|
750 | 660 |
self.Pt = Pt |
|
|
751 | 661 |
self.Gt = Gt |
|
|
752 | 662 |
self.Gr = Gr |
|
|
753 | 663 |
self.Lambda = Lambda |
|
|
754 | 664 |
self.aL = aL |
|
|
755 | 665 |
self.tauW = tauW |
|
|
756 | 666 |
self.ThetaT = ThetaT |
|
|
757 | 667 |
self.ThetaR = ThetaR |
|
|
758 | 668 |
|
|
|
759 | 669 |
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
760 | 670 |
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
|
761 |
RadarConstant = 5e-2 |
|
|
|
762 |
|
|
|
|
763 | print '*** RadarConstant' , RadarConstant, '****' | |
|
|
764 | print '***' | |
|
|
671 | RadarConstant = 5e-26 * Numerator / Denominator # | |
|
|
672 | ||
|
|
765 | 673 |
''' ============================= ''' |
|
|
766 | 674 |
|
|
|
767 | 675 |
self.spc[0] = (self.spc[0]-dataOut.noise[0]) |
|
|
768 | 676 |
self.spc[1] = (self.spc[1]-dataOut.noise[1]) |
|
|
769 | 677 |
self.spc[2] = (self.spc[2]-dataOut.noise[2]) |
|
|
770 | 678 |
|
|
|
771 | 679 |
self.spc[ numpy.where(self.spc < 0)] = 0 |
|
|
772 | 680 |
|
|
|
773 | 681 |
SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise)) |
|
|
774 | 682 |
SPCmean[ numpy.where(SPCmean < 0)] = 0 |
|
|
775 | 683 |
|
|
|
776 | 684 |
ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
777 | 685 |
ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
778 | 686 |
ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
779 | 687 |
|
|
|
780 | 688 |
Pr = SPCmean[:,:] |
|
|
781 | 689 |
|
|
|
782 | 690 |
VelMeteoro = numpy.mean(SPCmean,axis=0) |
|
|
783 | 691 |
|
|
|
784 | #print '==================== Vel SHAPE',VelMeteoro | |
|
|
785 | ||
|
|
786 | 692 |
D_range = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
787 | 693 |
SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
788 | 694 |
N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
789 | 695 |
V_mean = numpy.zeros(self.Num_Hei) |
|
|
790 | 696 |
del_V = numpy.zeros(self.Num_Hei) |
|
|
791 | 697 |
Z = numpy.zeros(self.Num_Hei) |
|
|
792 | 698 |
Ze = numpy.zeros(self.Num_Hei) |
|
|
793 | 699 |
RR = numpy.zeros(self.Num_Hei) |
|
|
794 | 700 |
|
|
|
795 | 701 |
Range = dataOut.heightList*1000. |
|
|
796 | 702 |
|
|
|
797 | 703 |
for R in range(self.Num_Hei): |
|
|
798 | 704 |
|
|
|
799 | 705 |
h = Range[R] + Altitude #Range from ground to radar pulse altitude |
|
|
800 | 706 |
del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity |
|
|
801 | 707 |
|
|
|
802 | 708 |
D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3 |
|
|
803 | 709 |
|
|
|
804 | 710 |
'''NOTA: ETA(n) dn = ETA(f) df |
|
|
805 | 711 |
|
|
|
806 | 712 |
dn = 1 Diferencial de muestreo |
|
|
807 | 713 |
df = ETA(n) / ETA(f) |
|
|
808 | 714 |
|
|
|
809 | 715 |
''' |
|
|
810 | 716 |
|
|
|
811 | 717 |
ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA) |
|
|
812 | 718 |
|
|
|
813 | 719 |
ETAv[:,R]=ETAn[:,R]/dV |
|
|
814 | 720 |
|
|
|
815 | 721 |
ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R]) |
|
|
816 | 722 |
|
|
|
817 | 723 |
SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4 #Equivalent Section of drops (sigma) |
|
|
818 | 724 |
|
|
|
819 | 725 |
N_dist[:,R] = ETAn[:,R] / SIGMA[:,R] |
|
|
820 | 726 |
|
|
|
821 | 727 |
DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin]) |
|
|
822 | 728 |
|
|
|
823 | 729 |
try: |
|
|
824 | 730 |
popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments) |
|
|
825 | 731 |
except: |
|
|
826 | 732 |
popt01=numpy.zeros(3) |
|
|
827 | 733 |
popt01[1]= DMoments[1] |
|
|
828 | 734 |
|
|
|
829 | 735 |
if popt01[1]<0 or popt01[1]>20: |
|
|
830 | 736 |
popt01[1]=numpy.NaN |
|
|
831 | 737 |
|
|
|
832 | 738 |
|
|
|
833 | 739 |
V_mean[R]=popt01[1] |
|
|
834 | 740 |
|
|
|
835 | 741 |
Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18 |
|
|
836 | 742 |
|
|
|
837 | 743 |
RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate |
|
|
838 | 744 |
|
|
|
839 | 745 |
Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km) |
|
|
840 | 746 |
|
|
|
841 | 747 |
|
|
|
842 | 748 |
|
|
|
843 | 749 |
RR2 = (Z/200)**(1/1.6) |
|
|
844 | 750 |
dBRR = 10*numpy.log10(RR) |
|
|
845 | 751 |
dBRR2 = 10*numpy.log10(RR2) |
|
|
846 | 752 |
|
|
|
847 | 753 |
dBZe = 10*numpy.log10(Ze) |
|
|
848 | 754 |
dBZ = 10*numpy.log10(Z) |
|
|
849 | 755 |
|
|
|
850 |
dataOut.data_output = |
|
|
|
756 | dataOut.data_output = RR[8] | |
|
|
851 | 757 |
dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
|
852 | 758 |
dataOut.channelList = [0,1,2] |
|
|
853 | 759 |
|
|
|
854 | 760 |
dataOut.data_param[0]=dBZ |
|
|
855 | 761 |
dataOut.data_param[1]=V_mean |
|
|
856 | 762 |
dataOut.data_param[2]=RR |
|
|
857 | 763 |
|
|
|
858 | #print 'VELRANGE', Velrange | |
|
|
859 | #print 'Range', len(Range) | |
|
|
860 | #print 'delv',del_V | |
|
|
861 | # print 'DRANGE', D_range[:,56] | |
|
|
862 | # #print 'NOISE', dataOut.noise[0], 10*numpy.log10(dataOut.noise[0]) | |
|
|
863 | # print 'radarconstant', RadarConstant | |
|
|
864 | # #print 'ETAn SHAPE', ETAn.shape | |
|
|
865 | # # print 'ETAn ', numpy.nansum(ETAn, axis=0) | |
|
|
866 | # # print 'ETAd ', numpy.nansum(ETAd, axis=0) | |
|
|
867 | # print 'Pr ', numpy.nansum(Pr, axis=0) | |
|
|
868 | # print 'dataOut.SPCparam[1]', numpy.nansum(dataOut.SPCparam[1][0], axis=0) | |
|
|
869 | # print 'Ze ', dBZe | |
|
|
870 | print 'Z ', dBZ | |
|
|
871 | # print 'Ndist',N_dist[:,56] | |
|
|
872 | print 'RR2 ', RR2 | |
|
|
873 | print 'RR ', RR | |
|
|
874 | print 'Vr', V_mean | |
|
|
875 | #print 'RR2 ', dBRR2 | |
|
|
876 | #print 'D_mean', D_mean | |
|
|
877 | #print 'del_V', del_V | |
|
|
878 | #print 'D_range',D_range.shape, D_range[:,30] | |
|
|
879 | #print 'Velrange', Velrange | |
|
|
880 | #print 'numpy.nansum( N_dist[:,R]', numpy.nansum( N_dist, axis=0) | |
|
|
881 | #print 'dataOut.data_param SHAPE', dataOut.data_param.shape | |
|
|
882 | ||
|
|
883 | 764 |
|
|
|
884 | 765 |
def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
885 | 766 |
|
|
|
886 | 767 |
NPW = dataOut.NPW |
|
|
887 | 768 |
COFA = dataOut.COFA |
|
|
888 | 769 |
|
|
|
889 | 770 |
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
890 | 771 |
RadarConst = dataOut.RadarConst |
|
|
891 | 772 |
#frequency = 34.85*10**9 |
|
|
892 | 773 |
|
|
|
893 | 774 |
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
894 | 775 |
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
895 | 776 |
|
|
|
896 | 777 |
ETA = numpy.sum(SNR,1) |
|
|
897 | print 'ETA' , ETA | |
|
|
778 | ||
|
|
898 | 779 |
ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN) |
|
|
899 | 780 |
|
|
|
900 | 781 |
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
901 | 782 |
|
|
|
902 | 783 |
for r in range(self.Num_Hei): |
|
|
903 | 784 |
|
|
|
904 | 785 |
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
905 | 786 |
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
906 | 787 |
|
|
|
907 | 788 |
return Ze |
|
|
908 | 789 |
|
|
|
909 | 790 |
# def GetRadarConstant(self): |
|
|
910 | 791 |
# |
|
|
911 | 792 |
# """ |
|
|
912 | 793 |
# Constants: |
|
|
913 | 794 |
# |
|
|
914 | 795 |
# Pt: Transmission Power dB 5kW 5000 |
|
|
915 | 796 |
# Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
|
916 | 797 |
# Gr: Reception Gain dB 18.5 dB 70.7945 |
|
|
917 | 798 |
# Lambda: Wavelenght m 0.6741 m 0.6741 |
|
|
918 | 799 |
# aL: Attenuation loses dB 4dB 2.5118 |
|
|
919 | 800 |
# tauW: Width of transmission pulse s 4us 4e-6 |
|
|
920 | 801 |
# ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
|
921 | 802 |
# ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
|
922 | 803 |
# |
|
|
923 | 804 |
# """ |
|
|
924 | 805 |
# |
|
|
925 | 806 |
# Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
926 | 807 |
# Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
927 | 808 |
# RadarConstant = Numerator / Denominator |
|
|
928 | 809 |
# |
|
|
929 | 810 |
# return RadarConstant |
|
|
930 | 811 |
|
|
|
931 | 812 |
|
|
|
932 | 813 |
|
|
|
933 | 814 |
class FullSpectralAnalysis(Operation): |
|
|
934 | 815 |
|
|
|
935 | 816 |
""" |
|
|
936 | 817 |
Function that implements Full Spectral Analisys technique. |
|
|
937 | 818 |
|
|
|
938 | 819 |
Input: |
|
|
939 | 820 |
self.dataOut.data_pre : SelfSpectra and CrossSPectra data |
|
|
940 | 821 |
self.dataOut.groupList : Pairlist of channels |
|
|
941 | 822 |
self.dataOut.ChanDist : Physical distance between receivers |
|
|
942 | 823 |
|
|
|
943 | 824 |
|
|
|
944 | 825 |
Output: |
|
|
945 | 826 |
|
|
|
946 | 827 |
self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind |
|
|
947 | 828 |
|
|
|
948 | 829 |
|
|
|
949 | 830 |
Parameters affected: Winds, height range, SNR |
|
|
950 | 831 |
|
|
|
951 | 832 |
""" |
|
|
952 | 833 |
def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7): |
|
|
953 | 834 |
|
|
|
954 | 835 |
self.indice=int(numpy.random.rand()*1000) |
|
|
955 | 836 |
|
|
|
956 | 837 |
spc = dataOut.data_pre[0].copy() |
|
|
957 | 838 |
cspc = dataOut.data_pre[1] |
|
|
958 | 839 |
|
|
|
840 | """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" | |
|
|
841 | ||
|
|
842 | SNRspc = spc.copy() | |
|
|
843 | SNRspc[:,:,0:7]= numpy.NaN | |
|
|
844 | ||
|
|
845 | """##########################################""" | |
|
|
846 | ||
|
|
847 | ||
|
|
959 | 848 |
nChannel = spc.shape[0] |
|
|
960 | 849 |
nProfiles = spc.shape[1] |
|
|
961 | 850 |
nHeights = spc.shape[2] |
|
|
962 | 851 |
|
|
|
963 | 852 |
pairsList = dataOut.groupList |
|
|
964 | 853 |
if dataOut.ChanDist is not None : |
|
|
965 | 854 |
ChanDist = dataOut.ChanDist |
|
|
966 | 855 |
else: |
|
|
967 | 856 |
ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
|
968 | 857 |
|
|
|
969 | 858 |
FrecRange = dataOut.spc_range[0] |
|
|
970 | 859 |
|
|
|
971 | 860 |
ySamples=numpy.ones([nChannel,nProfiles]) |
|
|
972 | 861 |
phase=numpy.ones([nChannel,nProfiles]) |
|
|
973 | 862 |
CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_) |
|
|
974 | 863 |
coherence=numpy.ones([nChannel,nProfiles]) |
|
|
975 | 864 |
PhaseSlope=numpy.ones(nChannel) |
|
|
976 | 865 |
PhaseInter=numpy.ones(nChannel) |
|
|
977 | 866 |
data_SNR=numpy.zeros([nProfiles]) |
|
|
978 | 867 |
|
|
|
979 | 868 |
data = dataOut.data_pre |
|
|
980 | 869 |
noise = dataOut.noise |
|
|
981 | 870 |
|
|
|
982 |
dataOut.data_SNR = (numpy.mean(spc,axis=1)- noise[0]) / noise[0] |
|
|
|
871 | dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0] | |
|
|
983 | 872 |
|
|
|
984 | 873 |
dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20 |
|
|
985 | ||
|
|
986 | 874 |
|
|
|
987 | #FirstMoment = dataOut.moments[0,1,:]#numpy.average(dataOut.data_param[:,1,:],0) | |
|
|
988 | #SecondMoment = numpy.average(dataOut.moments[:,2,:],0) | |
|
|
989 | ||
|
|
990 | #SNRdBMean = [] | |
|
|
991 | 875 |
|
|
|
992 | 876 |
data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN |
|
|
993 | 877 |
|
|
|
994 | 878 |
velocityX=[] |
|
|
995 | 879 |
velocityY=[] |
|
|
996 | 880 |
velocityV=[] |
|
|
997 | 881 |
PhaseLine=[] |
|
|
998 | 882 |
|
|
|
999 | 883 |
dbSNR = 10*numpy.log10(dataOut.data_SNR) |
|
|
1000 | 884 |
dbSNR = numpy.average(dbSNR,0) |
|
|
1001 | 885 |
|
|
|
1002 | 886 |
for Height in range(nHeights): |
|
|
1003 |
|
|
|
|
887 | ||
|
|
1004 | 888 |
[Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range.copy(), dbSNR[Height], SNRlimit) |
|
|
1005 | 889 |
PhaseLine = numpy.append(PhaseLine, PhaseSlope) |
|
|
1006 | 890 |
|
|
|
1007 | 891 |
if abs(Vzon)<100. and abs(Vzon)> 0.: |
|
|
1008 | 892 |
velocityX=numpy.append(velocityX, Vzon)#Vmag |
|
|
1009 | 893 |
|
|
|
1010 | 894 |
else: |
|
|
1011 | #print 'Vzon',Vzon | |
|
|
1012 | 895 |
velocityX=numpy.append(velocityX, numpy.NaN) |
|
|
1013 | 896 |
|
|
|
1014 | 897 |
if abs(Vmer)<100. and abs(Vmer) > 0.: |
|
|
1015 | 898 |
velocityY=numpy.append(velocityY, -Vmer)#Vang |
|
|
1016 | 899 |
|
|
|
1017 | 900 |
else: |
|
|
1018 | #print 'Vmer',Vmer | |
|
|
1019 | 901 |
velocityY=numpy.append(velocityY, numpy.NaN) |
|
|
1020 | 902 |
|
|
|
1021 | 903 |
if dbSNR[Height] > SNRlimit: |
|
|
1022 | 904 |
velocityV=numpy.append(velocityV, -Vver)#FirstMoment[Height]) |
|
|
1023 | 905 |
else: |
|
|
1024 | 906 |
velocityV=numpy.append(velocityV, numpy.NaN) |
|
|
1025 | #FirstMoment[Height]= numpy.NaN | |
|
|
1026 | # if SNRdBMean[Height] <12: | |
|
|
1027 | # FirstMoment[Height] = numpy.NaN | |
|
|
1028 | # velocityX[Height] = numpy.NaN | |
|
|
1029 | # velocityY[Height] = numpy.NaN | |
|
|
907 | ||
|
|
1030 | 908 |
|
|
|
1031 | 909 |
|
|
|
1032 | 910 |
|
|
|
1033 | 911 |
data_output[0] = numpy.array(velocityX) #self.moving_average(numpy.array(velocityX) , N=1) |
|
|
1034 | 912 |
data_output[1] = numpy.array(velocityY) #self.moving_average(numpy.array(velocityY) , N=1) |
|
|
1035 | 913 |
data_output[2] = velocityV#FirstMoment |
|
|
1036 | 914 |
|
|
|
1037 | print 'data_output', data_output.shape | |
|
|
1038 | #print FirstMoment | |
|
|
1039 | # print 'velocityX',numpy.shape(data_output[0]) | |
|
|
1040 | # print 'velocityX',data_output[0] | |
|
|
1041 | # print ' ' | |
|
|
1042 | # print 'velocityY',numpy.shape(data_output[1]) | |
|
|
1043 | # print 'velocityY',data_output[1] | |
|
|
1044 | # print 'velocityV',data_output[2] | |
|
|
1045 | # print 'PhaseLine',PhaseLine | |
|
|
1046 | #print numpy.array(velocityY) | |
|
|
1047 | #print 'SNR' | |
|
|
1048 | #print 10*numpy.log10(dataOut.data_SNR) | |
|
|
1049 | #print numpy.shape(10*numpy.log10(dataOut.data_SNR)) | |
|
|
1050 | print ' ' | |
|
|
1051 | ||
|
|
1052 | 915 |
xFrec=FrecRange[0:spc.shape[1]] |
|
|
1053 | 916 |
|
|
|
1054 | 917 |
dataOut.data_output=data_output |
|
|
1055 | 918 |
|
|
|
1056 | 919 |
return |
|
|
1057 | 920 |
|
|
|
1058 | 921 |
|
|
|
1059 | 922 |
def moving_average(self,x, N=2): |
|
|
1060 | 923 |
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
1061 | 924 |
|
|
|
1062 | 925 |
def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
1063 | 926 |
return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
1064 | 927 |
|
|
|
1065 | 928 |
|
|
|
1066 | 929 |
|
|
|
1067 | 930 |
def Moments(self, ySamples, xSamples): |
|
|
1068 | 931 |
Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|
1069 | 932 |
yNorm = ySamples / Pot |
|
|
1070 | 933 |
Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|
1071 | 934 |
Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|
1072 | 935 |
Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|
1073 | 936 |
|
|
|
1074 | 937 |
return numpy.array([Pot, Vr, Desv]) |
|
|
1075 | 938 |
|
|
|
1076 | 939 |
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): |
|
|
1077 | 940 |
|
|
|
1078 | 941 | |
|
|
1079 | 942 |
|
|
|
1080 | 943 |
ySamples=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1081 | 944 |
phase=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1082 | 945 |
CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) |
|
|
1083 | 946 |
coherence=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
1084 | 947 |
PhaseSlope=numpy.zeros(spc.shape[0]) |
|
|
1085 | 948 |
PhaseInter=numpy.ones(spc.shape[0]) |
|
|
1086 | 949 |
xFrec=AbbsisaRange[0][0:spc.shape[1]] |
|
|
1087 | 950 |
xVel =AbbsisaRange[2][0:spc.shape[1]] |
|
|
1088 | 951 |
Vv=numpy.empty(spc.shape[2])*0 |
|
|
1089 | 952 |
SPCav = numpy.average(spc, axis=0)-numpy.average(noise) #spc[0]-noise[0]# |
|
|
1090 | 953 |
|
|
|
1091 | 954 |
SPCmoments = self.Moments(SPCav[:,Height], xVel ) |
|
|
1092 | 955 |
CSPCmoments = [] |
|
|
1093 | 956 |
cspcNoise = numpy.empty(3) |
|
|
1094 | 957 |
|
|
|
1095 | 958 |
'''Getting Eij and Nij''' |
|
|
1096 | 959 |
|
|
|
1097 | 960 |
Xi01=ChanDist[0][0] |
|
|
1098 | 961 |
Eta01=ChanDist[0][1] |
|
|
1099 | 962 |
|
|
|
1100 | 963 |
Xi02=ChanDist[1][0] |
|
|
1101 | 964 |
Eta02=ChanDist[1][1] |
|
|
1102 | 965 |
|
|
|
1103 | 966 |
Xi12=ChanDist[2][0] |
|
|
1104 | 967 |
Eta12=ChanDist[2][1] |
|
|
1105 | 968 |
|
|
|
1106 | 969 |
z = spc.copy() |
|
|
1107 | 970 |
z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1108 | 971 |
|
|
|
1109 | 972 |
for i in range(spc.shape[0]): |
|
|
1110 | 973 |
|
|
|
1111 | 974 |
'''****** Line of Data SPC ******''' |
|
|
1112 | 975 |
zline=z[i,:,Height].copy() - noise[i] # Se resta ruido |
|
|
1113 | 976 |
|
|
|
1114 | 977 |
'''****** SPC is normalized ******''' |
|
|
1115 | 978 |
SmoothSPC =self.moving_average(zline.copy(),N=1) # Se suaviza el ruido |
|
|
1116 | 979 |
FactNorm = SmoothSPC/numpy.nansum(SmoothSPC) # SPC Normalizado y suavizado |
|
|
1117 | 980 |
|
|
|
1118 | 981 |
xSamples = xFrec # Se toma el rango de frecuncias |
|
|
1119 | 982 |
ySamples[i] = FactNorm # Se toman los valores de SPC normalizado |
|
|
1120 | 983 |
|
|
|
1121 | 984 |
for i in range(spc.shape[0]): |
|
|
1122 | 985 |
|
|
|
1123 | 986 |
'''****** Line of Data CSPC ******''' |
|
|
1124 | 987 |
cspcLine = ( cspc[i,:,Height].copy())# - noise[i] ) # no! Se resta el ruido |
|
|
1125 | 988 |
SmoothCSPC =self.moving_average(cspcLine,N=1) # Se suaviza el ruido |
|
|
1126 | 989 |
cspcNorm = SmoothCSPC/numpy.nansum(SmoothCSPC) # CSPC normalizado y suavizado |
|
|
1127 | 990 |
|
|
|
1128 | 991 |
'''****** CSPC is normalized with respect to Briggs and Vincent ******''' |
|
|
1129 | 992 |
chan_index0 = pairsList[i][0] |
|
|
1130 | 993 |
chan_index1 = pairsList[i][1] |
|
|
1131 | 994 |
|
|
|
1132 | 995 |
CSPCFactor= numpy.abs(numpy.nansum(ySamples[chan_index0]))**2 * numpy.abs(numpy.nansum(ySamples[chan_index1]))**2 |
|
|
1133 | 996 |
CSPCNorm = cspcNorm / numpy.sqrt(CSPCFactor) |
|
|
1134 | 997 |
|
|
|
1135 | 998 |
CSPCSamples[i] = CSPCNorm |
|
|
1136 | 999 |
|
|
|
1137 | 1000 |
coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor) |
|
|
1138 | 1001 |
|
|
|
1139 | 1002 |
#coherence[i]= self.moving_average(coherence[i],N=1) |
|
|
1140 | 1003 |
|
|
|
1141 | 1004 |
phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi |
|
|
1142 | 1005 |
|
|
|
1143 | 1006 |
CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPCSamples[0]), xSamples), |
|
|
1144 | 1007 |
self.Moments(numpy.abs(CSPCSamples[1]), xSamples), |
|
|
1145 | 1008 |
self.Moments(numpy.abs(CSPCSamples[2]), xSamples)]) |
|
|
1146 | 1009 |
|
|
|
1147 | #print '##### SUMA de SPC #####', len(ySamples) | |
|
|
1148 | #print numpy.sum(ySamples[0]) | |
|
|
1149 | #print '##### SUMA de CSPC #####', len(coherence) | |
|
|
1150 | #print numpy.sum(numpy.abs(CSPCNorm)) | |
|
|
1151 | #print numpy.sum(coherence[0]) | |
|
|
1152 | # print 'len',len(xSamples) | |
|
|
1153 | # print 'CSPCmoments', numpy.shape(CSPCmoments) | |
|
|
1154 | # print CSPCmoments | |
|
|
1155 | # print '#######################' | |
|
|
1156 | ||
|
|
1010 | ||
|
|
1157 | 1011 |
popt=[1e-10,0,1e-10] |
|
|
1158 | 1012 |
popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] |
|
|
1159 | 1013 |
FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 |
|
|
1160 | 1014 |
|
|
|
1161 | 1015 |
CSPCMask01 = numpy.abs(CSPCSamples[0]) |
|
|
1162 | 1016 |
CSPCMask02 = numpy.abs(CSPCSamples[1]) |
|
|
1163 | 1017 |
CSPCMask12 = numpy.abs(CSPCSamples[2]) |
|
|
1164 | 1018 |
|
|
|
1165 | 1019 |
mask01 = ~numpy.isnan(CSPCMask01) |
|
|
1166 | 1020 |
mask02 = ~numpy.isnan(CSPCMask02) |
|
|
1167 | 1021 |
mask12 = ~numpy.isnan(CSPCMask12) |
|
|
1168 | 1022 |
|
|
|
1169 | 1023 |
#mask = ~numpy.isnan(CSPCMask01) |
|
|
1170 | 1024 |
CSPCMask01 = CSPCMask01[mask01] |
|
|
1171 | 1025 |
CSPCMask02 = CSPCMask02[mask02] |
|
|
1172 | 1026 |
CSPCMask12 = CSPCMask12[mask12] |
|
|
1173 | 1027 |
#CSPCMask01 = numpy.ma.masked_invalid(CSPCMask01) |
|
|
1174 | 1028 |
|
|
|
1175 | 1029 |
|
|
|
1176 | 1030 |
|
|
|
1177 | 1031 |
'''***Fit Gauss CSPC01***''' |
|
|
1178 | 1032 |
if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3 : |
|
|
1179 | 1033 |
try: |
|
|
1180 | 1034 |
popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0]) |
|
|
1181 | 1035 |
popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) |
|
|
1182 | 1036 |
popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) |
|
|
1183 | 1037 |
FitGauss01 = self.gaus(xSamples,*popt01) |
|
|
1184 | 1038 |
FitGauss02 = self.gaus(xSamples,*popt02) |
|
|
1185 | 1039 |
FitGauss12 = self.gaus(xSamples,*popt12) |
|
|
1186 | 1040 |
except: |
|
|
1187 | 1041 |
FitGauss01=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[0])) |
|
|
1188 | 1042 |
FitGauss02=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[1])) |
|
|
1189 | 1043 |
FitGauss12=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[2])) |
|
|
1190 | 1044 |
|
|
|
1191 | 1045 |
|
|
|
1192 | 1046 |
CSPCopt = numpy.vstack([popt01,popt02,popt12]) |
|
|
1193 | 1047 |
|
|
|
1194 | 1048 |
'''****** Getting fij width ******''' |
|
|
1195 | 1049 |
|
|
|
1196 | 1050 |
yMean = numpy.average(ySamples, axis=0) # ySamples[0] |
|
|
1197 | 1051 |
|
|
|
1198 | 1052 |
'''******* Getting fitting Gaussian *******''' |
|
|
1199 | 1053 |
meanGauss = sum(xSamples*yMean) / len(xSamples) # Mu, velocidad radial (frecuencia) |
|
|
1200 | 1054 |
sigma2 = sum(yMean*(xSamples-meanGauss)**2) / len(xSamples) # Varianza, Ancho espectral (frecuencia) |
|
|
1201 | 1055 |
|
|
|
1202 | 1056 |
yMoments = self.Moments(yMean, xSamples) |
|
|
1203 | 1057 |
|
|
|
1204 | 1058 |
if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3: # and abs(meanGauss/sigma2) > 0.00001: |
|
|
1205 | 1059 |
try: |
|
|
1206 | 1060 |
popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=yMoments) |
|
|
1207 | 1061 |
FitGauss=self.gaus(xSamples,*popt) |
|
|
1208 | 1062 |
|
|
|
1209 | 1063 |
except :#RuntimeError: |
|
|
1210 | 1064 |
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1211 | 1065 |
|
|
|
1212 | 1066 |
|
|
|
1213 | 1067 |
else: |
|
|
1214 | 1068 |
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1215 | 1069 |
|
|
|
1216 | 1070 |
|
|
|
1217 | 1071 |
|
|
|
1218 | 1072 |
'''****** Getting Fij ******''' |
|
|
1219 | 1073 |
Fijcspc = CSPCopt[:,2]/2*3 |
|
|
1220 | 1074 |
|
|
|
1221 | 1075 |
|
|
|
1222 | 1076 |
GaussCenter = popt[1] #xFrec[GCpos] |
|
|
1223 | 1077 |
#Punto en Eje X de la Gaussiana donde se encuentra el centro |
|
|
1224 | 1078 |
ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] |
|
|
1225 | 1079 |
PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] |
|
|
1226 | 1080 |
|
|
|
1227 | 1081 |
#Punto e^-1 hubicado en la Gaussiana |
|
|
1228 | 1082 |
PeMinus1 = numpy.max(FitGauss)* numpy.exp(-1) |
|
|
1229 | 1083 |
FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss" |
|
|
1230 | 1084 |
PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
|
1231 | 1085 |
|
|
|
1232 | 1086 |
if xSamples[PointFij] > xSamples[PointGauCenter]: |
|
|
1233 | 1087 |
Fij = xSamples[PointFij] - xSamples[PointGauCenter] |
|
|
1234 | 1088 |
|
|
|
1235 | 1089 |
else: |
|
|
1236 | 1090 |
Fij = xSamples[PointGauCenter] - xSamples[PointFij] |
|
|
1237 | 1091 |
|
|
|
1238 | # print 'CSPCopt' | |
|
|
1239 | # print CSPCopt | |
|
|
1240 | # print 'popt' | |
|
|
1241 | # print popt | |
|
|
1242 | # print '#######################################' | |
|
|
1243 | #print 'dataOut.data_param', numpy.shape(data_param) | |
|
|
1244 | #print 'dataOut.data_param0', data_param[0,0,Height] | |
|
|
1245 | #print 'dataOut.data_param1', data_param[0,1,Height] | |
|
|
1246 | #print 'dataOut.data_param2', data_param[0,2,Height] | |
|
|
1247 | ||
|
|
1248 | ||
|
|
1249 | # print 'yMoments', yMoments | |
|
|
1250 | # print 'Moments', SPCmoments | |
|
|
1251 | # print 'Fij2 Moment', Fij | |
|
|
1252 | # #print 'Fij', Fij, 'popt[2]/2',popt[2]/2 | |
|
|
1253 | # print 'Fijcspc',Fijcspc | |
|
|
1254 | # print '#######################################' | |
|
|
1255 | ||
|
|
1256 | 1092 |
|
|
|
1257 | 1093 |
'''****** Taking frequency ranges from SPCs ******''' |
|
|
1258 | 1094 |
|
|
|
1259 | 1095 |
|
|
|
1260 | 1096 |
#GaussCenter = popt[1] #Primer momento 01 |
|
|
1261 | 1097 |
GauWidth = popt[2] *3/2 #Ancho de banda de Gau01 |
|
|
1262 | 1098 |
Range = numpy.empty(2) |
|
|
1263 | 1099 |
Range[0] = GaussCenter - GauWidth |
|
|
1264 | 1100 |
Range[1] = GaussCenter + GauWidth |
|
|
1265 | 1101 |
#Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) |
|
|
1266 | 1102 |
ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] |
|
|
1267 | 1103 |
ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] |
|
|
1268 | 1104 |
|
|
|
1269 | 1105 |
PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0] |
|
|
1270 | 1106 |
PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] |
|
|
1271 | 1107 |
|
|
|
1272 | 1108 |
Range=numpy.array([ PointRangeMin, PointRangeMax ]) |
|
|
1273 | 1109 |
|
|
|
1274 | 1110 |
FrecRange = xFrec[ Range[0] : Range[1] ] |
|
|
1275 | 1111 |
VelRange = xVel[ Range[0] : Range[1] ] |
|
|
1276 | 1112 |
|
|
|
1277 | 1113 |
|
|
|
1278 | #print 'RANGE: ', Range | |
|
|
1279 | #print 'FrecRange', numpy.shape(FrecRange)#,FrecRange | |
|
|
1280 | #print 'len: ', len(FrecRange) | |
|
|
1281 | ||
|
|
1282 | 1114 |
'''****** Getting SCPC Slope ******''' |
|
|
1283 | 1115 |
|
|
|
1284 | 1116 |
for i in range(spc.shape[0]): |
|
|
1285 | 1117 |
|
|
|
1286 | 1118 |
if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.3: |
|
|
1287 | 1119 |
PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3) |
|
|
1288 |
|
|
|
|
1289 | #print 'Ancho espectral Frecuencias', FrecRange[-1]-FrecRange[0], 'Hz' | |
|
|
1290 | #print 'Ancho espectral Velocidades', VelRange[-1]-VelRange[0], 'm/s' | |
|
|
1291 | #print 'FrecRange', len(FrecRange) , FrecRange | |
|
|
1292 | #print 'VelRange', len(VelRange) , VelRange | |
|
|
1293 | #print 'PhaseRange', numpy.shape(PhaseRange), PhaseRange | |
|
|
1294 | #print ' ' | |
|
|
1295 | ||
|
|
1120 | ||
|
|
1296 | 1121 |
'''***********************VelRange******************''' |
|
|
1297 | 1122 |
|
|
|
1298 | 1123 |
mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
|
1299 | 1124 |
|
|
|
1300 | 1125 |
if len(FrecRange) == len(PhaseRange): |
|
|
1301 | 1126 |
try: |
|
|
1302 | 1127 |
slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange[mask], PhaseRange[mask]) |
|
|
1303 | 1128 |
PhaseSlope[i]=slope |
|
|
1304 | 1129 |
PhaseInter[i]=intercept |
|
|
1305 | 1130 |
except: |
|
|
1306 | 1131 |
PhaseSlope[i]=0 |
|
|
1307 | 1132 |
PhaseInter[i]=0 |
|
|
1308 | 1133 |
else: |
|
|
1309 | 1134 |
PhaseSlope[i]=0 |
|
|
1310 | 1135 |
PhaseInter[i]=0 |
|
|
1311 | 1136 |
else: |
|
|
1312 | 1137 |
PhaseSlope[i]=0 |
|
|
1313 | 1138 |
PhaseInter[i]=0 |
|
|
1314 | 1139 |
|
|
|
1315 | 1140 |
|
|
|
1316 | 1141 |
'''Getting constant C''' |
|
|
1317 | 1142 |
cC=(Fij*numpy.pi)**2 |
|
|
1318 | 1143 |
|
|
|
1319 | 1144 |
'''****** Getting constants F and G ******''' |
|
|
1320 | 1145 |
MijEijNij=numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1321 | 1146 |
MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi) |
|
|
1322 | 1147 |
MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi) |
|
|
1323 | 1148 |
MijResults=numpy.array([MijResult0,MijResult1]) |
|
|
1324 | 1149 |
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
1325 | 1150 |
|
|
|
1326 | 1151 |
'''****** Getting constants A, B and H ******''' |
|
|
1327 | 1152 |
W01=numpy.nanmax( FitGauss01 ) #numpy.abs(CSPCSamples[0])) |
|
|
1328 | 1153 |
W02=numpy.nanmax( FitGauss02 ) #numpy.abs(CSPCSamples[1])) |
|
|
1329 | 1154 |
W12=numpy.nanmax( FitGauss12 ) #numpy.abs(CSPCSamples[2])) |
|
|
1330 | 1155 |
|
|
|
1331 | 1156 |
WijResult0=((cF*Xi01+cG*Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC)) |
|
|
1332 | 1157 |
WijResult1=((cF*Xi02+cG*Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC)) |
|
|
1333 | 1158 |
WijResult2=((cF*Xi12+cG*Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC)) |
|
|
1334 | 1159 |
|
|
|
1335 | 1160 |
WijResults=numpy.array([WijResult0, WijResult1, WijResult2]) |
|
|
1336 | 1161 |
|
|
|
1337 | 1162 |
WijEijNij=numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
|
1338 | 1163 |
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1339 | 1164 |
|
|
|
1340 | 1165 |
VxVy=numpy.array([[cA,cH],[cH,cB]]) |
|
|
1341 | 1166 |
VxVyResults=numpy.array([-cF,-cG]) |
|
|
1342 | 1167 |
(Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1343 | 1168 |
|
|
|
1344 | #print 'MijResults, cC, PhaseSlope', MijResults, cC, PhaseSlope | |
|
|
1345 | #print 'W01,02,12', W01, W02, W12 | |
|
|
1346 | #print 'WijResult0,1,2',WijResult0, WijResult1, WijResult2, 'Results', WijResults | |
|
|
1347 | #print 'cA,cB,cH, cF, cG', cA, cB, cH, cF, cG | |
|
|
1348 | #print 'VxVy', VxVyResults | |
|
|
1349 | #print '###########################****************************************' | |
|
|
1350 | 1169 |
Vzon = Vy |
|
|
1351 | 1170 |
Vmer = Vx |
|
|
1352 | 1171 |
Vmag=numpy.sqrt(Vzon**2+Vmer**2) |
|
|
1353 | 1172 |
Vang=numpy.arctan2(Vmer,Vzon) |
|
|
1354 | 1173 |
if numpy.abs( popt[1] ) < 3.5 and len(FrecRange)>4: |
|
|
1355 | 1174 |
Vver=popt[1] |
|
|
1356 | 1175 |
else: |
|
|
1357 | 1176 |
Vver=numpy.NaN |
|
|
1358 | 1177 |
FitGaussCSPC = numpy.array([FitGauss01,FitGauss02,FitGauss12]) |
|
|
1359 | 1178 |
|
|
|
1360 | 1179 |
|
|
|
1361 | # ''' Ploteo por altura ''' | |
|
|
1362 | # if Height == 28: | |
|
|
1363 | # for i in range(3): | |
|
|
1364 | # #print 'FASE', numpy.shape(phase), y[25] | |
|
|
1365 | # #print numpy.shape(coherence) | |
|
|
1366 | # fig = plt.figure(10+self.indice) | |
|
|
1367 | # #plt.plot( x[0:256],coherence[:,25] ) | |
|
|
1368 | # #cohAv = numpy.average(coherence[i],1) | |
|
|
1369 | # Pendiente = FrecRange * PhaseSlope[i] | |
|
|
1370 | # plt.plot( FrecRange, Pendiente) | |
|
|
1371 | # plt.plot( xFrec,phase[i]) | |
|
|
1372 | # | |
|
|
1373 | # CSPCmean = numpy.mean(numpy.abs(CSPCSamples),0) | |
|
|
1374 | # #plt.plot(xFrec, FitGauss01) | |
|
|
1375 | # #plt.plot(xFrec, CSPCmean) | |
|
|
1376 | # #plt.plot(xFrec, numpy.abs(CSPCSamples[0])) | |
|
|
1377 | # #plt.plot(xFrec, FitGauss) | |
|
|
1378 | # #plt.plot(xFrec, yMean) | |
|
|
1379 | # #plt.plot(xFrec, numpy.abs(coherence[0])) | |
|
|
1380 | # | |
|
|
1381 | # #plt.axis([-12, 12, 15, 50]) | |
|
|
1382 | # #plt.title("%s" %( '%s %s, Channel %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S") , i))) | |
|
|
1383 | # plt.ylabel('Desfase [rad]') | |
|
|
1384 | # #plt.ylabel('CSPC normalizado') | |
|
|
1385 | # plt.xlabel('Frec range [Hz]') | |
|
|
1386 | ||
|
|
1387 | #fig.savefig('/home/erick/Documents/Pics/to{}.png'.format(self.indice)) | |
|
|
1388 | ||
|
|
1389 | # plt.show() | |
|
|
1390 | # self.indice=self.indice+1 | |
|
|
1391 | ||
|
|
1392 | ||
|
|
1393 | ||
|
|
1394 | ||
|
|
1395 | ||
|
|
1396 | # print 'vzon y vmer', Vzon, Vmer | |
|
|
1397 | 1180 |
return Vzon, Vmer, Vver, GaussCenter, PhaseSlope, FitGaussCSPC |
|
|
1398 | 1181 |
|
|
|
1399 | 1182 |
class SpectralMoments(Operation): |
|
|
1400 | 1183 |
|
|
|
1401 | 1184 |
''' |
|
|
1402 | 1185 |
Function SpectralMoments() |
|
|
1403 | 1186 |
|
|
|
1404 | 1187 |
Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1405 | 1188 |
|
|
|
1406 | 1189 |
Type of dataIn: Spectra |
|
|
1407 | 1190 |
|
|
|
1408 | 1191 |
Configuration Parameters: |
|
|
1409 | 1192 |
|
|
|
1410 | 1193 |
dirCosx : Cosine director in X axis |
|
|
1411 | 1194 |
dirCosy : Cosine director in Y axis |
|
|
1412 | 1195 |
|
|
|
1413 | 1196 |
elevation : |
|
|
1414 | 1197 |
azimuth : |
|
|
1415 | 1198 |
|
|
|
1416 | 1199 |
Input: |
|
|
1417 | 1200 |
channelList : simple channel list to select e.g. [2,3,7] |
|
|
1418 | 1201 |
self.dataOut.data_pre : Spectral data |
|
|
1419 | 1202 |
self.dataOut.abscissaList : List of frequencies |
|
|
1420 | 1203 |
self.dataOut.noise : Noise level per channel |
|
|
1421 | 1204 |
|
|
|
1422 | 1205 |
Affected: |
|
|
1423 | 1206 |
self.dataOut.moments : Parameters per channel |
|
|
1424 | 1207 |
self.dataOut.data_SNR : SNR per channel |
|
|
1425 | 1208 |
|
|
|
1426 | 1209 |
''' |
|
|
1427 | 1210 |
|
|
|
1428 | 1211 |
def run(self, dataOut): |
|
|
1429 | 1212 |
|
|
|
1430 | 1213 |
#dataOut.data_pre = dataOut.data_pre[0] |
|
|
1431 | 1214 |
data = dataOut.data_pre[0] |
|
|
1432 | 1215 |
absc = dataOut.abscissaList[:-1] |
|
|
1433 | 1216 |
noise = dataOut.noise |
|
|
1434 | 1217 |
nChannel = data.shape[0] |
|
|
1435 | 1218 |
data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1436 | 1219 |
|
|
|
1437 | 1220 |
for ind in range(nChannel): |
|
|
1438 | 1221 |
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
|
1439 | 1222 |
|
|
|
1440 | 1223 |
dataOut.moments = data_param[:,1:,:] |
|
|
1441 | 1224 |
dataOut.data_SNR = data_param[:,0] |
|
|
1442 | 1225 |
return |
|
|
1443 | 1226 |
|
|
|
1444 | 1227 |
def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
1445 | 1228 |
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
1446 | 1229 |
|
|
|
1447 | 1230 |
if (nicoh == None): nicoh = 1 |
|
|
1448 | 1231 |
if (graph == None): graph = 0 |
|
|
1449 | 1232 |
if (smooth == None): smooth = 0 |
|
|
1450 | 1233 |
elif (self.smooth < 3): smooth = 0 |
|
|
1451 | 1234 | |
|
|
1452 | 1235 |
if (type1 == None): type1 = 0 |
|
|
1453 | 1236 |
if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1454 | 1237 |
if (snrth == None): snrth = -3 |
|
|
1455 | 1238 |
if (dc == None): dc = 0 |
|
|
1456 | 1239 |
if (aliasing == None): aliasing = 0 |
|
|
1457 | 1240 |
if (oldfd == None): oldfd = 0 |
|
|
1458 | 1241 |
if (wwauto == None): wwauto = 0 |
|
|
1459 | 1242 |
|
|
|
1460 | 1243 |
if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1461 | 1244 |
|
|
|
1462 | 1245 |
freq = oldfreq |
|
|
1463 | 1246 |
vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1464 | 1247 |
vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1465 | 1248 |
vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1466 | 1249 |
vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1467 | 1250 |
|
|
|
1468 | 1251 |
oldspec = numpy.ma.masked_invalid(oldspec) |
|
|
1469 | 1252 | |
|
|
1470 | 1253 |
for ind in range(oldspec.shape[1]): |
|
|
1471 | 1254 |
|
|
|
1472 | 1255 |
spec = oldspec[:,ind] |
|
|
1473 | 1256 |
aux = spec*fwindow |
|
|
1474 | 1257 |
max_spec = aux.max() |
|
|
1475 | 1258 |
m = list(aux).index(max_spec) |
|
|
1476 | 1259 |
|
|
|
1477 | 1260 |
#Smooth |
|
|
1478 | 1261 |
if (smooth == 0): spec2 = spec |
|
|
1479 | 1262 |
else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1480 | 1263 |
|
|
|
1481 | 1264 |
# Calculo de Momentos |
|
|
1482 | 1265 |
bb = spec2[range(m,spec2.size)] |
|
|
1483 | 1266 |
bb = (bb<n0).nonzero() |
|
|
1484 | 1267 |
bb = bb[0] |
|
|
1485 | 1268 |
|
|
|
1486 | 1269 |
ss = spec2[range(0,m + 1)] |
|
|
1487 | 1270 |
ss = (ss<n0).nonzero() |
|
|
1488 | 1271 |
ss = ss[0] |
|
|
1489 | 1272 |
|
|
|
1490 | 1273 |
if (bb.size == 0): |
|
|
1491 | 1274 |
bb0 = spec.size - 1 - m |
|
|
1492 | 1275 |
else: |
|
|
1493 | 1276 |
bb0 = bb[0] - 1 |
|
|
1494 | 1277 |
if (bb0 < 0): |
|
|
1495 | 1278 |
bb0 = 0 |
|
|
1496 | 1279 |
|
|
|
1497 | 1280 |
if (ss.size == 0): ss1 = 1 |
|
|
1498 | 1281 |
else: ss1 = max(ss) + 1 |
|
|
1499 | 1282 |
|
|
|
1500 | 1283 |
if (ss1 > m): ss1 = m |
|
|
1501 | 1284 |
|
|
|
1502 | 1285 |
valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
1503 | 1286 |
power = ( (spec2[valid] - n0) * fwindow[valid] ).sum() |
|
|
1504 | 1287 |
fd = ( (spec2[valid]- n0) * freq[valid] * fwindow[valid] ).sum() / power |
|
|
1505 | 1288 |
|
|
|
1506 | 1289 |
w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
1507 | 1290 |
snr = (spec2.mean()-n0)/n0 |
|
|
1508 | 1291 |
|
|
|
1509 | 1292 |
if (snr < 1.e-20) : |
|
|
1510 | 1293 |
snr = 1.e-20 |
|
|
1511 | 1294 |
|
|
|
1512 | 1295 |
vec_power[ind] = power |
|
|
1513 | 1296 |
vec_fd[ind] = fd |
|
|
1514 | 1297 |
vec_w[ind] = w |
|
|
1515 | 1298 |
vec_snr[ind] = snr |
|
|
1516 | 1299 |
|
|
|
1517 | 1300 |
moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1518 | 1301 |
return moments |
|
|
1519 | 1302 |
|
|
|
1520 | 1303 |
#------------------ Get SA Parameters -------------------------- |
|
|
1521 | 1304 |
|
|
|
1522 | 1305 |
def GetSAParameters(self): |
|
|
1523 | 1306 |
#SA en frecuencia |
|
|
1524 | 1307 |
pairslist = self.dataOut.groupList |
|
|
1525 | 1308 |
num_pairs = len(pairslist) |
|
|
1526 | 1309 |
|
|
|
1527 | 1310 |
vel = self.dataOut.abscissaList |
|
|
1528 | 1311 |
spectra = self.dataOut.data_pre |
|
|
1529 | 1312 |
cspectra = self.dataIn.data_cspc |
|
|
1530 | 1313 |
delta_v = vel[1] - vel[0] |
|
|
1531 | 1314 |
|
|
|
1532 | 1315 |
#Calculating the power spectrum |
|
|
1533 | 1316 |
spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1534 | 1317 |
#Normalizing Spectra |
|
|
1535 | 1318 |
norm_spectra = spectra/spc_pow |
|
|
1536 | 1319 |
#Calculating the norm_spectra at peak |
|
|
1537 | 1320 |
max_spectra = numpy.max(norm_spectra, 3) |
|
|
1538 | 1321 |
|
|
|
1539 | 1322 |
#Normalizing Cross Spectra |
|
|
1540 | 1323 |
norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1541 | 1324 |
|
|
|
1542 | 1325 |
for i in range(num_chan): |
|
|
1543 | 1326 |
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1544 | 1327 |
|
|
|
1545 | 1328 |
max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1546 | 1329 |
max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1547 | 1330 |
|
|
|
1548 | 1331 |
for i in range(num_pairs): |
|
|
1549 | 1332 |
cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1550 | 1333 |
#------------------- Get Lags ---------------------------------- |
|
|
1551 | 1334 |
|
|
|
1552 | 1335 |
class SALags(Operation): |
|
|
1553 | 1336 |
''' |
|
|
1554 | 1337 |
Function GetMoments() |
|
|
1555 | 1338 | |
|
|
1556 | 1339 |
Input: |
|
|
1557 | 1340 |
self.dataOut.data_pre |
|
|
1558 | 1341 |
self.dataOut.abscissaList |
|
|
1559 | 1342 |
self.dataOut.noise |
|
|
1560 | 1343 |
self.dataOut.normFactor |
|
|
1561 | 1344 |
self.dataOut.data_SNR |
|
|
1562 | 1345 |
self.dataOut.groupList |
|
|
1563 | 1346 |
self.dataOut.nChannels |
|
|
1564 | 1347 |
|
|
|
1565 | 1348 |
Affected: |
|
|
1566 | 1349 |
self.dataOut.data_param |
|
|
1567 | 1350 |
|
|
|
1568 | 1351 |
''' |
|
|
1569 | 1352 |
def run(self, dataOut): |
|
|
1570 | 1353 |
data_acf = dataOut.data_pre[0] |
|
|
1571 | 1354 |
data_ccf = dataOut.data_pre[1] |
|
|
1572 | 1355 |
normFactor_acf = dataOut.normFactor[0] |
|
|
1573 | 1356 |
normFactor_ccf = dataOut.normFactor[1] |
|
|
1574 | 1357 |
pairs_acf = dataOut.groupList[0] |
|
|
1575 | 1358 |
pairs_ccf = dataOut.groupList[1] |
|
|
1576 | 1359 |
|
|
|
1577 | 1360 |
nHeights = dataOut.nHeights |
|
|
1578 | 1361 |
absc = dataOut.abscissaList |
|
|
1579 | 1362 |
noise = dataOut.noise |
|
|
1580 | 1363 |
SNR = dataOut.data_SNR |
|
|
1581 | 1364 |
nChannels = dataOut.nChannels |
|
|
1582 | 1365 |
# pairsList = dataOut.groupList |
|
|
1583 | 1366 |
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1584 | 1367 | |
|
|
1585 | 1368 |
for l in range(len(pairs_acf)): |
|
|
1586 | 1369 |
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1587 | 1370 |
|
|
|
1588 | 1371 |
for l in range(len(pairs_ccf)): |
|
|
1589 | 1372 |
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1590 | 1373 |
|
|
|
1591 | 1374 |
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1592 | 1375 |
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1593 | 1376 |
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1594 | 1377 |
return |
|
|
1595 | 1378 |
|
|
|
1596 | 1379 |
# def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1597 | 1380 |
# |
|
|
1598 | 1381 |
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1599 | 1382 |
# |
|
|
1600 | 1383 |
# for l in range(len(pairsList)): |
|
|
1601 | 1384 |
# firstChannel = pairsList[l][0] |
|
|
1602 | 1385 |
# secondChannel = pairsList[l][1] |
|
|
1603 | 1386 |
# |
|
|
1604 | 1387 |
# #Obteniendo pares de Autocorrelacion |
|
|
1605 | 1388 |
# if firstChannel == secondChannel: |
|
|
1606 | 1389 |
# pairsAutoCorr[firstChannel] = int(l) |
|
|
1607 | 1390 |
# |
|
|
1608 | 1391 |
# pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1609 | 1392 |
# |
|
|
1610 | 1393 |
# pairsCrossCorr = range(len(pairsList)) |
|
|
1611 | 1394 |
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1612 | 1395 |
# |
|
|
1613 | 1396 |
# return pairsAutoCorr, pairsCrossCorr |
|
|
1614 | 1397 |
|
|
|
1615 | 1398 |
def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1616 | 1399 |
|
|
|
1617 | 1400 |
lag0 = data_acf.shape[1]/2 |
|
|
1618 | 1401 |
#Funcion de Autocorrelacion |
|
|
1619 | 1402 |
mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1620 | 1403 |
|
|
|
1621 | 1404 |
#Obtencion Indice de TauCross |
|
|
1622 | 1405 |
ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1623 | 1406 |
#Obtencion Indice de TauAuto |
|
|
1624 | 1407 |
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1625 | 1408 |
ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1626 | 1409 |
|
|
|
1627 | 1410 |
for i in range(ccf_lag0.shape[0]): |
|
|
1628 | 1411 |
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1629 | 1412 |
|
|
|
1630 | 1413 |
#Obtencion de TauCross y TauAuto |
|
|
1631 | 1414 |
tau_ccf = lagRange[ind_ccf] |
|
|
1632 | 1415 |
tau_acf = lagRange[ind_acf] |
|
|
1633 | 1416 |
|
|
|
1634 | 1417 |
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1635 | 1418 |
|
|
|
1636 | 1419 |
tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1637 | 1420 |
tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1638 | 1421 |
tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1639 | 1422 |
|
|
|
1640 | 1423 |
return tau |
|
|
1641 | 1424 |
|
|
|
1642 | 1425 |
def __calculateLag1Phase(self, data, lagTRange): |
|
|
1643 | 1426 |
data1 = stats.nanmean(data, axis = 0) |
|
|
1644 | 1427 |
lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1645 | 1428 | |
|
|
1646 | 1429 |
phase = numpy.angle(data1[lag1,:]) |
|
|
1647 | 1430 |
|
|
|
1648 | 1431 |
return phase |
|
|
1649 | 1432 |
|
|
|
1650 | 1433 |
class SpectralFitting(Operation): |
|
|
1651 | 1434 |
''' |
|
|
1652 | 1435 |
Function GetMoments() |
|
|
1653 | 1436 |
|
|
|
1654 | 1437 |
Input: |
|
|
1655 | 1438 |
Output: |
|
|
1656 | 1439 |
Variables modified: |
|
|
1657 | 1440 |
''' |
|
|
1658 | 1441 |
|
|
|
1659 | 1442 |
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
1660 | 1443 |
|
|
|
1661 | 1444 |
|
|
|
1662 | 1445 |
if path != None: |
|
|
1663 | 1446 |
sys.path.append(path) |
|
|
1664 | 1447 |
self.dataOut.library = importlib.import_module(file) |
|
|
1665 | 1448 |
|
|
|
1666 | 1449 |
#To be inserted as a parameter |
|
|
1667 | 1450 |
groupArray = numpy.array(groupList) |
|
|
1668 | 1451 |
# groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1669 | 1452 |
self.dataOut.groupList = groupArray |
|
|
1670 | 1453 |
|
|
|
1671 | 1454 |
nGroups = groupArray.shape[0] |
|
|
1672 | 1455 |
nChannels = self.dataIn.nChannels |
|
|
1673 | 1456 |
nHeights=self.dataIn.heightList.size |
|
|
1674 | 1457 |
|
|
|
1675 | 1458 |
#Parameters Array |
|
|
1676 | 1459 |
self.dataOut.data_param = None |
|
|
1677 | 1460 |
|
|
|
1678 | 1461 |
#Set constants |
|
|
1679 | 1462 |
constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1680 | 1463 |
self.dataOut.constants = constants |
|
|
1681 | 1464 |
M = self.dataIn.normFactor |
|
|
1682 | 1465 |
N = self.dataIn.nFFTPoints |
|
|
1683 | 1466 |
ippSeconds = self.dataIn.ippSeconds |
|
|
1684 | 1467 |
K = self.dataIn.nIncohInt |
|
|
1685 | 1468 |
pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1686 | 1469 |
|
|
|
1687 | 1470 |
#List of possible combinations |
|
|
1688 | 1471 |
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1689 | 1472 |
indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1690 | 1473 |
|
|
|
1691 | 1474 |
if getSNR: |
|
|
1692 | 1475 |
listChannels = groupArray.reshape((groupArray.size)) |
|
|
1693 | 1476 |
listChannels.sort() |
|
|
1694 | 1477 |
noise = self.dataIn.getNoise() |
|
|
1695 | 1478 |
self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1696 | 1479 |
|
|
|
1697 | 1480 |
for i in range(nGroups): |
|
|
1698 | 1481 |
coord = groupArray[i,:] |
|
|
1699 | 1482 |
|
|
|
1700 | 1483 |
#Input data array |
|
|
1701 | 1484 |
data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1702 | 1485 |
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1703 | 1486 |
|
|
|
1704 | 1487 |
#Cross Spectra data array for Covariance Matrixes |
|
|
1705 | 1488 |
ind = 0 |
|
|
1706 | 1489 |
for pairs in listComb: |
|
|
1707 | 1490 |
pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1708 | 1491 |
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1709 | 1492 |
ind += 1 |
|
|
1710 | 1493 |
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1711 | 1494 |
dataCross = dataCross**2/K |
|
|
1712 | 1495 |
|
|
|
1713 | 1496 |
for h in range(nHeights): |
|
|
1714 | # print self.dataOut.heightList[h] | |
|
|
1715 | 1497 |
|
|
|
1716 | 1498 |
#Input |
|
|
1717 | 1499 |
d = data[:,h] |
|
|
1718 | 1500 | |
|
|
1719 | 1501 |
#Covariance Matrix |
|
|
1720 | 1502 |
D = numpy.diag(d**2/K) |
|
|
1721 | 1503 |
ind = 0 |
|
|
1722 | 1504 |
for pairs in listComb: |
|
|
1723 | 1505 |
#Coordinates in Covariance Matrix |
|
|
1724 | 1506 |
x = pairs[0] |
|
|
1725 | 1507 |
y = pairs[1] |
|
|
1726 | 1508 |
#Channel Index |
|
|
1727 | 1509 |
S12 = dataCross[ind,:,h] |
|
|
1728 | 1510 |
D12 = numpy.diag(S12) |
|
|
1729 | 1511 |
#Completing Covariance Matrix with Cross Spectras |
|
|
1730 | 1512 |
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1731 | 1513 |
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1732 | 1514 |
ind += 1 |
|
|
1733 | 1515 |
Dinv=numpy.linalg.inv(D) |
|
|
1734 | 1516 |
L=numpy.linalg.cholesky(Dinv) |
|
|
1735 | 1517 |
LT=L.T |
|
|
1736 | 1518 | |
|
|
1737 | 1519 |
dp = numpy.dot(LT,d) |
|
|
1738 | 1520 |
|
|
|
1739 | 1521 |
#Initial values |
|
|
1740 | 1522 |
data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1741 | 1523 |
|
|
|
1742 | 1524 |
if (h>0)and(error1[3]<5): |
|
|
1743 | 1525 |
p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1744 | 1526 |
else: |
|
|
1745 | 1527 |
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1746 | 1528 |
|
|
|
1747 | 1529 |
try: |
|
|
1748 | 1530 |
#Least Squares |
|
|
1749 | 1531 |
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1750 | 1532 |
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1751 | 1533 |
#Chi square error |
|
|
1752 | 1534 |
error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1753 | 1535 |
#Error with Jacobian |
|
|
1754 | 1536 |
error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1755 | 1537 |
except: |
|
|
1756 | 1538 |
minp = p0*numpy.nan |
|
|
1757 | 1539 |
error0 = numpy.nan |
|
|
1758 | 1540 |
error1 = p0*numpy.nan |
|
|
1759 | 1541 |
|
|
|
1760 | 1542 |
#Save |
|
|
1761 | 1543 |
if self.dataOut.data_param == None: |
|
|
1762 | 1544 |
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1763 | 1545 |
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1764 | 1546 |
|
|
|
1765 | 1547 |
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1766 | 1548 |
self.dataOut.data_param[i,:,h] = minp |
|
|
1767 | 1549 |
return |
|
|
1768 | 1550 |
|
|
|
1769 | 1551 |
def __residFunction(self, p, dp, LT, constants): |
|
|
1770 | 1552 | |
|
|
1771 | 1553 |
fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1772 | 1554 |
fmp=numpy.dot(LT,fm) |
|
|
1773 | 1555 |
|
|
|
1774 | 1556 |
return dp-fmp |
|
|
1775 | 1557 | |
|
|
1776 | 1558 |
def __getSNR(self, z, noise): |
|
|
1777 | 1559 |
|
|
|
1778 | 1560 |
avg = numpy.average(z, axis=1) |
|
|
1779 | 1561 |
SNR = (avg.T-noise)/noise |
|
|
1780 | 1562 |
SNR = SNR.T |
|
|
1781 | 1563 |
return SNR |
|
|
1782 | 1564 |
|
|
|
1783 | 1565 |
def __chisq(p,chindex,hindex): |
|
|
1784 | 1566 |
#similar to Resid but calculates CHI**2 |
|
|
1785 | 1567 |
[LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1786 | 1568 |
dp=numpy.dot(LT,d) |
|
|
1787 | 1569 |
fmp=numpy.dot(LT,fm) |
|
|
1788 | 1570 |
chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1789 | 1571 |
return chisq |
|
|
1790 | 1572 |
|
|
|
1791 | 1573 |
class WindProfiler(Operation): |
|
|
1792 | 1574 |
|
|
|
1793 | 1575 |
__isConfig = False |
|
|
1794 | 1576 |
|
|
|
1795 | 1577 |
__initime = None |
|
|
1796 | 1578 |
__lastdatatime = None |
|
|
1797 | 1579 |
__integrationtime = None |
|
|
1798 | 1580 |
|
|
|
1799 | 1581 |
__buffer = None |
|
|
1800 | 1582 |
|
|
|
1801 | 1583 |
__dataReady = False |
|
|
1802 | 1584 |
|
|
|
1803 | 1585 |
__firstdata = None |
|
|
1804 | 1586 |
|
|
|
1805 | 1587 |
n = None |
|
|
1806 | 1588 |
|
|
|
1807 | 1589 |
def __init__(self, **kwargs): |
|
|
1808 | 1590 |
Operation.__init__(self, **kwargs) |
|
|
1809 | 1591 |
|
|
|
1810 | 1592 |
def __calculateCosDir(self, elev, azim): |
|
|
1811 | 1593 |
zen = (90 - elev)*numpy.pi/180 |
|
|
1812 | 1594 |
azim = azim*numpy.pi/180 |
|
|
1813 | 1595 |
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1814 | 1596 |
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1815 | 1597 |
|
|
|
1816 | 1598 |
signX = numpy.sign(numpy.cos(azim)) |
|
|
1817 | 1599 |
signY = numpy.sign(numpy.sin(azim)) |
|
|
1818 | 1600 |
|
|
|
1819 | 1601 |
cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1820 | 1602 |
cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1821 | 1603 |
return cosDirX, cosDirY |
|
|
1822 | 1604 |
|
|
|
1823 | 1605 |
def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1824 | 1606 |
|
|
|
1825 | 1607 |
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1826 | 1608 |
zenith_arr = numpy.arccos(dir_cosw) |
|
|
1827 | 1609 |
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1828 | 1610 |
|
|
|
1829 | 1611 |
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1830 | 1612 |
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1831 | 1613 |
|
|
|
1832 | 1614 |
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1833 | 1615 | |
|
|
1834 | 1616 |
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1835 | 1617 |
|
|
|
1836 | 1618 |
# |
|
|
1837 | 1619 |
if horOnly: |
|
|
1838 | 1620 |
A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1839 | 1621 |
else: |
|
|
1840 | 1622 |
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1841 | 1623 |
A = numpy.asmatrix(A) |
|
|
1842 | 1624 |
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1843 | 1625 | |
|
|
1844 | 1626 |
return A1 |
|
|
1845 | 1627 | |
|
|
1846 | 1628 |
def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1847 | 1629 |
listPhi = phi.tolist() |
|
|
1848 | 1630 |
maxid = listPhi.index(max(listPhi)) |
|
|
1849 | 1631 |
minid = listPhi.index(min(listPhi)) |
|
|
1850 | 1632 |
|
|
|
1851 | 1633 |
rango = range(len(phi)) |
|
|
1852 | 1634 |
# rango = numpy.delete(rango,maxid) |
|
|
1853 | 1635 |
|
|
|
1854 | 1636 |
heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1855 | 1637 |
heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1856 | 1638 |
indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1857 | 1639 |
heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1858 | 1640 |
|
|
|
1859 | 1641 |
velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1860 | 1642 |
SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1861 | 1643 |
|
|
|
1862 | 1644 |
for i in rango: |
|
|
1863 | 1645 |
x = heiRang*math.cos(phi[i]) |
|
|
1864 | 1646 |
y1 = velRadial[i,:] |
|
|
1865 | 1647 |
f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1866 | 1648 |
|
|
|
1867 | 1649 |
x1 = heiRang1 |
|
|
1868 | 1650 |
y11 = f1(x1) |
|
|
1869 | 1651 |
|
|
|
1870 | 1652 |
y2 = SNR[i,:] |
|
|
1871 | 1653 |
f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1872 | 1654 |
y21 = f2(x1) |
|
|
1873 | 1655 |
|
|
|
1874 | 1656 |
velRadial1[i,:] = y11 |
|
|
1875 | 1657 |
SNR1[i,:] = y21 |
|
|
1876 | 1658 |
|
|
|
1877 | 1659 |
return heiRang1, velRadial1, SNR1 |
|
|
1878 | 1660 | |
|
|
1879 | 1661 |
def __calculateVelUVW(self, A, velRadial): |
|
|
1880 | 1662 |
|
|
|
1881 | 1663 |
#Operacion Matricial |
|
|
1882 | 1664 |
# velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1883 | 1665 |
# for ind in range(velRadial.shape[1]): |
|
|
1884 | 1666 |
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1885 | 1667 |
# velUVW = velUVW.transpose() |
|
|
1886 | 1668 |
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1887 | 1669 |
velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1888 | 1670 |
|
|
|
1889 | 1671 |
|
|
|
1890 | 1672 |
return velUVW |
|
|
1891 | 1673 |
|
|
|
1892 | 1674 |
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1893 | 1675 |
|
|
|
1894 | 1676 |
def techniqueDBS(self, kwargs): |
|
|
1895 | 1677 |
""" |
|
|
1896 | 1678 |
Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1897 | 1679 |
|
|
|
1898 | 1680 |
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1899 | 1681 |
Direction correction (if necessary), Ranges and SNR |
|
|
1900 | 1682 |
|
|
|
1901 | 1683 |
Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1902 | 1684 |
|
|
|
1903 | 1685 |
Parameters affected: Winds, height range, SNR |
|
|
1904 | 1686 |
""" |
|
|
1905 | 1687 |
velRadial0 = kwargs['velRadial'] |
|
|
1906 | 1688 |
heiRang = kwargs['heightList'] |
|
|
1907 | 1689 |
SNR0 = kwargs['SNR'] |
|
|
1908 | 1690 |
|
|
|
1909 | 1691 |
if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1910 | 1692 |
theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1911 | 1693 |
theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1912 | 1694 |
else: |
|
|
1913 | 1695 |
elev = numpy.array(kwargs['elevation']) |
|
|
1914 | 1696 |
azim = numpy.array(kwargs['azimuth']) |
|
|
1915 | 1697 |
theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1916 | 1698 |
azimuth = kwargs['correctAzimuth'] |
|
|
1917 | 1699 |
if kwargs.has_key('horizontalOnly'): |
|
|
1918 | 1700 |
horizontalOnly = kwargs['horizontalOnly'] |
|
|
1919 | 1701 |
else: horizontalOnly = False |
|
|
1920 | 1702 |
if kwargs.has_key('correctFactor'): |
|
|
1921 | 1703 |
correctFactor = kwargs['correctFactor'] |
|
|
1922 | 1704 |
else: correctFactor = 1 |
|
|
1923 | 1705 |
if kwargs.has_key('channelList'): |
|
|
1924 | 1706 |
channelList = kwargs['channelList'] |
|
|
1925 | 1707 |
if len(channelList) == 2: |
|
|
1926 | 1708 |
horizontalOnly = True |
|
|
1927 | 1709 |
arrayChannel = numpy.array(channelList) |
|
|
1928 | 1710 |
param = param[arrayChannel,:,:] |
|
|
1929 | 1711 |
theta_x = theta_x[arrayChannel] |
|
|
1930 | 1712 |
theta_y = theta_y[arrayChannel] |
|
|
1931 | 1713 |
|
|
|
1932 | 1714 |
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1933 | 1715 |
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
1934 | 1716 |
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1935 | 1717 |
|
|
|
1936 | 1718 |
#Calculo de Componentes de la velocidad con DBS |
|
|
1937 | 1719 |
winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1938 | 1720 |
|
|
|
1939 | 1721 |
return winds, heiRang1, SNR1 |
|
|
1940 | 1722 |
|
|
|
1941 | 1723 |
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1942 | 1724 |
|
|
|
1943 | 1725 |
nPairs = len(pairs_ccf) |
|
|
1944 | 1726 |
posx = numpy.asarray(posx) |
|
|
1945 | 1727 |
posy = numpy.asarray(posy) |
|
|
1946 | 1728 |
|
|
|
1947 | 1729 |
#Rotacion Inversa para alinear con el azimuth |
|
|
1948 | 1730 |
if azimuth!= None: |
|
|
1949 | 1731 |
azimuth = azimuth*math.pi/180 |
|
|
1950 | 1732 |
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1951 | 1733 |
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1952 | 1734 |
else: |
|
|
1953 | 1735 |
posx1 = posx |
|
|
1954 | 1736 |
posy1 = posy |
|
|
1955 | 1737 |
|
|
|
1956 | 1738 |
#Calculo de Distancias |
|
|
1957 | 1739 |
distx = numpy.zeros(nPairs) |
|
|
1958 | 1740 |
disty = numpy.zeros(nPairs) |
|
|
1959 | 1741 |
dist = numpy.zeros(nPairs) |
|
|
1960 | 1742 |
ang = numpy.zeros(nPairs) |
|
|
1961 | 1743 |
|
|
|
1962 | 1744 |
for i in range(nPairs): |
|
|
1963 | 1745 |
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1964 | 1746 |
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
1965 | 1747 |
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1966 | 1748 |
ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1967 | 1749 |
|
|
|
1968 | 1750 |
return distx, disty, dist, ang |
|
|
1969 | 1751 |
#Calculo de Matrices |
|
|
1970 | 1752 |
# nPairs = len(pairs) |
|
|
1971 | 1753 |
# ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1972 | 1754 |
# dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1973 | 1755 |
# |
|
|
1974 | 1756 |
# for j in range(nPairs): |
|
|
1975 | 1757 |
# dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1976 | 1758 |
# dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1977 | 1759 |
# ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1978 | 1760 |
# ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1979 | 1761 |
# |
|
|
1980 | 1762 |
# return distx,disty, dist1,ang1 |
|
|
1981 | 1763 | |
|
|
1982 | 1764 |
|
|
|
1983 | 1765 |
def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1984 | 1766 | |
|
|
1985 | 1767 |
Ts = lagTRange[1] - lagTRange[0] |
|
|
1986 | 1768 |
velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1987 | 1769 |
|
|
|
1988 | 1770 |
return velW |
|
|
1989 | 1771 |
|
|
|
1990 | 1772 |
def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1991 | 1773 |
nPairs = tau1.shape[0] |
|
|
1992 | 1774 |
nHeights = tau1.shape[1] |
|
|
1993 | 1775 |
vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
1994 | 1776 |
dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1995 | 1777 |
|
|
|
1996 | 1778 |
angCos = numpy.cos(ang) |
|
|
1997 | 1779 |
angSin = numpy.sin(ang) |
|
|
1998 | 1780 |
|
|
|
1999 | 1781 |
vel0 = dist1*tau1/(2*tau2**2) |
|
|
2000 | 1782 |
vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
2001 | 1783 |
vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
2002 | 1784 |
|
|
|
2003 | 1785 |
ind = numpy.where(numpy.isinf(vel)) |
|
|
2004 | 1786 |
vel[ind] = numpy.nan |
|
|
2005 | 1787 |
|
|
|
2006 | 1788 |
return vel |
|
|
2007 | 1789 |
|
|
|
2008 | 1790 |
# def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
2009 | 1791 |
# |
|
|
2010 | 1792 |
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
2011 | 1793 |
# |
|
|
2012 | 1794 |
# for l in range(len(pairsList)): |
|
|
2013 | 1795 |
# firstChannel = pairsList[l][0] |
|
|
2014 | 1796 |
# secondChannel = pairsList[l][1] |
|
|
2015 | 1797 |
# |
|
|
2016 | 1798 |
# #Obteniendo pares de Autocorrelacion |
|
|
2017 | 1799 |
# if firstChannel == secondChannel: |
|
|
2018 | 1800 |
# pairsAutoCorr[firstChannel] = int(l) |
|
|
2019 | 1801 |
# |
|
|
2020 | 1802 |
# pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
2021 | 1803 |
# |
|
|
2022 | 1804 |
# pairsCrossCorr = range(len(pairsList)) |
|
|
2023 | 1805 |
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
2024 | 1806 |
# |
|
|
2025 | 1807 |
# return pairsAutoCorr, pairsCrossCorr |
|
|
2026 | 1808 |
|
|
|
2027 | 1809 |
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
2028 | 1810 |
def techniqueSA(self, kwargs): |
|
|
2029 | 1811 |
|
|
|
2030 | 1812 |
""" |
|
|
2031 | 1813 |
Function that implements Spaced Antenna (SA) technique. |
|
|
2032 | 1814 |
|
|
|
2033 | 1815 |
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
2034 | 1816 |
Direction correction (if necessary), Ranges and SNR |
|
|
2035 | 1817 |
|
|
|
2036 | 1818 |
Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
2037 | 1819 |
|
|
|
2038 | 1820 |
Parameters affected: Winds |
|
|
2039 | 1821 |
""" |
|
|
2040 | 1822 |
position_x = kwargs['positionX'] |
|
|
2041 | 1823 |
position_y = kwargs['positionY'] |
|
|
2042 | 1824 |
azimuth = kwargs['azimuth'] |
|
|
2043 | 1825 |
|
|
|
2044 | 1826 |
if kwargs.has_key('correctFactor'): |
|
|
2045 | 1827 |
correctFactor = kwargs['correctFactor'] |
|
|
2046 | 1828 |
else: |
|
|
2047 | 1829 |
correctFactor = 1 |
|
|
2048 | 1830 |
|
|
|
2049 | 1831 |
groupList = kwargs['groupList'] |
|
|
2050 | 1832 |
pairs_ccf = groupList[1] |
|
|
2051 | 1833 |
tau = kwargs['tau'] |
|
|
2052 | 1834 |
_lambda = kwargs['_lambda'] |
|
|
2053 | 1835 |
|
|
|
2054 | 1836 |
#Cross Correlation pairs obtained |
|
|
2055 | 1837 |
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
2056 | 1838 |
# pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
2057 | 1839 |
# pairsSelArray = numpy.array(pairsSelected) |
|
|
2058 | 1840 |
# pairs = [] |
|
|
2059 | 1841 |
# |
|
|
2060 | 1842 |
# #Wind estimation pairs obtained |
|
|
2061 | 1843 |
# for i in range(pairsSelArray.shape[0]/2): |
|
|
2062 | 1844 |
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
2063 | 1845 |
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
2064 | 1846 |
# pairs.append((ind1,ind2)) |
|
|
2065 | 1847 |
|
|
|
2066 | 1848 |
indtau = tau.shape[0]/2 |
|
|
2067 | 1849 |
tau1 = tau[:indtau,:] |
|
|
2068 | 1850 |
tau2 = tau[indtau:-1,:] |
|
|
2069 | 1851 |
# tau1 = tau1[pairs,:] |
|
|
2070 | 1852 |
# tau2 = tau2[pairs,:] |
|
|
2071 | 1853 |
phase1 = tau[-1,:] |
|
|
2072 | 1854 |
|
|
|
2073 | 1855 |
#--------------------------------------------------------------------- |
|
|
2074 | 1856 |
#Metodo Directo |
|
|
2075 | 1857 |
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
2076 | 1858 |
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
2077 | 1859 |
winds = stats.nanmean(winds, axis=0) |
|
|
2078 | 1860 |
#--------------------------------------------------------------------- |
|
|
2079 | 1861 |
#Metodo General |
|
|
2080 | 1862 |
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
2081 | 1863 |
# #Calculo Coeficientes de Funcion de Correlacion |
|
|
2082 | 1864 |
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
2083 | 1865 |
# #Calculo de Velocidades |
|
|
2084 | 1866 |
# winds = self.calculateVelUV(F,G,A,B,H) |
|
|
2085 | 1867 | |
|
|
2086 | 1868 |
#--------------------------------------------------------------------- |
|
|
2087 | 1869 |
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
2088 | 1870 |
winds = correctFactor*winds |
|
|
2089 | 1871 |
return winds |
|
|
2090 | 1872 |
|
|
|
2091 | 1873 |
def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
2092 | 1874 |
|
|
|
2093 | 1875 |
dataTime = currentTime + paramInterval |
|
|
2094 | 1876 |
deltaTime = dataTime - self.__initime |
|
|
2095 | 1877 |
|
|
|
2096 | 1878 |
if deltaTime >= outputInterval or deltaTime < 0: |
|
|
2097 | 1879 |
self.__dataReady = True |
|
|
2098 | 1880 |
return |
|
|
2099 | 1881 |
|
|
|
2100 | 1882 |
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
2101 | 1883 |
''' |
|
|
2102 | 1884 |
Function that implements winds estimation technique with detected meteors. |
|
|
2103 | 1885 |
|
|
|
2104 | 1886 |
Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
2105 | 1887 |
|
|
|
2106 | 1888 |
Output: Winds estimation (Zonal and Meridional) |
|
|
2107 | 1889 |
|
|
|
2108 | 1890 |
Parameters affected: Winds |
|
|
2109 | 1891 |
''' |
|
|
2110 | 1892 |
# print arrayMeteor.shape |
|
|
2111 | 1893 |
#Settings |
|
|
2112 | 1894 |
nInt = (heightMax - heightMin)/2 |
|
|
2113 | 1895 |
# print nInt |
|
|
2114 | 1896 |
nInt = int(nInt) |
|
|
2115 | 1897 |
# print nInt |
|
|
2116 | 1898 |
winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
2117 | 1899 |
|
|
|
2118 | 1900 |
#Filter errors |
|
|
2119 | 1901 |
error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
2120 | 1902 |
finalMeteor = arrayMeteor[error,:] |
|
|
2121 | 1903 |
|
|
|
2122 | 1904 |
#Meteor Histogram |
|
|
2123 | 1905 |
finalHeights = finalMeteor[:,2] |
|
|
2124 | 1906 |
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
2125 | 1907 |
nMeteorsPerI = hist[0] |
|
|
2126 | 1908 |
heightPerI = hist[1] |
|
|
2127 | 1909 |
|
|
|
2128 | 1910 |
#Sort of meteors |
|
|
2129 | 1911 |
indSort = finalHeights.argsort() |
|
|
2130 | 1912 |
finalMeteor2 = finalMeteor[indSort,:] |
|
|
2131 | 1913 |
|
|
|
2132 | 1914 |
# Calculating winds |
|
|
2133 | 1915 |
ind1 = 0 |
|
|
2134 | 1916 |
ind2 = 0 |
|
|
2135 | 1917 |
|
|
|
2136 | 1918 |
for i in range(nInt): |
|
|
2137 | 1919 |
nMet = nMeteorsPerI[i] |
|
|
2138 | 1920 |
ind1 = ind2 |
|
|
2139 | 1921 |
ind2 = ind1 + nMet |
|
|
2140 | 1922 |
|
|
|
2141 | 1923 |
meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
2142 | 1924 |
|
|
|
2143 | 1925 |
if meteorAux.shape[0] >= meteorThresh: |
|
|
2144 | 1926 |
vel = meteorAux[:, 6] |
|
|
2145 | 1927 |
zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
2146 | 1928 |
azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
2147 | 1929 |
|
|
|
2148 | 1930 |
n = numpy.cos(zen) |
|
|
2149 | 1931 |
# m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
2150 | 1932 |
# l = m*numpy.tan(azim) |
|
|
2151 | 1933 |
l = numpy.sin(zen)*numpy.sin(azim) |
|
|
2152 | 1934 |
m = numpy.sin(zen)*numpy.cos(azim) |
|
|
2153 | 1935 |
|
|
|
2154 | 1936 |
A = numpy.vstack((l, m)).transpose() |
|
|
2155 | 1937 |
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
2156 | 1938 |
windsAux = numpy.dot(A1, vel) |
|
|
2157 | 1939 |
|
|
|
2158 | 1940 |
winds[0,i] = windsAux[0] |
|
|
2159 | 1941 |
winds[1,i] = windsAux[1] |
|
|
2160 | 1942 |
|
|
|
2161 | 1943 |
return winds, heightPerI[:-1] |
|
|
2162 | 1944 |
|
|
|
2163 | 1945 |
def techniqueNSM_SA(self, **kwargs): |
|
|
2164 | 1946 |
metArray = kwargs['metArray'] |
|
|
2165 | 1947 |
heightList = kwargs['heightList'] |
|
|
2166 | 1948 |
timeList = kwargs['timeList'] |
|
|
2167 | 1949 |
|
|
|
2168 | 1950 |
rx_location = kwargs['rx_location'] |
|
|
2169 | 1951 |
groupList = kwargs['groupList'] |
|
|
2170 | 1952 |
azimuth = kwargs['azimuth'] |
|
|
2171 | 1953 |
dfactor = kwargs['dfactor'] |
|
|
2172 | 1954 |
k = kwargs['k'] |
|
|
2173 | 1955 |
|
|
|
2174 | 1956 |
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
2175 | 1957 |
d = dist*dfactor |
|
|
2176 | 1958 |
#Phase calculation |
|
|
2177 | 1959 |
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
2178 | 1960 |
|
|
|
2179 | 1961 |
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
2180 | 1962 |
|
|
|
2181 | 1963 |
velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2182 | 1964 |
azimuth1 = azimuth1*numpy.pi/180 |
|
|
2183 | 1965 |
|
|
|
2184 | 1966 |
for i in range(heightList.size): |
|
|
2185 | 1967 |
h = heightList[i] |
|
|
2186 | 1968 |
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
2187 | 1969 |
metHeight = metArray1[indH,:] |
|
|
2188 | 1970 |
if metHeight.shape[0] >= 2: |
|
|
2189 | 1971 |
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
2190 | 1972 |
iazim = metHeight[:,1].astype(int) |
|
|
2191 | 1973 |
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
2192 | 1974 |
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
2193 | 1975 |
A = numpy.asmatrix(A) |
|
|
2194 | 1976 |
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
2195 | 1977 |
velHor = numpy.dot(A1,velAux) |
|
|
2196 | 1978 |
|
|
|
2197 | 1979 |
velEst[i,:] = numpy.squeeze(velHor) |
|
|
2198 | 1980 |
return velEst |
|
|
2199 | 1981 |
|
|
|
2200 | 1982 |
def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
2201 | 1983 |
meteorList = [] |
|
|
2202 | 1984 |
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
2203 | 1985 |
#Putting back together the meteor matrix |
|
|
2204 | 1986 |
utctime = metArray[:,0] |
|
|
2205 | 1987 |
uniqueTime = numpy.unique(utctime) |
|
|
2206 | 1988 |
|
|
|
2207 | 1989 |
phaseDerThresh = 0.5 |
|
|
2208 | 1990 |
ippSeconds = timeList[1] - timeList[0] |
|
|
2209 | 1991 |
sec = numpy.where(timeList>1)[0][0] |
|
|
2210 | 1992 |
nPairs = metArray.shape[1] - 6 |
|
|
2211 | 1993 |
nHeights = len(heightList) |
|
|
2212 | 1994 |
|
|
|
2213 | 1995 |
for t in uniqueTime: |
|
|
2214 | 1996 |
metArray1 = metArray[utctime==t,:] |
|
|
2215 | 1997 |
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
2216 | 1998 |
tmet = metArray1[:,1].astype(int) |
|
|
2217 | 1999 |
hmet = metArray1[:,2].astype(int) |
|
|
2218 | 2000 |
|
|
|
2219 | 2001 |
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
2220 | 2002 |
metPhase[:,:] = numpy.nan |
|
|
2221 | 2003 |
metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
2222 | 2004 |
|
|
|
2223 | 2005 |
#Delete short trails |
|
|
2224 | 2006 |
metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
2225 | 2007 |
heightVect = numpy.sum(metBool, axis = 1) |
|
|
2226 | 2008 |
metBool[heightVect<sec,:] = False |
|
|
2227 | 2009 |
metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2228 | 2010 |
|
|
|
2229 | 2011 |
#Derivative |
|
|
2230 | 2012 |
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2231 | 2013 |
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2232 | 2014 |
metPhase[phDerAux] = numpy.nan |
|
|
2233 | 2015 |
|
|
|
2234 | 2016 |
#--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2235 | 2017 |
indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2236 | 2018 |
|
|
|
2237 | 2019 |
for p in numpy.arange(nPairs): |
|
|
2238 | 2020 |
phase = metPhase[p,:,:] |
|
|
2239 | 2021 |
phDer = metDer[p,:,:] |
|
|
2240 | 2022 |
|
|
|
2241 | 2023 |
for h in indMet: |
|
|
2242 | 2024 |
height = heightList[h] |
|
|
2243 | 2025 |
phase1 = phase[h,:] #82 |
|
|
2244 | 2026 |
phDer1 = phDer[h,:] |
|
|
2245 | 2027 |
|
|
|
2246 | 2028 |
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2247 | 2029 |
|
|
|
2248 | 2030 |
indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2249 | 2031 |
initMet = indValid[0] |
|
|
2250 | 2032 |
endMet = 0 |
|
|
2251 | 2033 |
|
|
|
2252 | 2034 |
for i in range(len(indValid)-1): |
|
|
2253 | 2035 |
|
|
|
2254 | 2036 |
#Time difference |
|
|
2255 | 2037 |
inow = indValid[i] |
|
|
2256 | 2038 |
inext = indValid[i+1] |
|
|
2257 | 2039 |
idiff = inext - inow |
|
|
2258 | 2040 |
#Phase difference |
|
|
2259 | 2041 |
phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
2260 | 2042 |
|
|
|
2261 | 2043 |
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2262 | 2044 |
sizeTrail = inow - initMet + 1 |
|
|
2263 | 2045 |
if sizeTrail>3*sec: #Too short meteors |
|
|
2264 | 2046 |
x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2265 | 2047 |
y = phase1[initMet:inow+1] |
|
|
2266 | 2048 |
ynnan = ~numpy.isnan(y) |
|
|
2267 | 2049 |
x = x[ynnan] |
|
|
2268 | 2050 |
y = y[ynnan] |
|
|
2269 | 2051 |
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2270 | 2052 |
ylin = x*slope + intercept |
|
|
2271 | 2053 |
rsq = r_value**2 |
|
|
2272 | 2054 |
if rsq > 0.5: |
|
|
2273 | 2055 |
vel = slope#*height*1000/(k*d) |
|
|
2274 | 2056 |
estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2275 | 2057 |
meteorList.append(estAux) |
|
|
2276 | 2058 |
initMet = inext |
|
|
2277 | 2059 |
metArray2 = numpy.array(meteorList) |
|
|
2278 | 2060 |
|
|
|
2279 | 2061 |
return metArray2 |
|
|
2280 | 2062 |
|
|
|
2281 | 2063 |
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2282 | 2064 |
|
|
|
2283 | 2065 |
azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2284 | 2066 |
dist = numpy.zeros(len(pairslist)) |
|
|
2285 | 2067 |
|
|
|
2286 | 2068 |
for i in range(len(rx_location)): |
|
|
2287 | 2069 |
ch0 = pairslist[i][0] |
|
|
2288 | 2070 |
ch1 = pairslist[i][1] |
|
|
2289 | 2071 |
|
|
|
2290 | 2072 |
diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2291 | 2073 |
diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2292 | 2074 |
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2293 | 2075 |
dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2294 | 2076 |
|
|
|
2295 | 2077 |
azimuth1 -= azimuth0 |
|
|
2296 | 2078 |
return azimuth1, dist |
|
|
2297 | 2079 |
|
|
|
2298 | 2080 |
def techniqueNSM_DBS(self, **kwargs): |
|
|
2299 | 2081 |
metArray = kwargs['metArray'] |
|
|
2300 | 2082 |
heightList = kwargs['heightList'] |
|
|
2301 | 2083 |
timeList = kwargs['timeList'] |
|
|
2302 | 2084 |
zenithList = kwargs['zenithList'] |
|
|
2303 | 2085 |
nChan = numpy.max(cmet) + 1 |
|
|
2304 | 2086 |
nHeights = len(heightList) |
|
|
2305 | 2087 |
|
|
|
2306 | 2088 |
utctime = metArray[:,0] |
|
|
2307 | 2089 |
cmet = metArray[:,1] |
|
|
2308 | 2090 |
hmet = metArray1[:,3].astype(int) |
|
|
2309 | 2091 |
h1met = heightList[hmet]*zenithList[cmet] |
|
|
2310 | 2092 |
vmet = metArray1[:,5] |
|
|
2311 | 2093 |
|
|
|
2312 | 2094 |
for i in range(nHeights - 1): |
|
|
2313 | 2095 |
hmin = heightList[i] |
|
|
2314 | 2096 |
hmax = heightList[i + 1] |
|
|
2315 | 2097 |
|
|
|
2316 | 2098 |
vthisH = vmet[(h1met>=hmin) & (h1met<hmax)] |
|
|
2317 | 2099 |
|
|
|
2318 | 2100 |
|
|
|
2319 | 2101 |
|
|
|
2320 | 2102 |
return data_output |
|
|
2321 | 2103 |
|
|
|
2322 | 2104 |
def run(self, dataOut, technique, positionY, positionX, azimuth, **kwargs): |
|
|
2323 | 2105 |
|
|
|
2324 | 2106 |
param = dataOut.data_param |
|
|
2325 | 2107 |
if dataOut.abscissaList != None: |
|
|
2326 | 2108 |
absc = dataOut.abscissaList[:-1] |
|
|
2327 | 2109 |
noise = dataOut.noise |
|
|
2328 | 2110 |
heightList = dataOut.heightList |
|
|
2329 | 2111 |
SNR = dataOut.data_SNR |
|
|
2330 | 2112 |
|
|
|
2331 | 2113 |
if technique == 'DBS': |
|
|
2332 | 2114 |
|
|
|
2333 | 2115 |
kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
2334 | 2116 |
kwargs['heightList'] = heightList |
|
|
2335 | 2117 |
kwargs['SNR'] = SNR |
|
|
2336 | 2118 |
|
|
|
2337 | 2119 |
dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
2338 | 2120 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2339 | 2121 |
dataOut.outputInterval = dataOut.paramInterval |
|
|
2340 | 2122 |
|
|
|
2341 | 2123 |
elif technique == 'SA': |
|
|
2342 | 2124 |
|
|
|
2343 | 2125 |
#Parameters |
|
|
2344 | 2126 |
# position_x = kwargs['positionX'] |
|
|
2345 | 2127 |
# position_y = kwargs['positionY'] |
|
|
2346 | 2128 |
# azimuth = kwargs['azimuth'] |
|
|
2347 | 2129 |
# |
|
|
2348 | 2130 |
# if kwargs.has_key('crosspairsList'): |
|
|
2349 | 2131 |
# pairs = kwargs['crosspairsList'] |
|
|
2350 | 2132 |
# else: |
|
|
2351 | 2133 |
# pairs = None |
|
|
2352 | 2134 |
# |
|
|
2353 | 2135 |
# if kwargs.has_key('correctFactor'): |
|
|
2354 | 2136 |
# correctFactor = kwargs['correctFactor'] |
|
|
2355 | 2137 |
# else: |
|
|
2356 | 2138 |
# correctFactor = 1 |
|
|
2357 | 2139 |
|
|
|
2358 | 2140 |
# tau = dataOut.data_param |
|
|
2359 | 2141 |
# _lambda = dataOut.C/dataOut.frequency |
|
|
2360 | 2142 |
# pairsList = dataOut.groupList |
|
|
2361 | 2143 |
# nChannels = dataOut.nChannels |
|
|
2362 | 2144 |
|
|
|
2363 | 2145 |
kwargs['groupList'] = dataOut.groupList |
|
|
2364 | 2146 |
kwargs['tau'] = dataOut.data_param |
|
|
2365 | 2147 |
kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2366 | 2148 |
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2367 | 2149 |
dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2368 | 2150 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2369 | 2151 |
dataOut.outputInterval = dataOut.timeInterval |
|
|
2370 | 2152 |
|
|
|
2371 | 2153 |
elif technique == 'Meteors': |
|
|
2372 | 2154 |
dataOut.flagNoData = True |
|
|
2373 | 2155 |
self.__dataReady = False |
|
|
2374 | 2156 |
|
|
|
2375 | 2157 |
if kwargs.has_key('nHours'): |
|
|
2376 | 2158 |
nHours = kwargs['nHours'] |
|
|
2377 | 2159 |
else: |
|
|
2378 | 2160 |
nHours = 1 |
|
|
2379 | 2161 |
|
|
|
2380 | 2162 |
if kwargs.has_key('meteorsPerBin'): |
|
|
2381 | 2163 |
meteorThresh = kwargs['meteorsPerBin'] |
|
|
2382 | 2164 |
else: |
|
|
2383 | 2165 |
meteorThresh = 6 |
|
|
2384 | 2166 |
|
|
|
2385 | 2167 |
if kwargs.has_key('hmin'): |
|
|
2386 | 2168 |
hmin = kwargs['hmin'] |
|
|
2387 | 2169 |
else: hmin = 70 |
|
|
2388 | 2170 |
if kwargs.has_key('hmax'): |
|
|
2389 | 2171 |
hmax = kwargs['hmax'] |
|
|
2390 | 2172 |
else: hmax = 110 |
|
|
2391 | 2173 |
|
|
|
2392 | 2174 |
dataOut.outputInterval = nHours*3600 |
|
|
2393 | 2175 |
|
|
|
2394 | 2176 |
if self.__isConfig == False: |
|
|
2395 | 2177 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2396 | 2178 |
#Get Initial LTC time |
|
|
2397 | 2179 |
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2398 | 2180 |
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2399 | 2181 | |
|
|
2400 | 2182 |
self.__isConfig = True |
|
|
2401 | 2183 |
|
|
|
2402 | 2184 |
if self.__buffer == None: |
|
|
2403 | 2185 |
self.__buffer = dataOut.data_param |
|
|
2404 | 2186 |
self.__firstdata = copy.copy(dataOut) |
|
|
2405 | 2187 | |
|
|
2406 | 2188 |
else: |
|
|
2407 | 2189 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2408 | 2190 |
|
|
|
2409 | 2191 |
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2410 | 2192 |
|
|
|
2411 | 2193 |
if self.__dataReady: |
|
|
2412 | 2194 |
dataOut.utctimeInit = self.__initime |
|
|
2413 | 2195 |
|
|
|
2414 | 2196 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2415 | 2197 |
|
|
|
2416 | 2198 |
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2417 | 2199 |
dataOut.flagNoData = False |
|
|
2418 | 2200 |
self.__buffer = None |
|
|
2419 | 2201 |
|
|
|
2420 | 2202 |
elif technique == 'Meteors1': |
|
|
2421 | 2203 |
dataOut.flagNoData = True |
|
|
2422 | 2204 |
self.__dataReady = False |
|
|
2423 | 2205 |
|
|
|
2424 | 2206 |
if kwargs.has_key('nMins'): |
|
|
2425 | 2207 |
nMins = kwargs['nMins'] |
|
|
2426 | 2208 |
else: nMins = 20 |
|
|
2427 | 2209 |
if kwargs.has_key('rx_location'): |
|
|
2428 | 2210 |
rx_location = kwargs['rx_location'] |
|
|
2429 | 2211 |
else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2430 | 2212 |
if kwargs.has_key('azimuth'): |
|
|
2431 | 2213 |
azimuth = kwargs['azimuth'] |
|
|
2432 | 2214 |
else: azimuth = 51 |
|
|
2433 | 2215 |
if kwargs.has_key('dfactor'): |
|
|
2434 | 2216 |
dfactor = kwargs['dfactor'] |
|
|
2435 | 2217 |
if kwargs.has_key('mode'): |
|
|
2436 | 2218 |
mode = kwargs['mode'] |
|
|
2437 | 2219 |
else: mode = 'SA' |
|
|
2438 | 2220 |
|
|
|
2439 | 2221 |
#Borrar luego esto |
|
|
2440 | 2222 |
if dataOut.groupList == None: |
|
|
2441 | 2223 |
dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2442 | 2224 |
groupList = dataOut.groupList |
|
|
2443 | 2225 |
C = 3e8 |
|
|
2444 | 2226 |
freq = 50e6 |
|
|
2445 | 2227 |
lamb = C/freq |
|
|
2446 | 2228 |
k = 2*numpy.pi/lamb |
|
|
2447 | 2229 |
|
|
|
2448 | 2230 |
timeList = dataOut.abscissaList |
|
|
2449 | 2231 |
heightList = dataOut.heightList |
|
|
2450 | 2232 |
|
|
|
2451 | 2233 |
if self.__isConfig == False: |
|
|
2452 | 2234 |
dataOut.outputInterval = nMins*60 |
|
|
2453 | 2235 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2454 | 2236 |
#Get Initial LTC time |
|
|
2455 | 2237 |
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2456 | 2238 |
minuteAux = initime.minute |
|
|
2457 | 2239 |
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2458 | 2240 |
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2459 | 2241 | |
|
|
2460 | 2242 |
self.__isConfig = True |
|
|
2461 | 2243 |
|
|
|
2462 | 2244 |
if self.__buffer == None: |
|
|
2463 | 2245 |
self.__buffer = dataOut.data_param |
|
|
2464 | 2246 |
self.__firstdata = copy.copy(dataOut) |
|
|
2465 | 2247 | |
|
|
2466 | 2248 |
else: |
|
|
2467 | 2249 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2468 | 2250 |
|
|
|
2469 | 2251 |
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2470 | 2252 |
|
|
|
2471 | 2253 |
if self.__dataReady: |
|
|
2472 | 2254 |
dataOut.utctimeInit = self.__initime |
|
|
2473 | 2255 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2474 | 2256 |
|
|
|
2475 | 2257 |
metArray = self.__buffer |
|
|
2476 | 2258 |
if mode == 'SA': |
|
|
2477 | 2259 |
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2478 | 2260 |
elif mode == 'DBS': |
|
|
2479 | 2261 |
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList) |
|
|
2480 | 2262 |
dataOut.data_output = dataOut.data_output.T |
|
|
2481 | 2263 |
dataOut.flagNoData = False |
|
|
2482 | 2264 |
self.__buffer = None |
|
|
2483 | 2265 | |
|
|
2484 | 2266 |
return |
|
|
2485 | 2267 |
|
|
|
2486 | 2268 |
class EWDriftsEstimation(Operation): |
|
|
2487 | 2269 |
|
|
|
2488 | 2270 |
def __init__(self): |
|
|
2489 | 2271 |
Operation.__init__(self) |
|
|
2490 | 2272 |
|
|
|
2491 | 2273 |
def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2492 | 2274 |
listPhi = phi.tolist() |
|
|
2493 | 2275 |
maxid = listPhi.index(max(listPhi)) |
|
|
2494 | 2276 |
minid = listPhi.index(min(listPhi)) |
|
|
2495 | 2277 |
|
|
|
2496 | 2278 |
rango = range(len(phi)) |
|
|
2497 | 2279 |
# rango = numpy.delete(rango,maxid) |
|
|
2498 | 2280 |
|
|
|
2499 | 2281 |
heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2500 | 2282 |
heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2501 | 2283 |
indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2502 | 2284 |
heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2503 | 2285 |
|
|
|
2504 | 2286 |
velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2505 | 2287 |
SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2506 | 2288 |
|
|
|
2507 | 2289 |
for i in rango: |
|
|
2508 | 2290 |
x = heiRang*math.cos(phi[i]) |
|
|
2509 | 2291 |
y1 = velRadial[i,:] |
|
|
2510 | 2292 |
f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2511 | 2293 |
|
|
|
2512 | 2294 |
x1 = heiRang1 |
|
|
2513 | 2295 |
y11 = f1(x1) |
|
|
2514 | 2296 |
|
|
|
2515 | 2297 |
y2 = SNR[i,:] |
|
|
2516 | 2298 |
f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2517 | 2299 |
y21 = f2(x1) |
|
|
2518 | 2300 |
|
|
|
2519 | 2301 |
velRadial1[i,:] = y11 |
|
|
2520 | 2302 |
SNR1[i,:] = y21 |
|
|
2521 | 2303 |
|
|
|
2522 | 2304 |
return heiRang1, velRadial1, SNR1 |
|
|
2523 | 2305 | |
|
|
2524 | 2306 |
def run(self, dataOut, zenith, zenithCorrection): |
|
|
2525 | 2307 |
heiRang = dataOut.heightList |
|
|
2526 | 2308 |
velRadial = dataOut.data_param[:,3,:] |
|
|
2527 | 2309 |
SNR = dataOut.data_SNR |
|
|
2528 | 2310 |
|
|
|
2529 | 2311 |
zenith = numpy.array(zenith) |
|
|
2530 | 2312 |
zenith -= zenithCorrection |
|
|
2531 | 2313 |
zenith *= numpy.pi/180 |
|
|
2532 | 2314 |
|
|
|
2533 | 2315 |
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2534 | 2316 |
|
|
|
2535 | 2317 |
alp = zenith[0] |
|
|
2536 | 2318 |
bet = zenith[1] |
|
|
2537 | 2319 |
|
|
|
2538 | 2320 |
w_w = velRadial1[0,:] |
|
|
2539 | 2321 |
w_e = velRadial1[1,:] |
|
|
2540 | 2322 |
|
|
|
2541 | 2323 |
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
2542 | 2324 |
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
2543 | 2325 |
|
|
|
2544 | 2326 |
winds = numpy.vstack((u,w)) |
|
|
2545 | 2327 |
|
|
|
2546 | 2328 |
dataOut.heightList = heiRang1 |
|
|
2547 | 2329 |
dataOut.data_output = winds |
|
|
2548 | 2330 |
dataOut.data_SNR = SNR1 |
|
|
2549 | 2331 |
|
|
|
2550 | 2332 |
dataOut.utctimeInit = dataOut.utctime |
|
|
2551 | 2333 |
dataOut.outputInterval = dataOut.timeInterval |
|
|
2552 | 2334 |
return |
|
|
2553 | 2335 | |
|
|
2554 | 2336 |
#--------------- Non Specular Meteor ---------------- |
|
|
2555 | 2337 | |
|
|
2556 | 2338 |
class NonSpecularMeteorDetection(Operation): |
|
|
2557 | 2339 | |
|
|
2558 | 2340 |
def run(self, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2559 | 2341 |
data_acf = self.dataOut.data_pre[0] |
|
|
2560 | 2342 |
data_ccf = self.dataOut.data_pre[1] |
|
|
2561 | 2343 |
|
|
|
2562 | 2344 |
lamb = self.dataOut.C/self.dataOut.frequency |
|
|
2563 | 2345 |
tSamp = self.dataOut.ippSeconds*self.dataOut.nCohInt |
|
|
2564 | 2346 |
paramInterval = self.dataOut.paramInterval |
|
|
2565 | 2347 |
|
|
|
2566 | 2348 |
nChannels = data_acf.shape[0] |
|
|
2567 | 2349 |
nLags = data_acf.shape[1] |
|
|
2568 | 2350 |
nProfiles = data_acf.shape[2] |
|
|
2569 | 2351 |
nHeights = self.dataOut.nHeights |
|
|
2570 | 2352 |
nCohInt = self.dataOut.nCohInt |
|
|
2571 | 2353 |
sec = numpy.round(nProfiles/self.dataOut.paramInterval) |
|
|
2572 | 2354 |
heightList = self.dataOut.heightList |
|
|
2573 | 2355 |
ippSeconds = self.dataOut.ippSeconds*self.dataOut.nCohInt*self.dataOut.nAvg |
|
|
2574 | 2356 |
utctime = self.dataOut.utctime |
|
|
2575 | 2357 |
|
|
|
2576 | 2358 |
self.dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2577 | 2359 |
|
|
|
2578 | 2360 |
#------------------------ SNR -------------------------------------- |
|
|
2579 | 2361 |
power = data_acf[:,0,:,:].real |
|
|
2580 | 2362 |
noise = numpy.zeros(nChannels) |
|
|
2581 | 2363 |
SNR = numpy.zeros(power.shape) |
|
|
2582 | 2364 |
for i in range(nChannels): |
|
|
2583 | 2365 |
noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2584 | 2366 |
SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2585 | 2367 |
SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2586 | 2368 |
SNRdB = 10*numpy.log10(SNR) |
|
|
2587 | 2369 |
|
|
|
2588 | 2370 |
if mode == 'SA': |
|
|
2589 | 2371 |
nPairs = data_ccf.shape[0] |
|
|
2590 | 2372 |
#---------------------- Coherence and Phase -------------------------- |
|
|
2591 | 2373 |
phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2592 | 2374 |
# phase1 = numpy.copy(phase) |
|
|
2593 | 2375 |
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2594 | 2376 |
|
|
|
2595 | 2377 |
for p in range(nPairs): |
|
|
2596 | 2378 |
ch0 = self.dataOut.groupList[p][0] |
|
|
2597 | 2379 |
ch1 = self.dataOut.groupList[p][1] |
|
|
2598 | 2380 |
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2599 | 2381 |
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
2600 | 2382 |
# phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
2601 | 2383 |
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
2602 | 2384 |
# coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
2603 | 2385 |
coh = numpy.nanmax(coh1, axis = 0) |
|
|
2604 | 2386 |
# struc = numpy.ones((5,1)) |
|
|
2605 | 2387 |
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2606 | 2388 |
#---------------------- Radial Velocity ---------------------------- |
|
|
2607 | 2389 |
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2608 | 2390 |
velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2609 | 2391 |
|
|
|
2610 | 2392 |
if allData: |
|
|
2611 | 2393 |
boolMetFin = ~numpy.isnan(SNRm) |
|
|
2612 | 2394 |
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2613 | 2395 |
else: |
|
|
2614 | 2396 |
#------------------------ Meteor mask --------------------------------- |
|
|
2615 | 2397 |
# #SNR mask |
|
|
2616 | 2398 |
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2617 | 2399 |
# |
|
|
2618 | 2400 |
# #Erase small objects |
|
|
2619 | 2401 |
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
2620 | 2402 |
# |
|
|
2621 | 2403 |
# auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2622 | 2404 |
# indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2623 | 2405 |
# indEEJ = numpy.where(indOver)[0] |
|
|
2624 | 2406 |
# indNEEJ = numpy.where(~indOver)[0] |
|
|
2625 | 2407 |
# |
|
|
2626 | 2408 |
# boolMetFin = boolMet1 |
|
|
2627 | 2409 |
# |
|
|
2628 | 2410 |
# if indEEJ.size > 0: |
|
|
2629 | 2411 |
# boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
2630 | 2412 |
# |
|
|
2631 | 2413 |
# boolMet2 = coh > cohThresh |
|
|
2632 | 2414 |
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2633 | 2415 |
# |
|
|
2634 | 2416 |
# #Final Meteor mask |
|
|
2635 | 2417 |
# boolMetFin = boolMet1|boolMet2 |
|
|
2636 | 2418 |
|
|
|
2637 | 2419 |
#Coherence mask |
|
|
2638 | 2420 |
boolMet1 = coh > 0.75 |
|
|
2639 | 2421 |
struc = numpy.ones((30,1)) |
|
|
2640 | 2422 |
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2641 | 2423 |
|
|
|
2642 | 2424 |
#Derivative mask |
|
|
2643 | 2425 |
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2644 | 2426 |
boolMet2 = derPhase < 0.2 |
|
|
2645 | 2427 |
# boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2646 | 2428 |
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2647 | 2429 |
boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2648 | 2430 |
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2649 | 2431 |
# #Final mask |
|
|
2650 | 2432 |
# boolMetFin = boolMet2 |
|
|
2651 | 2433 |
boolMetFin = boolMet1&boolMet2 |
|
|
2652 | 2434 |
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2653 | 2435 |
#Creating data_param |
|
|
2654 | 2436 |
coordMet = numpy.where(boolMetFin) |
|
|
2655 | 2437 | |
|
|
2656 | 2438 |
tmet = coordMet[0] |
|
|
2657 | 2439 |
hmet = coordMet[1] |
|
|
2658 | 2440 |
|
|
|
2659 | 2441 |
data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2660 | 2442 |
data_param[:,0] = utctime |
|
|
2661 | 2443 |
data_param[:,1] = tmet |
|
|
2662 | 2444 |
data_param[:,2] = hmet |
|
|
2663 | 2445 |
data_param[:,3] = SNRm[tmet,hmet] |
|
|
2664 | 2446 |
data_param[:,4] = velRad[tmet,hmet] |
|
|
2665 | 2447 |
data_param[:,5] = coh[tmet,hmet] |
|
|
2666 | 2448 |
data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2667 | 2449 |
|
|
|
2668 | 2450 |
elif mode == 'DBS': |
|
|
2669 | 2451 |
self.dataOut.groupList = numpy.arange(nChannels) |
|
|
2670 | 2452 |
|
|
|
2671 | 2453 |
#Radial Velocities |
|
|
2672 | 2454 |
# phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2673 | 2455 |
phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2674 | 2456 |
velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2675 | 2457 |
|
|
|
2676 | 2458 |
#Spectral width |
|
|
2677 | 2459 |
acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2678 | 2460 |
acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2679 | 2461 |
|
|
|
2680 | 2462 |
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2681 | 2463 |
# velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2682 | 2464 |
if allData: |
|
|
2683 | 2465 |
boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2684 | 2466 |
else: |
|
|
2685 | 2467 |
#SNR |
|
|
2686 | 2468 |
boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2687 | 2469 |
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2688 | 2470 |
|
|
|
2689 | 2471 |
#Radial velocity |
|
|
2690 | 2472 |
boolMet2 = numpy.abs(velRad) < 30 |
|
|
2691 | 2473 |
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2692 | 2474 |
|
|
|
2693 | 2475 |
#Spectral Width |
|
|
2694 | 2476 |
boolMet3 = spcWidth < 30 |
|
|
2695 | 2477 |
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2696 | 2478 |
# boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2697 | 2479 |
boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2698 | 2480 |
|
|
|
2699 | 2481 |
#Creating data_param |
|
|
2700 | 2482 |
coordMet = numpy.where(boolMetFin) |
|
|
2701 | 2483 | |
|
|
2702 | 2484 |
cmet = coordMet[0] |
|
|
2703 | 2485 |
tmet = coordMet[1] |
|
|
2704 | 2486 |
hmet = coordMet[2] |
|
|
2705 | 2487 |
|
|
|
2706 | 2488 |
data_param = numpy.zeros((tmet.size, 7)) |
|
|
2707 | 2489 |
data_param[:,0] = utctime |
|
|
2708 | 2490 |
data_param[:,1] = cmet |
|
|
2709 | 2491 |
data_param[:,2] = tmet |
|
|
2710 | 2492 |
data_param[:,3] = hmet |
|
|
2711 | 2493 |
data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2712 | 2494 |
data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2713 | 2495 |
data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2714 | 2496 |
|
|
|
2715 | 2497 |
# self.dataOut.data_param = data_int |
|
|
2716 | 2498 |
if len(data_param) == 0: |
|
|
2717 | 2499 |
self.dataOut.flagNoData = True |
|
|
2718 | 2500 |
else: |
|
|
2719 | 2501 |
self.dataOut.data_param = data_param |
|
|
2720 | 2502 | |
|
|
2721 | 2503 |
def __erase_small(self, binArray, threshX, threshY): |
|
|
2722 | 2504 |
labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2723 | 2505 |
binArray1 = numpy.copy(binArray) |
|
|
2724 | 2506 |
|
|
|
2725 | 2507 |
for i in range(1,numfeat + 1): |
|
|
2726 | 2508 |
auxBin = (labarray==i) |
|
|
2727 | 2509 |
auxSize = auxBin.sum() |
|
|
2728 | 2510 |
|
|
|
2729 | 2511 |
x,y = numpy.where(auxBin) |
|
|
2730 | 2512 |
widthX = x.max() - x.min() |
|
|
2731 | 2513 |
widthY = y.max() - y.min() |
|
|
2732 | 2514 |
|
|
|
2733 | 2515 |
#width X: 3 seg -> 12.5*3 |
|
|
2734 | 2516 |
#width Y: |
|
|
2735 | 2517 |
|
|
|
2736 | 2518 |
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2737 | 2519 |
binArray1[auxBin] = False |
|
|
2738 | 2520 |
|
|
|
2739 | 2521 |
return binArray1 |
|
|
2740 | 2522 | |
|
|
2741 | 2523 |
#--------------- Specular Meteor ---------------- |
|
|
2742 | 2524 | |
|
|
2743 | 2525 |
class SMDetection(Operation): |
|
|
2744 | 2526 |
''' |
|
|
2745 | 2527 |
Function DetectMeteors() |
|
|
2746 | 2528 |
Project developed with paper: |
|
|
2747 | 2529 |
HOLDSWORTH ET AL. 2004 |
|
|
2748 | 2530 |
|
|
|
2749 | 2531 |
Input: |
|
|
2750 | 2532 |
self.dataOut.data_pre |
|
|
2751 | 2533 |
|
|
|
2752 | 2534 |
centerReceiverIndex: From the channels, which is the center receiver |
|
|
2753 | 2535 |
|
|
|
2754 | 2536 |
hei_ref: Height reference for the Beacon signal extraction |
|
|
2755 | 2537 |
tauindex: |
|
|
2756 | 2538 |
predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2757 | 2539 |
|
|
|
2758 | 2540 |
cohDetection: Whether to user Coherent detection or not |
|
|
2759 | 2541 |
cohDet_timeStep: Coherent Detection calculation time step |
|
|
2760 | 2542 |
cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2761 | 2543 |
|
|
|
2762 | 2544 |
noise_timeStep: Noise calculation time step |
|
|
2763 | 2545 |
noise_multiple: Noise multiple to define signal threshold |
|
|
2764 | 2546 |
|
|
|
2765 | 2547 |
multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2766 | 2548 |
multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2767 | 2549 |
|
|
|
2768 | 2550 |
phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2769 | 2551 |
SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
2770 | 2552 |
|
|
|
2771 | 2553 |
hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2772 | 2554 |
hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2773 | 2555 |
azimuth: Azimuth angle correction |
|
|
2774 | 2556 |
|
|
|
2775 | 2557 |
Affected: |
|
|
2776 | 2558 |
self.dataOut.data_param |
|
|
2777 | 2559 |
|
|
|
2778 | 2560 |
Rejection Criteria (Errors): |
|
|
2779 | 2561 |
0: No error; analysis OK |
|
|
2780 | 2562 |
1: SNR < SNR threshold |
|
|
2781 | 2563 |
2: angle of arrival (AOA) ambiguously determined |
|
|
2782 | 2564 |
3: AOA estimate not feasible |
|
|
2783 | 2565 |
4: Large difference in AOAs obtained from different antenna baselines |
|
|
2784 | 2566 |
5: echo at start or end of time series |
|
|
2785 | 2567 |
6: echo less than 5 examples long; too short for analysis |
|
|
2786 | 2568 |
7: echo rise exceeds 0.3s |
|
|
2787 | 2569 |
8: echo decay time less than twice rise time |
|
|
2788 | 2570 |
9: large power level before echo |
|
|
2789 | 2571 |
10: large power level after echo |
|
|
2790 | 2572 |
11: poor fit to amplitude for estimation of decay time |
|
|
2791 | 2573 |
12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2792 | 2574 |
13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2793 | 2575 |
14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2794 | 2576 |
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2795 | 2577 |
16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2796 | 2578 |
|
|
|
2797 | 2579 |
17: phase difference in meteor Reestimation |
|
|
2798 | 2580 |
|
|
|
2799 | 2581 |
Data Storage: |
|
|
2800 | 2582 |
Meteors for Wind Estimation (8): |
|
|
2801 | 2583 |
Utc Time | Range Height |
|
|
2802 | 2584 |
Azimuth Zenith errorCosDir |
|
|
2803 | 2585 |
VelRad errorVelRad |
|
|
2804 | 2586 |
Phase0 Phase1 Phase2 Phase3 |
|
|
2805 | 2587 |
TypeError |
|
|
2806 | 2588 |
|
|
|
2807 | 2589 |
''' |
|
|
2808 | 2590 |
|
|
|
2809 | 2591 |
def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2810 | 2592 |
phaseOffsets = None, |
|
|
2811 | 2593 |
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
2812 | 2594 |
noise_timeStep = 4, noise_multiple = 4, |
|
|
2813 | 2595 |
multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2814 | 2596 |
phaseThresh = 20, SNRThresh = 5, |
|
|
2815 | 2597 |
hmin = 50, hmax=150, azimuth = 0, |
|
|
2816 | 2598 |
channelPositions = None) : |
|
|
2817 | 2599 |
|
|
|
2818 | 2600 |
|
|
|
2819 | 2601 |
#Getting Pairslist |
|
|
2820 | 2602 |
if channelPositions == None: |
|
|
2821 | 2603 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2822 | 2604 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2823 | 2605 |
meteorOps = SMOperations() |
|
|
2824 | 2606 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2825 | 2607 |
heiRang = dataOut.getHeiRange() |
|
|
2826 | 2608 |
#Get Beacon signal - No Beacon signal anymore |
|
|
2827 | 2609 |
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2828 | 2610 |
# |
|
|
2829 | 2611 |
# if hei_ref != None: |
|
|
2830 | 2612 |
# newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2831 | 2613 |
# |
|
|
2832 | 2614 |
|
|
|
2833 | 2615 |
|
|
|
2834 | 2616 |
#****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2835 | 2617 |
# see if the user put in pre defined phase shifts |
|
|
2836 | 2618 |
voltsPShift = dataOut.data_pre.copy() |
|
|
2837 | 2619 |
|
|
|
2838 | 2620 |
# if predefinedPhaseShifts != None: |
|
|
2839 | 2621 |
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2840 | 2622 |
# |
|
|
2841 | 2623 |
# # elif beaconPhaseShifts: |
|
|
2842 | 2624 |
# # #get hardware phase shifts using beacon signal |
|
|
2843 | 2625 |
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2844 | 2626 |
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2845 | 2627 |
# |
|
|
2846 | 2628 |
# else: |
|
|
2847 | 2629 |
# hardwarePhaseShifts = numpy.zeros(5) |
|
|
2848 | 2630 |
# |
|
|
2849 | 2631 |
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2850 | 2632 |
# for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2851 | 2633 |
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2852 | 2634 | |
|
|
2853 | 2635 |
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2854 | 2636 |
|
|
|
2855 | 2637 |
#Remove DC |
|
|
2856 | 2638 |
voltsDC = numpy.mean(voltsPShift,1) |
|
|
2857 | 2639 |
voltsDC = numpy.mean(voltsDC,1) |
|
|
2858 | 2640 |
for i in range(voltsDC.shape[0]): |
|
|
2859 | 2641 |
voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2860 | 2642 |
|
|
|
2861 | 2643 |
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
2862 | 2644 |
# voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2863 | 2645 |
|
|
|
2864 | 2646 |
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2865 | 2647 |
#Coherent Detection |
|
|
2866 | 2648 |
if cohDetection: |
|
|
2867 | 2649 |
#use coherent detection to get the net power |
|
|
2868 | 2650 |
cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2869 | 2651 |
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2870 | 2652 |
|
|
|
2871 | 2653 |
#Non-coherent detection! |
|
|
2872 | 2654 |
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2873 | 2655 |
#********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2874 | 2656 |
|
|
|
2875 | 2657 |
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2876 | 2658 |
#Get noise |
|
|
2877 | 2659 |
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2878 | 2660 |
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2879 | 2661 |
#Get signal threshold |
|
|
2880 | 2662 |
signalThresh = noise_multiple*noise |
|
|
2881 | 2663 |
#Meteor echoes detection |
|
|
2882 | 2664 |
listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2883 | 2665 |
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2884 | 2666 |
|
|
|
2885 | 2667 |
#************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2886 | 2668 |
#Parameters |
|
|
2887 | 2669 |
heiRange = dataOut.getHeiRange() |
|
|
2888 | 2670 |
rangeInterval = heiRange[1] - heiRange[0] |
|
|
2889 | 2671 |
rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2890 | 2672 |
timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2891 | 2673 |
#Multiple detection removals |
|
|
2892 | 2674 |
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2893 | 2675 |
#************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2894 | 2676 |
|
|
|
2895 | 2677 |
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2896 | 2678 |
#Parameters |
|
|
2897 | 2679 |
phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2898 | 2680 |
thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2899 | 2681 |
#Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2900 | 2682 |
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2901 | 2683 |
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2902 | 2684 |
#Estimation of decay times (Errors N 7, 8, 11) |
|
|
2903 | 2685 |
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2904 | 2686 |
#******************* END OF METEOR REESTIMATION ******************* |
|
|
2905 | 2687 |
|
|
|
2906 | 2688 |
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2907 | 2689 |
#Calculating Radial Velocity (Error N 15) |
|
|
2908 | 2690 |
radialStdThresh = 10 |
|
|
2909 | 2691 |
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
2910 | 2692 | |
|
|
2911 | 2693 |
if len(listMeteors4) > 0: |
|
|
2912 | 2694 |
#Setting New Array |
|
|
2913 | 2695 |
date = dataOut.utctime |
|
|
2914 | 2696 |
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2915 | 2697 |
|
|
|
2916 | 2698 |
#Correcting phase offset |
|
|
2917 | 2699 |
if phaseOffsets != None: |
|
|
2918 | 2700 |
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2919 | 2701 |
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2920 | 2702 |
|
|
|
2921 | 2703 |
#Second Pairslist |
|
|
2922 | 2704 |
pairsList = [] |
|
|
2923 | 2705 |
pairx = (0,1) |
|
|
2924 | 2706 |
pairy = (2,3) |
|
|
2925 | 2707 |
pairsList.append(pairx) |
|
|
2926 | 2708 |
pairsList.append(pairy) |
|
|
2927 | 2709 |
|
|
|
2928 | 2710 |
jph = numpy.array([0,0,0,0]) |
|
|
2929 | 2711 |
h = (hmin,hmax) |
|
|
2930 | 2712 |
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2931 | 2713 |
|
|
|
2932 | 2714 |
# #Calculate AOA (Error N 3, 4) |
|
|
2933 | 2715 |
# #JONES ET AL. 1998 |
|
|
2934 | 2716 |
# error = arrayParameters[:,-1] |
|
|
2935 | 2717 |
# AOAthresh = numpy.pi/8 |
|
|
2936 | 2718 |
# phases = -arrayParameters[:,9:13] |
|
|
2937 | 2719 |
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2938 | 2720 |
# |
|
|
2939 | 2721 |
# #Calculate Heights (Error N 13 and 14) |
|
|
2940 | 2722 |
# error = arrayParameters[:,-1] |
|
|
2941 | 2723 |
# Ranges = arrayParameters[:,2] |
|
|
2942 | 2724 |
# zenith = arrayParameters[:,5] |
|
|
2943 | 2725 |
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2944 | 2726 |
# error = arrayParameters[:,-1] |
|
|
2945 | 2727 |
#********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2946 | 2728 |
|
|
|
2947 | 2729 |
#***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
2948 | 2730 |
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2949 | 2731 |
dataOut.data_param = arrayParameters |
|
|
2950 | 2732 |
|
|
|
2951 | 2733 |
if arrayParameters == None: |
|
|
2952 | 2734 |
dataOut.flagNoData = True |
|
|
2953 | 2735 |
else: |
|
|
2954 | 2736 |
dataOut.flagNoData = True |
|
|
2955 | 2737 |
|
|
|
2956 | 2738 |
return |
|
|
2957 | 2739 |
|
|
|
2958 | 2740 |
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2959 | 2741 |
|
|
|
2960 | 2742 |
minIndex = min(newheis[0]) |
|
|
2961 | 2743 |
maxIndex = max(newheis[0]) |
|
|
2962 | 2744 |
|
|
|
2963 | 2745 |
voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2964 | 2746 |
nLength = voltage.shape[1]/n |
|
|
2965 | 2747 |
nMin = 0 |
|
|
2966 | 2748 |
nMax = 0 |
|
|
2967 | 2749 |
phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2968 | 2750 |
|
|
|
2969 | 2751 |
for i in range(n): |
|
|
2970 | 2752 |
nMax += nLength |
|
|
2971 | 2753 |
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2972 | 2754 |
phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2973 | 2755 |
phaseOffset[:,i] = phaseCCF.transpose() |
|
|
2974 | 2756 |
nMin = nMax |
|
|
2975 | 2757 |
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2976 | 2758 |
|
|
|
2977 | 2759 |
#Remove Outliers |
|
|
2978 | 2760 |
factor = 2 |
|
|
2979 | 2761 |
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2980 | 2762 |
dw = numpy.std(wt,axis = 1) |
|
|
2981 | 2763 |
dw = dw.reshape((dw.size,1)) |
|
|
2982 | 2764 |
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
2983 | 2765 |
phaseOffset[ind] = numpy.nan |
|
|
2984 | 2766 |
phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
2985 | 2767 |
|
|
|
2986 | 2768 |
return phaseOffset |
|
|
2987 | 2769 |
|
|
|
2988 | 2770 |
def __shiftPhase(self, data, phaseShift): |
|
|
2989 | 2771 |
#this will shift the phase of a complex number |
|
|
2990 | 2772 |
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
2991 | 2773 |
return dataShifted |
|
|
2992 | 2774 |
|
|
|
2993 | 2775 |
def __estimatePhaseDifference(self, array, pairslist): |
|
|
2994 | 2776 |
nChannel = array.shape[0] |
|
|
2995 | 2777 |
nHeights = array.shape[2] |
|
|
2996 | 2778 |
numPairs = len(pairslist) |
|
|
2997 | 2779 |
# phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2998 | 2780 |
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2999 | 2781 |
|
|
|
3000 | 2782 |
#Correct phases |
|
|
3001 | 2783 |
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
3002 | 2784 |
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3003 | 2785 |
|
|
|
3004 | 2786 |
if indDer[0].shape[0] > 0: |
|
|
3005 | 2787 |
for i in range(indDer[0].shape[0]): |
|
|
3006 | 2788 |
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
3007 | 2789 |
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
3008 | 2790 |
|
|
|
3009 | 2791 |
# for j in range(numSides): |
|
|
3010 | 2792 |
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
3011 | 2793 |
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
3012 | 2794 |
# |
|
|
3013 | 2795 |
#Linear |
|
|
3014 | 2796 |
phaseInt = numpy.zeros((numPairs,1)) |
|
|
3015 | 2797 |
angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
3016 | 2798 |
for j in range(numPairs): |
|
|
3017 | 2799 |
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
3018 | 2800 |
phaseInt[j] = fit[1] |
|
|
3019 | 2801 |
#Phase Differences |
|
|
3020 | 2802 |
phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
3021 | 2803 |
phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
3022 | 2804 |
|
|
|
3023 | 2805 |
#Dealias |
|
|
3024 | 2806 |
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
3025 | 2807 |
# indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
3026 | 2808 |
# phaseArrival[indAlias] -= 2*numpy.pi |
|
|
3027 | 2809 |
# indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
3028 | 2810 |
# phaseArrival[indAlias] += 2*numpy.pi |
|
|
3029 | 2811 |
|
|
|
3030 | 2812 |
return phaseDiff, phaseArrival |
|
|
3031 | 2813 |
|
|
|
3032 | 2814 |
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
3033 | 2815 |
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
3034 | 2816 |
#find the phase shifts of each channel over 1 second intervals |
|
|
3035 | 2817 |
#only look at ranges below the beacon signal |
|
|
3036 | 2818 |
numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
3037 | 2819 |
numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
3038 | 2820 |
numHeights = volts.shape[2] |
|
|
3039 | 2821 |
nChannel = volts.shape[0] |
|
|
3040 | 2822 |
voltsCohDet = volts.copy() |
|
|
3041 | 2823 |
|
|
|
3042 | 2824 |
pairsarray = numpy.array(pairslist) |
|
|
3043 | 2825 |
indSides = pairsarray[:,1] |
|
|
3044 | 2826 |
# indSides = numpy.array(range(nChannel)) |
|
|
3045 | 2827 |
# indSides = numpy.delete(indSides, indCenter) |
|
|
3046 | 2828 |
# |
|
|
3047 | 2829 |
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
3048 | 2830 |
listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
3049 | 2831 |
|
|
|
3050 | 2832 |
startInd = 0 |
|
|
3051 | 2833 |
endInd = 0 |
|
|
3052 | 2834 |
|
|
|
3053 | 2835 |
for i in range(numBlocks): |
|
|
3054 | 2836 |
startInd = endInd |
|
|
3055 | 2837 |
endInd = endInd + listBlocks[i].shape[1] |
|
|
3056 | 2838 |
|
|
|
3057 | 2839 |
arrayBlock = listBlocks[i] |
|
|
3058 | 2840 |
# arrayBlockCenter = listCenter[i] |
|
|
3059 | 2841 |
|
|
|
3060 | 2842 |
#Estimate the Phase Difference |
|
|
3061 | 2843 |
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
3062 | 2844 |
#Phase Difference RMS |
|
|
3063 | 2845 |
arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
3064 | 2846 |
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
3065 | 2847 |
indPhase = numpy.where(phaseRMSaux==4) |
|
|
3066 | 2848 |
#Shifting |
|
|
3067 | 2849 |
if indPhase[0].shape[0] > 0: |
|
|
3068 | 2850 |
for j in range(indSides.size): |
|
|
3069 | 2851 |
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
3070 | 2852 |
voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
3071 | 2853 |
|
|
|
3072 | 2854 |
return voltsCohDet |
|
|
3073 | 2855 |
|
|
|
3074 | 2856 |
def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
3075 | 2857 |
|
|
|
3076 | 2858 |
nHeights = volts.shape[2] |
|
|
3077 | 2859 |
nPoints = volts.shape[1] |
|
|
3078 | 2860 |
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
3079 | 2861 |
|
|
|
3080 | 2862 |
for i in range(len(pairslist)): |
|
|
3081 | 2863 |
volts1 = volts[pairslist[i][0]] |
|
|
3082 | 2864 |
volts2 = volts[pairslist[i][1]] |
|
|
3083 | 2865 |
|
|
|
3084 | 2866 |
for t in range(len(laglist)): |
|
|
3085 | 2867 |
idxT = laglist[t] |
|
|
3086 | 2868 |
if idxT >= 0: |
|
|
3087 | 2869 |
vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
3088 | 2870 |
numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
3089 | 2871 |
else: |
|
|
3090 | 2872 |
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
3091 | 2873 |
volts2[:(nPoints + idxT),:])) |
|
|
3092 | 2874 |
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
3093 | 2875 |
|
|
|
3094 | 2876 |
vStacked = None |
|
|
3095 | 2877 |
return voltsCCF |
|
|
3096 | 2878 |
|
|
|
3097 | 2879 |
def __getNoise(self, power, timeSegment, timeInterval): |
|
|
3098 | 2880 |
numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
3099 | 2881 |
numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
3100 | 2882 |
numHeights = power.shape[1] |
|
|
3101 | 2883 | |
|
|
3102 | 2884 |
listPower = numpy.array_split(power, numBlocks, 0) |
|
|
3103 | 2885 |
noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3104 | 2886 |
noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
3105 | 2887 |
|
|
|
3106 | 2888 |
startInd = 0 |
|
|
3107 | 2889 |
endInd = 0 |
|
|
3108 | 2890 |
|
|
|
3109 | 2891 |
for i in range(numBlocks): #split por canal |
|
|
3110 | 2892 |
startInd = endInd |
|
|
3111 | 2893 |
endInd = endInd + listPower[i].shape[0] |
|
|
3112 | 2894 |
|
|
|
3113 | 2895 |
arrayBlock = listPower[i] |
|
|
3114 | 2896 |
noiseAux = numpy.mean(arrayBlock, 0) |
|
|
3115 | 2897 |
# noiseAux = numpy.median(noiseAux) |
|
|
3116 | 2898 |
# noiseAux = numpy.mean(arrayBlock) |
|
|
3117 | 2899 |
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
3118 | 2900 |
|
|
|
3119 | 2901 |
noiseAux1 = numpy.mean(arrayBlock) |
|
|
3120 | 2902 |
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
3121 | 2903 |
|
|
|
3122 | 2904 |
return noise, noise1 |
|
|
3123 | 2905 |
|
|
|
3124 | 2906 |
def __findMeteors(self, power, thresh): |
|
|
3125 | 2907 |
nProf = power.shape[0] |
|
|
3126 | 2908 |
nHeights = power.shape[1] |
|
|
3127 | 2909 |
listMeteors = [] |
|
|
3128 | 2910 |
|
|
|
3129 | 2911 |
for i in range(nHeights): |
|
|
3130 | 2912 |
powerAux = power[:,i] |
|
|
3131 | 2913 |
threshAux = thresh[:,i] |
|
|
3132 | 2914 |
|
|
|
3133 | 2915 |
indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
3134 | 2916 |
indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
3135 | 2917 |
|
|
|
3136 | 2918 |
j = 0 |
|
|
3137 | 2919 |
|
|
|
3138 | 2920 |
while (j < indUPthresh.size - 2): |
|
|
3139 | 2921 |
if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
3140 | 2922 |
indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
3141 | 2923 |
indDNthresh = indDNthresh[indDNAux] |
|
|
3142 | 2924 |
|
|
|
3143 | 2925 |
if (indDNthresh.size > 0): |
|
|
3144 | 2926 |
indEnd = indDNthresh[0] - 1 |
|
|
3145 | 2927 |
indInit = indUPthresh[j] |
|
|
3146 | 2928 |
|
|
|
3147 | 2929 |
meteor = powerAux[indInit:indEnd + 1] |
|
|
3148 | 2930 |
indPeak = meteor.argmax() + indInit |
|
|
3149 | 2931 |
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
3150 | 2932 |
|
|
|
3151 | 2933 |
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
3152 | 2934 |
j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
3153 | 2935 |
else: j+=1 |
|
|
3154 | 2936 |
else: j+=1 |
|
|
3155 | 2937 |
|
|
|
3156 | 2938 |
return listMeteors |
|
|
3157 | 2939 |
|
|
|
3158 | 2940 |
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
3159 | 2941 |
|
|
|
3160 | 2942 |
arrayMeteors = numpy.asarray(listMeteors) |
|
|
3161 | 2943 |
listMeteors1 = [] |
|
|
3162 | 2944 |
|
|
|
3163 | 2945 |
while arrayMeteors.shape[0] > 0: |
|
|
3164 | 2946 |
FLAs = arrayMeteors[:,4] |
|
|
3165 | 2947 |
maxFLA = FLAs.argmax() |
|
|
3166 | 2948 |
listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
3167 | 2949 |
|
|
|
3168 | 2950 |
MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
3169 | 2951 |
MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
3170 | 2952 |
MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
3171 | 2953 |
|
|
|
3172 | 2954 |
#Check neighborhood |
|
|
3173 | 2955 |
maxHeightIndex = MeteorHeight + rangeLimit |
|
|
3174 | 2956 |
minHeightIndex = MeteorHeight - rangeLimit |
|
|
3175 | 2957 |
minTimeIndex = MeteorInitTime - timeLimit |
|
|
3176 | 2958 |
maxTimeIndex = MeteorEndTime + timeLimit |
|
|
3177 | 2959 |
|
|
|
3178 | 2960 |
#Check Heights |
|
|
3179 | 2961 |
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
3180 | 2962 |
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
3181 | 2963 |
indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
3182 | 2964 |
|
|
|
3183 | 2965 |
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
3184 | 2966 |
|
|
|
3185 | 2967 |
return listMeteors1 |
|
|
3186 | 2968 |
|
|
|
3187 | 2969 |
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
3188 | 2970 |
numHeights = volts.shape[2] |
|
|
3189 | 2971 |
nChannel = volts.shape[0] |
|
|
3190 | 2972 |
|
|
|
3191 | 2973 |
thresholdPhase = thresh[0] |
|
|
3192 | 2974 |
thresholdNoise = thresh[1] |
|
|
3193 | 2975 |
thresholdDB = float(thresh[2]) |
|
|
3194 | 2976 |
|
|
|
3195 | 2977 |
thresholdDB1 = 10**(thresholdDB/10) |
|
|
3196 | 2978 |
pairsarray = numpy.array(pairslist) |
|
|
3197 | 2979 |
indSides = pairsarray[:,1] |
|
|
3198 | 2980 |
|
|
|
3199 | 2981 |
pairslist1 = list(pairslist) |
|
|
3200 | 2982 |
pairslist1.append((0,1)) |
|
|
3201 | 2983 |
pairslist1.append((3,4)) |
|
|
3202 | 2984 | |
|
|
3203 | 2985 |
listMeteors1 = [] |
|
|
3204 | 2986 |
listPowerSeries = [] |
|
|
3205 | 2987 |
listVoltageSeries = [] |
|
|
3206 | 2988 |
#volts has the war data |
|
|
3207 | 2989 |
|
|
|
3208 | 2990 |
if frequency == 30e6: |
|
|
3209 | 2991 |
timeLag = 45*10**-3 |
|
|
3210 | 2992 |
else: |
|
|
3211 | 2993 |
timeLag = 15*10**-3 |
|
|
3212 | 2994 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3213 | 2995 |
|
|
|
3214 | 2996 |
for i in range(len(listMeteors)): |
|
|
3215 | 2997 |
|
|
|
3216 | 2998 |
###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3217 | 2999 |
meteorAux = numpy.zeros(16) |
|
|
3218 | 3000 |
|
|
|
3219 | 3001 |
#Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3220 | 3002 |
mHeight = listMeteors[i][0] |
|
|
3221 | 3003 |
mStart = listMeteors[i][1] |
|
|
3222 | 3004 |
mPeak = listMeteors[i][2] |
|
|
3223 | 3005 |
mEnd = listMeteors[i][3] |
|
|
3224 | 3006 |
|
|
|
3225 | 3007 |
#get the volt data between the start and end times of the meteor |
|
|
3226 | 3008 |
meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3227 | 3009 |
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3228 | 3010 |
|
|
|
3229 | 3011 |
#3.6. Phase Difference estimation |
|
|
3230 | 3012 |
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3231 | 3013 |
|
|
|
3232 | 3014 |
#3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3233 | 3015 |
#meteorVolts0.- all Channels, all Profiles |
|
|
3234 | 3016 |
meteorVolts0 = volts[:,:,mHeight] |
|
|
3235 | 3017 |
meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3236 | 3018 |
meteorNoise = noise[:,mHeight] |
|
|
3237 | 3019 |
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3238 | 3020 |
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3239 | 3021 |
|
|
|
3240 | 3022 |
#Times reestimation |
|
|
3241 | 3023 |
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3242 | 3024 |
if mStart1.size > 0: |
|
|
3243 | 3025 |
mStart1 = mStart1[-1] + 1 |
|
|
3244 | 3026 |
|
|
|
3245 | 3027 |
else: |
|
|
3246 | 3028 |
mStart1 = mPeak |
|
|
3247 | 3029 |
|
|
|
3248 | 3030 |
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3249 | 3031 |
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3250 | 3032 |
if mEndDecayTime1.size == 0: |
|
|
3251 | 3033 |
mEndDecayTime1 = powerNet0.size |
|
|
3252 | 3034 |
else: |
|
|
3253 | 3035 |
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3254 | 3036 |
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3255 | 3037 |
|
|
|
3256 | 3038 |
#meteorVolts1.- all Channels, from start to end |
|
|
3257 | 3039 |
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3258 | 3040 |
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3259 | 3041 |
if meteorVolts2.shape[1] == 0: |
|
|
3260 | 3042 |
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3261 | 3043 |
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3262 | 3044 |
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3263 | 3045 |
##################### END PARAMETERS REESTIMATION ######################### |
|
|
3264 | 3046 |
|
|
|
3265 | 3047 |
##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3266 | 3048 |
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3267 | 3049 |
if meteorVolts2.shape[1] > 0: |
|
|
3268 | 3050 |
#Phase Difference re-estimation |
|
|
3269 | 3051 |
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3270 | 3052 |
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3271 | 3053 |
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3272 | 3054 |
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3273 | 3055 |
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3274 | 3056 |
|
|
|
3275 | 3057 |
#Phase Difference RMS |
|
|
3276 | 3058 |
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3277 | 3059 |
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3278 | 3060 |
#Data from Meteor |
|
|
3279 | 3061 |
mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3280 | 3062 |
mPeakPower1 = powerNet1.max() |
|
|
3281 | 3063 |
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3282 | 3064 |
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3283 | 3065 |
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3284 | 3066 |
Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3285 | 3067 |
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3286 | 3068 |
#Vectorize |
|
|
3287 | 3069 |
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3288 | 3070 |
meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3289 | 3071 |
|
|
|
3290 | 3072 |
#Rejection Criterions |
|
|
3291 | 3073 |
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3292 | 3074 |
meteorAux[-1] = 17 |
|
|
3293 | 3075 |
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3294 | 3076 |
meteorAux[-1] = 1 |
|
|
3295 | 3077 |
|
|
|
3296 | 3078 |
|
|
|
3297 | 3079 |
else: |
|
|
3298 | 3080 |
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3299 | 3081 |
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3300 | 3082 |
PowerSeries = 0 |
|
|
3301 | 3083 |
|
|
|
3302 | 3084 |
listMeteors1.append(meteorAux) |
|
|
3303 | 3085 |
listPowerSeries.append(PowerSeries) |
|
|
3304 | 3086 |
listVoltageSeries.append(meteorVolts1) |
|
|
3305 | 3087 |
|
|
|
3306 | 3088 |
return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
3307 | 3089 |
|
|
|
3308 | 3090 |
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3309 | 3091 |
|
|
|
3310 | 3092 |
threshError = 10 |
|
|
3311 | 3093 |
#Depending if it is 30 or 50 MHz |
|
|
3312 | 3094 |
if frequency == 30e6: |
|
|
3313 | 3095 |
timeLag = 45*10**-3 |
|
|
3314 | 3096 |
else: |
|
|
3315 | 3097 |
timeLag = 15*10**-3 |
|
|
3316 | 3098 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3317 | 3099 |
|
|
|
3318 | 3100 |
listMeteors1 = [] |
|
|
3319 | 3101 |
|
|
|
3320 | 3102 |
for i in range(len(listMeteors)): |
|
|
3321 | 3103 |
meteorPower = listPower[i] |
|
|
3322 | 3104 |
meteorAux = listMeteors[i] |
|
|
3323 | 3105 |
|
|
|
3324 | 3106 |
if meteorAux[-1] == 0: |
|
|
3325 | 3107 | |
|
|
3326 | 3108 |
try: |
|
|
3327 | 3109 |
indmax = meteorPower.argmax() |
|
|
3328 | 3110 |
indlag = indmax + lag |
|
|
3329 | 3111 |
|
|
|
3330 | 3112 |
y = meteorPower[indlag:] |
|
|
3331 | 3113 |
x = numpy.arange(0, y.size)*timeLag |
|
|
3332 | 3114 |
|
|
|
3333 | 3115 |
#first guess |
|
|
3334 | 3116 |
a = y[0] |
|
|
3335 | 3117 |
tau = timeLag |
|
|
3336 | 3118 |
#exponential fit |
|
|
3337 | 3119 |
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3338 | 3120 |
y1 = self.__exponential_function(x, *popt) |
|
|
3339 | 3121 |
#error estimation |
|
|
3340 | 3122 |
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3341 | 3123 |
|
|
|
3342 | 3124 |
decayTime = popt[1] |
|
|
3343 | 3125 |
riseTime = indmax*timeInterval |
|
|
3344 | 3126 |
meteorAux[11:13] = [decayTime, error] |
|
|
3345 | 3127 |
|
|
|
3346 | 3128 |
#Table items 7, 8 and 11 |
|
|
3347 | 3129 |
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3348 | 3130 |
meteorAux[-1] = 7 |
|
|
3349 | 3131 |
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3350 | 3132 |
meteorAux[-1] = 8 |
|
|
3351 | 3133 |
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3352 | 3134 |
meteorAux[-1] = 11 |
|
|
3353 | 3135 |
|
|
|
3354 | 3136 |
|
|
|
3355 | 3137 |
except: |
|
|
3356 | 3138 |
meteorAux[-1] = 11 |
|
|
3357 | 3139 |
|
|
|
3358 | 3140 |
|
|
|
3359 | 3141 |
listMeteors1.append(meteorAux) |
|
|
3360 | 3142 |
|
|
|
3361 | 3143 |
return listMeteors1 |
|
|
3362 | 3144 | |
|
|
3363 | 3145 |
#Exponential Function |
|
|
3364 | 3146 | |
|
|
3365 | 3147 |
def __exponential_function(self, x, a, tau): |
|
|
3366 | 3148 |
y = a*numpy.exp(-x/tau) |
|
|
3367 | 3149 |
return y |
|
|
3368 | 3150 |
|
|
|
3369 | 3151 |
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3370 | 3152 |
|
|
|
3371 | 3153 |
pairslist1 = list(pairslist) |
|
|
3372 | 3154 |
pairslist1.append((0,1)) |
|
|
3373 | 3155 |
pairslist1.append((3,4)) |
|
|
3374 | 3156 |
numPairs = len(pairslist1) |
|
|
3375 | 3157 |
#Time Lag |
|
|
3376 | 3158 |
timeLag = 45*10**-3 |
|
|
3377 | 3159 |
c = 3e8 |
|
|
3378 | 3160 |
lag = numpy.ceil(timeLag/timeInterval) |
|
|
3379 | 3161 |
freq = 30e6 |
|
|
3380 | 3162 |
|
|
|
3381 | 3163 |
listMeteors1 = [] |
|
|
3382 | 3164 |
|
|
|
3383 | 3165 |
for i in range(len(listMeteors)): |
|
|
3384 | 3166 |
meteorAux = listMeteors[i] |
|
|
3385 | 3167 |
if meteorAux[-1] == 0: |
|
|
3386 | 3168 |
mStart = listMeteors[i][1] |
|
|
3387 | 3169 |
mPeak = listMeteors[i][2] |
|
|
3388 | 3170 |
mLag = mPeak - mStart + lag |
|
|
3389 | 3171 |
|
|
|
3390 | 3172 |
#get the volt data between the start and end times of the meteor |
|
|
3391 | 3173 |
meteorVolts = listVolts[i] |
|
|
3392 | 3174 |
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3393 | 3175 | |
|
|
3394 | 3176 |
#Get CCF |
|
|
3395 | 3177 |
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3396 | 3178 |
|
|
|
3397 | 3179 |
#Method 2 |
|
|
3398 | 3180 |
slopes = numpy.zeros(numPairs) |
|
|
3399 | 3181 |
time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3400 | 3182 |
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3401 | 3183 |
|
|
|
3402 | 3184 |
#Correct phases |
|
|
3403 | 3185 |
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3404 | 3186 |
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3405 | 3187 |
|
|
|
3406 | 3188 |
if indDer[0].shape[0] > 0: |
|
|
3407 | 3189 |
for i in range(indDer[0].shape[0]): |
|
|
3408 | 3190 |
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3409 | 3191 |
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3410 | 3192 | |
|
|
3411 | 3193 |
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3412 | 3194 |
for j in range(numPairs): |
|
|
3413 | 3195 |
fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3414 | 3196 |
slopes[j] = fit[0] |
|
|
3415 | 3197 |
|
|
|
3416 | 3198 |
#Remove Outlier |
|
|
3417 | 3199 |
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3418 | 3200 |
# slopes = numpy.delete(slopes,indOut) |
|
|
3419 | 3201 |
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3420 | 3202 |
# slopes = numpy.delete(slopes,indOut) |
|
|
3421 | 3203 |
|
|
|
3422 | 3204 |
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3423 | 3205 |
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3424 | 3206 |
meteorAux[-2] = radialError |
|
|
3425 | 3207 |
meteorAux[-3] = radialVelocity |
|
|
3426 | 3208 |
|
|
|
3427 | 3209 |
#Setting Error |
|
|
3428 | 3210 |
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3429 | 3211 |
if numpy.abs(radialVelocity) > 200: |
|
|
3430 | 3212 |
meteorAux[-1] = 15 |
|
|
3431 | 3213 |
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3432 | 3214 |
elif radialError > radialStdThresh: |
|
|
3433 | 3215 |
meteorAux[-1] = 12 |
|
|
3434 | 3216 |
|
|
|
3435 | 3217 |
listMeteors1.append(meteorAux) |
|
|
3436 | 3218 |
return listMeteors1 |
|
|
3437 | 3219 |
|
|
|
3438 | 3220 |
def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3439 | 3221 |
|
|
|
3440 | 3222 |
#New arrays |
|
|
3441 | 3223 |
arrayMeteors = numpy.array(listMeteors) |
|
|
3442 | 3224 |
arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3443 | 3225 |
|
|
|
3444 | 3226 |
#Date inclusion |
|
|
3445 | 3227 |
# date = re.findall(r'\((.*?)\)', date) |
|
|
3446 | 3228 |
# date = date[0].split(',') |
|
|
3447 | 3229 |
# date = map(int, date) |
|
|
3448 | 3230 |
# |
|
|
3449 | 3231 |
# if len(date)<6: |
|
|
3450 | 3232 |
# date.append(0) |
|
|
3451 | 3233 |
# |
|
|
3452 | 3234 |
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3453 | 3235 |
# arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3454 | 3236 |
arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3455 | 3237 |
|
|
|
3456 | 3238 |
#Meteor array |
|
|
3457 | 3239 |
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3458 | 3240 |
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3459 | 3241 |
|
|
|
3460 | 3242 |
#Parameters Array |
|
|
3461 | 3243 |
arrayParameters[:,0] = arrayDate #Date |
|
|
3462 | 3244 |
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3463 | 3245 |
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3464 | 3246 |
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3465 | 3247 |
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3466 | 3248 | |
|
|
3467 | 3249 |
|
|
|
3468 | 3250 |
return arrayParameters |
|
|
3469 | 3251 |
|
|
|
3470 | 3252 |
class CorrectSMPhases(Operation): |
|
|
3471 | 3253 |
|
|
|
3472 | 3254 |
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3473 | 3255 |
|
|
|
3474 | 3256 |
arrayParameters = dataOut.data_param |
|
|
3475 | 3257 |
pairsList = [] |
|
|
3476 | 3258 |
pairx = (0,1) |
|
|
3477 | 3259 |
pairy = (2,3) |
|
|
3478 | 3260 |
pairsList.append(pairx) |
|
|
3479 | 3261 |
pairsList.append(pairy) |
|
|
3480 | 3262 |
jph = numpy.zeros(4) |
|
|
3481 | 3263 |
|
|
|
3482 | 3264 |
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3483 | 3265 |
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3484 | 3266 |
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3485 | 3267 |
|
|
|
3486 | 3268 |
meteorOps = SMOperations() |
|
|
3487 | 3269 |
if channelPositions == None: |
|
|
3488 | 3270 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3489 | 3271 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3490 | 3272 |
|
|
|
3491 | 3273 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3492 | 3274 |
h = (hmin,hmax) |
|
|
3493 | 3275 |
|
|
|
3494 | 3276 |
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3495 | 3277 |
|
|
|
3496 | 3278 |
dataOut.data_param = arrayParameters |
|
|
3497 | 3279 |
return |
|
|
3498 | 3280 | |
|
|
3499 | 3281 |
class SMPhaseCalibration(Operation): |
|
|
3500 | 3282 |
|
|
|
3501 | 3283 |
__buffer = None |
|
|
3502 | 3284 | |
|
|
3503 | 3285 |
__initime = None |
|
|
3504 | 3286 | |
|
|
3505 | 3287 |
__dataReady = False |
|
|
3506 | 3288 |
|
|
|
3507 | 3289 |
__isConfig = False |
|
|
3508 | 3290 |
|
|
|
3509 | 3291 |
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3510 | 3292 |
|
|
|
3511 | 3293 |
dataTime = currentTime + paramInterval |
|
|
3512 | 3294 |
deltaTime = dataTime - initTime |
|
|
3513 | 3295 |
|
|
|
3514 | 3296 |
if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3515 | 3297 |
return True |
|
|
3516 | 3298 |
|
|
|
3517 | 3299 |
return False |
|
|
3518 | 3300 |
|
|
|
3519 | 3301 |
def __getGammas(self, pairs, d, phases): |
|
|
3520 | 3302 |
gammas = numpy.zeros(2) |
|
|
3521 | 3303 |
|
|
|
3522 | 3304 |
for i in range(len(pairs)): |
|
|
3523 | 3305 |
|
|
|
3524 | 3306 |
pairi = pairs[i] |
|
|
3525 | 3307 |
|
|
|
3526 | 3308 |
phip3 = phases[:,pairi[1]] |
|
|
3527 | 3309 |
d3 = d[pairi[1]] |
|
|
3528 | 3310 |
phip2 = phases[:,pairi[0]] |
|
|
3529 | 3311 |
d2 = d[pairi[0]] |
|
|
3530 | 3312 |
#Calculating gamma |
|
|
3531 | 3313 |
# jdcos = alp1/(k*d1) |
|
|
3532 | 3314 |
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3533 | 3315 |
jgamma = -phip2*d3/d2 - phip3 |
|
|
3534 | 3316 |
jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3535 | 3317 |
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3536 | 3318 |
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3537 | 3319 |
|
|
|
3538 | 3320 |
#Revised distribution |
|
|
3539 | 3321 |
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3540 | 3322 | |
|
|
3541 | 3323 |
#Histogram |
|
|
3542 | 3324 |
nBins = 64.0 |
|
|
3543 | 3325 |
rmin = -0.5*numpy.pi |
|
|
3544 | 3326 |
rmax = 0.5*numpy.pi |
|
|
3545 | 3327 |
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3546 | 3328 |
|
|
|
3547 | 3329 |
meteorsY = phaseHisto[0] |
|
|
3548 | 3330 |
phasesX = phaseHisto[1][:-1] |
|
|
3549 | 3331 |
width = phasesX[1] - phasesX[0] |
|
|
3550 | 3332 |
phasesX += width/2 |
|
|
3551 | 3333 |
|
|
|
3552 | 3334 |
#Gaussian aproximation |
|
|
3553 | 3335 |
bpeak = meteorsY.argmax() |
|
|
3554 | 3336 |
peak = meteorsY.max() |
|
|
3555 | 3337 |
jmin = bpeak - 5 |
|
|
3556 | 3338 |
jmax = bpeak + 5 + 1 |
|
|
3557 | 3339 |
|
|
|
3558 | 3340 |
if jmin<0: |
|
|
3559 | 3341 |
jmin = 0 |
|
|
3560 | 3342 |
jmax = 6 |
|
|
3561 | 3343 |
elif jmax > meteorsY.size: |
|
|
3562 | 3344 |
jmin = meteorsY.size - 6 |
|
|
3563 | 3345 |
jmax = meteorsY.size |
|
|
3564 | 3346 |
|
|
|
3565 | 3347 |
x0 = numpy.array([peak,bpeak,50]) |
|
|
3566 | 3348 |
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3567 | 3349 |
|
|
|
3568 | 3350 |
#Gammas |
|
|
3569 | 3351 |
gammas[i] = coeff[0][1] |
|
|
3570 | 3352 |
|
|
|
3571 | 3353 |
return gammas |
|
|
3572 | 3354 |
|
|
|
3573 | 3355 |
def __residualFunction(self, coeffs, y, t): |
|
|
3574 | 3356 |
|
|
|
3575 | 3357 |
return y - self.__gauss_function(t, coeffs) |
|
|
3576 | 3358 | |
|
|
3577 | 3359 |
def __gauss_function(self, t, coeffs): |
|
|
3578 | 3360 |
|
|
|
3579 | 3361 |
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3580 | 3362 | |
|
|
3581 | 3363 |
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3582 | 3364 |
meteorOps = SMOperations() |
|
|
3583 | 3365 |
nchan = 4 |
|
|
3584 | 3366 |
pairx = pairsList[0] |
|
|
3585 | 3367 |
pairy = pairsList[1] |
|
|
3586 | 3368 |
center_xangle = 0 |
|
|
3587 | 3369 |
center_yangle = 0 |
|
|
3588 | 3370 |
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3589 | 3371 |
ntimes = len(range_angle) |
|
|
3590 | 3372 |
|
|
|
3591 | 3373 |
nstepsx = 20.0 |
|
|
3592 | 3374 |
nstepsy = 20.0 |
|
|
3593 | 3375 |
|
|
|
3594 | 3376 |
for iz in range(ntimes): |
|
|
3595 | 3377 |
min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3596 | 3378 |
max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3597 | 3379 |
min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3598 | 3380 |
max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3599 | 3381 |
|
|
|
3600 | 3382 |
inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3601 | 3383 |
inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3602 | 3384 |
|
|
|
3603 | 3385 |
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3604 | 3386 |
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3605 | 3387 |
penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3606 | 3388 |
jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3607 | 3389 |
jph = numpy.zeros(nchan) |
|
|
3608 | 3390 |
|
|
|
3609 | 3391 |
# Iterations looking for the offset |
|
|
3610 | 3392 |
for iy in range(int(nstepsy)): |
|
|
3611 | 3393 |
for ix in range(int(nstepsx)): |
|
|
3612 | 3394 |
jph[pairy[1]] = alpha_y[iy] |
|
|
3613 | 3395 |
jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
3614 | 3396 |
|
|
|
3615 | 3397 |
jph[pairx[1]] = alpha_x[ix] |
|
|
3616 | 3398 |
jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3617 | 3399 |
|
|
|
3618 | 3400 |
jph_array[:,ix,iy] = jph |
|
|
3619 | 3401 |
|
|
|
3620 | 3402 |
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3621 | 3403 |
error = meteorsArray1[:,-1] |
|
|
3622 | 3404 |
ind1 = numpy.where(error==0)[0] |
|
|
3623 | 3405 |
penalty[ix,iy] = ind1.size |
|
|
3624 | 3406 |
|
|
|
3625 | 3407 |
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3626 | 3408 |
phOffset = jph_array[:,i,j] |
|
|
3627 | 3409 |
|
|
|
3628 | 3410 |
center_xangle = phOffset[pairx[1]] |
|
|
3629 | 3411 |
center_yangle = phOffset[pairy[1]] |
|
|
3630 | 3412 |
|
|
|
3631 | 3413 |
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3632 | 3414 |
phOffset = phOffset*180/numpy.pi |
|
|
3633 | 3415 |
return phOffset |
|
|
3634 | 3416 |
|
|
|
3635 | 3417 |
|
|
|
3636 | 3418 |
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3637 | 3419 |
|
|
|
3638 | 3420 |
dataOut.flagNoData = True |
|
|
3639 | 3421 |
self.__dataReady = False |
|
|
3640 | 3422 |
dataOut.outputInterval = nHours*3600 |
|
|
3641 | 3423 |
|
|
|
3642 | 3424 |
if self.__isConfig == False: |
|
|
3643 | 3425 |
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3644 | 3426 |
#Get Initial LTC time |
|
|
3645 | 3427 |
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3646 | 3428 |
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3647 | 3429 | |
|
|
3648 | 3430 |
self.__isConfig = True |
|
|
3649 | 3431 |
|
|
|
3650 | 3432 |
if self.__buffer == None: |
|
|
3651 | 3433 |
self.__buffer = dataOut.data_param.copy() |
|
|
3652 | 3434 | |
|
|
3653 | 3435 |
else: |
|
|
3654 | 3436 |
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3655 | 3437 |
|
|
|
3656 | 3438 |
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3657 | 3439 |
|
|
|
3658 | 3440 |
if self.__dataReady: |
|
|
3659 | 3441 |
dataOut.utctimeInit = self.__initime |
|
|
3660 | 3442 |
self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3661 | 3443 |
|
|
|
3662 | 3444 |
freq = dataOut.frequency |
|
|
3663 | 3445 |
c = dataOut.C #m/s |
|
|
3664 | 3446 |
lamb = c/freq |
|
|
3665 | 3447 |
k = 2*numpy.pi/lamb |
|
|
3666 | 3448 |
azimuth = 0 |
|
|
3667 | 3449 |
h = (hmin, hmax) |
|
|
3668 | 3450 |
pairs = ((0,1),(2,3)) |
|
|
3669 | 3451 |
|
|
|
3670 | 3452 |
if channelPositions == None: |
|
|
3671 | 3453 |
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3672 | 3454 |
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3673 | 3455 |
meteorOps = SMOperations() |
|
|
3674 | 3456 |
pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3675 | 3457 |
|
|
|
3676 | 3458 |
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3677 | 3459 |
|
|
|
3678 | 3460 |
meteorsArray = self.__buffer |
|
|
3679 | 3461 |
error = meteorsArray[:,-1] |
|
|
3680 | 3462 |
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3681 | 3463 |
ind1 = numpy.where(boolError)[0] |
|
|
3682 | 3464 |
meteorsArray = meteorsArray[ind1,:] |
|
|
3683 | 3465 |
meteorsArray[:,-1] = 0 |
|
|
3684 | 3466 |
phases = meteorsArray[:,8:12] |
|
|
3685 | 3467 |
|
|
|
3686 | 3468 |
#Calculate Gammas |
|
|
3687 | 3469 |
gammas = self.__getGammas(pairs, distances, phases) |
|
|
3688 | 3470 |
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3689 | 3471 |
#Calculate Phases |
|
|
3690 | 3472 |
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3691 | 3473 |
phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3692 | 3474 |
dataOut.data_output = -phasesOff |
|
|
3693 | 3475 |
dataOut.flagNoData = False |
|
|
3694 | 3476 |
self.__buffer = None |
|
|
3695 | 3477 |
|
|
|
3696 | 3478 |
|
|
|
3697 | 3479 |
return |
|
|
3698 | 3480 |
|
|
|
3699 | 3481 |
class SMOperations(): |
|
|
3700 | 3482 |
|
|
|
3701 | 3483 |
def __init__(self): |
|
|
3702 | 3484 |
|
|
|
3703 | 3485 |
return |
|
|
3704 | 3486 |
|
|
|
3705 | 3487 |
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3706 | 3488 |
|
|
|
3707 | 3489 |
arrayParameters = arrayParameters0.copy() |
|
|
3708 | 3490 |
hmin = h[0] |
|
|
3709 | 3491 |
hmax = h[1] |
|
|
3710 | 3492 |
|
|
|
3711 | 3493 |
#Calculate AOA (Error N 3, 4) |
|
|
3712 | 3494 |
#JONES ET AL. 1998 |
|
|
3713 | 3495 |
AOAthresh = numpy.pi/8 |
|
|
3714 | 3496 |
error = arrayParameters[:,-1] |
|
|
3715 | 3497 |
phases = -arrayParameters[:,8:12] + jph |
|
|
3716 | 3498 |
# phases = numpy.unwrap(phases) |
|
|
3717 | 3499 |
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3718 | 3500 |
|
|
|
3719 | 3501 |
#Calculate Heights (Error N 13 and 14) |
|
|
3720 | 3502 |
error = arrayParameters[:,-1] |
|
|
3721 | 3503 |
Ranges = arrayParameters[:,1] |
|
|
3722 | 3504 |
zenith = arrayParameters[:,4] |
|
|
3723 | 3505 |
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3724 | 3506 |
|
|
|
3725 | 3507 |
#----------------------- Get Final data ------------------------------------ |
|
|
3726 | 3508 |
# error = arrayParameters[:,-1] |
|
|
3727 | 3509 |
# ind1 = numpy.where(error==0)[0] |
|
|
3728 | 3510 |
# arrayParameters = arrayParameters[ind1,:] |
|
|
3729 | 3511 |
|
|
|
3730 | 3512 |
return arrayParameters |
|
|
3731 | 3513 |
|
|
|
3732 | 3514 |
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3733 | 3515 |
|
|
|
3734 | 3516 |
arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3735 | 3517 |
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3736 | 3518 |
|
|
|
3737 | 3519 |
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3738 | 3520 |
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3739 | 3521 |
arrayAOA[:,2] = cosDirError |
|
|
3740 | 3522 |
|
|
|
3741 | 3523 |
azimuthAngle = arrayAOA[:,0] |
|
|
3742 | 3524 |
zenithAngle = arrayAOA[:,1] |
|
|
3743 | 3525 |
|
|
|
3744 | 3526 |
#Setting Error |
|
|
3745 | 3527 |
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3746 | 3528 |
error[indError] = 0 |
|
|
3747 | 3529 |
#Number 3: AOA not fesible |
|
|
3748 | 3530 |
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3749 | 3531 |
error[indInvalid] = 3 |
|
|
3750 | 3532 |
#Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3751 | 3533 |
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3752 | 3534 |
error[indInvalid] = 4 |
|
|
3753 | 3535 |
return arrayAOA, error |
|
|
3754 | 3536 |
|
|
|
3755 | 3537 |
def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3756 | 3538 |
|
|
|
3757 | 3539 |
#Initializing some variables |
|
|
3758 | 3540 |
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3759 | 3541 |
ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3760 | 3542 |
|
|
|
3761 | 3543 |
cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3762 | 3544 |
cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3763 | 3545 |
|
|
|
3764 | 3546 |
|
|
|
3765 | 3547 |
for i in range(2): |
|
|
3766 | 3548 |
ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3767 | 3549 |
ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3768 | 3550 |
d0 = distances[pairsList[i][0]] |
|
|
3769 | 3551 |
d1 = distances[pairsList[i][1]] |
|
|
3770 | 3552 |
|
|
|
3771 | 3553 |
ph0_aux = ph0 + ph1 |
|
|
3772 | 3554 |
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3773 | 3555 |
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3774 | 3556 |
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
3775 | 3557 |
#First Estimation |
|
|
3776 | 3558 |
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3777 | 3559 |
|
|
|
3778 | 3560 |
#Most-Accurate Second Estimation |
|
|
3779 | 3561 |
phi1_aux = ph0 - ph1 |
|
|
3780 | 3562 |
phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3781 | 3563 |
#Direction Cosine 1 |
|
|
3782 | 3564 |
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3783 | 3565 |
|
|
|
3784 | 3566 |
#Searching the correct Direction Cosine |
|
|
3785 | 3567 |
cosdir0_aux = cosdir0[:,i] |
|
|
3786 | 3568 |
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3787 | 3569 |
#Minimum Distance |
|
|
3788 | 3570 |
cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3789 | 3571 |
indcos = cosDiff.argmin(axis = 1) |
|
|
3790 | 3572 |
#Saving Value obtained |
|
|
3791 | 3573 |
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3792 | 3574 |
|
|
|
3793 | 3575 |
return cosdir0, cosdir |
|
|
3794 | 3576 |
|
|
|
3795 | 3577 |
def __calculateAOA(self, cosdir, azimuth): |
|
|
3796 | 3578 |
cosdirX = cosdir[:,0] |
|
|
3797 | 3579 |
cosdirY = cosdir[:,1] |
|
|
3798 | 3580 |
|
|
|
3799 | 3581 |
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3800 | 3582 |
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3801 | 3583 |
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3802 | 3584 |
|
|
|
3803 | 3585 |
return angles |
|
|
3804 | 3586 |
|
|
|
3805 | 3587 |
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3806 | 3588 |
|
|
|
3807 | 3589 |
Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3808 | 3590 |
Re = 6371 #Earth Radius |
|
|
3809 | 3591 |
heights = numpy.zeros(Ranges.shape) |
|
|
3810 | 3592 |
|
|
|
3811 | 3593 |
R_aux = numpy.array([0,1,2])*Ramb |
|
|
3812 | 3594 |
R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3813 | 3595 | |
|
|
3814 | 3596 |
Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3815 | 3597 |
|
|
|
3816 | 3598 |
Ri = Ranges + R_aux |
|
|
3817 | 3599 |
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3818 | 3600 |
|
|
|
3819 | 3601 |
#Check if there is a height between 70 and 110 km |
|
|
3820 | 3602 |
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3821 | 3603 |
ind_h = numpy.where(h_bool == 1)[0] |
|
|
3822 | 3604 |
|
|
|
3823 | 3605 |
hCorr = hi[ind_h, :] |
|
|
3824 | 3606 |
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3825 | 3607 |
|
|
|
3826 | 3608 |
hCorr = hi[ind_hCorr] |
|
|
3827 | 3609 |
heights[ind_h] = hCorr |
|
|
3828 | 3610 |
|
|
|
3829 | 3611 |
#Setting Error |
|
|
3830 | 3612 |
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3831 | 3613 |
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3832 | 3614 |
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3833 | 3615 |
error[indError] = 0 |
|
|
3834 | 3616 |
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3835 | 3617 |
error[indInvalid2] = 14 |
|
|
3836 | 3618 |
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3837 | 3619 |
error[indInvalid1] = 13 |
|
|
3838 | 3620 |
|
|
|
3839 | 3621 |
return heights, error |
|
|
3840 | 3622 |
|
|
|
3841 | 3623 |
def getPhasePairs(self, channelPositions): |
|
|
3842 | 3624 |
chanPos = numpy.array(channelPositions) |
|
|
3843 | 3625 |
listOper = list(itertools.combinations(range(5),2)) |
|
|
3844 | 3626 |
|
|
|
3845 | 3627 |
distances = numpy.zeros(4) |
|
|
3846 | 3628 |
axisX = [] |
|
|
3847 | 3629 |
axisY = [] |
|
|
3848 | 3630 |
distX = numpy.zeros(3) |
|
|
3849 | 3631 |
distY = numpy.zeros(3) |
|
|
3850 | 3632 |
ix = 0 |
|
|
3851 | 3633 |
iy = 0 |
|
|
3852 | 3634 |
|
|
|
3853 | 3635 |
pairX = numpy.zeros((2,2)) |
|
|
3854 | 3636 |
pairY = numpy.zeros((2,2)) |
|
|
3855 | 3637 |
|
|
|
3856 | 3638 |
for i in range(len(listOper)): |
|
|
3857 | 3639 |
pairi = listOper[i] |
|
|
3858 | 3640 |
|
|
|
3859 | 3641 |
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3860 | 3642 |
|
|
|
3861 | 3643 |
if posDif[0] == 0: |
|
|
3862 | 3644 |
axisY.append(pairi) |
|
|
3863 | 3645 |
distY[iy] = posDif[1] |
|
|
3864 | 3646 |
iy += 1 |
|
|
3865 | 3647 |
elif posDif[1] == 0: |
|
|
3866 | 3648 |
axisX.append(pairi) |
|
|
3867 | 3649 |
distX[ix] = posDif[0] |
|
|
3868 | 3650 |
ix += 1 |
|
|
3869 | 3651 |
|
|
|
3870 | 3652 |
for i in range(2): |
|
|
3871 | 3653 |
if i==0: |
|
|
3872 | 3654 |
dist0 = distX |
|
|
3873 | 3655 |
axis0 = axisX |
|
|
3874 | 3656 |
else: |
|
|
3875 | 3657 |
dist0 = distY |
|
|
3876 | 3658 |
axis0 = axisY |
|
|
3877 | 3659 |
|
|
|
3878 | 3660 |
side = numpy.argsort(dist0)[:-1] |
|
|
3879 | 3661 |
axis0 = numpy.array(axis0)[side,:] |
|
|
3880 | 3662 |
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3881 | 3663 |
axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3882 | 3664 |
side = axis1[axis1 != chanC] |
|
|
3883 | 3665 |
diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3884 | 3666 |
diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3885 | 3667 |
if diff1<0: |
|
|
3886 | 3668 |
chan2 = side[0] |
|
|
3887 | 3669 |
d2 = numpy.abs(diff1) |
|
|
3888 | 3670 |
chan1 = side[1] |
|
|
3889 | 3671 |
d1 = numpy.abs(diff2) |
|
|
3890 | 3672 |
else: |
|
|
3891 | 3673 |
chan2 = side[1] |
|
|
3892 | 3674 |
d2 = numpy.abs(diff2) |
|
|
3893 | 3675 |
chan1 = side[0] |
|
|
3894 | 3676 |
d1 = numpy.abs(diff1) |
|
|
3895 | 3677 |
|
|
|
3896 | 3678 |
if i==0: |
|
|
3897 | 3679 |
chanCX = chanC |
|
|
3898 | 3680 |
chan1X = chan1 |
|
|
3899 | 3681 |
chan2X = chan2 |
|
|
3900 | 3682 |
distances[0:2] = numpy.array([d1,d2]) |
|
|
3901 | 3683 |
else: |
|
|
3902 | 3684 |
chanCY = chanC |
|
|
3903 | 3685 |
chan1Y = chan1 |
|
|
3904 | 3686 |
chan2Y = chan2 |
|
|
3905 | 3687 |
distances[2:4] = numpy.array([d1,d2]) |
|
|
3906 | 3688 |
# axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3907 | 3689 |
# |
|
|
3908 | 3690 |
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3909 | 3691 |
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3910 | 3692 |
# |
|
|
3911 | 3693 |
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3912 | 3694 |
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3913 | 3695 |
# channel25X = int(pairX[0,ind25X]) |
|
|
3914 | 3696 |
# channel20X = int(pairX[1,ind20X]) |
|
|
3915 | 3697 |
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3916 | 3698 |
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3917 | 3699 |
# channel25Y = int(pairY[0,ind25Y]) |
|
|
3918 | 3700 |
# channel20Y = int(pairY[1,ind20Y]) |
|
|
3919 | 3701 |
|
|
|
3920 | 3702 |
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3921 | 3703 |
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
3922 | 3704 |
|
|
|
3923 | 3705 |
return pairslist, distances |
|
|
3924 | 3706 |
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3925 | 3707 |
# |
|
|
3926 | 3708 |
# arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3927 | 3709 |
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3928 | 3710 |
# |
|
|
3929 | 3711 |
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3930 | 3712 |
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3931 | 3713 |
# arrayAOA[:,2] = cosDirError |
|
|
3932 | 3714 |
# |
|
|
3933 | 3715 |
# azimuthAngle = arrayAOA[:,0] |
|
|
3934 | 3716 |
# zenithAngle = arrayAOA[:,1] |
|
|
3935 | 3717 |
# |
|
|
3936 | 3718 |
# #Setting Error |
|
|
3937 | 3719 |
# #Number 3: AOA not fesible |
|
|
3938 | 3720 |
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3939 | 3721 |
# error[indInvalid] = 3 |
|
|
3940 | 3722 |
# #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3941 | 3723 |
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3942 | 3724 |
# error[indInvalid] = 4 |
|
|
3943 | 3725 |
# return arrayAOA, error |
|
|
3944 | 3726 |
# |
|
|
3945 | 3727 |
# def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3946 | 3728 |
# |
|
|
3947 | 3729 |
# #Initializing some variables |
|
|
3948 | 3730 |
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3949 | 3731 |
# ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3950 | 3732 |
# |
|
|
3951 | 3733 |
# cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3952 | 3734 |
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3953 | 3735 |
# |
|
|
3954 | 3736 |
# |
|
|
3955 | 3737 |
# for i in range(2): |
|
|
3956 | 3738 |
# #First Estimation |
|
|
3957 | 3739 |
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3958 | 3740 |
# #Dealias |
|
|
3959 | 3741 |
# indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3960 | 3742 |
# phi0_aux[indcsi] -= 2*numpy.pi |
|
|
3961 | 3743 |
# indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3962 | 3744 |
# phi0_aux[indcsi] += 2*numpy.pi |
|
|
3963 | 3745 |
# #Direction Cosine 0 |
|
|
3964 | 3746 |
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3965 | 3747 |
# |
|
|
3966 | 3748 |
# #Most-Accurate Second Estimation |
|
|
3967 | 3749 |
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3968 | 3750 |
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3969 | 3751 |
# #Direction Cosine 1 |
|
|
3970 | 3752 |
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3971 | 3753 |
# |
|
|
3972 | 3754 |
# #Searching the correct Direction Cosine |
|
|
3973 | 3755 |
# cosdir0_aux = cosdir0[:,i] |
|
|
3974 | 3756 |
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3975 | 3757 |
# #Minimum Distance |
|
|
3976 | 3758 |
# cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3977 | 3759 |
# indcos = cosDiff.argmin(axis = 1) |
|
|
3978 | 3760 |
# #Saving Value obtained |
|
|
3979 | 3761 |
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3980 | 3762 |
# |
|
|
3981 | 3763 |
# return cosdir0, cosdir |
|
|
3982 | 3764 |
# |
|
|
3983 | 3765 |
# def __calculateAOA(self, cosdir, azimuth): |
|
|
3984 | 3766 |
# cosdirX = cosdir[:,0] |
|
|
3985 | 3767 |
# cosdirY = cosdir[:,1] |
|
|
3986 | 3768 |
# |
|
|
3987 | 3769 |
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3988 | 3770 |
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
3989 | 3771 |
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3990 | 3772 |
# |
|
|
3991 | 3773 |
# return angles |
|
|
3992 | 3774 |
# |
|
|
3993 | 3775 |
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3994 | 3776 |
# |
|
|
3995 | 3777 |
# Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3996 | 3778 |
# Re = 6371 #Earth Radius |
|
|
3997 | 3779 |
# heights = numpy.zeros(Ranges.shape) |
|
|
3998 | 3780 |
# |
|
|
3999 | 3781 |
# R_aux = numpy.array([0,1,2])*Ramb |
|
|
4000 | 3782 |
# R_aux = R_aux.reshape(1,R_aux.size) |
|
|
4001 | 3783 |
# |
|
|
4002 | 3784 |
# Ranges = Ranges.reshape(Ranges.size,1) |
|
|
4003 | 3785 |
# |
|
|
4004 | 3786 |
# Ri = Ranges + R_aux |
|
|
4005 | 3787 |
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
4006 | 3788 |
# |
|
|
4007 | 3789 |
# #Check if there is a height between 70 and 110 km |
|
|
4008 | 3790 |
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
4009 | 3791 |
# ind_h = numpy.where(h_bool == 1)[0] |
|
|
4010 | 3792 |
# |
|
|
4011 | 3793 |
# hCorr = hi[ind_h, :] |
|
|
4012 | 3794 |
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
4013 | 3795 |
# |
|
|
4014 | 3796 |
# hCorr = hi[ind_hCorr] |
|
|
4015 | 3797 |
# heights[ind_h] = hCorr |
|
|
4016 | 3798 |
# |
|
|
4017 | 3799 |
# #Setting Error |
|
|
4018 | 3800 |
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
4019 | 3801 |
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
4020 | 3802 |
# |
|
|
4021 | 3803 |
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
4022 | 3804 |
# error[indInvalid2] = 14 |
|
|
4023 | 3805 |
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
4024 | 3806 |
# error[indInvalid1] = 13 |
|
|
4025 | 3807 |
# |
|
|
4026 | 3808 |
# return heights, error |
|
|
4027 | 3809 |
|
| @@ -1,1067 +1,1078 | |||
|
|
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 | import matplotlib.pyplot as plt |
|
|
8 | 8 | |
|
|
9 | 9 | class SpectraProc(ProcessingUnit): |
|
|
10 | 10 | |
|
|
11 | 11 | def __init__(self, **kwargs): |
|
|
12 | 12 | |
|
|
13 | 13 | ProcessingUnit.__init__(self, **kwargs) |
|
|
14 | 14 | |
|
|
15 | 15 | self.buffer = None |
|
|
16 | 16 | self.firstdatatime = None |
|
|
17 | 17 | self.profIndex = 0 |
|
|
18 | 18 | self.dataOut = Spectra() |
|
|
19 | 19 | self.id_min = None |
|
|
20 | 20 | self.id_max = None |
|
|
21 | 21 | |
|
|
22 | 22 | def __updateSpecFromVoltage(self): |
|
|
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.channelList = self.dataIn.channelList |
|
|
32 | 32 | self.dataOut.heightList = self.dataIn.heightList |
|
|
33 | 33 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
34 | 34 | |
|
|
35 | 35 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
36 | 36 | self.dataOut.nCode = self.dataIn.nCode |
|
|
37 | 37 | self.dataOut.code = self.dataIn.code |
|
|
38 | 38 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
39 | 39 | |
|
|
40 | 40 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
41 | 41 | self.dataOut.utctime = self.firstdatatime |
|
|
42 | 42 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
43 | 43 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
44 | 44 | self.dataOut.flagShiftFFT = False |
|
|
45 | 45 | |
|
|
46 | 46 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
47 | 47 | self.dataOut.nIncohInt = 1 |
|
|
48 | 48 | |
|
|
49 | 49 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
50 | 50 | |
|
|
51 | 51 | self.dataOut.frequency = self.dataIn.frequency |
|
|
52 | 52 | self.dataOut.realtime = self.dataIn.realtime |
|
|
53 | 53 | |
|
|
54 | 54 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
55 | 55 | self.dataOut.zenith = self.dataIn.zenith |
|
|
56 | 56 | |
|
|
57 | 57 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
58 | 58 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
59 | 59 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
60 | 60 | |
|
|
61 | 61 | def __getFft(self): |
|
|
62 | 62 | """ |
|
|
63 | 63 | Convierte valores de Voltaje a Spectra |
|
|
64 | 64 | |
|
|
65 | 65 | Affected: |
|
|
66 | 66 | self.dataOut.data_spc |
|
|
67 | 67 | self.dataOut.data_cspc |
|
|
68 | 68 | self.dataOut.data_dc |
|
|
69 | 69 | self.dataOut.heightList |
|
|
70 | 70 | self.profIndex |
|
|
71 | 71 | self.buffer |
|
|
72 | 72 | self.dataOut.flagNoData |
|
|
73 | 73 | """ |
|
|
74 | 74 | fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1) |
|
|
75 | 75 | |
|
|
76 | 76 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
|
77 | 77 | dc = fft_volt[:,0,:] |
|
|
78 | 78 | |
|
|
79 | 79 | #calculo de self-spectra |
|
|
80 | 80 | fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,)) |
|
|
81 | 81 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
|
82 | 82 | spc = spc.real |
|
|
83 | 83 | |
|
|
84 | 84 | blocksize = 0 |
|
|
85 | 85 | blocksize += dc.size |
|
|
86 | 86 | blocksize += spc.size |
|
|
87 | 87 | |
|
|
88 | 88 | cspc = None |
|
|
89 | 89 | pairIndex = 0 |
|
|
90 | 90 | if self.dataOut.pairsList != None: |
|
|
91 | 91 | #calculo de cross-spectra |
|
|
92 | 92 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
|
93 | 93 | for pair in self.dataOut.pairsList: |
|
|
94 | 94 | if pair[0] not in self.dataOut.channelList: |
|
|
95 | 95 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
96 | 96 | if pair[1] not in self.dataOut.channelList: |
|
|
97 | 97 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
|
|
98 | 98 | |
|
|
99 | 99 | cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]) |
|
|
100 | 100 | pairIndex += 1 |
|
|
101 | 101 | blocksize += cspc.size |
|
|
102 | 102 | |
|
|
103 | 103 | self.dataOut.data_spc = spc |
|
|
104 | 104 | self.dataOut.data_cspc = cspc |
|
|
105 | 105 | self.dataOut.data_dc = dc |
|
|
106 | 106 | self.dataOut.blockSize = blocksize |
|
|
107 | 107 | self.dataOut.flagShiftFFT = True |
|
|
108 | 108 | |
|
|
109 | 109 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None): |
|
|
110 | 110 | |
|
|
111 | 111 | self.dataOut.flagNoData = True |
|
|
112 | 112 | |
|
|
113 | 113 | if self.dataIn.type == "Spectra": |
|
|
114 | 114 | self.dataOut.copy(self.dataIn) |
|
|
115 | 115 | # self.__selectPairs(pairsList) |
|
|
116 | 116 | return True |
|
|
117 | 117 | |
|
|
118 | 118 | if self.dataIn.type == "Voltage": |
|
|
119 | 119 | |
|
|
120 | 120 | if nFFTPoints == None: |
|
|
121 | 121 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" |
|
|
122 | 122 | |
|
|
123 | 123 | if nProfiles == None: |
|
|
124 | 124 | nProfiles = nFFTPoints |
|
|
125 | 125 | |
|
|
126 | 126 | if ippFactor == None: |
|
|
127 | 127 | ippFactor = 1 |
|
|
128 | 128 | |
|
|
129 | 129 | self.dataOut.ippFactor = ippFactor |
|
|
130 | 130 | |
|
|
131 | 131 | self.dataOut.nFFTPoints = nFFTPoints |
|
|
132 | 132 | self.dataOut.pairsList = pairsList |
|
|
133 | 133 | |
|
|
134 | 134 | if self.buffer is None: |
|
|
135 | 135 | self.buffer = numpy.zeros( (self.dataIn.nChannels, |
|
|
136 | 136 | nProfiles, |
|
|
137 | 137 | self.dataIn.nHeights), |
|
|
138 | 138 | dtype='complex') |
|
|
139 | 139 | |
|
|
140 | 140 | if self.dataIn.flagDataAsBlock: |
|
|
141 | 141 | #data dimension: [nChannels, nProfiles, nSamples] |
|
|
142 | 142 | |
|
|
143 | 143 | nVoltProfiles = self.dataIn.data.shape[1] |
|
|
144 | 144 | # nVoltProfiles = self.dataIn.nProfiles |
|
|
145 | 145 | |
|
|
146 | 146 | if nVoltProfiles == nProfiles: |
|
|
147 | 147 | self.buffer = self.dataIn.data.copy() |
|
|
148 | 148 | self.profIndex = nVoltProfiles |
|
|
149 | 149 | |
|
|
150 | 150 | elif nVoltProfiles < nProfiles: |
|
|
151 | 151 | |
|
|
152 | 152 | if self.profIndex == 0: |
|
|
153 | 153 | self.id_min = 0 |
|
|
154 | 154 | self.id_max = nVoltProfiles |
|
|
155 | 155 | |
|
|
156 | 156 | self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data |
|
|
157 | 157 | self.profIndex += nVoltProfiles |
|
|
158 | 158 | self.id_min += nVoltProfiles |
|
|
159 | 159 | self.id_max += nVoltProfiles |
|
|
160 | 160 | else: |
|
|
161 | 161 | raise ValueError, "The type object %s has %d profiles, it should just has %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles) |
|
|
162 | 162 | self.dataOut.flagNoData = True |
|
|
163 | 163 | return 0 |
|
|
164 | 164 | else: |
|
|
165 | 165 | |
|
|
166 | 166 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
|
|
167 | 167 | self.profIndex += 1 |
|
|
168 | 168 | |
|
|
169 | 169 | if self.firstdatatime == None: |
|
|
170 | 170 | self.firstdatatime = self.dataIn.utctime |
|
|
171 | 171 | |
|
|
172 | 172 | if self.profIndex == nProfiles: |
|
|
173 | 173 | self.__updateSpecFromVoltage() |
|
|
174 | 174 | self.__getFft() |
|
|
175 | 175 | |
|
|
176 | 176 | self.dataOut.flagNoData = False |
|
|
177 | 177 | self.firstdatatime = None |
|
|
178 | 178 | self.profIndex = 0 |
|
|
179 | 179 | |
|
|
180 | 180 | return True |
|
|
181 | 181 | |
|
|
182 | 182 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) |
|
|
183 | 183 | |
|
|
184 | 184 | def __selectPairs(self, pairsList): |
|
|
185 | 185 | |
|
|
186 | 186 | if channelList == None: |
|
|
187 | 187 | return |
|
|
188 | 188 | |
|
|
189 | 189 | pairsIndexListSelected = [] |
|
|
190 | 190 | |
|
|
191 | 191 | for thisPair in pairsList: |
|
|
192 | 192 | |
|
|
193 | 193 | if thisPair not in self.dataOut.pairsList: |
|
|
194 | 194 | continue |
|
|
195 | 195 | |
|
|
196 | 196 | pairIndex = self.dataOut.pairsList.index(thisPair) |
|
|
197 | 197 | |
|
|
198 | 198 | pairsIndexListSelected.append(pairIndex) |
|
|
199 | 199 | |
|
|
200 | 200 | if not pairsIndexListSelected: |
|
|
201 | 201 | self.dataOut.data_cspc = None |
|
|
202 | 202 | self.dataOut.pairsList = [] |
|
|
203 | 203 | return |
|
|
204 | 204 | |
|
|
205 | 205 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
206 | 206 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
207 | 207 | |
|
|
208 | 208 | return |
|
|
209 | 209 | |
|
|
210 | 210 | def __selectPairsByChannel(self, channelList=None): |
|
|
211 | 211 | |
|
|
212 | 212 | if channelList == None: |
|
|
213 | 213 | return |
|
|
214 | 214 | |
|
|
215 | 215 | pairsIndexListSelected = [] |
|
|
216 | 216 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
217 | 217 | #First pair |
|
|
218 | 218 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
219 | 219 | continue |
|
|
220 | 220 | #Second pair |
|
|
221 | 221 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
222 | 222 | continue |
|
|
223 | 223 | |
|
|
224 | 224 | pairsIndexListSelected.append(pairIndex) |
|
|
225 | 225 | |
|
|
226 | 226 | if not pairsIndexListSelected: |
|
|
227 | 227 | self.dataOut.data_cspc = None |
|
|
228 | 228 | self.dataOut.pairsList = [] |
|
|
229 | 229 | return |
|
|
230 | 230 | |
|
|
231 | 231 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
232 | 232 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
|
|
233 | 233 | |
|
|
234 | 234 | return |
|
|
235 | 235 | |
|
|
236 | 236 | def selectChannels(self, channelList): |
|
|
237 | 237 | |
|
|
238 | 238 | channelIndexList = [] |
|
|
239 | 239 | |
|
|
240 | 240 | for channel in channelList: |
|
|
241 | 241 | if channel not in self.dataOut.channelList: |
|
|
242 | 242 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) |
|
|
243 | 243 | |
|
|
244 | 244 | index = self.dataOut.channelList.index(channel) |
|
|
245 | 245 | channelIndexList.append(index) |
|
|
246 | 246 | |
|
|
247 | 247 | self.selectChannelsByIndex(channelIndexList) |
|
|
248 | 248 | |
|
|
249 | 249 | def selectChannelsByIndex(self, channelIndexList): |
|
|
250 | 250 | """ |
|
|
251 | 251 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
252 | 252 | |
|
|
253 | 253 | Input: |
|
|
254 | 254 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
255 | 255 | |
|
|
256 | 256 | Affected: |
|
|
257 | 257 | self.dataOut.data_spc |
|
|
258 | 258 | self.dataOut.channelIndexList |
|
|
259 | 259 | self.dataOut.nChannels |
|
|
260 | 260 | |
|
|
261 | 261 | Return: |
|
|
262 | 262 | None |
|
|
263 | 263 | """ |
|
|
264 | 264 | |
|
|
265 | 265 | for channelIndex in channelIndexList: |
|
|
266 | 266 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
267 | 267 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) |
|
|
268 | 268 | |
|
|
269 | 269 | # nChannels = len(channelIndexList) |
|
|
270 | 270 | |
|
|
271 | 271 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
|
|
272 | 272 | data_dc = self.dataOut.data_dc[channelIndexList,:] |
|
|
273 | 273 | |
|
|
274 | 274 | self.dataOut.data_spc = data_spc |
|
|
275 | 275 | self.dataOut.data_dc = data_dc |
|
|
276 | 276 | |
|
|
277 | 277 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
278 | 278 | # self.dataOut.nChannels = nChannels |
|
|
279 | 279 | |
|
|
280 | 280 | self.__selectPairsByChannel(self.dataOut.channelList) |
|
|
281 | 281 | |
|
|
282 | 282 | return 1 |
|
|
283 | 283 | |
|
|
284 | 284 | |
|
|
285 | 285 | def selectFFTs(self, minFFT, maxFFT ): |
|
|
286 | 286 | """ |
|
|
287 | 287 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
|
|
288 | 288 | minFFT<= FFT <= maxFFT |
|
|
289 | 289 | """ |
|
|
290 | 290 | |
|
|
291 | 291 | if (minFFT > maxFFT): |
|
|
292 | 292 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT) |
|
|
293 | 293 | |
|
|
294 | 294 | if (minFFT < self.dataOut.getFreqRange()[0]): |
|
|
295 | 295 | minFFT = self.dataOut.getFreqRange()[0] |
|
|
296 | 296 | |
|
|
297 | 297 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
|
|
298 | 298 | maxFFT = self.dataOut.getFreqRange()[-1] |
|
|
299 | 299 | |
|
|
300 | 300 | minIndex = 0 |
|
|
301 | 301 | maxIndex = 0 |
|
|
302 | 302 | FFTs = self.dataOut.getFreqRange() |
|
|
303 | 303 | |
|
|
304 | 304 | inda = numpy.where(FFTs >= minFFT) |
|
|
305 | 305 | indb = numpy.where(FFTs <= maxFFT) |
|
|
306 | 306 | |
|
|
307 | 307 | try: |
|
|
308 | 308 | minIndex = inda[0][0] |
|
|
309 | 309 | except: |
|
|
310 | 310 | minIndex = 0 |
|
|
311 | 311 | |
|
|
312 | 312 | try: |
|
|
313 | 313 | maxIndex = indb[0][-1] |
|
|
314 | 314 | except: |
|
|
315 | 315 | maxIndex = len(FFTs) |
|
|
316 | 316 | |
|
|
317 | 317 | self.selectFFTsByIndex(minIndex, maxIndex) |
|
|
318 | 318 | |
|
|
319 | 319 | return 1 |
|
|
320 | 320 | |
|
|
321 | 321 | |
|
|
322 | def setH0(self, h0, deltaHeight = None): | |
|
|
323 | ||
|
|
324 | if not deltaHeight: | |
|
|
325 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] | |
|
|
326 | ||
|
|
327 | nHeights = self.dataOut.nHeights | |
|
|
328 | ||
|
|
329 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight | |
|
|
330 | ||
|
|
331 | self.dataOut.heightList = newHeiRange | |
|
|
332 | ||
|
|
322 | 333 | |
|
|
323 | 334 | def selectHeights(self, minHei, maxHei): |
|
|
324 | 335 | """ |
|
|
325 | 336 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
326 | 337 | minHei <= height <= maxHei |
|
|
327 | 338 | |
|
|
328 | 339 | Input: |
|
|
329 | 340 | minHei : valor minimo de altura a considerar |
|
|
330 | 341 | maxHei : valor maximo de altura a considerar |
|
|
331 | 342 | |
|
|
332 | 343 | Affected: |
|
|
333 | 344 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
334 | 345 | |
|
|
335 | 346 | Return: |
|
|
336 | 347 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
337 | 348 | """ |
|
|
338 | 349 | |
|
|
339 | 350 | |
|
|
340 | 351 | if (minHei > maxHei): |
|
|
341 | 352 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) |
|
|
342 | 353 | |
|
|
343 | 354 | if (minHei < self.dataOut.heightList[0]): |
|
|
344 | 355 | minHei = self.dataOut.heightList[0] |
|
|
345 | 356 | |
|
|
346 | 357 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
347 | 358 | maxHei = self.dataOut.heightList[-1] |
|
|
348 | 359 | |
|
|
349 | 360 | minIndex = 0 |
|
|
350 | 361 | maxIndex = 0 |
|
|
351 | 362 | heights = self.dataOut.heightList |
|
|
352 | 363 | |
|
|
353 | 364 | inda = numpy.where(heights >= minHei) |
|
|
354 | 365 | indb = numpy.where(heights <= maxHei) |
|
|
355 | 366 | |
|
|
356 | 367 | try: |
|
|
357 | 368 | minIndex = inda[0][0] |
|
|
358 | 369 | except: |
|
|
359 | 370 | minIndex = 0 |
|
|
360 | 371 | |
|
|
361 | 372 | try: |
|
|
362 | 373 | maxIndex = indb[0][-1] |
|
|
363 | 374 | except: |
|
|
364 | 375 | maxIndex = len(heights) |
|
|
365 | 376 | |
|
|
366 | 377 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
367 | 378 | |
|
|
368 | 379 | |
|
|
369 | 380 | return 1 |
|
|
370 | 381 | |
|
|
371 | 382 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): |
|
|
372 | 383 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
373 | 384 | |
|
|
374 | 385 | if hei_ref != None: |
|
|
375 | 386 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
376 | 387 | |
|
|
377 | 388 | minIndex = min(newheis[0]) |
|
|
378 | 389 | maxIndex = max(newheis[0]) |
|
|
379 | 390 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
380 | 391 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
381 | 392 | |
|
|
382 | 393 | # determina indices |
|
|
383 | 394 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) |
|
|
384 | 395 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) |
|
|
385 | 396 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
386 | 397 | beacon_heiIndexList = [] |
|
|
387 | 398 | for val in avg_dB.tolist(): |
|
|
388 | 399 | if val >= beacon_dB[0]: |
|
|
389 | 400 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
|
390 | 401 | |
|
|
391 | 402 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
|
|
392 | 403 | data_cspc = None |
|
|
393 | 404 | if self.dataOut.data_cspc is not None: |
|
|
394 | 405 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
395 | 406 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
396 | 407 | |
|
|
397 | 408 | data_dc = None |
|
|
398 | 409 | if self.dataOut.data_dc is not None: |
|
|
399 | 410 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
400 | 411 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
401 | 412 | |
|
|
402 | 413 | self.dataOut.data_spc = data_spc |
|
|
403 | 414 | self.dataOut.data_cspc = data_cspc |
|
|
404 | 415 | self.dataOut.data_dc = data_dc |
|
|
405 | 416 | self.dataOut.heightList = heightList |
|
|
406 | 417 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
407 | 418 | |
|
|
408 | 419 | return 1 |
|
|
409 | 420 | |
|
|
410 | 421 | def selectFFTsByIndex(self, minIndex, maxIndex): |
|
|
411 | 422 | """ |
|
|
412 | 423 | |
|
|
413 | 424 | """ |
|
|
414 | 425 | |
|
|
415 | 426 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
416 | 427 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
417 | 428 | |
|
|
418 | 429 | if (maxIndex >= self.dataOut.nProfiles): |
|
|
419 | 430 | maxIndex = self.dataOut.nProfiles-1 |
|
|
420 | 431 | |
|
|
421 | 432 | #Spectra |
|
|
422 | 433 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
|
|
423 | 434 | |
|
|
424 | 435 | data_cspc = None |
|
|
425 | 436 | if self.dataOut.data_cspc is not None: |
|
|
426 | 437 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
|
|
427 | 438 | |
|
|
428 | 439 | data_dc = None |
|
|
429 | 440 | if self.dataOut.data_dc is not None: |
|
|
430 | 441 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
|
|
431 | 442 | |
|
|
432 | 443 | self.dataOut.data_spc = data_spc |
|
|
433 | 444 | self.dataOut.data_cspc = data_cspc |
|
|
434 | 445 | self.dataOut.data_dc = data_dc |
|
|
435 | 446 | |
|
|
436 | 447 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
|
|
437 | 448 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
|
|
438 | 449 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
|
|
439 | 450 | |
|
|
440 | 451 | #self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
441 | 452 | |
|
|
442 | 453 | return 1 |
|
|
443 | 454 | |
|
|
444 | 455 | |
|
|
445 | 456 | |
|
|
446 | 457 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
447 | 458 | """ |
|
|
448 | 459 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
449 | 460 | minIndex <= index <= maxIndex |
|
|
450 | 461 | |
|
|
451 | 462 | Input: |
|
|
452 | 463 | minIndex : valor de indice minimo de altura a considerar |
|
|
453 | 464 | maxIndex : valor de indice maximo de altura a considerar |
|
|
454 | 465 | |
|
|
455 | 466 | Affected: |
|
|
456 | 467 | self.dataOut.data_spc |
|
|
457 | 468 | self.dataOut.data_cspc |
|
|
458 | 469 | self.dataOut.data_dc |
|
|
459 | 470 | self.dataOut.heightList |
|
|
460 | 471 | |
|
|
461 | 472 | Return: |
|
|
462 | 473 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
463 | 474 | """ |
|
|
464 | 475 | |
|
|
465 | 476 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
466 | 477 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
467 | 478 | |
|
|
468 | 479 | if (maxIndex >= self.dataOut.nHeights): |
|
|
469 | 480 | maxIndex = self.dataOut.nHeights-1 |
|
|
470 | 481 | |
|
|
471 | 482 | #Spectra |
|
|
472 | 483 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
473 | 484 | |
|
|
474 | 485 | data_cspc = None |
|
|
475 | 486 | if self.dataOut.data_cspc is not None: |
|
|
476 | 487 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
477 | 488 | |
|
|
478 | 489 | data_dc = None |
|
|
479 | 490 | if self.dataOut.data_dc is not None: |
|
|
480 | 491 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
481 | 492 | |
|
|
482 | 493 | self.dataOut.data_spc = data_spc |
|
|
483 | 494 | self.dataOut.data_cspc = data_cspc |
|
|
484 | 495 | self.dataOut.data_dc = data_dc |
|
|
485 | 496 | |
|
|
486 | 497 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
487 | 498 | |
|
|
488 | 499 | return 1 |
|
|
489 | 500 | |
|
|
490 | 501 | |
|
|
491 | 502 | def removeDC(self, mode = 2): |
|
|
492 | 503 | jspectra = self.dataOut.data_spc |
|
|
493 | 504 | jcspectra = self.dataOut.data_cspc |
|
|
494 | 505 | |
|
|
495 | 506 | |
|
|
496 | 507 | num_chan = jspectra.shape[0] |
|
|
497 | 508 | num_hei = jspectra.shape[2] |
|
|
498 | 509 | |
|
|
499 | 510 | if jcspectra is not None: |
|
|
500 | 511 | jcspectraExist = True |
|
|
501 | 512 | num_pairs = jcspectra.shape[0] |
|
|
502 | 513 | else: jcspectraExist = False |
|
|
503 | 514 | |
|
|
504 | 515 | freq_dc = jspectra.shape[1]/2 |
|
|
505 | 516 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
506 | 517 | |
|
|
507 | 518 | if ind_vel[0]<0: |
|
|
508 | 519 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
509 | 520 | |
|
|
510 | 521 | if mode == 1: |
|
|
511 | 522 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
512 | 523 | |
|
|
513 | 524 | if jcspectraExist: |
|
|
514 | 525 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 |
|
|
515 | 526 | |
|
|
516 | 527 | if mode == 2: |
|
|
517 | 528 | |
|
|
518 | 529 | vel = numpy.array([-2,-1,1,2]) |
|
|
519 | 530 | xx = numpy.zeros([4,4]) |
|
|
520 | 531 | |
|
|
521 | 532 | for fil in range(4): |
|
|
522 | 533 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
523 | 534 | |
|
|
524 | 535 | xx_inv = numpy.linalg.inv(xx) |
|
|
525 | 536 | xx_aux = xx_inv[0,:] |
|
|
526 | 537 | |
|
|
527 | 538 | for ich in range(num_chan): |
|
|
528 | 539 | yy = jspectra[ich,ind_vel,:] |
|
|
529 | 540 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
530 | 541 | |
|
|
531 | 542 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
532 | 543 | cjunkid = sum(junkid) |
|
|
533 | 544 | |
|
|
534 | 545 | if cjunkid.any(): |
|
|
535 | 546 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
536 | 547 | |
|
|
537 | 548 | if jcspectraExist: |
|
|
538 | 549 | for ip in range(num_pairs): |
|
|
539 | 550 | yy = jcspectra[ip,ind_vel,:] |
|
|
540 | 551 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
541 | 552 | |
|
|
542 | 553 | |
|
|
543 | 554 | self.dataOut.data_spc = jspectra |
|
|
544 | 555 | self.dataOut.data_cspc = jcspectra |
|
|
545 | 556 | |
|
|
546 | 557 | return 1 |
|
|
547 | 558 | |
|
|
548 | 559 | def removeInterference2(self): |
|
|
549 | 560 | |
|
|
550 | 561 | cspc = self.dataOut.data_cspc |
|
|
551 | 562 | spc = self.dataOut.data_spc |
|
|
552 | 563 | print numpy.shape(spc) |
|
|
553 | 564 | Heights = numpy.arange(cspc.shape[2]) |
|
|
554 | 565 | realCspc = numpy.abs(cspc) |
|
|
555 | 566 | |
|
|
556 | 567 | for i in range(cspc.shape[0]): |
|
|
557 | 568 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
|
558 | 569 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
559 | 570 | SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] |
|
|
560 | 571 | #print numpy.shape(realCspc) |
|
|
561 | 572 | #print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights]) |
|
|
562 | 573 | InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) |
|
|
563 | 574 | print SelectedHeights |
|
|
564 | 575 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
565 | 576 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
|
566 | 577 | |
|
|
567 | 578 | |
|
|
568 | 579 | InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
|
569 | 580 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
|
570 | 581 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
|
571 | 582 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
|
572 | 583 | |
|
|
573 | 584 | print '########################################################################################' |
|
|
574 | 585 | print 'Len interference sum',len(InterferenceSum) |
|
|
575 | 586 | print 'InterferenceThresholdMin', InterferenceThresholdMin, 'InterferenceThresholdMax', InterferenceThresholdMax |
|
|
576 | 587 | print 'InterferenceRange',InterferenceRange |
|
|
577 | 588 | print '########################################################################################' |
|
|
578 | 589 | |
|
|
579 | 590 | ''' Ploteo ''' |
|
|
580 | 591 | |
|
|
581 | 592 | #for i in range(3): |
|
|
582 | 593 | #print 'FASE', numpy.shape(phase), y[25] |
|
|
583 | 594 | #print numpy.shape(coherence) |
|
|
584 | 595 | #fig = plt.figure(10+ int(numpy.random.rand()*100)) |
|
|
585 | 596 | #plt.plot( x[0:256],coherence[:,25] ) |
|
|
586 | 597 | #cohAv = numpy.average(coherence[i],1) |
|
|
587 | 598 | #Pendiente = FrecRange * PhaseSlope[i] |
|
|
588 | 599 | #plt.plot( InterferenceSum) |
|
|
589 | 600 | #plt.plot( numpy.sort(InterferenceSum)) |
|
|
590 | 601 | #plt.plot( LinePower ) |
|
|
591 | 602 | #plt.plot( xFrec,phase[i]) |
|
|
592 | 603 | |
|
|
593 | 604 | #CSPCmean = numpy.mean(numpy.abs(CSPCSamples),0) |
|
|
594 | 605 | #plt.plot(xFrec, FitGauss01) |
|
|
595 | 606 | #plt.plot(xFrec, CSPCmean) |
|
|
596 | 607 | #plt.plot(xFrec, numpy.abs(CSPCSamples[0])) |
|
|
597 | 608 | #plt.plot(xFrec, FitGauss) |
|
|
598 | 609 | #plt.plot(xFrec, yMean) |
|
|
599 | 610 | #plt.plot(xFrec, numpy.abs(coherence[0])) |
|
|
600 | 611 | |
|
|
601 | 612 | #plt.axis([-12, 12, 15, 50]) |
|
|
602 | 613 | #plt.title("%s" %( '%s %s, Channel %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S") , i))) |
|
|
603 | 614 | |
|
|
604 | 615 | |
|
|
605 | 616 | #fig.savefig('/home/erick/Documents/Pics/nom{}.png'.format(int(numpy.random.rand()*100))) |
|
|
606 | 617 | |
|
|
607 | 618 | #plt.show() |
|
|
608 | 619 | #self.indice=self.indice+1 |
|
|
609 | 620 | #raise |
|
|
610 | 621 | |
|
|
611 | 622 | |
|
|
612 | 623 | self.dataOut.data_cspc = cspc |
|
|
613 | 624 | |
|
|
614 | 625 | # for i in range(spc.shape[0]): |
|
|
615 | 626 | # LinePower= numpy.sum(spc[i], axis=0) |
|
|
616 | 627 | # Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
617 | 628 | # SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] |
|
|
618 | 629 | # #print numpy.shape(realCspc) |
|
|
619 | 630 | # #print '',SelectedHeights, '', numpy.shape(realCspc[i,:,SelectedHeights]) |
|
|
620 | 631 | # InterferenceSum = numpy.sum( spc[i,:,SelectedHeights], axis=0 ) |
|
|
621 | 632 | # InterferenceThreshold = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
622 | 633 | # InterferenceRange = numpy.where( InterferenceSum > InterferenceThreshold ) |
|
|
623 | 634 | # if len(InterferenceRange)<int(spc.shape[1]*0.03): |
|
|
624 | 635 | # spc[i,InterferenceRange,:] = numpy.NaN |
|
|
625 | 636 | |
|
|
626 | 637 | #self.dataOut.data_spc = spc |
|
|
627 | 638 | |
|
|
628 | 639 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
629 | 640 | |
|
|
630 | 641 | jspectra = self.dataOut.data_spc |
|
|
631 | 642 | jcspectra = self.dataOut.data_cspc |
|
|
632 | 643 | jnoise = self.dataOut.getNoise() |
|
|
633 | 644 | num_incoh = self.dataOut.nIncohInt |
|
|
634 | 645 | |
|
|
635 | 646 | num_channel = jspectra.shape[0] |
|
|
636 | 647 | num_prof = jspectra.shape[1] |
|
|
637 | 648 | num_hei = jspectra.shape[2] |
|
|
638 | 649 | |
|
|
639 | 650 | #hei_interf |
|
|
640 | 651 | if hei_interf is None: |
|
|
641 | 652 | count_hei = num_hei/2 #Como es entero no importa |
|
|
642 | 653 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei |
|
|
643 | 654 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
644 | 655 | #nhei_interf |
|
|
645 | 656 | if (nhei_interf == None): |
|
|
646 | 657 | nhei_interf = 5 |
|
|
647 | 658 | if (nhei_interf < 1): |
|
|
648 | 659 | nhei_interf = 1 |
|
|
649 | 660 | if (nhei_interf > count_hei): |
|
|
650 | 661 | nhei_interf = count_hei |
|
|
651 | 662 | if (offhei_interf == None): |
|
|
652 | 663 | offhei_interf = 0 |
|
|
653 | 664 | |
|
|
654 | 665 | ind_hei = range(num_hei) |
|
|
655 | 666 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
656 | 667 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
657 | 668 | mask_prof = numpy.asarray(range(num_prof)) |
|
|
658 | 669 | num_mask_prof = mask_prof.size |
|
|
659 | 670 | comp_mask_prof = [0, num_prof/2] |
|
|
660 | 671 | |
|
|
661 | 672 | |
|
|
662 | 673 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
663 | 674 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
664 | 675 | jnoise = numpy.nan |
|
|
665 | 676 | noise_exist = jnoise[0] < numpy.Inf |
|
|
666 | 677 | |
|
|
667 | 678 | #Subrutina de Remocion de la Interferencia |
|
|
668 | 679 | for ich in range(num_channel): |
|
|
669 | 680 | #Se ordena los espectros segun su potencia (menor a mayor) |
|
|
670 | 681 | power = jspectra[ich,mask_prof,:] |
|
|
671 | 682 | power = power[:,hei_interf] |
|
|
672 | 683 | power = power.sum(axis = 0) |
|
|
673 | 684 | psort = power.ravel().argsort() |
|
|
674 | 685 | |
|
|
675 | 686 | #Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
676 | 687 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
677 | 688 | |
|
|
678 | 689 | if noise_exist: |
|
|
679 | 690 | # tmp_noise = jnoise[ich] / num_prof |
|
|
680 | 691 | tmp_noise = jnoise[ich] |
|
|
681 | 692 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
682 | 693 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
683 | 694 | |
|
|
684 | 695 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf |
|
|
685 | 696 | jspc_interf = jspc_interf.transpose() |
|
|
686 | 697 | #Calculando el espectro de interferencia promedio |
|
|
687 | 698 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
688 | 699 | noiseid = noiseid[0] |
|
|
689 | 700 | cnoiseid = noiseid.size |
|
|
690 | 701 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
691 | 702 | interfid = interfid[0] |
|
|
692 | 703 | cinterfid = interfid.size |
|
|
693 | 704 | |
|
|
694 | 705 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 |
|
|
695 | 706 | |
|
|
696 | 707 | #Expandiendo los perfiles a limpiar |
|
|
697 | 708 | if (cinterfid > 0): |
|
|
698 | 709 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof |
|
|
699 | 710 | new_interfid = numpy.asarray(new_interfid) |
|
|
700 | 711 | new_interfid = {x for x in new_interfid} |
|
|
701 | 712 | new_interfid = numpy.array(list(new_interfid)) |
|
|
702 | 713 | new_cinterfid = new_interfid.size |
|
|
703 | 714 | else: new_cinterfid = 0 |
|
|
704 | 715 | |
|
|
705 | 716 | for ip in range(new_cinterfid): |
|
|
706 | 717 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() |
|
|
707 | 718 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] |
|
|
708 | 719 | |
|
|
709 | 720 | |
|
|
710 | 721 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices |
|
|
711 | 722 | |
|
|
712 | 723 | #Removiendo la interferencia del punto de mayor interferencia |
|
|
713 | 724 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
714 | 725 | maxid = ListAux.index(max(ListAux)) |
|
|
715 | 726 | |
|
|
716 | 727 | |
|
|
717 | 728 | if cinterfid > 0: |
|
|
718 | 729 | for ip in range(cinterfid*(interf == 2) - 1): |
|
|
719 | 730 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() |
|
|
720 | 731 | cind = len(ind) |
|
|
721 | 732 | |
|
|
722 | 733 | if (cind > 0): |
|
|
723 | 734 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) |
|
|
724 | 735 | |
|
|
725 | 736 | ind = numpy.array([-2,-1,1,2]) |
|
|
726 | 737 | xx = numpy.zeros([4,4]) |
|
|
727 | 738 | |
|
|
728 | 739 | for id1 in range(4): |
|
|
729 | 740 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
730 | 741 | |
|
|
731 | 742 | xx_inv = numpy.linalg.inv(xx) |
|
|
732 | 743 | xx = xx_inv[:,0] |
|
|
733 | 744 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
734 | 745 | yy = jspectra[ich,mask_prof[ind],:] |
|
|
735 | 746 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
736 | 747 | |
|
|
737 | 748 | |
|
|
738 | 749 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() |
|
|
739 | 750 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) |
|
|
740 | 751 | |
|
|
741 | 752 | #Remocion de Interferencia en el Cross Spectra |
|
|
742 | 753 | if jcspectra is None: return jspectra, jcspectra |
|
|
743 | 754 | num_pairs = jcspectra.size/(num_prof*num_hei) |
|
|
744 | 755 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
745 | 756 | |
|
|
746 | 757 | for ip in range(num_pairs): |
|
|
747 | 758 | |
|
|
748 | 759 | #------------------------------------------- |
|
|
749 | 760 | |
|
|
750 | 761 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) |
|
|
751 | 762 | cspower = cspower[:,hei_interf] |
|
|
752 | 763 | cspower = cspower.sum(axis = 0) |
|
|
753 | 764 | |
|
|
754 | 765 | cspsort = cspower.ravel().argsort() |
|
|
755 | 766 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
756 | 767 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
757 | 768 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf |
|
|
758 | 769 | |
|
|
759 | 770 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
760 | 771 | |
|
|
761 | 772 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
762 | 773 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
763 | 774 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) |
|
|
764 | 775 | |
|
|
765 | 776 | for iprof in range(num_prof): |
|
|
766 | 777 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() |
|
|
767 | 778 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] |
|
|
768 | 779 | |
|
|
769 | 780 | #Removiendo la Interferencia |
|
|
770 | 781 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf |
|
|
771 | 782 | |
|
|
772 | 783 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
773 | 784 | maxid = ListAux.index(max(ListAux)) |
|
|
774 | 785 | |
|
|
775 | 786 | ind = numpy.array([-2,-1,1,2]) |
|
|
776 | 787 | xx = numpy.zeros([4,4]) |
|
|
777 | 788 | |
|
|
778 | 789 | for id1 in range(4): |
|
|
779 | 790 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
780 | 791 | |
|
|
781 | 792 | xx_inv = numpy.linalg.inv(xx) |
|
|
782 | 793 | xx = xx_inv[:,0] |
|
|
783 | 794 | |
|
|
784 | 795 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
785 | 796 | yy = jcspectra[ip,mask_prof[ind],:] |
|
|
786 | 797 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
787 | 798 | |
|
|
788 | 799 | #Guardar Resultados |
|
|
789 | 800 | self.dataOut.data_spc = jspectra |
|
|
790 | 801 | self.dataOut.data_cspc = jcspectra |
|
|
791 | 802 | |
|
|
792 | 803 | return 1 |
|
|
793 | 804 | |
|
|
794 | 805 | def setRadarFrequency(self, frequency=None): |
|
|
795 | 806 | |
|
|
796 | 807 | if frequency != None: |
|
|
797 | 808 | self.dataOut.frequency = frequency |
|
|
798 | 809 | |
|
|
799 | 810 | return 1 |
|
|
800 | 811 | |
|
|
801 | 812 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
802 | 813 | #validacion de rango |
|
|
803 | 814 | if minHei == None: |
|
|
804 | 815 | minHei = self.dataOut.heightList[0] |
|
|
805 | 816 | |
|
|
806 | 817 | if maxHei == None: |
|
|
807 | 818 | maxHei = self.dataOut.heightList[-1] |
|
|
808 | 819 | |
|
|
809 | 820 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
810 | 821 | print 'minHei: %.2f is out of the heights range'%(minHei) |
|
|
811 | 822 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) |
|
|
812 | 823 | minHei = self.dataOut.heightList[0] |
|
|
813 | 824 | |
|
|
814 | 825 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
815 | 826 | print 'maxHei: %.2f is out of the heights range'%(maxHei) |
|
|
816 | 827 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) |
|
|
817 | 828 | maxHei = self.dataOut.heightList[-1] |
|
|
818 | 829 | |
|
|
819 | 830 | # validacion de velocidades |
|
|
820 | 831 | velrange = self.dataOut.getVelRange(1) |
|
|
821 | 832 | |
|
|
822 | 833 | if minVel == None: |
|
|
823 | 834 | minVel = velrange[0] |
|
|
824 | 835 | |
|
|
825 | 836 | if maxVel == None: |
|
|
826 | 837 | maxVel = velrange[-1] |
|
|
827 | 838 | |
|
|
828 | 839 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
829 | 840 | print 'minVel: %.2f is out of the velocity range'%(minVel) |
|
|
830 | 841 | print 'minVel is setting to %.2f'%(velrange[0]) |
|
|
831 | 842 | minVel = velrange[0] |
|
|
832 | 843 | |
|
|
833 | 844 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
834 | 845 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) |
|
|
835 | 846 | print 'maxVel is setting to %.2f'%(velrange[-1]) |
|
|
836 | 847 | maxVel = velrange[-1] |
|
|
837 | 848 | |
|
|
838 | 849 | # seleccion de indices para rango |
|
|
839 | 850 | minIndex = 0 |
|
|
840 | 851 | maxIndex = 0 |
|
|
841 | 852 | heights = self.dataOut.heightList |
|
|
842 | 853 | |
|
|
843 | 854 | inda = numpy.where(heights >= minHei) |
|
|
844 | 855 | indb = numpy.where(heights <= maxHei) |
|
|
845 | 856 | |
|
|
846 | 857 | try: |
|
|
847 | 858 | minIndex = inda[0][0] |
|
|
848 | 859 | except: |
|
|
849 | 860 | minIndex = 0 |
|
|
850 | 861 | |
|
|
851 | 862 | try: |
|
|
852 | 863 | maxIndex = indb[0][-1] |
|
|
853 | 864 | except: |
|
|
854 | 865 | maxIndex = len(heights) |
|
|
855 | 866 | |
|
|
856 | 867 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
857 | 868 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
858 | 869 | |
|
|
859 | 870 | if (maxIndex >= self.dataOut.nHeights): |
|
|
860 | 871 | maxIndex = self.dataOut.nHeights-1 |
|
|
861 | 872 | |
|
|
862 | 873 | # seleccion de indices para velocidades |
|
|
863 | 874 | indminvel = numpy.where(velrange >= minVel) |
|
|
864 | 875 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
865 | 876 | try: |
|
|
866 | 877 | minIndexVel = indminvel[0][0] |
|
|
867 | 878 | except: |
|
|
868 | 879 | minIndexVel = 0 |
|
|
869 | 880 | |
|
|
870 | 881 | try: |
|
|
871 | 882 | maxIndexVel = indmaxvel[0][-1] |
|
|
872 | 883 | except: |
|
|
873 | 884 | maxIndexVel = len(velrange) |
|
|
874 | 885 | |
|
|
875 | 886 | #seleccion del espectro |
|
|
876 | 887 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] |
|
|
877 | 888 | #estimacion de ruido |
|
|
878 | 889 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
879 | 890 | |
|
|
880 | 891 | for channel in range(self.dataOut.nChannels): |
|
|
881 | 892 | daux = data_spc[channel,:,:] |
|
|
882 | 893 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) |
|
|
883 | 894 | |
|
|
884 | 895 | self.dataOut.noise_estimation = noise.copy() |
|
|
885 | 896 | |
|
|
886 | 897 | return 1 |
|
|
887 | 898 | |
|
|
888 | 899 | class IncohInt(Operation): |
|
|
889 | 900 | |
|
|
890 | 901 | |
|
|
891 | 902 | __profIndex = 0 |
|
|
892 | 903 | __withOverapping = False |
|
|
893 | 904 | |
|
|
894 | 905 | __byTime = False |
|
|
895 | 906 | __initime = None |
|
|
896 | 907 | __lastdatatime = None |
|
|
897 | 908 | __integrationtime = None |
|
|
898 | 909 | |
|
|
899 | 910 | __buffer_spc = None |
|
|
900 | 911 | __buffer_cspc = None |
|
|
901 | 912 | __buffer_dc = None |
|
|
902 | 913 | |
|
|
903 | 914 | __dataReady = False |
|
|
904 | 915 | |
|
|
905 | 916 | __timeInterval = None |
|
|
906 | 917 | |
|
|
907 | 918 | n = None |
|
|
908 | 919 | |
|
|
909 | 920 | |
|
|
910 | 921 | |
|
|
911 | 922 | def __init__(self, **kwargs): |
|
|
912 | 923 | |
|
|
913 | 924 | Operation.__init__(self, **kwargs) |
|
|
914 | 925 | # self.isConfig = False |
|
|
915 | 926 | |
|
|
916 | 927 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
917 | 928 | """ |
|
|
918 | 929 | Set the parameters of the integration class. |
|
|
919 | 930 | |
|
|
920 | 931 | Inputs: |
|
|
921 | 932 | |
|
|
922 | 933 | n : Number of coherent integrations |
|
|
923 | 934 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
924 | 935 | overlapping : |
|
|
925 | 936 | |
|
|
926 | 937 | """ |
|
|
927 | 938 | |
|
|
928 | 939 | self.__initime = None |
|
|
929 | 940 | self.__lastdatatime = 0 |
|
|
930 | 941 | |
|
|
931 | 942 | self.__buffer_spc = 0 |
|
|
932 | 943 | self.__buffer_cspc = 0 |
|
|
933 | 944 | self.__buffer_dc = 0 |
|
|
934 | 945 | |
|
|
935 | 946 | self.__profIndex = 0 |
|
|
936 | 947 | self.__dataReady = False |
|
|
937 | 948 | self.__byTime = False |
|
|
938 | 949 | |
|
|
939 | 950 | if n is None and timeInterval is None: |
|
|
940 | 951 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
941 | 952 | |
|
|
942 | 953 | if n is not None: |
|
|
943 | 954 | self.n = int(n) |
|
|
944 | 955 | else: |
|
|
945 | 956 | self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line |
|
|
946 | 957 | self.n = None |
|
|
947 | 958 | self.__byTime = True |
|
|
948 | 959 | |
|
|
949 | 960 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
950 | 961 | |
|
|
951 | 962 | """ |
|
|
952 | 963 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
953 | 964 | |
|
|
954 | 965 | """ |
|
|
955 | 966 | |
|
|
956 | 967 | self.__buffer_spc += data_spc |
|
|
957 | 968 | |
|
|
958 | 969 | if data_cspc is None: |
|
|
959 | 970 | self.__buffer_cspc = None |
|
|
960 | 971 | else: |
|
|
961 | 972 | self.__buffer_cspc += data_cspc |
|
|
962 | 973 | |
|
|
963 | 974 | if data_dc is None: |
|
|
964 | 975 | self.__buffer_dc = None |
|
|
965 | 976 | else: |
|
|
966 | 977 | self.__buffer_dc += data_dc |
|
|
967 | 978 | |
|
|
968 | 979 | self.__profIndex += 1 |
|
|
969 | 980 | |
|
|
970 | 981 | return |
|
|
971 | 982 | |
|
|
972 | 983 | def pushData(self): |
|
|
973 | 984 | """ |
|
|
974 | 985 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
975 | 986 | |
|
|
976 | 987 | Affected: |
|
|
977 | 988 | |
|
|
978 | 989 | self.__profileIndex |
|
|
979 | 990 | |
|
|
980 | 991 | """ |
|
|
981 | 992 | |
|
|
982 | 993 | data_spc = self.__buffer_spc |
|
|
983 | 994 | data_cspc = self.__buffer_cspc |
|
|
984 | 995 | data_dc = self.__buffer_dc |
|
|
985 | 996 | n = self.__profIndex |
|
|
986 | 997 | |
|
|
987 | 998 | self.__buffer_spc = 0 |
|
|
988 | 999 | self.__buffer_cspc = 0 |
|
|
989 | 1000 | self.__buffer_dc = 0 |
|
|
990 | 1001 | self.__profIndex = 0 |
|
|
991 | 1002 | |
|
|
992 | 1003 | return data_spc, data_cspc, data_dc, n |
|
|
993 | 1004 | |
|
|
994 | 1005 | def byProfiles(self, *args): |
|
|
995 | 1006 | |
|
|
996 | 1007 | self.__dataReady = False |
|
|
997 | 1008 | avgdata_spc = None |
|
|
998 | 1009 | avgdata_cspc = None |
|
|
999 | 1010 | avgdata_dc = None |
|
|
1000 | 1011 | |
|
|
1001 | 1012 | self.putData(*args) |
|
|
1002 | 1013 | |
|
|
1003 | 1014 | if self.__profIndex == self.n: |
|
|
1004 | 1015 | |
|
|
1005 | 1016 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1006 | 1017 | self.n = n |
|
|
1007 | 1018 | self.__dataReady = True |
|
|
1008 | 1019 | |
|
|
1009 | 1020 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1010 | 1021 | |
|
|
1011 | 1022 | def byTime(self, datatime, *args): |
|
|
1012 | 1023 | |
|
|
1013 | 1024 | self.__dataReady = False |
|
|
1014 | 1025 | avgdata_spc = None |
|
|
1015 | 1026 | avgdata_cspc = None |
|
|
1016 | 1027 | avgdata_dc = None |
|
|
1017 | 1028 | |
|
|
1018 | 1029 | self.putData(*args) |
|
|
1019 | 1030 | |
|
|
1020 | 1031 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1021 | 1032 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1022 | 1033 | self.n = n |
|
|
1023 | 1034 | self.__dataReady = True |
|
|
1024 | 1035 | |
|
|
1025 | 1036 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1026 | 1037 | |
|
|
1027 | 1038 | def integrate(self, datatime, *args): |
|
|
1028 | 1039 | |
|
|
1029 | 1040 | if self.__profIndex == 0: |
|
|
1030 | 1041 | self.__initime = datatime |
|
|
1031 | 1042 | |
|
|
1032 | 1043 | if self.__byTime: |
|
|
1033 | 1044 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args) |
|
|
1034 | 1045 | else: |
|
|
1035 | 1046 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1036 | 1047 | |
|
|
1037 | 1048 | if not self.__dataReady: |
|
|
1038 | 1049 | return None, None, None, None |
|
|
1039 | 1050 | |
|
|
1040 | 1051 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1041 | 1052 | |
|
|
1042 | 1053 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
1043 | 1054 | |
|
|
1044 | 1055 | if n==1: |
|
|
1045 | 1056 | return |
|
|
1046 | 1057 | |
|
|
1047 | 1058 | dataOut.flagNoData = True |
|
|
1048 | 1059 | |
|
|
1049 | 1060 | if not self.isConfig: |
|
|
1050 | 1061 | self.setup(n, timeInterval, overlapping) |
|
|
1051 | 1062 | self.isConfig = True |
|
|
1052 | 1063 | |
|
|
1053 | 1064 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
1054 | 1065 | dataOut.data_spc, |
|
|
1055 | 1066 | dataOut.data_cspc, |
|
|
1056 | 1067 | dataOut.data_dc) |
|
|
1057 | 1068 | |
|
|
1058 | 1069 | if self.__dataReady: |
|
|
1059 | 1070 | |
|
|
1060 | 1071 | dataOut.data_spc = avgdata_spc |
|
|
1061 | 1072 | dataOut.data_cspc = avgdata_cspc |
|
|
1062 | 1073 | dataOut.data_dc = avgdata_dc |
|
|
1063 | 1074 | |
|
|
1064 | 1075 | dataOut.nIncohInt *= self.n |
|
|
1065 | 1076 | dataOut.utctime = avgdatatime |
|
|
1066 | 1077 | dataOut.flagNoData = False |
|
|
1067 | 1078 | |
| @@ -1,1 +1,1 | |||
|
|
1 | <Project description="Segundo Test" id="191" name="test01"><ReadUnit datatype="SpectraReader" id="1911" inputId="0" name="SpectraReader"><Operation id="19111" name="run" priority="1" type="self"><Parameter format="str" id="191111" name="datatype" value="SpectraReader" /><Parameter format="str" id="191112" name="path" value="/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/2018" /><Parameter format="date" id="191113" name="startDate" value="2018/01/26" /><Parameter format="date" id="191114" name="endDate" value="2018/01/26" /><Parameter format="time" id="191115" name="startTime" value="17:45:00" /><Parameter format="time" id="191116" name="endTime" value="23:59:00" /><Parameter format="int" id="191118" name="online" value="0" /><Parameter format="int" id="191119" name="walk" value="1" /></Operation><Operation id="19112" name="printInfo" priority="2" type="self" /><Operation id="19113" name="printNumberOfBlock" priority="3" type="self" /></ReadUnit><ProcUnit datatype="Parameters" id="1913" inputId="1912" name="ParametersProc"><Operation id="19131" name="run" priority="1" type="self" /><Operation id="19132" name="SpectralFilters" priority="2" type="other"><Parameter format="float" id="191321" name="PositiveLimit" value="1.5" /><Parameter format="float" id="191322" name="NegativeLimit" value="12.5" /></Operation><Operation id="19133" name="PrecipitationProc" priority="3" type="other" /><Operation id="19134" name="ParametersPlot" priority="4" type="other"><Parameter format="int" id="191341" name="id" value="10" /><Parameter format="str" id="191342" name="wintitle" value="First_gg" /><Parameter format="str" id="191343" name="colormap" value="ocean_r" /><Parameter format="int" id="191344" name="zmin" value="00" /><Parameter format="int" id="191345" name="zmax" value="40" /><Parameter format="int" id="191346" name="ymin" value="0" /><Parameter format="int" id="191347" name="ymax" value="11" /><Parameter format="int" id="191348" name="xmin" value="17" /><Parameter format="int" id="191349" name="xmax" value="24" /><Parameter format="int" id="191350" name="save" value="1" /><Parameter format="str" id="191351" name="figpath" value="/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/2018" /></Operation><Operation id="19135" name="SpcParamPlot" priority="5" type="other"><Parameter format="int" id="191351" name="id" value="21" /><Parameter format="str" id="191352" name="wintitle" value="Primer eco removido" /><Parameter format="str" id="191353" name="xaxis" value="velocity" /><Parameter format="int" id="191354" name="showprofile" value="1" /><Parameter format="int" id="191355" name="zmin" value="10" /><Parameter format="int" id="191356" name="zmax" value="40" /><Parameter format="int" id="191357" name="ymin" value="0" /><Parameter format="int" id="191358" name="ymax" value="10" /><Parameter format="int" id="191359" name="Selector" value="1" /></Operation></ProcUnit><ProcUnit datatype="SpectraProc" id="1912" inputId="1911" name="SpectraProc"><Operation id="19121" name="run" priority="1" type="self" /><Operation id="19122" name="setRadarFrequency" priority="2" type="self"><Parameter format="float" id="191221" name="frequency" value="445.09e6" /></Operation></ProcUnit></Project> No newline at end of file | |
|
|
1 | <Project description="Segundo Test" id="191" name="test01"><ReadUnit datatype="SpectraReader" id="1911" inputId="0" name="SpectraReader"><Operation id="19111" name="run" priority="1" type="self"><Parameter format="str" id="191111" name="datatype" value="SpectraReader" /><Parameter format="str" id="191112" name="path" value="/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/Extra" /><Parameter format="date" id="191113" name="startDate" value="2018/02/23" /><Parameter format="date" id="191114" name="endDate" value="2018/02/23" /><Parameter format="time" id="191115" name="startTime" value="00:00:00" /><Parameter format="time" id="191116" name="endTime" value="03:59:50" /><Parameter format="int" id="191118" name="online" value="0" /><Parameter format="int" id="191119" name="walk" value="1" /></Operation><Operation id="19112" name="printInfo" priority="2" type="self" /><Operation id="19113" name="printNumberOfBlock" priority="3" type="self" /></ReadUnit><ProcUnit datatype="Parameters" id="1913" inputId="1912" name="ParametersProc"><Operation id="19131" name="run" priority="1" type="self" /><Operation id="19132" name="SpectralFilters" priority="2" type="other"><Parameter format="float" id="191321" name="PositiveLimit" value="1.5" /><Parameter format="float" id="191322" name="NegativeLimit" value="12.5" /></Operation><Operation id="19133" name="FullSpectralAnalysis" priority="3" type="other"><Parameter format="float" id="191331" name="SNRlimit" value="-16" /><Parameter format="float" id="191332" name="Xi01" value="1.500" /><Parameter format="float" id="191333" name="Xi02" value="1.500" /><Parameter format="float" id="191334" name="Xi12" value="0" /><Parameter format="float" id="191335" name="Eta01" value="0.875" /><Parameter format="float" id="191336" name="Eta02" value="-0.875" /><Parameter format="float" id="191337" name="Eta12" value="-1.750" /></Operation><Operation id="19134" name="WindProfilerPlot" priority="4" type="other"><Parameter format="int" id="191341" name="id" value="4" /><Parameter format="str" id="191342" name="wintitle" value="Wind Profiler" /><Parameter format="float" id="191343" name="xmin" value="0" /><Parameter format="float" id="191344" name="xmax" value="4" /><Parameter format="float" id="191345" name="ymin" value="0" /><Parameter format="int" id="191346" name="ymax" value="4" /><Parameter format="float" id="191347" name="zmin" value="-20" /><Parameter format="float" id="191348" name="zmax" value="20" /><Parameter format="float" id="191349" name="SNRmin" value="-20" /><Parameter format="float" id="191350" name="SNRmax" value="20" /><Parameter format="float" id="191351" name="zmin_ver" value="-200" /><Parameter format="float" id="191352" name="zmax_ver" value="200" /><Parameter format="float" id="191353" name="SNRthresh" value="-20" /><Parameter format="int" id="191354" name="save" value="1" /><Parameter format="str" id="191355" name="figpath" value="/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/ImagenesTesis" /></Operation><Operation id="19135" name="ParamWriter" priority="5" type="other"><Parameter format="str" id="191351" name="path" value="/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/ImagenesTesis" /><Parameter format="int" id="191352" name="blocksPerFile" value="500" /><Parameter format="list" id="191353" name="metadataList" value="heightList,timeZone,paramInterval" /><Parameter format="list" id="191354" name="dataList" value="data_output,utctime,utctimeInit" /></Operation></ProcUnit><ProcUnit datatype="SpectraProc" id="1912" inputId="1911" name="SpectraProc"><Operation id="19121" name="run" priority="1" type="self" /><Operation id="19122" name="setRadarFrequency" priority="2" type="self"><Parameter format="float" id="191221" name="frequency" value="445.09e6" /></Operation><Operation id="19123" name="setH0" priority="3" type="self"><Parameter format="float" id="191231" name="h0" value="0.500" /></Operation></ProcUnit></Project> No newline at end of file | |
| @@ -1,151 +1,152 | |||
|
|
1 | 1 | #!/usr/bin/env python |
|
|
2 | 2 | import os, sys |
|
|
3 | 3 | |
|
|
4 | 4 | # path = os.path.dirname(os.getcwd()) |
|
|
5 | 5 | # path = os.path.join(path, 'source') |
|
|
6 | 6 | # sys.path.insert(0, '../') |
|
|
7 | 7 | |
|
|
8 | 8 | from schainpy.controller import Project |
|
|
9 | 9 | |
|
|
10 | 10 | xmin = '15.5' |
|
|
11 | 11 | xmax = '24' |
|
|
12 | 12 | |
|
|
13 | 13 | |
|
|
14 | 14 | desc = "ProcBLTR Test" |
|
|
15 | 15 | filename = "ProcBLTR.xml" |
|
|
16 | figpath = '/media/erick/6F60F7113095A154/BLTR' | |
|
|
16 | figpath = '/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/2018' | |
|
|
17 | 17 | |
|
|
18 | 18 | controllerObj = Project() |
|
|
19 | 19 | |
|
|
20 | 20 | |
|
|
21 | 21 | controllerObj.setup(id='191', name='test01', description=desc) |
|
|
22 | 22 | |
|
|
23 | 23 | readUnitConfObj = controllerObj.addReadUnit(datatype='BLTRSpectraReader', |
|
|
24 | 24 | #path='/media/erick/6F60F7113095A154/BLTR/', |
|
|
25 | #path='/data/BLTR', | |
|
|
25 | 26 | path='/home/erick/Documents/Data/BLTR_Data/fdt/', |
|
|
26 |
endDate='201 |
|
|
|
27 |
startTime=' |
|
|
|
28 |
startDate='2016/11/ |
|
|
|
27 | endDate='2016/11/01', | |
|
|
28 | startTime='0:00:00', | |
|
|
29 | startDate='2016/11/01', | |
|
|
29 | 30 | endTime='23:59:59', |
|
|
30 | 31 | |
|
|
31 | 32 | |
|
|
32 | 33 | online=0, |
|
|
33 | 34 | walk=0, |
|
|
34 | 35 | ReadMode='1') |
|
|
35 | 36 | # expLabel='') |
|
|
36 | 37 | |
|
|
37 | 38 | # opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') |
|
|
38 | 39 | |
|
|
39 | 40 | procUnitConfObj1 = controllerObj.addProcUnit(datatype='Spectra', inputId=readUnitConfObj.getId()) |
|
|
40 | 41 | |
|
|
41 | 42 | |
|
|
42 | 43 | opObj11 = procUnitConfObj1.addOperation(name='IncohInt', optype='other') |
|
|
43 | 44 | opObj11.addParameter(name='n', value='2', format='float') |
|
|
44 | 45 | |
|
|
45 | 46 | opObj10 = procUnitConfObj1.addOperation(name='removeDC') |
|
|
46 | 47 | #opObj10 = procUnitConfObj1.addOperation(name='removeInterference2') |
|
|
47 | 48 | |
|
|
48 | 49 | # opObj10 = procUnitConfObj1.addOperation(name='calcMag') |
|
|
49 | 50 | |
|
|
50 | 51 | # opObj11 = procUnitConfObj1.addOperation(name='SpectraPlot', optype='other') |
|
|
51 | 52 | # opObj11.addParameter(name='id', value='21', format='int') |
|
|
52 | 53 | # opObj11.addParameter(name='wintitle', value='SpectraCutPlot', format='str') |
|
|
53 | 54 | # opObj11.addParameter(name='xaxis', value='frequency', format='str') |
|
|
54 | 55 | # opObj11.addParameter(name='colormap', value='winter', format='str') |
|
|
55 | 56 | # opObj11.addParameter(name='xmin', value='-0.005', format='float') |
|
|
56 | 57 | # opObj11.addParameter(name='xmax', value='0.005', format='float') |
|
|
57 | 58 | # #opObj10 = procUnitConfObj1.addOperation(name='selectChannels') |
|
|
58 | 59 | # #opObj10.addParameter(name='channelList', value='0,1', format='intlist') |
|
|
59 | 60 | # opObj11 = procUnitConfObj1.addOperation(name='SpectraPlot', optype='other') |
|
|
60 | 61 | # opObj11.addParameter(name='id', value='21', format='int') |
|
|
61 | 62 | # opObj11.addParameter(name='wintitle', value='SpectraPlot', format='str') |
|
|
62 | 63 | # #opObj11.addParameter(name='xaxis', value='Velocity', format='str') |
|
|
63 | 64 | |
|
|
64 | 65 | # opObj11.addParameter(name='xaxis', value='velocity', format='str') |
|
|
65 | 66 | # opObj11.addParameter(name='xmin', value='-0.005', format='float') |
|
|
66 | 67 | # opObj11.addParameter(name='xmax', value='0.005', format='float') |
|
|
67 | 68 | |
|
|
68 | 69 | # opObj11.addParameter(name='ymin', value='225', format='float') |
|
|
69 | 70 | # opObj11.addParameter(name='ymax', value='3000', format='float') |
|
|
70 | 71 | # opObj11.addParameter(name='zmin', value='-100', format='int') |
|
|
71 | 72 | # opObj11.addParameter(name='zmax', value='-65', format='int') |
|
|
72 | 73 | |
|
|
73 | 74 | # opObj11 = procUnitConfObj1.addOperation(name='RTIPlot', optype='other') |
|
|
74 | 75 | # opObj11.addParameter(name='id', value='10', format='int') |
|
|
75 | 76 | # opObj11.addParameter(name='wintitle', value='RTI', format='str') |
|
|
76 | 77 | # opObj11.addParameter(name='ymin', value='0', format='float') |
|
|
77 | 78 | # opObj11.addParameter(name='ymax', value='4000', format='float') |
|
|
78 | 79 | # #opObj11.addParameter(name='zmin', value='-100', format='int') |
|
|
79 | 80 | # #opObj11.addParameter(name='zmax', value='-70', format='int') |
|
|
80 | 81 | # opObj11.addParameter(name='zmin', value='-90', format='int') |
|
|
81 | 82 | # opObj11.addParameter(name='zmax', value='-40', format='int') |
|
|
82 | 83 | # opObj11.addParameter(name='showprofile', value='1', format='int') |
|
|
83 | 84 | # opObj11.addParameter(name='timerange', value=str(2*60*60), format='int') |
|
|
84 | 85 | |
|
|
85 | opObj11 = procUnitConfObj1.addOperation(name='CrossSpectraPlot', optype='other') | |
|
|
86 | procUnitConfObj1.addParameter(name='pairsList', value='(0,1),(0,2),(1,2)', format='pairsList') | |
|
|
87 | opObj11.addParameter(name='id', value='2005', format='int') | |
|
|
88 | opObj11.addParameter(name='wintitle', value='CrossSpectraPlot_ShortPulse', format='str') | |
|
|
89 | # opObj11.addParameter(name='exp_code', value='13', format='int') | |
|
|
90 | opObj11.addParameter(name='xaxis', value='Velocity', format='str') | |
|
|
86 | # opObj11 = procUnitConfObj1.addOperation(name='CrossSpectraPlot', optype='other') | |
|
|
87 | # procUnitConfObj1.addParameter(name='pairsList', value='(0,1),(0,2),(1,2)', format='pairsList') | |
|
|
88 | # opObj11.addParameter(name='id', value='2005', format='int') | |
|
|
89 | # opObj11.addParameter(name='wintitle', value='CrossSpectraPlot_ShortPulse', format='str') | |
|
|
90 | # # opObj11.addParameter(name='exp_code', value='13', format='int') | |
|
|
91 | # opObj11.addParameter(name='xaxis', value='Velocity', format='str') | |
|
|
91 | 92 | #opObj11.addParameter(name='xmin', value='-10', format='float') |
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92 | 93 | #opObj11.addParameter(name='xmax', value='10', format='float') |
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93 | 94 | #opObj11.addParameter(name='ymin', value='225', format='float') |
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94 | 95 | #opObj11.addParameter(name='ymax', value='3000', format='float') |
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95 | 96 | #opObj11.addParameter(name='phase_min', value='-4', format='int') |
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96 | 97 | #opObj11.addParameter(name='phase_max', value='4', format='int') |
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97 | 98 | |
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98 | 99 | # procUnitConfObj2 = controllerObj.addProcUnit(datatype='CorrelationProc', inputId=procUnitConfObj1.getId()) |
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99 | 100 | # procUnitConfObj2.addParameter(name='pairsList', value='(0,1),(0,2),(1,2)', format='pairsList') |
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100 | 101 | |
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101 | 102 | procUnitConfObj2 = controllerObj.addProcUnit(datatype='Parameters', inputId=procUnitConfObj1.getId()) |
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102 | opObj11 = procUnitConfObj2.addOperation(name='SpectralMoments', optype='other') | |
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103 | #opObj11 = procUnitConfObj2.addOperation(name='SpectralMoments', optype='other') | |
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103 | 104 | opObj22 = procUnitConfObj2.addOperation(name='FullSpectralAnalysis', optype='other') |
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104 |
opObj22.addParameter(name='SNRlimit', value=' |
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105 | opObj22.addParameter(name='SNRlimit', value='2', format='float') | |
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105 | 106 | |
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106 | 107 | opObj22 = procUnitConfObj2.addOperation(name='WindProfilerPlot', optype='other') |
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107 | 108 | opObj22.addParameter(name='id', value='4', format='int') |
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108 | 109 | opObj22.addParameter(name='wintitle', value='Wind Profiler', format='str') |
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109 | opObj22.addParameter(name='save', value='1', format='bool') | |
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110 | #opObj22.addParameter(name='save', value='1', format='bool') | |
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110 | 111 | # opObj22.addParameter(name='figpath', value = '/home/erick/Pictures', format='str') |
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111 | 112 | |
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112 | 113 | opObj22.addParameter(name='zmin', value='-20', format='int') |
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113 | 114 | opObj22.addParameter(name='zmax', value='20', format='int') |
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114 | 115 | opObj22.addParameter(name='zmin_ver', value='-300', format='float') |
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115 | 116 | opObj22.addParameter(name='zmax_ver', value='300', format='float') |
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116 | 117 | opObj22.addParameter(name='SNRmin', value='-5', format='int') |
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117 | 118 | opObj22.addParameter(name='SNRmax', value='30', format='int') |
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118 | 119 | # opObj22.addParameter(name='SNRthresh', value='-3.5', format='float') |
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119 | 120 | opObj22.addParameter(name='xmin', value='0', format='float') |
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120 | 121 | opObj22.addParameter(name='xmax', value='24', format='float') |
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121 | 122 | opObj22.addParameter(name='ymin', value='225', format='float') |
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122 | 123 | #opObj22.addParameter(name='ymax', value='2000', format='float') |
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123 | 124 | opObj22.addParameter(name='save', value='1', format='int') |
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124 | 125 | opObj22.addParameter(name='figpath', value=figpath, format='str') |
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125 | 126 | |
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126 | 127 | |
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127 | 128 | # opObj11.addParameter(name='pairlist', value='(1,0),(0,2),(1,2)', format='pairsList') |
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128 | 129 | #opObj10 = procUnitConfObj1.addOperation(name='selectHeights') |
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129 | 130 | #opObj10.addParameter(name='minHei', value='225', format='float') |
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130 | 131 | #opObj10.addParameter(name='maxHei', value='1000', format='float') |
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131 | 132 | |
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132 | 133 | # opObj11 = procUnitConfObj1.addOperation(name='CoherenceMap', optype='other') |
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133 | 134 | # opObj11.addParameter(name='id', value='102', format='int') |
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134 | 135 | # opObj11.addParameter(name='wintitle', value='Coherence', format='str') |
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135 | 136 | # opObj11.addParameter(name='ymin', value='225', format='float') |
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136 | 137 | # opObj11.addParameter(name='ymax', value='4000', format='float') |
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137 | 138 | |
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138 | 139 | # opObj11.addParameter(name='phase_cmap', value='jet', format='str') |
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139 | 140 | # opObj11.addParameter(name='xmin', value='8.5', format='float') |
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140 | 141 | # opObj11.addParameter(name='xmax', value='9.5', format='float') |
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141 | 142 | # opObj11.addParameter(name='figpath', value=figpath, format='str') |
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142 | 143 | # opObj11.addParameter(name='save', value=1, format='bool') |
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143 | 144 | # opObj11.addParameter(name='pairsList', value='(1,0),(3,2)', format='pairsList') |
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144 | 145 | |
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145 | 146 | # opObj12 = procUnitConfObj1.addOperation(name='PublishData', optype='other') |
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146 | 147 | # opObj12.addParameter(name='zeromq', value=1, format='int') |
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147 | 148 | # opObj12.addParameter(name='verbose', value=0, format='bool') |
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148 | 149 | # opObj12.addParameter(name='server', value='erick2', format='str') |
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149 | 150 | controllerObj.start() |
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150 | 151 | |
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151 | 152 | |
| @@ -1,239 +1,239 | |||
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1 | 1 | import os, sys |
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2 | 2 | |
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3 | 3 | from schainpy.controller import Project |
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4 | 4 | |
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5 | 5 | if __name__ == '__main__': |
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6 | 6 | |
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7 | 7 | desc = "Segundo Test" |
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8 | 8 | filename = "schain.xml" |
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9 | 9 | |
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10 |
pathW='/ |
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|
|
11 |
figpath = '/ |
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|
|
10 | pathW='/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/Extra' | |
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11 | figpath = '/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA/pdataCLAIRE/Extra' | |
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12 | 12 | |
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13 | 13 | controllerObj = Project() |
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14 | 14 | |
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15 | 15 | controllerObj.setup(id='191', name='test01', description=desc) |
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16 | 16 | |
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17 | 17 | readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader', |
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18 |
path='/ |
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18 | path='/data/CLAIRE/CLAIRE_WINDS_2MHZ/DATA', | |
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19 | 19 | #path='/home/erick/Documents/Data/Claire_Data/raw', |
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20 |
startDate='2018/02/ |
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21 |
endDate='2018/02/ |
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|
22 |
startTime=' |
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|
23 |
endTime='2 |
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|
20 | startDate='2018/02/22', | |
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21 | endDate='2018/02/24', | |
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22 | startTime='00:00:00', | |
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23 | endTime='23:59:00', | |
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24 | 24 | online=0, |
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25 | 25 | walk=1) |
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26 | 26 | |
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27 | 27 | opObj00 = readUnitConfObj.addOperation(name='printInfo') |
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28 | 28 | # |
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29 | 29 | # procUnitConfObj0 = controllerObj.addProcUnit(datatype='VoltageProc', |
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30 | 30 | # inputId=readUnitConfObj.getId()) |
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31 | 31 | # |
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32 | 32 | # opObj10 = procUnitConfObj0.addOperation(name='selectHeights') |
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33 | 33 | # opObj10.addParameter(name='minHei', value='0', format='float') |
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34 | 34 | # opObj10.addParameter(name='maxHei', value='8', format='float') |
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35 | 35 | # |
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36 | 36 | # opObj10 = procUnitConfObj0.addOperation(name='filterByHeights') |
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37 | 37 | # opObj10.addParameter(name='window', value='2', format='float') |
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38 | 38 | # |
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39 | 39 | # opObj10 = procUnitConfObj0.addOperation(name='Decoder', optype='external') |
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40 | 40 | # opObj10.addParameter(name='code', value='1,-1', format='intlist') |
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41 | 41 | # opObj10.addParameter(name='nCode', value='2', format='float') |
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42 | 42 | # opObj10.addParameter(name='nBaud', value='1', format='float') |
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43 | 43 | # |
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44 | 44 | # |
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45 | 45 | # opObj10 = procUnitConfObj0.addOperation(name='CohInt', optype='external') |
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46 | 46 | # opObj10.addParameter(name='n', value='1296', format='float') |
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47 | 47 | |
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48 | 48 | opObj00 = readUnitConfObj.addOperation(name='printNumberOfBlock') |
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49 | 49 | |
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50 | 50 | procUnitConfObj0 = controllerObj.addProcUnit(datatype='VoltageProc', |
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51 | 51 | inputId=readUnitConfObj.getId()) |
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52 | 52 | |
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53 | 53 | |
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54 | 54 | opObj10 = procUnitConfObj0.addOperation(name='setRadarFrequency') |
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55 | 55 | opObj10.addParameter(name='frequency', value='445.09e6', format='float') |
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56 | 56 | |
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57 | 57 | opObj10 = procUnitConfObj0.addOperation(name='CohInt', optype='external') |
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58 | 58 | opObj10.addParameter(name='n', value='2', format='float') |
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59 | 59 | |
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60 | 60 | #opObj10 = procUnitConfObj0.addOperation(name='selectHeights') |
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61 | 61 | #opObj10.addParameter(name='minHei', value='1', format='float') |
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62 | 62 | #opObj10.addParameter(name='maxHei', value='15', format='float') |
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63 | 63 | |
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64 | 64 | #opObj10 = procUnitConfObj0.addOperation(name='selectFFTs') |
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65 | 65 | #opObj10.addParameter(name='minHei', value='', format='float') |
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66 | 66 | #opObj10.addParameter(name='maxHei', value='', format='float') |
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67 | 67 | |
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68 | 68 | procUnitConfObj1 = controllerObj.addProcUnit(datatype='SpectraProc', |
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69 | 69 | inputId=procUnitConfObj0.getId()) |
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70 | 70 | |
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71 | 71 | # Creating a processing object with its parameters |
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72 | 72 | # schainpy.model.proc.jroproc_spectra.SpectraProc.run() |
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73 | 73 | # If you need to add more parameters can use the "addParameter method" |
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74 | 74 | procUnitConfObj1.addParameter(name='nFFTPoints', value='256', format='int') |
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75 | 75 | |
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76 | 76 | |
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77 | 77 | opObj10 = procUnitConfObj1.addOperation(name='removeDC') |
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78 | 78 | #opObj10 = procUnitConfObj1.addOperation(name='removeInterference') |
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79 | 79 | opObj10 = procUnitConfObj1.addOperation(name='IncohInt', optype='external') |
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80 | 80 | opObj10.addParameter(name='n', value='10', format='float') |
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81 | 81 | |
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82 | 82 | |
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83 | 83 | |
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84 | 84 | #opObj10 = procUnitConfObj1.addOperation(name='selectFFTs') |
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85 | 85 | #opObj10.addParameter(name='minFFT', value='-15', format='float') |
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86 | 86 | #opObj10.addParameter(name='maxFFT', value='15', format='float') |
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87 | 87 | |
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88 | 88 | |
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89 | 89 | |
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90 | 90 | opObj10 = procUnitConfObj1.addOperation(name='SpectraWriter', optype='other') |
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91 | 91 | opObj10.addParameter(name='blocksPerFile', value='64', format = 'int') |
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92 | 92 | opObj10.addParameter(name='path', value=pathW) |
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93 | 93 | # Using internal methods |
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|
94 | 94 | # schainpy.model.proc.jroproc_spectra.SpectraProc.selectChannels() |
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95 | 95 | # opObj10 = procUnitConfObj1.addOperation(name='selectChannels') |
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96 | 96 | # opObj10.addParameter(name='channelList', value='0,1', format='intlist') |
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97 | 97 | |
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98 | 98 | # Using internal methods |
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|
99 | 99 | # schainpy.model.proc.jroproc_spectra.SpectraProc.selectHeights() |
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100 | 100 | # opObj10 = procUnitConfObj1.addOperation(name='selectHeights') |
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101 | 101 | # opObj10.addParameter(name='minHei', value='90', format='float') |
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102 | 102 | # opObj10.addParameter(name='maxHei', value='180', format='float') |
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103 | 103 | |
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|
104 | 104 | # Using external methods (new modules) |
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|
105 | 105 | # #schainpy.model.proc.jroproc_spectra.IncohInt.setup() |
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106 | 106 | # opObj12 = procUnitConfObj1.addOperation(name='IncohInt', optype='other') |
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|
107 | 107 | # opObj12.addParameter(name='n', value='1', format='int') |
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|
108 | 108 | |
|
|
109 | 109 | # Using external methods (new modules) |
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|
110 | 110 | # schainpy.model.graphics.jroplot_spectra.SpectraPlot.setup() |
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|
111 | 111 | opObj11 = procUnitConfObj1.addOperation(name='SpectraPlot', optype='external') |
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112 | 112 | opObj11.addParameter(name='id', value='11', format='int') |
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113 | 113 | opObj11.addParameter(name='wintitle', value='SpectraPlot', format='str') |
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114 | 114 | opObj11.addParameter(name='xaxis', value='velocity', format='str') |
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|
115 | 115 | # opObj11.addParameter(name='xmin', value='-10', format='int') |
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116 | 116 | # opObj11.addParameter(name='xmax', value='10', format='int') |
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117 | 117 | |
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|
118 | 118 | # opObj11.addParameter(name='ymin', value='1', format='float') |
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|
119 | 119 | # opObj11.addParameter(name='ymax', value='3', format='int') |
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|
120 | 120 | #opObj11.addParameter(name='zmin', value='10', format='int') |
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|
121 | 121 | #opObj11.addParameter(name='zmax', value='35', format='int') |
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|
122 | 122 | # opObj11.addParameter(name='save', value='2', format='int') |
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|
123 | 123 | # opObj11.addParameter(name='save', value='5', format='int') |
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|
124 | 124 | # opObj11.addParameter(name='figpath', value=figpath, format='str') |
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125 | 125 | |
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126 | 126 | |
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127 | 127 | opObj11 = procUnitConfObj1.addOperation(name='CrossSpectraPlot', optype='other') |
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128 | 128 | procUnitConfObj1.addParameter(name='pairsList', value='(0,1),(0,2),(1,2)', format='pairsList') |
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129 | 129 | opObj11.addParameter(name='id', value='2005', format='int') |
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130 | 130 | #opObj11.addParameter(name='wintitle', value='CrossSpectraPlot_ShortPulse', format='str') |
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131 | 131 | #opObj11.addParameter(name='exp_code', value='13', format='int') |
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132 | 132 | opObj11.addParameter(name='xaxis', value='Velocity', format='str') |
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|
133 | 133 | #opObj11.addParameter(name='xmin', value='-6', format='float') |
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134 | 134 | #opObj11.addParameter(name='xmax', value='6', format='float') |
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135 | 135 | opObj11.addParameter(name='zmin', value='15', format='float') |
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136 | 136 | opObj11.addParameter(name='zmax', value='50', format='float') |
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137 | 137 | opObj11.addParameter(name='ymin', value='0', format='float') |
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|
138 | 138 | opObj11.addParameter(name='ymax', value='7', format='float') |
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|
139 | 139 | #opObj11.addParameter(name='phase_min', value='-4', format='int') |
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|
140 | 140 | #opObj11.addParameter(name='phase_max', value='4', format='int') |
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|
141 | 141 | # |
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|
142 | 142 | |
|
|
143 | 143 | # Using external methods (new modules) |
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|
144 | 144 | # schainpy.model.graphics.jroplot_spectra.RTIPlot.setup() |
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|
145 | 145 | opObj11 = procUnitConfObj1.addOperation(name='RTIPlot', optype='other') |
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|
146 | 146 | opObj11.addParameter(name='id', value='30', format='int') |
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147 | 147 | opObj11.addParameter(name='wintitle', value='RTI', format='str') |
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|
148 | 148 | opObj11.addParameter(name='zmin', value='15', format='int') |
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149 | 149 | opObj11.addParameter(name='zmax', value='40', format='int') |
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150 | 150 | opObj11.addParameter(name='ymin', value='1', format='int') |
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151 | 151 | opObj11.addParameter(name='ymax', value='7', format='int') |
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152 | 152 | opObj11.addParameter(name='showprofile', value='1', format='int') |
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153 | 153 | # opObj11.addParameter(name='timerange', value=str(5*60*60*60), format='int') |
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|
154 | 154 | #opObj11.addParameter(name='xmin', value='1', format='float') |
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|
155 | 155 | #opObj11.addParameter(name='xmax', value='6', format='float') |
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|
156 | 156 | opObj11.addParameter(name='save', value='1', format='int') |
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157 | 157 | opObj11.addParameter(name='figpath', value=figpath, format='str') |
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158 | 158 | |
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|
159 | 159 | |
|
|
160 | 160 | # '''#########################################################################################''' |
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|
161 | 161 | # |
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162 | 162 | # |
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163 | 163 | # procUnitConfObj2 = controllerObj.addProcUnit(datatype='Parameters', inputId=procUnitConfObj1.getId()) |
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|
164 | 164 | # opObj11 = procUnitConfObj2.addOperation(name='SpectralMoments', optype='other') |
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|
165 | 165 | # |
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|
166 | 166 | # ''' |
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|
167 | 167 | # # Discriminacion de ecos |
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|
168 | 168 | # opObj11 = procUnitConfObj2.addOperation(name='GaussianFit', optype='other') |
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|
169 | 169 | # opObj11.addParameter(name='SNRlimit', value='0', format='int') |
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170 | 170 | # ''' |
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|
171 | 171 | # |
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|
172 | 172 | # ''' |
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|
173 | 173 | # # Estimacion de Precipitacion |
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|
174 | 174 | # opObj11 = procUnitConfObj2.addOperation(name='PrecipitationProc', optype='other') |
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|
175 | 175 | # ''' |
|
|
176 | 176 | # |
|
|
177 | 177 | # opObj22 = procUnitConfObj2.addOperation(name='FullSpectralAnalysis', optype='other') |
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|
178 | 178 | # |
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|
179 | 179 | # opObj22.addParameter(name='SNRlimit', value='-10', format='float') |
|
|
180 | 180 | # opObj22.addParameter(name='E01', value='1.500', format='float') |
|
|
181 | 181 | # opObj22.addParameter(name='E02', value='1.500', format='float') |
|
|
182 | 182 | # opObj22.addParameter(name='E12', value='0', format='float') |
|
|
183 | 183 | # opObj22.addParameter(name='N01', value='0.875', format='float') |
|
|
184 | 184 | # opObj22.addParameter(name='N02', value='-0.875', format='float') |
|
|
185 | 185 | # opObj22.addParameter(name='N12', value='-1.750', format='float') |
|
|
186 | 186 | # |
|
|
187 | 187 | # |
|
|
188 | 188 | # opObj22 = procUnitConfObj2.addOperation(name='WindProfilerPlot', optype='other') |
|
|
189 | 189 | # opObj22.addParameter(name='id', value='4', format='int') |
|
|
190 | 190 | # opObj22.addParameter(name='wintitle', value='Wind Profiler', format='str') |
|
|
191 | 191 | # opObj22.addParameter(name='save', value='1', format='bool') |
|
|
192 | 192 | # opObj22.addParameter(name='xmin', value='0', format='float') |
|
|
193 | 193 | # opObj22.addParameter(name='xmax', value='6', format='float') |
|
|
194 | 194 | # opObj22.addParameter(name='ymin', value='1', format='float') |
|
|
195 | 195 | # opObj22.addParameter(name='ymax', value='3.5', format='float') |
|
|
196 | 196 | # opObj22.addParameter(name='zmin', value='-1', format='float') |
|
|
197 | 197 | # opObj22.addParameter(name='zmax', value='1', format='float') |
|
|
198 | 198 | # opObj22.addParameter(name='SNRmin', value='-15', format='float') |
|
|
199 | 199 | # opObj22.addParameter(name='SNRmax', value='20', format='float') |
|
|
200 | 200 | # opObj22.addParameter(name='zmin_ver', value='-200', format='float') |
|
|
201 | 201 | # opObj22.addParameter(name='zmax_ver', value='200', format='float') |
|
|
202 | 202 | # opObj22.addParameter(name='save', value='1', format='int') |
|
|
203 | 203 | # opObj22.addParameter(name='figpath', value=figpath, format='str') |
|
|
204 | 204 | # |
|
|
205 | 205 | # |
|
|
206 | 206 | # |
|
|
207 | 207 | # #opObj11.addParameter(name='zmin', value='75', format='int') |
|
|
208 | 208 | # |
|
|
209 | 209 | # #opObj12 = procUnitConfObj2.addOperation(name='ParametersPlot', optype='other') |
|
|
210 | 210 | # #opObj12.addParameter(name='id',value='4',format='int') |
|
|
211 | 211 | # #opObj12.addParameter(name='wintitle',value='First_gg',format='str') |
|
|
212 | 212 | # ''' |
|
|
213 | 213 | # #Ploteo de Discriminacion de Gaussianas |
|
|
214 | 214 | # |
|
|
215 | 215 | # opObj11 = procUnitConfObj2.addOperation(name='FitGauPlot', optype='other') |
|
|
216 | 216 | # opObj11.addParameter(name='id', value='21', format='int') |
|
|
217 | 217 | # opObj11.addParameter(name='wintitle', value='Rainfall Gaussian', format='str') |
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218 | 218 | # opObj11.addParameter(name='xaxis', value='velocity', format='str') |
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219 | 219 | # opObj11.addParameter(name='showprofile', value='1', format='int') |
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220 | 220 | # opObj11.addParameter(name='zmin', value='75', format='int') |
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221 | 221 | # opObj11.addParameter(name='zmax', value='100', format='int') |
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222 | 222 | # opObj11.addParameter(name='GauSelector', value='1', format='int') |
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223 | 223 | # #opObj11.addParameter(name='save', value='1', format='int') |
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224 | 224 | # #opObj11.addParameter(name='figpath', value='/home/erick/Documents/Data/d2015106') |
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225 | 225 | # |
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226 | 226 | # opObj11 = procUnitConfObj2.addOperation(name='FitGauPlot', optype='other') |
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227 | 227 | # opObj11.addParameter(name='id', value='22', format='int') |
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228 | 228 | # opObj11.addParameter(name='wintitle', value='Wind Gaussian', format='str') |
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229 | 229 | # opObj11.addParameter(name='xaxis', value='velocity', format='str') |
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230 | 230 | # opObj11.addParameter(name='showprofile', value='1', format='int') |
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231 | 231 | # opObj11.addParameter(name='zmin', value='75', format='int') |
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232 | 232 | # opObj11.addParameter(name='zmax', value='100', format='int') |
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233 | 233 | # opObj11.addParameter(name='GauSelector', value='0', format='int') |
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234 | 234 | # ''' |
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235 | 235 | # |
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236 | 236 | # |
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237 | 237 | |
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238 | 238 | |
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239 | 239 | controllerObj.start() |
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