| @@ -1,851 +1,851 | |||
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1 | 1 | ''' |
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2 | 2 | |
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3 | 3 | $Author: murco $ |
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4 | 4 | $Id: JROHeaderIO.py 151 2012-10-31 19:00:51Z murco $ |
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5 | 5 | ''' |
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6 | 6 | import sys |
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7 | 7 | import numpy |
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8 | 8 | import copy |
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9 | 9 | import datetime |
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10 | 10 | |
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11 | 11 | SPEED_OF_LIGHT = 299792458 |
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12 | 12 | SPEED_OF_LIGHT = 3e8 |
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13 | 13 | |
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14 | 14 | BASIC_STRUCTURE = numpy.dtype([ |
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15 | 15 | ('nSize','<u4'), |
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16 | 16 | ('nVersion','<u2'), |
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17 | 17 | ('nDataBlockId','<u4'), |
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18 | 18 | ('nUtime','<u4'), |
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19 | 19 | ('nMilsec','<u2'), |
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20 | 20 | ('nTimezone','<i2'), |
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21 | 21 | ('nDstflag','<i2'), |
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22 | 22 | ('nErrorCount','<u4') |
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23 | 23 | ]) |
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24 | 24 | |
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25 | 25 | SYSTEM_STRUCTURE = numpy.dtype([ |
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26 | 26 | ('nSize','<u4'), |
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27 | 27 | ('nNumSamples','<u4'), |
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28 | 28 | ('nNumProfiles','<u4'), |
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29 | 29 | ('nNumChannels','<u4'), |
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30 | 30 | ('nADCResolution','<u4'), |
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31 | 31 | ('nPCDIOBusWidth','<u4'), |
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32 | 32 | ]) |
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33 | 33 | |
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34 | 34 | RADAR_STRUCTURE = numpy.dtype([ |
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35 | 35 | ('nSize','<u4'), |
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36 | 36 | ('nExpType','<u4'), |
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37 | 37 | ('nNTx','<u4'), |
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38 | 38 | ('fIpp','<f4'), |
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39 | 39 | ('fTxA','<f4'), |
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40 | 40 | ('fTxB','<f4'), |
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41 | 41 | ('nNumWindows','<u4'), |
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42 | 42 | ('nNumTaus','<u4'), |
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43 | 43 | ('nCodeType','<u4'), |
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44 | 44 | ('nLine6Function','<u4'), |
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45 | 45 | ('nLine5Function','<u4'), |
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46 | 46 | ('fClock','<f4'), |
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47 | 47 | ('nPrePulseBefore','<u4'), |
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48 | 48 | ('nPrePulseAfter','<u4'), |
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49 | 49 | ('sRangeIPP','<a20'), |
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50 | 50 | ('sRangeTxA','<a20'), |
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51 | 51 | ('sRangeTxB','<a20'), |
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52 | 52 | ]) |
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53 | 53 | |
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54 | 54 | SAMPLING_STRUCTURE = numpy.dtype([('h0','<f4'),('dh','<f4'),('nsa','<u4')]) |
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55 | 55 | |
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56 | 56 | |
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57 | 57 | PROCESSING_STRUCTURE = numpy.dtype([ |
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58 | 58 | ('nSize','<u4'), |
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59 | 59 | ('nDataType','<u4'), |
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60 | 60 | ('nSizeOfDataBlock','<u4'), |
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61 | 61 | ('nProfilesperBlock','<u4'), |
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62 | 62 | ('nDataBlocksperFile','<u4'), |
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63 | 63 | ('nNumWindows','<u4'), |
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64 | 64 | ('nProcessFlags','<u4'), |
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65 | 65 | ('nCoherentIntegrations','<u4'), |
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66 | 66 | ('nIncoherentIntegrations','<u4'), |
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67 | 67 | ('nTotalSpectra','<u4') |
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68 | 68 | ]) |
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69 | 69 | |
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70 | 70 | class Header(object): |
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71 | 71 | |
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72 | 72 | def __init__(self): |
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73 | 73 | raise NotImplementedError |
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74 | 74 | |
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75 | 75 | def copy(self): |
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76 | 76 | return copy.deepcopy(self) |
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77 | 77 | |
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78 | 78 | def read(self): |
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79 | 79 | |
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80 | 80 | raise NotImplementedError |
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81 | 81 | |
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82 | 82 | def write(self): |
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83 | 83 | |
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84 | 84 | raise NotImplementedError |
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85 | 85 | |
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86 | 86 | def printInfo(self): |
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87 | 87 | |
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88 | 88 | message = "#"*50 + "\n" |
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89 | 89 | message += self.__class__.__name__.upper() + "\n" |
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90 | 90 | message += "#"*50 + "\n" |
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91 | 91 | |
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92 | 92 | keyList = self.__dict__.keys() |
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93 | 93 | keyList.sort() |
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94 | 94 | |
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95 | 95 | for key in keyList: |
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96 | 96 | message += "%s = %s" %(key, self.__dict__[key]) + "\n" |
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97 | 97 | |
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98 | 98 | if "size" not in keyList: |
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99 | 99 | attr = getattr(self, "size") |
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100 | 100 | |
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101 | 101 | if attr: |
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102 | 102 | message += "%s = %s" %("size", attr) + "\n" |
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103 | 103 | |
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104 | 104 | print message |
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105 | 105 | |
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106 | 106 | class BasicHeader(Header): |
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107 | 107 | |
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108 | 108 | size = None |
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109 | 109 | version = None |
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110 | 110 | dataBlock = None |
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111 | 111 | utc = None |
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112 | 112 | ltc = None |
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113 | 113 | miliSecond = None |
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114 | 114 | timeZone = None |
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115 | 115 | dstFlag = None |
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116 | 116 | errorCount = None |
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117 | 117 | datatime = None |
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118 | 118 | __LOCALTIME = None |
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119 | 119 | |
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120 | 120 | def __init__(self, useLocalTime=True): |
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121 | 121 | |
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122 | 122 | self.size = 24 |
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123 | 123 | self.version = 0 |
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124 | 124 | self.dataBlock = 0 |
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125 | 125 | self.utc = 0 |
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126 | 126 | self.miliSecond = 0 |
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127 | 127 | self.timeZone = 0 |
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128 | 128 | self.dstFlag = 0 |
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129 | 129 | self.errorCount = 0 |
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130 | 130 | |
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131 | 131 | self.useLocalTime = useLocalTime |
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132 | 132 | |
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133 | 133 | def read(self, fp): |
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134 | 134 | |
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135 | 135 | self.length = 0 |
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136 | 136 | try: |
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137 | 137 | if hasattr(fp, 'read'): |
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138 | 138 | header = numpy.fromfile(fp, BASIC_STRUCTURE,1) |
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139 | 139 | else: |
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140 | 140 | header = numpy.fromstring(fp, BASIC_STRUCTURE,1) |
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141 | 141 | except Exception, e: |
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142 | 142 | print "BasicHeader: " |
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143 | 143 | print e |
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144 | 144 | return 0 |
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145 | 145 | |
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146 | 146 | self.size = int(header['nSize'][0]) |
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147 | 147 | self.version = int(header['nVersion'][0]) |
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148 | 148 | self.dataBlock = int(header['nDataBlockId'][0]) |
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149 | 149 | self.utc = int(header['nUtime'][0]) |
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150 | 150 | self.miliSecond = int(header['nMilsec'][0]) |
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151 | 151 | self.timeZone = int(header['nTimezone'][0]) |
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152 | 152 | self.dstFlag = int(header['nDstflag'][0]) |
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153 | 153 | self.errorCount = int(header['nErrorCount'][0]) |
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154 | 154 | |
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155 | 155 | if self.size < 24: |
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156 | 156 | return 0 |
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157 | 157 | |
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158 | 158 | self.length = header.nbytes |
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159 | 159 | return 1 |
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160 | 160 | |
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161 | 161 | def write(self, fp): |
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162 | 162 | |
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163 | 163 | headerTuple = (self.size,self.version,self.dataBlock,self.utc,self.miliSecond,self.timeZone,self.dstFlag,self.errorCount) |
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164 | 164 | header = numpy.array(headerTuple, BASIC_STRUCTURE) |
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165 | 165 | header.tofile(fp) |
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166 | 166 | |
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167 | 167 | return 1 |
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168 | 168 | |
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169 | 169 | def get_ltc(self): |
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170 | 170 | |
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171 | 171 | return self.utc - self.timeZone*60 |
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172 | 172 | |
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173 | 173 | def set_ltc(self, value): |
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174 | 174 | |
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175 | 175 | self.utc = value + self.timeZone*60 |
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176 | 176 | |
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177 | 177 | def get_datatime(self): |
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178 | 178 | |
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179 | 179 | return datetime.datetime.utcfromtimestamp(self.ltc) |
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180 | 180 | |
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181 | 181 | ltc = property(get_ltc, set_ltc) |
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182 | 182 | datatime = property(get_datatime) |
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183 | 183 | |
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184 | 184 | class SystemHeader(Header): |
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185 | 185 | |
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186 | 186 | size = None |
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187 | 187 | nSamples = None |
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188 | 188 | nProfiles = None |
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189 | 189 | nChannels = None |
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190 | 190 | adcResolution = None |
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191 | 191 | pciDioBusWidth = None |
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192 | 192 | |
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193 | 193 | def __init__(self, nSamples=0, nProfiles=0, nChannels=0, adcResolution=14, pciDioBusWith=0): |
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194 | 194 | |
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195 | 195 | self.size = 24 |
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196 | 196 | self.nSamples = nSamples |
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197 | 197 | self.nProfiles = nProfiles |
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198 | 198 | self.nChannels = nChannels |
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199 | 199 | self.adcResolution = adcResolution |
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200 | 200 | self.pciDioBusWidth = pciDioBusWith |
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201 | 201 | |
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202 | 202 | def read(self, fp): |
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203 | 203 | self.length = 0 |
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204 | 204 | try: |
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205 | 205 | startFp = fp.tell() |
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206 | 206 | except Exception, e: |
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207 | 207 | startFp = None |
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208 | 208 | pass |
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209 | 209 | |
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210 | 210 | try: |
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211 | 211 | if hasattr(fp, 'read'): |
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212 | 212 | header = numpy.fromfile(fp, SYSTEM_STRUCTURE,1) |
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213 | 213 | else: |
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214 | 214 | header = numpy.fromstring(fp, SYSTEM_STRUCTURE,1) |
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215 | 215 | except Exception, e: |
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216 | 216 | print "System Header: " + str(e) |
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217 | 217 | return 0 |
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218 | 218 | |
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219 | 219 | self.size = header['nSize'][0] |
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220 | 220 | self.nSamples = header['nNumSamples'][0] |
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221 | 221 | self.nProfiles = header['nNumProfiles'][0] |
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222 | 222 | self.nChannels = header['nNumChannels'][0] |
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223 | 223 | self.adcResolution = header['nADCResolution'][0] |
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224 | 224 | self.pciDioBusWidth = header['nPCDIOBusWidth'][0] |
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225 | 225 | |
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226 | 226 | |
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227 | 227 | if startFp is not None: |
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228 | 228 | endFp = self.size + startFp |
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229 | 229 | |
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230 | 230 | if fp.tell() > endFp: |
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231 | 231 | sys.stderr.write("Warning %s: Size value read from System Header is lower than it has to be\n" %fp.name) |
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232 | 232 | return 0 |
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233 | 233 | |
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234 | 234 | if fp.tell() < endFp: |
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235 | 235 | sys.stderr.write("Warning %s: Size value read from System Header size is greater than it has to be\n" %fp.name) |
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236 | 236 | return 0 |
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237 | 237 | |
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238 | 238 | self.length = header.nbytes |
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239 | 239 | return 1 |
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240 | 240 | |
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241 | 241 | def write(self, fp): |
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242 | 242 | |
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243 | 243 | headerTuple = (self.size,self.nSamples,self.nProfiles,self.nChannels,self.adcResolution,self.pciDioBusWidth) |
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244 | 244 | header = numpy.array(headerTuple,SYSTEM_STRUCTURE) |
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245 | 245 | header.tofile(fp) |
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246 | 246 | |
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247 | 247 | return 1 |
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248 | 248 | |
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249 | 249 | class RadarControllerHeader(Header): |
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250 | 250 | |
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251 | 251 | expType = None |
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252 | 252 | nTx = None |
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253 | 253 | ipp = None |
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254 | 254 | txA = None |
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255 | 255 | txB = None |
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256 | 256 | nWindows = None |
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257 | 257 | numTaus = None |
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258 | 258 | codeType = None |
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259 | 259 | line6Function = None |
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260 | 260 | line5Function = None |
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261 | 261 | fClock = None |
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262 | 262 | prePulseBefore = None |
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263 | 263 | prePulserAfter = None |
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264 | 264 | rangeIpp = None |
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265 | 265 | rangeTxA = None |
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266 | 266 | rangeTxB = None |
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267 | 267 | |
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268 | 268 | __size = None |
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269 | 269 | |
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270 | 270 | def __init__(self, expType=2, nTx=1, |
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271 | 271 | ippKm=None, txA=0, txB=0, |
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272 | 272 | nWindows=None, nHeights=None, firstHeight=None, deltaHeight=None, |
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273 | 273 | numTaus=0, line6Function=0, line5Function=0, fClock=None, |
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274 | 274 | prePulseBefore=0, prePulseAfter=0, |
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275 | 275 | codeType=0, nCode=0, nBaud=0, code=None, |
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276 | 276 | flip1=0, flip2=0): |
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277 | 277 | |
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278 | 278 | # self.size = 116 |
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279 | 279 | self.expType = expType |
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280 | 280 | self.nTx = nTx |
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281 | 281 | self.ipp = ippKm |
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282 | 282 | self.txA = txA |
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283 | 283 | self.txB = txB |
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284 | 284 | self.rangeIpp = ippKm |
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285 | 285 | self.rangeTxA = txA |
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286 | 286 | self.rangeTxB = txB |
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287 | 287 | |
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288 | 288 | self.nWindows = nWindows |
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289 | 289 | self.numTaus = numTaus |
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290 | 290 | self.codeType = codeType |
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291 | 291 | self.line6Function = line6Function |
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292 | 292 | self.line5Function = line5Function |
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293 | 293 | self.fClock = fClock |
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294 | 294 | self.prePulseBefore = prePulseBefore |
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295 | 295 | self.prePulserAfter = prePulseAfter |
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296 | 296 | |
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297 | 297 | self.nHeights = nHeights |
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298 | 298 | self.firstHeight = firstHeight |
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299 | 299 | self.deltaHeight = deltaHeight |
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300 | 300 | self.samplesWin = nHeights |
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301 | 301 | |
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302 | 302 | self.nCode = nCode |
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303 | 303 | self.nBaud = nBaud |
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304 | 304 | self.code = code |
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305 | 305 | self.flip1 = flip1 |
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306 | 306 | self.flip2 = flip2 |
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307 | 307 | |
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308 | 308 | self.code_size = int(numpy.ceil(self.nBaud/32.))*self.nCode*4 |
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309 | 309 | # self.dynamic = numpy.array([],numpy.dtype('byte')) |
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310 | 310 | |
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311 | 311 | if self.fClock is None and self.deltaHeight is not None: |
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312 | 312 | self.fClock = 0.15/(deltaHeight*1e-6) #0.15Km / (height * 1u) |
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313 | 313 | |
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314 | 314 | def read(self, fp): |
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315 | 315 | self.length = 0 |
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316 | 316 | try: |
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317 | 317 | startFp = fp.tell() |
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318 | 318 | except Exception, e: |
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319 | 319 | startFp = None |
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320 | 320 | pass |
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321 | 321 | |
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322 | 322 | try: |
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323 | 323 | if hasattr(fp, 'read'): |
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324 | 324 | header = numpy.fromfile(fp, RADAR_STRUCTURE,1) |
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325 | 325 | else: |
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326 | 326 | header = numpy.fromstring(fp, RADAR_STRUCTURE,1) |
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327 | 327 | self.length += header.nbytes |
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328 | 328 | except Exception, e: |
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329 | 329 | print "RadarControllerHeader: " + str(e) |
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330 | 330 | return 0 |
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331 | 331 | |
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332 | 332 | size = int(header['nSize'][0]) |
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333 | 333 | self.expType = int(header['nExpType'][0]) |
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334 | 334 | self.nTx = int(header['nNTx'][0]) |
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335 | 335 | self.ipp = float(header['fIpp'][0]) |
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336 | 336 | self.txA = float(header['fTxA'][0]) |
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337 | 337 | self.txB = float(header['fTxB'][0]) |
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338 | 338 | self.nWindows = int(header['nNumWindows'][0]) |
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339 | 339 | self.numTaus = int(header['nNumTaus'][0]) |
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340 | 340 | self.codeType = int(header['nCodeType'][0]) |
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341 | 341 | self.line6Function = int(header['nLine6Function'][0]) |
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342 | 342 | self.line5Function = int(header['nLine5Function'][0]) |
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343 | 343 | self.fClock = float(header['fClock'][0]) |
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344 | 344 | self.prePulseBefore = int(header['nPrePulseBefore'][0]) |
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345 | 345 | self.prePulserAfter = int(header['nPrePulseAfter'][0]) |
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346 | 346 | self.rangeIpp = header['sRangeIPP'][0] |
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347 | 347 | self.rangeTxA = header['sRangeTxA'][0] |
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348 | 348 | self.rangeTxB = header['sRangeTxB'][0] |
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349 | 349 | |
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350 | 350 | try: |
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351 | 351 | if hasattr(fp, 'read'): |
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352 | 352 | samplingWindow = numpy.fromfile(fp, SAMPLING_STRUCTURE, self.nWindows) |
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353 | 353 | else: |
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354 | 354 | samplingWindow = numpy.fromstring(fp[self.length:], SAMPLING_STRUCTURE, self.nWindows) |
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355 | 355 | self.length += samplingWindow.nbytes |
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356 | 356 | except Exception, e: |
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357 | 357 | print "RadarControllerHeader: " + str(e) |
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358 | 358 | return 0 |
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359 | 359 | self.nHeights = int(numpy.sum(samplingWindow['nsa'])) |
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360 | 360 | self.firstHeight = samplingWindow['h0'] |
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361 | 361 | self.deltaHeight = samplingWindow['dh'] |
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362 | 362 | self.samplesWin = samplingWindow['nsa'] |
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363 | 363 | |
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364 | 364 | |
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365 | 365 | |
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366 | 366 | try: |
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367 | 367 | if hasattr(fp, 'read'): |
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368 | 368 | self.Taus = numpy.fromfile(fp, '<f4', self.numTaus) |
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369 | 369 | else: |
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370 | 370 | self.Taus = numpy.fromstring(fp[self.length:], '<f4', self.numTaus) |
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371 | 371 | self.length += self.Taus.nbytes |
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372 | 372 | except Exception, e: |
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373 | 373 | print "RadarControllerHeader: " + str(e) |
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374 | 374 | return 0 |
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375 | 375 | |
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376 | 376 | |
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377 | 377 | |
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378 | 378 | self.code_size = 0 |
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379 | 379 | if self.codeType != 0: |
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380 | 380 | |
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381 | 381 | try: |
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382 | 382 | if hasattr(fp, 'read'): |
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383 | self.nCode = numpy.fromfile(fp, '<u4', 1) | |
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383 | self.nCode = numpy.fromfile(fp, '<u4', 1)[0] | |
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384 | 384 | self.length += self.nCode.nbytes |
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385 | self.nBaud = numpy.fromfile(fp, '<u4', 1) | |
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385 | self.nBaud = numpy.fromfile(fp, '<u4', 1)[0] | |
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386 | 386 | self.length += self.nBaud.nbytes |
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387 | 387 | else: |
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388 | 388 | self.nCode = numpy.fromstring(fp[self.length:], '<u4', 1)[0] |
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389 | 389 | self.length += self.nCode.nbytes |
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390 | 390 | self.nBaud = numpy.fromstring(fp[self.length:], '<u4', 1)[0] |
|
|
391 | 391 | self.length += self.nBaud.nbytes |
|
|
392 | 392 | except Exception, e: |
|
|
393 | 393 | print "RadarControllerHeader: " + str(e) |
|
|
394 | 394 | return 0 |
|
|
395 | 395 | code = numpy.empty([self.nCode,self.nBaud],dtype='i1') |
|
|
396 | 396 | |
|
|
397 | 397 | for ic in range(self.nCode): |
|
|
398 | 398 | try: |
|
|
399 | 399 | if hasattr(fp, 'read'): |
|
|
400 | 400 | temp = numpy.fromfile(fp,'u4', int(numpy.ceil(self.nBaud/32.))) |
|
|
401 | 401 | else: |
|
|
402 | 402 | temp = numpy.fromstring(fp,'u4', int(numpy.ceil(self.nBaud/32.))) |
|
|
403 | 403 | self.length += temp.nbytes |
|
|
404 | 404 | except Exception, e: |
|
|
405 | 405 | print "RadarControllerHeader: " + str(e) |
|
|
406 | 406 | return 0 |
|
|
407 | 407 | |
|
|
408 | 408 | for ib in range(self.nBaud-1,-1,-1): |
|
|
409 | 409 | code[ic,ib] = temp[ib/32]%2 |
|
|
410 | 410 | temp[ib/32] = temp[ib/32]/2 |
|
|
411 | 411 | |
|
|
412 | 412 | self.code = 2.0*code - 1.0 |
|
|
413 | 413 | self.code_size = int(numpy.ceil(self.nBaud/32.))*self.nCode*4 |
|
|
414 | 414 | |
|
|
415 | 415 | # if self.line5Function == RCfunction.FLIP: |
|
|
416 | 416 | # self.flip1 = numpy.fromfile(fp,'<u4',1) |
|
|
417 | 417 | # |
|
|
418 | 418 | # if self.line6Function == RCfunction.FLIP: |
|
|
419 | 419 | # self.flip2 = numpy.fromfile(fp,'<u4',1) |
|
|
420 | 420 | if startFp is not None: |
|
|
421 | 421 | endFp = size + startFp |
|
|
422 | 422 | |
|
|
423 | 423 | if fp.tell() != endFp: |
|
|
424 | 424 | # fp.seek(endFp) |
|
|
425 | 425 | print "%s: Radar Controller Header size is not consistent: from data [%d] != from header field [%d]" %(fp.name, fp.tell()-startFp, size) |
|
|
426 | 426 | # return 0 |
|
|
427 | 427 | |
|
|
428 | 428 | if fp.tell() > endFp: |
|
|
429 | 429 | sys.stderr.write("Warning %s: Size value read from Radar Controller header is lower than it has to be\n" %fp.name) |
|
|
430 | 430 | # return 0 |
|
|
431 | 431 | |
|
|
432 | 432 | if fp.tell() < endFp: |
|
|
433 | 433 | sys.stderr.write("Warning %s: Size value read from Radar Controller header is greater than it has to be\n" %fp.name) |
|
|
434 | 434 | |
|
|
435 | 435 | |
|
|
436 | 436 | return 1 |
|
|
437 | 437 | |
|
|
438 | 438 | def write(self, fp): |
|
|
439 | 439 | |
|
|
440 | 440 | headerTuple = (self.size, |
|
|
441 | 441 | self.expType, |
|
|
442 | 442 | self.nTx, |
|
|
443 | 443 | self.ipp, |
|
|
444 | 444 | self.txA, |
|
|
445 | 445 | self.txB, |
|
|
446 | 446 | self.nWindows, |
|
|
447 | 447 | self.numTaus, |
|
|
448 | 448 | self.codeType, |
|
|
449 | 449 | self.line6Function, |
|
|
450 | 450 | self.line5Function, |
|
|
451 | 451 | self.fClock, |
|
|
452 | 452 | self.prePulseBefore, |
|
|
453 | 453 | self.prePulserAfter, |
|
|
454 | 454 | self.rangeIpp, |
|
|
455 | 455 | self.rangeTxA, |
|
|
456 | 456 | self.rangeTxB) |
|
|
457 | 457 | |
|
|
458 | 458 | header = numpy.array(headerTuple,RADAR_STRUCTURE) |
|
|
459 | 459 | header.tofile(fp) |
|
|
460 | 460 | |
|
|
461 | 461 | sampleWindowTuple = (self.firstHeight,self.deltaHeight,self.samplesWin) |
|
|
462 | 462 | samplingWindow = numpy.array(sampleWindowTuple,SAMPLING_STRUCTURE) |
|
|
463 | 463 | samplingWindow.tofile(fp) |
|
|
464 | 464 | |
|
|
465 | 465 | if self.numTaus > 0: |
|
|
466 | 466 | self.Taus.tofile(fp) |
|
|
467 | 467 | |
|
|
468 | 468 | if self.codeType !=0: |
|
|
469 | 469 | nCode = numpy.array(self.nCode, '<u4') |
|
|
470 | 470 | nCode.tofile(fp) |
|
|
471 | 471 | nBaud = numpy.array(self.nBaud, '<u4') |
|
|
472 | 472 | nBaud.tofile(fp) |
|
|
473 | 473 | code1 = (self.code + 1.0)/2. |
|
|
474 | 474 | |
|
|
475 | 475 | for ic in range(self.nCode): |
|
|
476 | 476 | tempx = numpy.zeros(numpy.ceil(self.nBaud/32.)) |
|
|
477 | 477 | start = 0 |
|
|
478 | 478 | end = 32 |
|
|
479 | 479 | for i in range(len(tempx)): |
|
|
480 | 480 | code_selected = code1[ic,start:end] |
|
|
481 | 481 | for j in range(len(code_selected)-1,-1,-1): |
|
|
482 | 482 | if code_selected[j] == 1: |
|
|
483 | 483 | tempx[i] = tempx[i] + 2**(len(code_selected)-1-j) |
|
|
484 | 484 | start = start + 32 |
|
|
485 | 485 | end = end + 32 |
|
|
486 | 486 | |
|
|
487 | 487 | tempx = tempx.astype('u4') |
|
|
488 | 488 | tempx.tofile(fp) |
|
|
489 | 489 | |
|
|
490 | 490 | # if self.line5Function == RCfunction.FLIP: |
|
|
491 | 491 | # self.flip1.tofile(fp) |
|
|
492 | 492 | # |
|
|
493 | 493 | # if self.line6Function == RCfunction.FLIP: |
|
|
494 | 494 | # self.flip2.tofile(fp) |
|
|
495 | 495 | |
|
|
496 | 496 | return 1 |
|
|
497 | 497 | |
|
|
498 | 498 | def get_ippSeconds(self): |
|
|
499 | 499 | ''' |
|
|
500 | 500 | ''' |
|
|
501 | 501 | ippSeconds = 2.0 * 1000 * self.ipp / SPEED_OF_LIGHT |
|
|
502 | 502 | |
|
|
503 | 503 | return ippSeconds |
|
|
504 | 504 | |
|
|
505 | 505 | def set_ippSeconds(self, ippSeconds): |
|
|
506 | 506 | ''' |
|
|
507 | 507 | ''' |
|
|
508 | 508 | |
|
|
509 | 509 | self.ipp = ippSeconds * SPEED_OF_LIGHT / (2.0*1000) |
|
|
510 | 510 | |
|
|
511 | 511 | return |
|
|
512 | 512 | |
|
|
513 | 513 | def get_size(self): |
|
|
514 | 514 | |
|
|
515 | 515 | self.__size = 116 + 12*self.nWindows + 4*self.numTaus |
|
|
516 | 516 | |
|
|
517 | 517 | if self.codeType != 0: |
|
|
518 | 518 | self.__size += 4 + 4 + 4*self.nCode*numpy.ceil(self.nBaud/32.) |
|
|
519 | 519 | |
|
|
520 | 520 | return self.__size |
|
|
521 | 521 | |
|
|
522 | 522 | def set_size(self, value): |
|
|
523 | 523 | |
|
|
524 | 524 | raise IOError, "size is a property and it cannot be set, just read" |
|
|
525 | 525 | |
|
|
526 | 526 | return |
|
|
527 | 527 | |
|
|
528 | 528 | ippSeconds = property(get_ippSeconds, set_ippSeconds) |
|
|
529 | 529 | size = property(get_size, set_size) |
|
|
530 | 530 | |
|
|
531 | 531 | class ProcessingHeader(Header): |
|
|
532 | 532 | |
|
|
533 | 533 | # size = None |
|
|
534 | 534 | dtype = None |
|
|
535 | 535 | blockSize = None |
|
|
536 | 536 | profilesPerBlock = None |
|
|
537 | 537 | dataBlocksPerFile = None |
|
|
538 | 538 | nWindows = None |
|
|
539 | 539 | processFlags = None |
|
|
540 | 540 | nCohInt = None |
|
|
541 | 541 | nIncohInt = None |
|
|
542 | 542 | totalSpectra = None |
|
|
543 | 543 | |
|
|
544 | 544 | flag_dc = None |
|
|
545 | 545 | flag_cspc = None |
|
|
546 | 546 | |
|
|
547 | 547 | def __init__(self): |
|
|
548 | 548 | |
|
|
549 | 549 | # self.size = 0 |
|
|
550 | 550 | self.dtype = 0 |
|
|
551 | 551 | self.blockSize = 0 |
|
|
552 | 552 | self.profilesPerBlock = 0 |
|
|
553 | 553 | self.dataBlocksPerFile = 0 |
|
|
554 | 554 | self.nWindows = 0 |
|
|
555 | 555 | self.processFlags = 0 |
|
|
556 | 556 | self.nCohInt = 0 |
|
|
557 | 557 | self.nIncohInt = 0 |
|
|
558 | 558 | self.totalSpectra = 0 |
|
|
559 | 559 | |
|
|
560 | 560 | self.nHeights = 0 |
|
|
561 | 561 | self.firstHeight = 0 |
|
|
562 | 562 | self.deltaHeight = 0 |
|
|
563 | 563 | self.samplesWin = 0 |
|
|
564 | 564 | self.spectraComb = 0 |
|
|
565 | 565 | self.nCode = None |
|
|
566 | 566 | self.code = None |
|
|
567 | 567 | self.nBaud = None |
|
|
568 | 568 | |
|
|
569 | 569 | self.shif_fft = False |
|
|
570 | 570 | self.flag_dc = False |
|
|
571 | 571 | self.flag_cspc = False |
|
|
572 | 572 | self.flag_decode = False |
|
|
573 | 573 | self.flag_deflip = False |
|
|
574 | 574 | self.length = 0 |
|
|
575 | 575 | def read(self, fp): |
|
|
576 | 576 | self.length = 0 |
|
|
577 | 577 | try: |
|
|
578 | 578 | startFp = fp.tell() |
|
|
579 | 579 | except Exception, e: |
|
|
580 | 580 | startFp = None |
|
|
581 | 581 | pass |
|
|
582 | 582 | |
|
|
583 | 583 | try: |
|
|
584 | 584 | if hasattr(fp, 'read'): |
|
|
585 | 585 | header = numpy.fromfile(fp, PROCESSING_STRUCTURE, 1) |
|
|
586 | 586 | else: |
|
|
587 | 587 | header = numpy.fromstring(fp, PROCESSING_STRUCTURE, 1) |
|
|
588 | 588 | self.length += header.nbytes |
|
|
589 | 589 | except Exception, e: |
|
|
590 | 590 | print "ProcessingHeader: " + str(e) |
|
|
591 | 591 | return 0 |
|
|
592 | 592 | |
|
|
593 | 593 | size = int(header['nSize'][0]) |
|
|
594 | 594 | self.dtype = int(header['nDataType'][0]) |
|
|
595 | 595 | self.blockSize = int(header['nSizeOfDataBlock'][0]) |
|
|
596 | 596 | self.profilesPerBlock = int(header['nProfilesperBlock'][0]) |
|
|
597 | 597 | self.dataBlocksPerFile = int(header['nDataBlocksperFile'][0]) |
|
|
598 | 598 | self.nWindows = int(header['nNumWindows'][0]) |
|
|
599 | 599 | self.processFlags = header['nProcessFlags'] |
|
|
600 | 600 | self.nCohInt = int(header['nCoherentIntegrations'][0]) |
|
|
601 | 601 | self.nIncohInt = int(header['nIncoherentIntegrations'][0]) |
|
|
602 | 602 | self.totalSpectra = int(header['nTotalSpectra'][0]) |
|
|
603 | 603 | |
|
|
604 | 604 | try: |
|
|
605 | 605 | if hasattr(fp, 'read'): |
|
|
606 | 606 | samplingWindow = numpy.fromfile(fp, SAMPLING_STRUCTURE, self.nWindows) |
|
|
607 | 607 | else: |
|
|
608 | 608 | samplingWindow = numpy.fromstring(fp[self.length:], SAMPLING_STRUCTURE, self.nWindows) |
|
|
609 | 609 | self.length += samplingWindow.nbytes |
|
|
610 | 610 | except Exception, e: |
|
|
611 | 611 | print "ProcessingHeader: " + str(e) |
|
|
612 | 612 | return 0 |
|
|
613 | 613 | |
|
|
614 | 614 | self.nHeights = int(numpy.sum(samplingWindow['nsa'])) |
|
|
615 | 615 | self.firstHeight = float(samplingWindow['h0'][0]) |
|
|
616 | 616 | self.deltaHeight = float(samplingWindow['dh'][0]) |
|
|
617 | 617 | self.samplesWin = samplingWindow['nsa'][0] |
|
|
618 | 618 | |
|
|
619 | 619 | |
|
|
620 | 620 | try: |
|
|
621 | 621 | if hasattr(fp, 'read'): |
|
|
622 | 622 | self.spectraComb = numpy.fromfile(fp, 'u1', 2*self.totalSpectra) |
|
|
623 | 623 | else: |
|
|
624 | 624 | self.spectraComb = numpy.fromstring(fp[self.length:], 'u1', 2*self.totalSpectra) |
|
|
625 | 625 | self.length += self.spectraComb.nbytes |
|
|
626 | 626 | except Exception, e: |
|
|
627 | 627 | print "ProcessingHeader: " + str(e) |
|
|
628 | 628 | return 0 |
|
|
629 | 629 | |
|
|
630 | 630 | if ((self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE) == PROCFLAG.DEFINE_PROCESS_CODE): |
|
|
631 | 631 | self.nCode = int(numpy.fromfile(fp,'<u4',1)) |
|
|
632 | 632 | self.nBaud = int(numpy.fromfile(fp,'<u4',1)) |
|
|
633 | 633 | self.code = numpy.fromfile(fp,'<f4',self.nCode*self.nBaud).reshape(self.nCode,self.nBaud) |
|
|
634 | 634 | |
|
|
635 | 635 | if ((self.processFlags & PROCFLAG.EXP_NAME_ESP) == PROCFLAG.EXP_NAME_ESP): |
|
|
636 | 636 | exp_name_len = int(numpy.fromfile(fp,'<u4',1)) |
|
|
637 | 637 | exp_name = numpy.fromfile(fp,'u1',exp_name_len+1) |
|
|
638 | 638 | |
|
|
639 | 639 | if ((self.processFlags & PROCFLAG.SHIFT_FFT_DATA) == PROCFLAG.SHIFT_FFT_DATA): |
|
|
640 | 640 | self.shif_fft = True |
|
|
641 | 641 | else: |
|
|
642 | 642 | self.shif_fft = False |
|
|
643 | 643 | |
|
|
644 | 644 | if ((self.processFlags & PROCFLAG.SAVE_CHANNELS_DC) == PROCFLAG.SAVE_CHANNELS_DC): |
|
|
645 | 645 | self.flag_dc = True |
|
|
646 | 646 | else: |
|
|
647 | 647 | self.flag_dc = False |
|
|
648 | 648 | |
|
|
649 | 649 | if ((self.processFlags & PROCFLAG.DECODE_DATA) == PROCFLAG.DECODE_DATA): |
|
|
650 | 650 | self.flag_decode = True |
|
|
651 | 651 | else: |
|
|
652 | 652 | self.flag_decode = False |
|
|
653 | 653 | |
|
|
654 | 654 | if ((self.processFlags & PROCFLAG.DEFLIP_DATA) == PROCFLAG.DEFLIP_DATA): |
|
|
655 | 655 | self.flag_deflip = True |
|
|
656 | 656 | else: |
|
|
657 | 657 | self.flag_deflip = False |
|
|
658 | 658 | |
|
|
659 | 659 | nChannels = 0 |
|
|
660 | 660 | nPairs = 0 |
|
|
661 | 661 | pairList = [] |
|
|
662 | 662 | |
|
|
663 | 663 | for i in range( 0, self.totalSpectra*2, 2 ): |
|
|
664 | 664 | if self.spectraComb[i] == self.spectraComb[i+1]: |
|
|
665 | 665 | nChannels = nChannels + 1 #par de canales iguales |
|
|
666 | 666 | else: |
|
|
667 | 667 | nPairs = nPairs + 1 #par de canales diferentes |
|
|
668 | 668 | pairList.append( (self.spectraComb[i], self.spectraComb[i+1]) ) |
|
|
669 | 669 | |
|
|
670 | 670 | self.flag_cspc = False |
|
|
671 | 671 | if nPairs > 0: |
|
|
672 | 672 | self.flag_cspc = True |
|
|
673 | 673 | |
|
|
674 | 674 | |
|
|
675 | 675 | |
|
|
676 | 676 | if startFp is not None: |
|
|
677 | 677 | endFp = size + startFp |
|
|
678 | 678 | if fp.tell() > endFp: |
|
|
679 | 679 | sys.stderr.write("Warning: Processing header size is lower than it has to be") |
|
|
680 | 680 | return 0 |
|
|
681 | 681 | |
|
|
682 | 682 | if fp.tell() < endFp: |
|
|
683 | 683 | sys.stderr.write("Warning: Processing header size is greater than it is considered") |
|
|
684 | 684 | |
|
|
685 | 685 | return 1 |
|
|
686 | 686 | |
|
|
687 | 687 | def write(self, fp): |
|
|
688 | 688 | #Clear DEFINE_PROCESS_CODE |
|
|
689 | 689 | self.processFlags = self.processFlags & (~PROCFLAG.DEFINE_PROCESS_CODE) |
|
|
690 | 690 | |
|
|
691 | 691 | headerTuple = (self.size, |
|
|
692 | 692 | self.dtype, |
|
|
693 | 693 | self.blockSize, |
|
|
694 | 694 | self.profilesPerBlock, |
|
|
695 | 695 | self.dataBlocksPerFile, |
|
|
696 | 696 | self.nWindows, |
|
|
697 | 697 | self.processFlags, |
|
|
698 | 698 | self.nCohInt, |
|
|
699 | 699 | self.nIncohInt, |
|
|
700 | 700 | self.totalSpectra) |
|
|
701 | 701 | |
|
|
702 | 702 | header = numpy.array(headerTuple,PROCESSING_STRUCTURE) |
|
|
703 | 703 | header.tofile(fp) |
|
|
704 | 704 | |
|
|
705 | 705 | if self.nWindows != 0: |
|
|
706 | 706 | sampleWindowTuple = (self.firstHeight,self.deltaHeight,self.samplesWin) |
|
|
707 | 707 | samplingWindow = numpy.array(sampleWindowTuple,SAMPLING_STRUCTURE) |
|
|
708 | 708 | samplingWindow.tofile(fp) |
|
|
709 | 709 | |
|
|
710 | 710 | if self.totalSpectra != 0: |
|
|
711 | 711 | # spectraComb = numpy.array([],numpy.dtype('u1')) |
|
|
712 | 712 | spectraComb = self.spectraComb |
|
|
713 | 713 | spectraComb.tofile(fp) |
|
|
714 | 714 | |
|
|
715 | 715 | # if self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE == PROCFLAG.DEFINE_PROCESS_CODE: |
|
|
716 | 716 | # nCode = numpy.array([self.nCode], numpy.dtype('u4')) #Probar con un dato que almacene codigo, hasta el momento no se hizo la prueba |
|
|
717 | 717 | # nCode.tofile(fp) |
|
|
718 | 718 | # |
|
|
719 | 719 | # nBaud = numpy.array([self.nBaud], numpy.dtype('u4')) |
|
|
720 | 720 | # nBaud.tofile(fp) |
|
|
721 | 721 | # |
|
|
722 | 722 | # code = self.code.reshape(self.nCode*self.nBaud) |
|
|
723 | 723 | # code = code.astype(numpy.dtype('<f4')) |
|
|
724 | 724 | # code.tofile(fp) |
|
|
725 | 725 | |
|
|
726 | 726 | return 1 |
|
|
727 | 727 | |
|
|
728 | 728 | def get_size(self): |
|
|
729 | 729 | |
|
|
730 | 730 | self.__size = 40 + 12*self.nWindows + 2*self.totalSpectra |
|
|
731 | 731 | |
|
|
732 | 732 | # if self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE == PROCFLAG.DEFINE_PROCESS_CODE: |
|
|
733 | 733 | # self.__size += 4 + 4 + 4*self.nCode*numpy.ceil(self.nBaud/32.) |
|
|
734 | 734 | # self.__size += 4 + 4 + 4 * self.nCode * self.nBaud |
|
|
735 | 735 | |
|
|
736 | 736 | return self.__size |
|
|
737 | 737 | |
|
|
738 | 738 | def set_size(self, value): |
|
|
739 | 739 | |
|
|
740 | 740 | raise IOError, "size is a property and it cannot be set, just read" |
|
|
741 | 741 | |
|
|
742 | 742 | return |
|
|
743 | 743 | |
|
|
744 | 744 | size = property(get_size, set_size) |
|
|
745 | 745 | |
|
|
746 | 746 | class RCfunction: |
|
|
747 | 747 | NONE=0 |
|
|
748 | 748 | FLIP=1 |
|
|
749 | 749 | CODE=2 |
|
|
750 | 750 | SAMPLING=3 |
|
|
751 | 751 | LIN6DIV256=4 |
|
|
752 | 752 | SYNCHRO=5 |
|
|
753 | 753 | |
|
|
754 | 754 | class nCodeType: |
|
|
755 | 755 | NONE=0 |
|
|
756 | 756 | USERDEFINE=1 |
|
|
757 | 757 | BARKER2=2 |
|
|
758 | 758 | BARKER3=3 |
|
|
759 | 759 | BARKER4=4 |
|
|
760 | 760 | BARKER5=5 |
|
|
761 | 761 | BARKER7=6 |
|
|
762 | 762 | BARKER11=7 |
|
|
763 | 763 | BARKER13=8 |
|
|
764 | 764 | AC128=9 |
|
|
765 | 765 | COMPLEMENTARYCODE2=10 |
|
|
766 | 766 | COMPLEMENTARYCODE4=11 |
|
|
767 | 767 | COMPLEMENTARYCODE8=12 |
|
|
768 | 768 | COMPLEMENTARYCODE16=13 |
|
|
769 | 769 | COMPLEMENTARYCODE32=14 |
|
|
770 | 770 | COMPLEMENTARYCODE64=15 |
|
|
771 | 771 | COMPLEMENTARYCODE128=16 |
|
|
772 | 772 | CODE_BINARY28=17 |
|
|
773 | 773 | |
|
|
774 | 774 | class PROCFLAG: |
|
|
775 | 775 | |
|
|
776 | 776 | COHERENT_INTEGRATION = numpy.uint32(0x00000001) |
|
|
777 | 777 | DECODE_DATA = numpy.uint32(0x00000002) |
|
|
778 | 778 | SPECTRA_CALC = numpy.uint32(0x00000004) |
|
|
779 | 779 | INCOHERENT_INTEGRATION = numpy.uint32(0x00000008) |
|
|
780 | 780 | POST_COHERENT_INTEGRATION = numpy.uint32(0x00000010) |
|
|
781 | 781 | SHIFT_FFT_DATA = numpy.uint32(0x00000020) |
|
|
782 | 782 | |
|
|
783 | 783 | DATATYPE_CHAR = numpy.uint32(0x00000040) |
|
|
784 | 784 | DATATYPE_SHORT = numpy.uint32(0x00000080) |
|
|
785 | 785 | DATATYPE_LONG = numpy.uint32(0x00000100) |
|
|
786 | 786 | DATATYPE_INT64 = numpy.uint32(0x00000200) |
|
|
787 | 787 | DATATYPE_FLOAT = numpy.uint32(0x00000400) |
|
|
788 | 788 | DATATYPE_DOUBLE = numpy.uint32(0x00000800) |
|
|
789 | 789 | |
|
|
790 | 790 | DATAARRANGE_CONTIGUOUS_CH = numpy.uint32(0x00001000) |
|
|
791 | 791 | DATAARRANGE_CONTIGUOUS_H = numpy.uint32(0x00002000) |
|
|
792 | 792 | DATAARRANGE_CONTIGUOUS_P = numpy.uint32(0x00004000) |
|
|
793 | 793 | |
|
|
794 | 794 | SAVE_CHANNELS_DC = numpy.uint32(0x00008000) |
|
|
795 | 795 | DEFLIP_DATA = numpy.uint32(0x00010000) |
|
|
796 | 796 | DEFINE_PROCESS_CODE = numpy.uint32(0x00020000) |
|
|
797 | 797 | |
|
|
798 | 798 | ACQ_SYS_NATALIA = numpy.uint32(0x00040000) |
|
|
799 | 799 | ACQ_SYS_ECHOTEK = numpy.uint32(0x00080000) |
|
|
800 | 800 | ACQ_SYS_ADRXD = numpy.uint32(0x000C0000) |
|
|
801 | 801 | ACQ_SYS_JULIA = numpy.uint32(0x00100000) |
|
|
802 | 802 | ACQ_SYS_XXXXXX = numpy.uint32(0x00140000) |
|
|
803 | 803 | |
|
|
804 | 804 | EXP_NAME_ESP = numpy.uint32(0x00200000) |
|
|
805 | 805 | CHANNEL_NAMES_ESP = numpy.uint32(0x00400000) |
|
|
806 | 806 | |
|
|
807 | 807 | OPERATION_MASK = numpy.uint32(0x0000003F) |
|
|
808 | 808 | DATATYPE_MASK = numpy.uint32(0x00000FC0) |
|
|
809 | 809 | DATAARRANGE_MASK = numpy.uint32(0x00007000) |
|
|
810 | 810 | ACQ_SYS_MASK = numpy.uint32(0x001C0000) |
|
|
811 | 811 | |
|
|
812 | 812 | dtype0 = numpy.dtype([('real','<i1'),('imag','<i1')]) |
|
|
813 | 813 | dtype1 = numpy.dtype([('real','<i2'),('imag','<i2')]) |
|
|
814 | 814 | dtype2 = numpy.dtype([('real','<i4'),('imag','<i4')]) |
|
|
815 | 815 | dtype3 = numpy.dtype([('real','<i8'),('imag','<i8')]) |
|
|
816 | 816 | dtype4 = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
817 | 817 | dtype5 = numpy.dtype([('real','<f8'),('imag','<f8')]) |
|
|
818 | 818 | |
|
|
819 | 819 | NUMPY_DTYPE_LIST = [dtype0, dtype1, dtype2, dtype3, dtype4, dtype5] |
|
|
820 | 820 | |
|
|
821 | 821 | PROCFLAG_DTYPE_LIST = [PROCFLAG.DATATYPE_CHAR, |
|
|
822 | 822 | PROCFLAG.DATATYPE_SHORT, |
|
|
823 | 823 | PROCFLAG.DATATYPE_LONG, |
|
|
824 | 824 | PROCFLAG.DATATYPE_INT64, |
|
|
825 | 825 | PROCFLAG.DATATYPE_FLOAT, |
|
|
826 | 826 | PROCFLAG.DATATYPE_DOUBLE] |
|
|
827 | 827 | |
|
|
828 | 828 | DTYPE_WIDTH = [1, 2, 4, 8, 4, 8] |
|
|
829 | 829 | |
|
|
830 | 830 | def get_dtype_index(numpy_dtype): |
|
|
831 | 831 | |
|
|
832 | 832 | index = None |
|
|
833 | 833 | |
|
|
834 | 834 | for i in range(len(NUMPY_DTYPE_LIST)): |
|
|
835 | 835 | if numpy_dtype == NUMPY_DTYPE_LIST[i]: |
|
|
836 | 836 | index = i |
|
|
837 | 837 | break |
|
|
838 | 838 | |
|
|
839 | 839 | return index |
|
|
840 | 840 | |
|
|
841 | 841 | def get_numpy_dtype(index): |
|
|
842 | 842 | |
|
|
843 | 843 | return NUMPY_DTYPE_LIST[index] |
|
|
844 | 844 | |
|
|
845 | 845 | def get_procflag_dtype(index): |
|
|
846 | 846 | |
|
|
847 | 847 | return PROCFLAG_DTYPE_LIST[index] |
|
|
848 | 848 | |
|
|
849 | 849 | def get_dtype_width(index): |
|
|
850 | 850 | |
|
|
851 | 851 | return DTYPE_WIDTH[index] No newline at end of file |
| @@ -1,2805 +1,2805 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import math |
|
|
3 | 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 | import re |
|
|
5 | 5 | import datetime |
|
|
6 | 6 | import copy |
|
|
7 | 7 | import sys |
|
|
8 | 8 | import importlib |
|
|
9 | 9 | import itertools |
|
|
10 | 10 | |
|
|
11 | 11 | from jroproc_base import ProcessingUnit, Operation |
|
|
12 | 12 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
13 | 13 | |
|
|
14 | 14 | |
|
|
15 | 15 | class ParametersProc(ProcessingUnit): |
|
|
16 | 16 | |
|
|
17 | 17 | nSeconds = None |
|
|
18 | 18 | |
|
|
19 | 19 | def __init__(self): |
|
|
20 | 20 | ProcessingUnit.__init__(self) |
|
|
21 | 21 | |
|
|
22 | 22 | # self.objectDict = {} |
|
|
23 | 23 | self.buffer = None |
|
|
24 | 24 | self.firstdatatime = None |
|
|
25 | 25 | self.profIndex = 0 |
|
|
26 | 26 | self.dataOut = Parameters() |
|
|
27 | 27 | |
|
|
28 | 28 | def __updateObjFromInput(self): |
|
|
29 | 29 | |
|
|
30 | 30 | self.dataOut.inputUnit = self.dataIn.type |
|
|
31 | 31 | |
|
|
32 | 32 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
33 | 33 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
34 | 34 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
35 | 35 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
36 | 36 | |
|
|
37 | 37 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
38 | 38 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 40 | self.dataOut.heightList = self.dataIn.heightList |
|
|
41 | 41 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
42 | 42 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
43 | 43 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
44 | 44 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
45 | 45 | self.dataOut.nCode = self.dataIn.nCode |
|
|
46 | 46 | self.dataOut.code = self.dataIn.code |
|
|
47 | 47 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
48 | 48 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
49 | 49 | # self.dataOut.utctime = self.firstdatatime |
|
|
50 | 50 | self.dataOut.utctime = self.dataIn.utctime |
|
|
51 | 51 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
52 | 52 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
53 | 53 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
54 | 54 | # self.dataOut.nIncohInt = 1 |
|
|
55 | 55 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
56 | 56 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
57 | 57 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
58 | 58 | self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
59 | 59 | self.dataOut.frequency = self.dataIn.frequency |
|
|
60 | 60 | #self.dataOut.noise = self.dataIn.noise |
|
|
61 | 61 | |
|
|
62 | 62 | def run(self): |
|
|
63 | 63 | |
|
|
64 | 64 | #---------------------- Voltage Data --------------------------- |
|
|
65 | 65 | |
|
|
66 | 66 | if self.dataIn.type == "Voltage": |
|
|
67 | 67 | |
|
|
68 | 68 | self.__updateObjFromInput() |
|
|
69 | 69 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
70 | 70 | self.dataOut.flagNoData = False |
|
|
71 | 71 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
72 | 72 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
73 | 73 | return |
|
|
74 | 74 | |
|
|
75 | 75 | #---------------------- Spectra Data --------------------------- |
|
|
76 | 76 | |
|
|
77 | 77 | if self.dataIn.type == "Spectra": |
|
|
78 | 78 | |
|
|
79 | 79 | self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc) |
|
|
80 | 80 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
81 | 81 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
82 | 82 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
83 | 83 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
84 | 84 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
85 | 85 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
86 | 86 | #self.dataOut.normFactor = self.dataIn.getNormFactor() |
|
|
87 | 87 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
88 | 88 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
89 | 89 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
90 | 90 | self.dataOut.flagNoData = False |
|
|
91 | 91 | |
|
|
92 | 92 | #---------------------- Correlation Data --------------------------- |
|
|
93 | 93 | |
|
|
94 | 94 | if self.dataIn.type == "Correlation": |
|
|
95 | 95 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
96 | 96 | |
|
|
97 | 97 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
98 | 98 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
99 | 99 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
100 | 100 | |
|
|
101 | 101 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
102 | 102 | self.dataOut.noise = self.dataIn.noise |
|
|
103 | 103 | self.dataOut.data_SNR = self.dataIn.SNR |
|
|
104 | 104 | self.dataOut.flagNoData = False |
|
|
105 | 105 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
106 | 106 | |
|
|
107 | 107 | #---------------------- Parameters Data --------------------------- |
|
|
108 | 108 | |
|
|
109 | 109 | if self.dataIn.type == "Parameters": |
|
|
110 | 110 | self.dataOut.copy(self.dataIn) |
|
|
111 | 111 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
112 | 112 | self.dataOut.flagNoData = False |
|
|
113 | 113 | |
|
|
114 | 114 | return True |
|
|
115 | 115 | |
|
|
116 | 116 | self.__updateObjFromInput() |
|
|
117 | 117 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
118 | 118 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
119 | 119 | |
|
|
120 | 120 | return |
|
|
121 | 121 | |
|
|
122 | 122 | class SpectralMoments(Operation): |
|
|
123 | 123 | |
|
|
124 | 124 | ''' |
|
|
125 | 125 | Function SpectralMoments() |
|
|
126 | 126 | |
|
|
127 | 127 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
128 | 128 | |
|
|
129 | 129 | Type of dataIn: Spectra |
|
|
130 | 130 | |
|
|
131 | 131 | Configuration Parameters: |
|
|
132 | 132 | |
|
|
133 | 133 | dirCosx : Cosine director in X axis |
|
|
134 | 134 | dirCosy : Cosine director in Y axis |
|
|
135 | 135 | |
|
|
136 | 136 | elevation : |
|
|
137 | 137 | azimuth : |
|
|
138 | 138 | |
|
|
139 | 139 | Input: |
|
|
140 | 140 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
141 | 141 | self.dataOut.data_pre : Spectral data |
|
|
142 | 142 | self.dataOut.abscissaList : List of frequencies |
|
|
143 | 143 | self.dataOut.noise : Noise level per channel |
|
|
144 | 144 | |
|
|
145 | 145 | Affected: |
|
|
146 | 146 | self.dataOut.data_param : Parameters per channel |
|
|
147 | 147 | self.dataOut.data_SNR : SNR per channel |
|
|
148 | 148 | |
|
|
149 | 149 | ''' |
|
|
150 | 150 | |
|
|
151 | 151 | def run(self, dataOut): |
|
|
152 | 152 | |
|
|
153 | 153 | #dataOut.data_pre = dataOut.data_pre[0] |
|
|
154 | 154 | data = dataOut.data_pre[0] |
|
|
155 | 155 | absc = dataOut.abscissaList[:-1] |
|
|
156 | 156 | noise = dataOut.noise |
|
|
157 | 157 | nChannel = data.shape[0] |
|
|
158 | 158 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
159 | 159 | |
|
|
160 | 160 | for ind in range(nChannel): |
|
|
161 | 161 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
|
|
162 | 162 | |
|
|
163 | 163 | dataOut.data_param = data_param[:,1:,:] |
|
|
164 | 164 | dataOut.data_SNR = data_param[:,0] |
|
|
165 | 165 | dataOut.data_DOP = data_param[:,1] |
|
|
166 | 166 | dataOut.data_MEAN = data_param[:,2] |
|
|
167 | 167 | dataOut.data_STD = data_param[:,3] |
|
|
168 | 168 | return |
|
|
169 | 169 | |
|
|
170 | 170 | def __calculateMoments(self, oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
171 | 171 | |
|
|
172 | 172 | if (nicoh is None): nicoh = 1 |
|
|
173 | 173 | if (graph is None): graph = 0 |
|
|
174 | 174 | if (smooth is None): smooth = 0 |
|
|
175 | 175 | elif (self.smooth < 3): smooth = 0 |
|
|
176 | 176 | |
|
|
177 | 177 | if (type1 is None): type1 = 0 |
|
|
178 | 178 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
179 | 179 | if (snrth is None): snrth = -3 |
|
|
180 | 180 | if (dc is None): dc = 0 |
|
|
181 | 181 | if (aliasing is None): aliasing = 0 |
|
|
182 | 182 | if (oldfd is None): oldfd = 0 |
|
|
183 | 183 | if (wwauto is None): wwauto = 0 |
|
|
184 | 184 | |
|
|
185 | 185 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
186 | 186 | |
|
|
187 | 187 | freq = oldfreq |
|
|
188 | 188 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
189 | 189 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
190 | 190 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
191 | 191 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
192 | 192 | |
|
|
193 | 193 | for ind in range(oldspec.shape[1]): |
|
|
194 | 194 | |
|
|
195 | 195 | spec = oldspec[:,ind] |
|
|
196 | 196 | aux = spec*fwindow |
|
|
197 | 197 | max_spec = aux.max() |
|
|
198 | 198 | m = list(aux).index(max_spec) |
|
|
199 | 199 | |
|
|
200 | 200 | #Smooth |
|
|
201 | 201 | if (smooth == 0): spec2 = spec |
|
|
202 | 202 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
203 | 203 | |
|
|
204 | 204 | # Calculo de Momentos |
|
|
205 | 205 | bb = spec2[range(m,spec2.size)] |
|
|
206 | 206 | bb = (bb<n0).nonzero() |
|
|
207 | 207 | bb = bb[0] |
|
|
208 | 208 | |
|
|
209 | 209 | ss = spec2[range(0,m + 1)] |
|
|
210 | 210 | ss = (ss<n0).nonzero() |
|
|
211 | 211 | ss = ss[0] |
|
|
212 | 212 | |
|
|
213 | 213 | if (bb.size == 0): |
|
|
214 | 214 | bb0 = spec.size - 1 - m |
|
|
215 | 215 | else: |
|
|
216 | 216 | bb0 = bb[0] - 1 |
|
|
217 | 217 | if (bb0 < 0): |
|
|
218 | 218 | bb0 = 0 |
|
|
219 | 219 | |
|
|
220 | 220 | if (ss.size == 0): ss1 = 1 |
|
|
221 | 221 | else: ss1 = max(ss) + 1 |
|
|
222 | 222 | |
|
|
223 | 223 | if (ss1 > m): ss1 = m |
|
|
224 | 224 | |
|
|
225 | 225 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
226 | 226 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
227 | 227 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
228 | 228 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
229 | 229 | snr = (spec2.mean()-n0)/n0 |
|
|
230 | 230 | |
|
|
231 | 231 | if (snr < 1.e-20) : |
|
|
232 | 232 | snr = 1.e-20 |
|
|
233 | 233 | |
|
|
234 | 234 | vec_power[ind] = power |
|
|
235 | 235 | vec_fd[ind] = fd |
|
|
236 | 236 | vec_w[ind] = w |
|
|
237 | 237 | vec_snr[ind] = snr |
|
|
238 | 238 | |
|
|
239 | 239 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
240 | 240 | return moments |
|
|
241 | 241 | |
|
|
242 | 242 | #------------------ Get SA Parameters -------------------------- |
|
|
243 | 243 | |
|
|
244 | 244 | def GetSAParameters(self): |
|
|
245 | 245 | #SA en frecuencia |
|
|
246 | 246 | pairslist = self.dataOut.groupList |
|
|
247 | 247 | num_pairs = len(pairslist) |
|
|
248 | 248 | |
|
|
249 | 249 | vel = self.dataOut.abscissaList |
|
|
250 | 250 | spectra = self.dataOut.data_pre[0] |
|
|
251 | 251 | cspectra = self.dataOut.data_pre[1] |
|
|
252 | 252 | delta_v = vel[1] - vel[0] |
|
|
253 | 253 | |
|
|
254 | 254 | #Calculating the power spectrum |
|
|
255 | 255 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
256 | 256 | #Normalizing Spectra |
|
|
257 | 257 | norm_spectra = spectra/spc_pow |
|
|
258 | 258 | #Calculating the norm_spectra at peak |
|
|
259 | 259 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
260 | 260 | |
|
|
261 | 261 | #Normalizing Cross Spectra |
|
|
262 | 262 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
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263 | 263 | |
|
|
264 | 264 | for i in range(num_chan): |
|
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265 | 265 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
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266 | 266 | |
|
|
267 | 267 | max_cspectra = numpy.max(norm_cspectra,2) |
|
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268 | 268 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
269 | 269 | |
|
|
270 | 270 | for i in range(num_pairs): |
|
|
271 | 271 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
272 | 272 | #------------------- Get Lags ---------------------------------- |
|
|
273 | 273 | |
|
|
274 | 274 | class SALags(Operation): |
|
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275 | 275 | ''' |
|
|
276 | 276 | Function GetMoments() |
|
|
277 | 277 | |
|
|
278 | 278 | Input: |
|
|
279 | 279 | self.dataOut.data_pre |
|
|
280 | 280 | self.dataOut.abscissaList |
|
|
281 | 281 | self.dataOut.noise |
|
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282 | 282 | self.dataOut.normFactor |
|
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283 | 283 | self.dataOut.data_SNR |
|
|
284 | 284 | self.dataOut.groupList |
|
|
285 | 285 | self.dataOut.nChannels |
|
|
286 | 286 | |
|
|
287 | 287 | Affected: |
|
|
288 | 288 | self.dataOut.data_param |
|
|
289 | 289 | |
|
|
290 | 290 | ''' |
|
|
291 | 291 | def run(self, dataOut): |
|
|
292 | 292 | data_acf = dataOut.data_pre[0] |
|
|
293 | 293 | data_ccf = dataOut.data_pre[1] |
|
|
294 | 294 | normFactor_acf = dataOut.normFactor[0] |
|
|
295 | 295 | normFactor_ccf = dataOut.normFactor[1] |
|
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296 | 296 | pairs_acf = dataOut.groupList[0] |
|
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297 | 297 | pairs_ccf = dataOut.groupList[1] |
|
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298 | 298 | |
|
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299 | 299 | nHeights = dataOut.nHeights |
|
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300 | 300 | absc = dataOut.abscissaList |
|
|
301 | 301 | noise = dataOut.noise |
|
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302 | 302 | SNR = dataOut.data_SNR |
|
|
303 | 303 | nChannels = dataOut.nChannels |
|
|
304 | 304 | # pairsList = dataOut.groupList |
|
|
305 | 305 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
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306 | 306 | |
|
|
307 | 307 | for l in range(len(pairs_acf)): |
|
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308 | 308 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
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309 | 309 | |
|
|
310 | 310 | for l in range(len(pairs_ccf)): |
|
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311 | 311 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
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312 | 312 | |
|
|
313 | 313 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
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314 | 314 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
315 | 315 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
316 | 316 | return |
|
|
317 | 317 | |
|
|
318 | 318 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
319 | 319 | # |
|
|
320 | 320 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
321 | 321 | # |
|
|
322 | 322 | # for l in range(len(pairsList)): |
|
|
323 | 323 | # firstChannel = pairsList[l][0] |
|
|
324 | 324 | # secondChannel = pairsList[l][1] |
|
|
325 | 325 | # |
|
|
326 | 326 | # #Obteniendo pares de Autocorrelacion |
|
|
327 | 327 | # if firstChannel == secondChannel: |
|
|
328 | 328 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
329 | 329 | # |
|
|
330 | 330 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
331 | 331 | # |
|
|
332 | 332 | # pairsCrossCorr = range(len(pairsList)) |
|
|
333 | 333 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
334 | 334 | # |
|
|
335 | 335 | # return pairsAutoCorr, pairsCrossCorr |
|
|
336 | 336 | |
|
|
337 | 337 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
338 | 338 | |
|
|
339 | 339 | lag0 = data_acf.shape[1]/2 |
|
|
340 | 340 | #Funcion de Autocorrelacion |
|
|
341 | 341 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
342 | 342 | |
|
|
343 | 343 | #Obtencion Indice de TauCross |
|
|
344 | 344 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
345 | 345 | #Obtencion Indice de TauAuto |
|
|
346 | 346 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
347 | 347 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
348 | 348 | |
|
|
349 | 349 | for i in range(ccf_lag0.shape[0]): |
|
|
350 | 350 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
351 | 351 | |
|
|
352 | 352 | #Obtencion de TauCross y TauAuto |
|
|
353 | 353 | tau_ccf = lagRange[ind_ccf] |
|
|
354 | 354 | tau_acf = lagRange[ind_acf] |
|
|
355 | 355 | |
|
|
356 | 356 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
357 | 357 | |
|
|
358 | 358 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
359 | 359 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
360 | 360 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
361 | 361 | |
|
|
362 | 362 | return tau |
|
|
363 | 363 | |
|
|
364 | 364 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
365 | 365 | data1 = stats.nanmean(data, axis = 0) |
|
|
366 | 366 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
367 | 367 | |
|
|
368 | 368 | phase = numpy.angle(data1[lag1,:]) |
|
|
369 | 369 | |
|
|
370 | 370 | return phase |
|
|
371 | 371 | |
|
|
372 | 372 | class SpectralFitting(Operation): |
|
|
373 | 373 | ''' |
|
|
374 | 374 | Function GetMoments() |
|
|
375 | 375 | |
|
|
376 | 376 | Input: |
|
|
377 | 377 | Output: |
|
|
378 | 378 | Variables modified: |
|
|
379 | 379 | ''' |
|
|
380 | 380 | |
|
|
381 | 381 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
382 | 382 | |
|
|
383 | 383 | |
|
|
384 | 384 | if path != None: |
|
|
385 | 385 | sys.path.append(path) |
|
|
386 | 386 | self.dataOut.library = importlib.import_module(file) |
|
|
387 | 387 | |
|
|
388 | 388 | #To be inserted as a parameter |
|
|
389 | 389 | groupArray = numpy.array(groupList) |
|
|
390 | 390 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
391 | 391 | self.dataOut.groupList = groupArray |
|
|
392 | 392 | |
|
|
393 | 393 | nGroups = groupArray.shape[0] |
|
|
394 | 394 | nChannels = self.dataIn.nChannels |
|
|
395 | 395 | nHeights=self.dataIn.heightList.size |
|
|
396 | 396 | |
|
|
397 | 397 | #Parameters Array |
|
|
398 | 398 | self.dataOut.data_param = None |
|
|
399 | 399 | |
|
|
400 | 400 | #Set constants |
|
|
401 | 401 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
402 | 402 | self.dataOut.constants = constants |
|
|
403 | 403 | M = self.dataIn.normFactor |
|
|
404 | 404 | N = self.dataIn.nFFTPoints |
|
|
405 | 405 | ippSeconds = self.dataIn.ippSeconds |
|
|
406 | 406 | K = self.dataIn.nIncohInt |
|
|
407 | 407 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
408 | 408 | |
|
|
409 | 409 | #List of possible combinations |
|
|
410 | 410 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
411 | 411 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
412 | 412 | |
|
|
413 | 413 | if getSNR: |
|
|
414 | 414 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
415 | 415 | listChannels.sort() |
|
|
416 | 416 | noise = self.dataIn.getNoise() |
|
|
417 | 417 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
418 | 418 | |
|
|
419 | 419 | for i in range(nGroups): |
|
|
420 | 420 | coord = groupArray[i,:] |
|
|
421 | 421 | |
|
|
422 | 422 | #Input data array |
|
|
423 | 423 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
424 | 424 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
425 | 425 | |
|
|
426 | 426 | #Cross Spectra data array for Covariance Matrixes |
|
|
427 | 427 | ind = 0 |
|
|
428 | 428 | for pairs in listComb: |
|
|
429 | 429 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
430 | 430 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
431 | 431 | ind += 1 |
|
|
432 | 432 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
433 | 433 | dataCross = dataCross**2/K |
|
|
434 | 434 | |
|
|
435 | 435 | for h in range(nHeights): |
|
|
436 | 436 | # print self.dataOut.heightList[h] |
|
|
437 | 437 | |
|
|
438 | 438 | #Input |
|
|
439 | 439 | d = data[:,h] |
|
|
440 | 440 | |
|
|
441 | 441 | #Covariance Matrix |
|
|
442 | 442 | D = numpy.diag(d**2/K) |
|
|
443 | 443 | ind = 0 |
|
|
444 | 444 | for pairs in listComb: |
|
|
445 | 445 | #Coordinates in Covariance Matrix |
|
|
446 | 446 | x = pairs[0] |
|
|
447 | 447 | y = pairs[1] |
|
|
448 | 448 | #Channel Index |
|
|
449 | 449 | S12 = dataCross[ind,:,h] |
|
|
450 | 450 | D12 = numpy.diag(S12) |
|
|
451 | 451 | #Completing Covariance Matrix with Cross Spectras |
|
|
452 | 452 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
453 | 453 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
454 | 454 | ind += 1 |
|
|
455 | 455 | Dinv=numpy.linalg.inv(D) |
|
|
456 | 456 | L=numpy.linalg.cholesky(Dinv) |
|
|
457 | 457 | LT=L.T |
|
|
458 | 458 | |
|
|
459 | 459 | dp = numpy.dot(LT,d) |
|
|
460 | 460 | |
|
|
461 | 461 | #Initial values |
|
|
462 | 462 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
463 | 463 | |
|
|
464 | 464 | if (h>0)and(error1[3]<5): |
|
|
465 | 465 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
466 | 466 | else: |
|
|
467 | 467 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
468 | 468 | |
|
|
469 | 469 | try: |
|
|
470 | 470 | #Least Squares |
|
|
471 | 471 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
472 | 472 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
473 | 473 | #Chi square error |
|
|
474 | 474 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
475 | 475 | #Error with Jacobian |
|
|
476 | 476 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
477 | 477 | except: |
|
|
478 | 478 | minp = p0*numpy.nan |
|
|
479 | 479 | error0 = numpy.nan |
|
|
480 | 480 | error1 = p0*numpy.nan |
|
|
481 | 481 | |
|
|
482 | 482 | #Save |
|
|
483 | 483 | if self.dataOut.data_param is None: |
|
|
484 | 484 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
485 | 485 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
486 | 486 | |
|
|
487 | 487 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
488 | 488 | self.dataOut.data_param[i,:,h] = minp |
|
|
489 | 489 | return |
|
|
490 | 490 | |
|
|
491 | 491 | def __residFunction(self, p, dp, LT, constants): |
|
|
492 | 492 | |
|
|
493 | 493 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
494 | 494 | fmp=numpy.dot(LT,fm) |
|
|
495 | 495 | |
|
|
496 | 496 | return dp-fmp |
|
|
497 | 497 | |
|
|
498 | 498 | def __getSNR(self, z, noise): |
|
|
499 | 499 | |
|
|
500 | 500 | avg = numpy.average(z, axis=1) |
|
|
501 | 501 | SNR = (avg.T-noise)/noise |
|
|
502 | 502 | SNR = SNR.T |
|
|
503 | 503 | return SNR |
|
|
504 | 504 | |
|
|
505 | 505 | def __chisq(p,chindex,hindex): |
|
|
506 | 506 | #similar to Resid but calculates CHI**2 |
|
|
507 | 507 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
508 | 508 | dp=numpy.dot(LT,d) |
|
|
509 | 509 | fmp=numpy.dot(LT,fm) |
|
|
510 | 510 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
511 | 511 | return chisq |
|
|
512 | 512 | |
|
|
513 | 513 | class WindProfiler(Operation): |
|
|
514 | 514 | |
|
|
515 | 515 | __isConfig = False |
|
|
516 | 516 | |
|
|
517 | 517 | __initime = None |
|
|
518 | 518 | __lastdatatime = None |
|
|
519 | 519 | __integrationtime = None |
|
|
520 | 520 | |
|
|
521 | 521 | __buffer = None |
|
|
522 | 522 | |
|
|
523 | 523 | __dataReady = False |
|
|
524 | 524 | |
|
|
525 | 525 | __firstdata = None |
|
|
526 | 526 | |
|
|
527 | 527 | n = None |
|
|
528 | 528 | |
|
|
529 | 529 | def __calculateCosDir(self, elev, azim): |
|
|
530 | 530 | zen = (90 - elev)*numpy.pi/180 |
|
|
531 | 531 | azim = azim*numpy.pi/180 |
|
|
532 | 532 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
533 | 533 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
534 | 534 | |
|
|
535 | 535 | signX = numpy.sign(numpy.cos(azim)) |
|
|
536 | 536 | signY = numpy.sign(numpy.sin(azim)) |
|
|
537 | 537 | |
|
|
538 | 538 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
539 | 539 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
540 | 540 | return cosDirX, cosDirY |
|
|
541 | 541 | |
|
|
542 | 542 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
543 | 543 | |
|
|
544 | 544 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
545 | 545 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
546 | 546 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
547 | 547 | |
|
|
548 | 548 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
549 | 549 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
550 | 550 | |
|
|
551 | 551 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
552 | 552 | |
|
|
553 | 553 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
554 | 554 | |
|
|
555 | 555 | # |
|
|
556 | 556 | if horOnly: |
|
|
557 | 557 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
558 | 558 | else: |
|
|
559 | 559 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
560 | 560 | A = numpy.asmatrix(A) |
|
|
561 | 561 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
562 | 562 | |
|
|
563 | 563 | return A1 |
|
|
564 | 564 | |
|
|
565 | 565 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
566 | 566 | listPhi = phi.tolist() |
|
|
567 | 567 | maxid = listPhi.index(max(listPhi)) |
|
|
568 | 568 | minid = listPhi.index(min(listPhi)) |
|
|
569 | 569 | |
|
|
570 | 570 | rango = range(len(phi)) |
|
|
571 | 571 | # rango = numpy.delete(rango,maxid) |
|
|
572 | 572 | |
|
|
573 | 573 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
574 | 574 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
575 | 575 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
576 | 576 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
577 | 577 | |
|
|
578 | 578 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
579 | 579 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
580 | 580 | |
|
|
581 | 581 | for i in rango: |
|
|
582 | 582 | x = heiRang*math.cos(phi[i]) |
|
|
583 | 583 | y1 = velRadial[i,:] |
|
|
584 | 584 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
585 | 585 | |
|
|
586 | 586 | x1 = heiRang1 |
|
|
587 | 587 | y11 = f1(x1) |
|
|
588 | 588 | |
|
|
589 | 589 | y2 = SNR[i,:] |
|
|
590 | 590 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
591 | 591 | y21 = f2(x1) |
|
|
592 | 592 | |
|
|
593 | 593 | velRadial1[i,:] = y11 |
|
|
594 | 594 | SNR1[i,:] = y21 |
|
|
595 | 595 | |
|
|
596 | 596 | return heiRang1, velRadial1, SNR1 |
|
|
597 | 597 | |
|
|
598 | 598 | def __calculateVelUVW(self, A, velRadial): |
|
|
599 | 599 | |
|
|
600 | 600 | #Operacion Matricial |
|
|
601 | 601 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
602 | 602 | # for ind in range(velRadial.shape[1]): |
|
|
603 | 603 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
604 | 604 | # velUVW = velUVW.transpose() |
|
|
605 | 605 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
606 | 606 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
607 | 607 | |
|
|
608 | 608 | |
|
|
609 | 609 | return velUVW |
|
|
610 | 610 | |
|
|
611 | 611 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
612 | 612 | |
|
|
613 | 613 | def techniqueDBS(self, kwargs): |
|
|
614 | 614 | """ |
|
|
615 | 615 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
616 | 616 | |
|
|
617 | 617 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
618 | 618 | Direction correction (if necessary), Ranges and SNR |
|
|
619 | 619 | |
|
|
620 | 620 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
621 | 621 | |
|
|
622 | 622 | Parameters affected: Winds, height range, SNR |
|
|
623 | 623 | """ |
|
|
624 | 624 | velRadial0 = kwargs['velRadial'] |
|
|
625 | 625 | heiRang = kwargs['heightList'] |
|
|
626 | 626 | SNR0 = kwargs['SNR'] |
|
|
627 | 627 | |
|
|
628 | 628 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
629 | 629 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
630 | 630 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
631 | 631 | else: |
|
|
632 | 632 | elev = numpy.array(kwargs['elevation']) |
|
|
633 | 633 | azim = numpy.array(kwargs['azimuth']) |
|
|
634 | 634 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
635 | 635 | azimuth = kwargs['correctAzimuth'] |
|
|
636 | 636 | if kwargs.has_key('horizontalOnly'): |
|
|
637 | 637 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
638 | 638 | else: horizontalOnly = False |
|
|
639 | 639 | if kwargs.has_key('correctFactor'): |
|
|
640 | 640 | correctFactor = kwargs['correctFactor'] |
|
|
641 | 641 | else: correctFactor = 1 |
|
|
642 | 642 | if kwargs.has_key('channelList'): |
|
|
643 | 643 | channelList = kwargs['channelList'] |
|
|
644 | 644 | if len(channelList) == 2: |
|
|
645 | 645 | horizontalOnly = True |
|
|
646 | 646 | arrayChannel = numpy.array(channelList) |
|
|
647 | 647 | param = param[arrayChannel,:,:] |
|
|
648 | 648 | theta_x = theta_x[arrayChannel] |
|
|
649 | 649 | theta_y = theta_y[arrayChannel] |
|
|
650 | 650 | |
|
|
651 | 651 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
652 | 652 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
653 | 653 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
654 | 654 | |
|
|
655 | 655 | #Calculo de Componentes de la velocidad con DBS |
|
|
656 | 656 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
657 | 657 | |
|
|
658 | 658 | return winds, heiRang1, SNR1 |
|
|
659 | 659 | |
|
|
660 | 660 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
661 | 661 | |
|
|
662 | 662 | nPairs = len(pairs_ccf) |
|
|
663 | 663 | posx = numpy.asarray(posx) |
|
|
664 | 664 | posy = numpy.asarray(posy) |
|
|
665 | 665 | |
|
|
666 | 666 | #Rotacion Inversa para alinear con el azimuth |
|
|
667 | 667 | if azimuth!= None: |
|
|
668 | 668 | azimuth = azimuth*math.pi/180 |
|
|
669 | 669 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
670 | 670 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
671 | 671 | else: |
|
|
672 | 672 | posx1 = posx |
|
|
673 | 673 | posy1 = posy |
|
|
674 | 674 | |
|
|
675 | 675 | #Calculo de Distancias |
|
|
676 | 676 | distx = numpy.zeros(nPairs) |
|
|
677 | 677 | disty = numpy.zeros(nPairs) |
|
|
678 | 678 | dist = numpy.zeros(nPairs) |
|
|
679 | 679 | ang = numpy.zeros(nPairs) |
|
|
680 | 680 | |
|
|
681 | 681 | for i in range(nPairs): |
|
|
682 | 682 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
683 | 683 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
684 | 684 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
685 | 685 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
686 | 686 | |
|
|
687 | 687 | return distx, disty, dist, ang |
|
|
688 | 688 | #Calculo de Matrices |
|
|
689 | 689 | # nPairs = len(pairs) |
|
|
690 | 690 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
691 | 691 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
692 | 692 | # |
|
|
693 | 693 | # for j in range(nPairs): |
|
|
694 | 694 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
695 | 695 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
696 | 696 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
697 | 697 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
698 | 698 | # |
|
|
699 | 699 | # return distx,disty, dist1,ang1 |
|
|
700 | 700 | |
|
|
701 | 701 | |
|
|
702 | 702 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
703 | 703 | |
|
|
704 | 704 | Ts = lagTRange[1] - lagTRange[0] |
|
|
705 | 705 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
706 | 706 | |
|
|
707 | 707 | return velW |
|
|
708 | 708 | |
|
|
709 | 709 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
710 | 710 | nPairs = tau1.shape[0] |
|
|
711 | 711 | nHeights = tau1.shape[1] |
|
|
712 | 712 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
713 | 713 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
714 | 714 | |
|
|
715 | 715 | angCos = numpy.cos(ang) |
|
|
716 | 716 | angSin = numpy.sin(ang) |
|
|
717 | 717 | |
|
|
718 | 718 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
719 | 719 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
720 | 720 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
721 | 721 | |
|
|
722 | 722 | ind = numpy.where(numpy.isinf(vel)) |
|
|
723 | 723 | vel[ind] = numpy.nan |
|
|
724 | 724 | |
|
|
725 | 725 | return vel |
|
|
726 | 726 | |
|
|
727 | 727 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
728 | 728 | # |
|
|
729 | 729 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
730 | 730 | # |
|
|
731 | 731 | # for l in range(len(pairsList)): |
|
|
732 | 732 | # firstChannel = pairsList[l][0] |
|
|
733 | 733 | # secondChannel = pairsList[l][1] |
|
|
734 | 734 | # |
|
|
735 | 735 | # #Obteniendo pares de Autocorrelacion |
|
|
736 | 736 | # if firstChannel == secondChannel: |
|
|
737 | 737 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
738 | 738 | # |
|
|
739 | 739 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
740 | 740 | # |
|
|
741 | 741 | # pairsCrossCorr = range(len(pairsList)) |
|
|
742 | 742 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
743 | 743 | # |
|
|
744 | 744 | # return pairsAutoCorr, pairsCrossCorr |
|
|
745 | 745 | |
|
|
746 | 746 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
747 | 747 | def techniqueSA(self, kwargs): |
|
|
748 | 748 | |
|
|
749 | 749 | """ |
|
|
750 | 750 | Function that implements Spaced Antenna (SA) technique. |
|
|
751 | 751 | |
|
|
752 | 752 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
753 | 753 | Direction correction (if necessary), Ranges and SNR |
|
|
754 | 754 | |
|
|
755 | 755 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
756 | 756 | |
|
|
757 | 757 | Parameters affected: Winds |
|
|
758 | 758 | """ |
|
|
759 | 759 | position_x = kwargs['positionX'] |
|
|
760 | 760 | position_y = kwargs['positionY'] |
|
|
761 | 761 | azimuth = kwargs['azimuth'] |
|
|
762 | 762 | |
|
|
763 | 763 | if kwargs.has_key('correctFactor'): |
|
|
764 | 764 | correctFactor = kwargs['correctFactor'] |
|
|
765 | 765 | else: |
|
|
766 | 766 | correctFactor = 1 |
|
|
767 | 767 | |
|
|
768 | 768 | groupList = kwargs['groupList'] |
|
|
769 | 769 | pairs_ccf = groupList[1] |
|
|
770 | 770 | tau = kwargs['tau'] |
|
|
771 | 771 | _lambda = kwargs['_lambda'] |
|
|
772 | 772 | |
|
|
773 | 773 | #Cross Correlation pairs obtained |
|
|
774 | 774 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
775 | 775 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
776 | 776 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
777 | 777 | # pairs = [] |
|
|
778 | 778 | # |
|
|
779 | 779 | # #Wind estimation pairs obtained |
|
|
780 | 780 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
781 | 781 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
782 | 782 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
783 | 783 | # pairs.append((ind1,ind2)) |
|
|
784 | 784 | |
|
|
785 | 785 | indtau = tau.shape[0]/2 |
|
|
786 | 786 | tau1 = tau[:indtau,:] |
|
|
787 | 787 | tau2 = tau[indtau:-1,:] |
|
|
788 | 788 | # tau1 = tau1[pairs,:] |
|
|
789 | 789 | # tau2 = tau2[pairs,:] |
|
|
790 | 790 | phase1 = tau[-1,:] |
|
|
791 | 791 | |
|
|
792 | 792 | #--------------------------------------------------------------------- |
|
|
793 | 793 | #Metodo Directo |
|
|
794 | 794 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
795 | 795 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
796 | 796 | winds = stats.nanmean(winds, axis=0) |
|
|
797 | 797 | #--------------------------------------------------------------------- |
|
|
798 | 798 | #Metodo General |
|
|
799 | 799 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
800 | 800 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
801 | 801 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
802 | 802 | # #Calculo de Velocidades |
|
|
803 | 803 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
804 | 804 | |
|
|
805 | 805 | #--------------------------------------------------------------------- |
|
|
806 | 806 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
807 | 807 | winds = correctFactor*winds |
|
|
808 | 808 | return winds |
|
|
809 | 809 | |
|
|
810 | 810 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
811 | 811 | |
|
|
812 | 812 | dataTime = currentTime + paramInterval |
|
|
813 | 813 | deltaTime = dataTime - self.__initime |
|
|
814 | 814 | |
|
|
815 | 815 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
816 | 816 | self.__dataReady = True |
|
|
817 | 817 | return |
|
|
818 | 818 | |
|
|
819 | 819 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax, binkm=2): |
|
|
820 | 820 | ''' |
|
|
821 | 821 | Function that implements winds estimation technique with detected meteors. |
|
|
822 | 822 | |
|
|
823 | 823 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
824 | 824 | |
|
|
825 | 825 | Output: Winds estimation (Zonal and Meridional) |
|
|
826 | 826 | |
|
|
827 | 827 | Parameters affected: Winds |
|
|
828 | 828 | ''' |
|
|
829 | 829 | # print arrayMeteor.shape |
|
|
830 | 830 | #Settings |
|
|
831 | 831 | nInt = (heightMax - heightMin)/binkm |
|
|
832 | 832 | # print nInt |
|
|
833 | 833 | nInt = int(nInt) |
|
|
834 | 834 | # print nInt |
|
|
835 | 835 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
836 | 836 | |
|
|
837 | 837 | #Filter errors |
|
|
838 | 838 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
839 | 839 | finalMeteor = arrayMeteor[error,:] |
|
|
840 | 840 | |
|
|
841 | 841 | #Meteor Histogram |
|
|
842 | 842 | finalHeights = finalMeteor[:,2] |
|
|
843 | 843 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
844 | 844 | nMeteorsPerI = hist[0] |
|
|
845 | 845 | heightPerI = hist[1] |
|
|
846 | 846 | |
|
|
847 | 847 | #Sort of meteors |
|
|
848 | 848 | indSort = finalHeights.argsort() |
|
|
849 | 849 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
850 | 850 | |
|
|
851 | 851 | # Calculating winds |
|
|
852 | 852 | ind1 = 0 |
|
|
853 | 853 | ind2 = 0 |
|
|
854 | 854 | |
|
|
855 | 855 | for i in range(nInt): |
|
|
856 | 856 | nMet = nMeteorsPerI[i] |
|
|
857 | 857 | ind1 = ind2 |
|
|
858 | 858 | ind2 = ind1 + nMet |
|
|
859 | 859 | |
|
|
860 | 860 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
861 | 861 | |
|
|
862 | 862 | if meteorAux.shape[0] >= meteorThresh: |
|
|
863 | 863 | vel = meteorAux[:, 6] |
|
|
864 | 864 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
865 | 865 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
866 | 866 | |
|
|
867 | 867 | n = numpy.cos(zen) |
|
|
868 | 868 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
869 | 869 | # l = m*numpy.tan(azim) |
|
|
870 | 870 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
871 | 871 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
872 | 872 | |
|
|
873 | 873 | A = numpy.vstack((l, m)).transpose() |
|
|
874 | 874 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
875 | 875 | windsAux = numpy.dot(A1, vel) |
|
|
876 | 876 | |
|
|
877 | 877 | winds[0,i] = windsAux[0] |
|
|
878 | 878 | winds[1,i] = windsAux[1] |
|
|
879 | 879 | |
|
|
880 | 880 | return winds, heightPerI[:-1] |
|
|
881 | 881 | |
|
|
882 | 882 | def techniqueNSM_SA(self, **kwargs): |
|
|
883 | 883 | metArray = kwargs['metArray'] |
|
|
884 | 884 | heightList = kwargs['heightList'] |
|
|
885 | 885 | timeList = kwargs['timeList'] |
|
|
886 | 886 | |
|
|
887 | 887 | rx_location = kwargs['rx_location'] |
|
|
888 | 888 | groupList = kwargs['groupList'] |
|
|
889 | 889 | azimuth = kwargs['azimuth'] |
|
|
890 | 890 | dfactor = kwargs['dfactor'] |
|
|
891 | 891 | k = kwargs['k'] |
|
|
892 | 892 | |
|
|
893 | 893 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
894 | 894 | d = dist*dfactor |
|
|
895 | 895 | #Phase calculation |
|
|
896 | 896 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
897 | 897 | |
|
|
898 | 898 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
899 | 899 | |
|
|
900 | 900 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
901 | 901 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
902 | 902 | |
|
|
903 | 903 | for i in range(heightList.size): |
|
|
904 | 904 | h = heightList[i] |
|
|
905 | 905 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
906 | 906 | metHeight = metArray1[indH,:] |
|
|
907 | 907 | if metHeight.shape[0] >= 2: |
|
|
908 | 908 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
909 | 909 | iazim = metHeight[:,1].astype(int) |
|
|
910 | 910 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
911 | 911 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
912 | 912 | A = numpy.asmatrix(A) |
|
|
913 | 913 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
914 | 914 | velHor = numpy.dot(A1,velAux) |
|
|
915 | 915 | |
|
|
916 | 916 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
917 | 917 | return velEst |
|
|
918 | 918 | |
|
|
919 | 919 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
920 | 920 | meteorList = [] |
|
|
921 | 921 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
922 | 922 | #Putting back together the meteor matrix |
|
|
923 | 923 | utctime = metArray[:,0] |
|
|
924 | 924 | uniqueTime = numpy.unique(utctime) |
|
|
925 | 925 | |
|
|
926 | 926 | phaseDerThresh = 0.5 |
|
|
927 | 927 | ippSeconds = timeList[1] - timeList[0] |
|
|
928 | 928 | sec = numpy.where(timeList>1)[0][0] |
|
|
929 | 929 | nPairs = metArray.shape[1] - 6 |
|
|
930 | 930 | nHeights = len(heightList) |
|
|
931 | 931 | |
|
|
932 | 932 | for t in uniqueTime: |
|
|
933 | 933 | metArray1 = metArray[utctime==t,:] |
|
|
934 | 934 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
935 | 935 | tmet = metArray1[:,1].astype(int) |
|
|
936 | 936 | hmet = metArray1[:,2].astype(int) |
|
|
937 | 937 | |
|
|
938 | 938 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
939 | 939 | metPhase[:,:] = numpy.nan |
|
|
940 | 940 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
941 | 941 | |
|
|
942 | 942 | #Delete short trails |
|
|
943 | 943 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
944 | 944 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
945 | 945 | metBool[heightVect<sec,:] = False |
|
|
946 | 946 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
947 | 947 | |
|
|
948 | 948 | #Derivative |
|
|
949 | 949 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
950 | 950 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
951 | 951 | metPhase[phDerAux] = numpy.nan |
|
|
952 | 952 | |
|
|
953 | 953 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
954 | 954 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
955 | 955 | |
|
|
956 | 956 | for p in numpy.arange(nPairs): |
|
|
957 | 957 | phase = metPhase[p,:,:] |
|
|
958 | 958 | phDer = metDer[p,:,:] |
|
|
959 | 959 | |
|
|
960 | 960 | for h in indMet: |
|
|
961 | 961 | height = heightList[h] |
|
|
962 | 962 | phase1 = phase[h,:] #82 |
|
|
963 | 963 | phDer1 = phDer[h,:] |
|
|
964 | 964 | |
|
|
965 | 965 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
966 | 966 | |
|
|
967 | 967 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
968 | 968 | initMet = indValid[0] |
|
|
969 | 969 | endMet = 0 |
|
|
970 | 970 | |
|
|
971 | 971 | for i in range(len(indValid)-1): |
|
|
972 | 972 | |
|
|
973 | 973 | #Time difference |
|
|
974 | 974 | inow = indValid[i] |
|
|
975 | 975 | inext = indValid[i+1] |
|
|
976 | 976 | idiff = inext - inow |
|
|
977 | 977 | #Phase difference |
|
|
978 | 978 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
979 | 979 | |
|
|
980 | 980 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
981 | 981 | sizeTrail = inow - initMet + 1 |
|
|
982 | 982 | if sizeTrail>3*sec: #Too short meteors |
|
|
983 | 983 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
984 | 984 | y = phase1[initMet:inow+1] |
|
|
985 | 985 | ynnan = ~numpy.isnan(y) |
|
|
986 | 986 | x = x[ynnan] |
|
|
987 | 987 | y = y[ynnan] |
|
|
988 | 988 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
989 | 989 | ylin = x*slope + intercept |
|
|
990 | 990 | rsq = r_value**2 |
|
|
991 | 991 | if rsq > 0.5: |
|
|
992 | 992 | vel = slope#*height*1000/(k*d) |
|
|
993 | 993 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
994 | 994 | meteorList.append(estAux) |
|
|
995 | 995 | initMet = inext |
|
|
996 | 996 | metArray2 = numpy.array(meteorList) |
|
|
997 | 997 | |
|
|
998 | 998 | return metArray2 |
|
|
999 | 999 | |
|
|
1000 | 1000 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
1003 | 1003 | dist = numpy.zeros(len(pairslist)) |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | for i in range(len(rx_location)): |
|
|
1006 | 1006 | ch0 = pairslist[i][0] |
|
|
1007 | 1007 | ch1 = pairslist[i][1] |
|
|
1008 | 1008 | |
|
|
1009 | 1009 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
1010 | 1010 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
1011 | 1011 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
1012 | 1012 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
1013 | 1013 | |
|
|
1014 | 1014 | azimuth1 -= azimuth0 |
|
|
1015 | 1015 | return azimuth1, dist |
|
|
1016 | 1016 | |
|
|
1017 | 1017 | def techniqueNSM_DBS(self, **kwargs): |
|
|
1018 | 1018 | metArray = kwargs['metArray'] |
|
|
1019 | 1019 | heightList = kwargs['heightList'] |
|
|
1020 | 1020 | timeList = kwargs['timeList'] |
|
|
1021 | 1021 | azimuth = kwargs['azimuth'] |
|
|
1022 | 1022 | theta_x = numpy.array(kwargs['theta_x']) |
|
|
1023 | 1023 | theta_y = numpy.array(kwargs['theta_y']) |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | utctime = metArray[:,0] |
|
|
1026 | 1026 | cmet = metArray[:,1].astype(int) |
|
|
1027 | 1027 | hmet = metArray[:,3].astype(int) |
|
|
1028 | 1028 | SNRmet = metArray[:,4] |
|
|
1029 | 1029 | vmet = metArray[:,5] |
|
|
1030 | 1030 | spcmet = metArray[:,6] |
|
|
1031 | 1031 | |
|
|
1032 | 1032 | nChan = numpy.max(cmet) + 1 |
|
|
1033 | 1033 | nHeights = len(heightList) |
|
|
1034 | 1034 | |
|
|
1035 | 1035 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1036 | 1036 | hmet = heightList[hmet] |
|
|
1037 | 1037 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
1038 | 1038 | |
|
|
1039 | 1039 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
1040 | 1040 | |
|
|
1041 | 1041 | for i in range(nHeights - 1): |
|
|
1042 | 1042 | hmin = heightList[i] |
|
|
1043 | 1043 | hmax = heightList[i + 1] |
|
|
1044 | 1044 | |
|
|
1045 | 1045 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
1046 | 1046 | indthisH = numpy.where(thisH) |
|
|
1047 | 1047 | |
|
|
1048 | 1048 | if numpy.size(indthisH) > 3: |
|
|
1049 | 1049 | |
|
|
1050 | 1050 | vel_aux = vmet[thisH] |
|
|
1051 | 1051 | chan_aux = cmet[thisH] |
|
|
1052 | 1052 | cosu_aux = dir_cosu[chan_aux] |
|
|
1053 | 1053 | cosv_aux = dir_cosv[chan_aux] |
|
|
1054 | 1054 | cosw_aux = dir_cosw[chan_aux] |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | nch = numpy.size(numpy.unique(chan_aux)) |
|
|
1057 | 1057 | if nch > 1: |
|
|
1058 | 1058 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
1059 | 1059 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
1060 | 1060 | |
|
|
1061 | 1061 | return velEst |
|
|
1062 | 1062 | |
|
|
1063 | def run(self, dataOut, technique, **kwargs): | |
|
|
1063 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): | |
|
|
1064 | 1064 | |
|
|
1065 | 1065 | param = dataOut.data_param |
|
|
1066 | 1066 | if dataOut.abscissaList != None: |
|
|
1067 | 1067 | absc = dataOut.abscissaList[:-1] |
|
|
1068 | noise = dataOut.noise | |
|
|
1068 | # noise = dataOut.noise | |
|
|
1069 | 1069 | heightList = dataOut.heightList |
|
|
1070 | 1070 | SNR = dataOut.data_SNR |
|
|
1071 | 1071 | |
|
|
1072 | 1072 | if technique == 'DBS': |
|
|
1073 | 1073 | |
|
|
1074 | 1074 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
1075 | 1075 | kwargs['heightList'] = heightList |
|
|
1076 | 1076 | kwargs['SNR'] = SNR |
|
|
1077 | 1077 | |
|
|
1078 | 1078 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
1079 | 1079 | dataOut.utctimeInit = dataOut.utctime |
|
|
1080 | 1080 | dataOut.outputInterval = dataOut.paramInterval |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | elif technique == 'SA': |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | #Parameters |
|
|
1085 | 1085 | # position_x = kwargs['positionX'] |
|
|
1086 | 1086 | # position_y = kwargs['positionY'] |
|
|
1087 | 1087 | # azimuth = kwargs['azimuth'] |
|
|
1088 | 1088 | # |
|
|
1089 | 1089 | # if kwargs.has_key('crosspairsList'): |
|
|
1090 | 1090 | # pairs = kwargs['crosspairsList'] |
|
|
1091 | 1091 | # else: |
|
|
1092 | 1092 | # pairs = None |
|
|
1093 | 1093 | # |
|
|
1094 | 1094 | # if kwargs.has_key('correctFactor'): |
|
|
1095 | 1095 | # correctFactor = kwargs['correctFactor'] |
|
|
1096 | 1096 | # else: |
|
|
1097 | 1097 | # correctFactor = 1 |
|
|
1098 | 1098 | |
|
|
1099 | 1099 | # tau = dataOut.data_param |
|
|
1100 | 1100 | # _lambda = dataOut.C/dataOut.frequency |
|
|
1101 | 1101 | # pairsList = dataOut.groupList |
|
|
1102 | 1102 | # nChannels = dataOut.nChannels |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | kwargs['groupList'] = dataOut.groupList |
|
|
1105 | 1105 | kwargs['tau'] = dataOut.data_param |
|
|
1106 | 1106 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
1107 | 1107 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
1108 | 1108 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
1109 | 1109 | dataOut.utctimeInit = dataOut.utctime |
|
|
1110 | 1110 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | elif technique == 'Meteors': |
|
|
1113 | 1113 | dataOut.flagNoData = True |
|
|
1114 | 1114 | self.__dataReady = False |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | if kwargs.has_key('nHours'): |
|
|
1117 | 1117 | nHours = kwargs['nHours'] |
|
|
1118 | 1118 | else: |
|
|
1119 | 1119 | nHours = 1 |
|
|
1120 | 1120 | |
|
|
1121 | 1121 | if kwargs.has_key('meteorsPerBin'): |
|
|
1122 | 1122 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1123 | 1123 | else: |
|
|
1124 | 1124 | meteorThresh = 6 |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | if kwargs.has_key('hmin'): |
|
|
1127 | 1127 | hmin = kwargs['hmin'] |
|
|
1128 | 1128 | else: hmin = 70 |
|
|
1129 | 1129 | if kwargs.has_key('hmax'): |
|
|
1130 | 1130 | hmax = kwargs['hmax'] |
|
|
1131 | 1131 | else: hmax = 110 |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | if kwargs.has_key('BinKm'): |
|
|
1134 | 1134 | binkm = kwargs['BinKm'] |
|
|
1135 | 1135 | else: |
|
|
1136 | 1136 | binkm = 2 |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | dataOut.outputInterval = nHours*3600 |
|
|
1139 | 1139 | |
|
|
1140 | 1140 | if self.__isConfig == False: |
|
|
1141 | 1141 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1142 | 1142 | #Get Initial LTC time |
|
|
1143 | 1143 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1144 | 1144 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1145 | 1145 | |
|
|
1146 | 1146 | self.__isConfig = True |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | if self.__buffer is None: |
|
|
1149 | 1149 | self.__buffer = dataOut.data_param |
|
|
1150 | 1150 | self.__firstdata = copy.copy(dataOut) |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | else: |
|
|
1153 | 1153 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1154 | 1154 | |
|
|
1155 | 1155 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | if self.__dataReady: |
|
|
1158 | 1158 | dataOut.utctimeInit = self.__initime |
|
|
1159 | 1159 | |
|
|
1160 | 1160 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1161 | 1161 | |
|
|
1162 | 1162 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax, binkm) |
|
|
1163 | 1163 | dataOut.flagNoData = False |
|
|
1164 | 1164 | self.__buffer = None |
|
|
1165 | 1165 | |
|
|
1166 | 1166 | elif technique == 'Meteors1': |
|
|
1167 | 1167 | dataOut.flagNoData = True |
|
|
1168 | 1168 | self.__dataReady = False |
|
|
1169 | 1169 | |
|
|
1170 | 1170 | if kwargs.has_key('nMins'): |
|
|
1171 | 1171 | nMins = kwargs['nMins'] |
|
|
1172 | 1172 | else: nMins = 20 |
|
|
1173 | 1173 | if kwargs.has_key('rx_location'): |
|
|
1174 | 1174 | rx_location = kwargs['rx_location'] |
|
|
1175 | 1175 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
1176 | 1176 | if kwargs.has_key('azimuth'): |
|
|
1177 | 1177 | azimuth = kwargs['azimuth'] |
|
|
1178 | 1178 | else: azimuth = 51.06 |
|
|
1179 | 1179 | if kwargs.has_key('dfactor'): |
|
|
1180 | 1180 | dfactor = kwargs['dfactor'] |
|
|
1181 | 1181 | if kwargs.has_key('mode'): |
|
|
1182 | 1182 | mode = kwargs['mode'] |
|
|
1183 | 1183 | if kwargs.has_key('theta_x'): |
|
|
1184 | 1184 | theta_x = kwargs['theta_x'] |
|
|
1185 | 1185 | if kwargs.has_key('theta_y'): |
|
|
1186 | 1186 | theta_y = kwargs['theta_y'] |
|
|
1187 | 1187 | else: mode = 'SA' |
|
|
1188 | 1188 | |
|
|
1189 | 1189 | #Borrar luego esto |
|
|
1190 | 1190 | if dataOut.groupList is None: |
|
|
1191 | 1191 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
1192 | 1192 | groupList = dataOut.groupList |
|
|
1193 | 1193 | C = 3e8 |
|
|
1194 | 1194 | freq = 50e6 |
|
|
1195 | 1195 | lamb = C/freq |
|
|
1196 | 1196 | k = 2*numpy.pi/lamb |
|
|
1197 | 1197 | |
|
|
1198 | 1198 | timeList = dataOut.abscissaList |
|
|
1199 | 1199 | heightList = dataOut.heightList |
|
|
1200 | 1200 | |
|
|
1201 | 1201 | if self.__isConfig == False: |
|
|
1202 | 1202 | dataOut.outputInterval = nMins*60 |
|
|
1203 | 1203 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1204 | 1204 | #Get Initial LTC time |
|
|
1205 | 1205 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1206 | 1206 | minuteAux = initime.minute |
|
|
1207 | 1207 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
1208 | 1208 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1209 | 1209 | |
|
|
1210 | 1210 | self.__isConfig = True |
|
|
1211 | 1211 | |
|
|
1212 | 1212 | if self.__buffer is None: |
|
|
1213 | 1213 | self.__buffer = dataOut.data_param |
|
|
1214 | 1214 | self.__firstdata = copy.copy(dataOut) |
|
|
1215 | 1215 | |
|
|
1216 | 1216 | else: |
|
|
1217 | 1217 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1218 | 1218 | |
|
|
1219 | 1219 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1220 | 1220 | |
|
|
1221 | 1221 | if self.__dataReady: |
|
|
1222 | 1222 | dataOut.utctimeInit = self.__initime |
|
|
1223 | 1223 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1224 | 1224 | |
|
|
1225 | 1225 | metArray = self.__buffer |
|
|
1226 | 1226 | if mode == 'SA': |
|
|
1227 | 1227 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
1228 | 1228 | elif mode == 'DBS': |
|
|
1229 | 1229 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
1230 | 1230 | dataOut.data_output = dataOut.data_output.T |
|
|
1231 | 1231 | dataOut.flagNoData = False |
|
|
1232 | 1232 | self.__buffer = None |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | return |
|
|
1235 | 1235 | |
|
|
1236 | 1236 | class EWDriftsEstimation(Operation): |
|
|
1237 | 1237 | |
|
|
1238 | 1238 | |
|
|
1239 | 1239 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1240 | 1240 | listPhi = phi.tolist() |
|
|
1241 | 1241 | maxid = listPhi.index(max(listPhi)) |
|
|
1242 | 1242 | minid = listPhi.index(min(listPhi)) |
|
|
1243 | 1243 | |
|
|
1244 | 1244 | rango = range(len(phi)) |
|
|
1245 | 1245 | # rango = numpy.delete(rango,maxid) |
|
|
1246 | 1246 | |
|
|
1247 | 1247 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1248 | 1248 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1249 | 1249 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1250 | 1250 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1251 | 1251 | |
|
|
1252 | 1252 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1253 | 1253 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1254 | 1254 | |
|
|
1255 | 1255 | for i in rango: |
|
|
1256 | 1256 | x = heiRang*math.cos(phi[i]) |
|
|
1257 | 1257 | y1 = velRadial[i,:] |
|
|
1258 | 1258 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1259 | 1259 | |
|
|
1260 | 1260 | x1 = heiRang1 |
|
|
1261 | 1261 | y11 = f1(x1) |
|
|
1262 | 1262 | |
|
|
1263 | 1263 | y2 = SNR[i,:] |
|
|
1264 | 1264 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1265 | 1265 | y21 = f2(x1) |
|
|
1266 | 1266 | |
|
|
1267 | 1267 | velRadial1[i,:] = y11 |
|
|
1268 | 1268 | SNR1[i,:] = y21 |
|
|
1269 | 1269 | |
|
|
1270 | 1270 | return heiRang1, velRadial1, SNR1 |
|
|
1271 | 1271 | |
|
|
1272 | 1272 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
1273 | 1273 | heiRang = dataOut.heightList |
|
|
1274 | 1274 | velRadial = dataOut.data_param[:,3,:] |
|
|
1275 | 1275 | SNR = dataOut.data_SNR |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | zenith = numpy.array(zenith) |
|
|
1278 | 1278 | zenith -= zenithCorrection |
|
|
1279 | 1279 | zenith *= numpy.pi/180 |
|
|
1280 | 1280 | |
|
|
1281 | 1281 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
1282 | 1282 | |
|
|
1283 | 1283 | alp = zenith[0] |
|
|
1284 | 1284 | bet = zenith[1] |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | w_w = velRadial1[0,:] |
|
|
1287 | 1287 | w_e = velRadial1[1,:] |
|
|
1288 | 1288 | |
|
|
1289 | 1289 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
1290 | 1290 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
1291 | 1291 | |
|
|
1292 | 1292 | winds = numpy.vstack((u,w)) |
|
|
1293 | 1293 | |
|
|
1294 | 1294 | dataOut.heightList = heiRang1 |
|
|
1295 | 1295 | dataOut.data_output = winds |
|
|
1296 | 1296 | dataOut.data_SNR = SNR1 |
|
|
1297 | 1297 | |
|
|
1298 | 1298 | dataOut.utctimeInit = dataOut.utctime |
|
|
1299 | 1299 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1300 | 1300 | return |
|
|
1301 | 1301 | |
|
|
1302 | 1302 | #--------------- Non Specular Meteor ---------------- |
|
|
1303 | 1303 | |
|
|
1304 | 1304 | class NonSpecularMeteorDetection(Operation): |
|
|
1305 | 1305 | |
|
|
1306 | 1306 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
1307 | 1307 | data_acf = dataOut.data_pre[0] |
|
|
1308 | 1308 | data_ccf = dataOut.data_pre[1] |
|
|
1309 | 1309 | pairsList = dataOut.groupList[1] |
|
|
1310 | 1310 | |
|
|
1311 | 1311 | lamb = dataOut.C/dataOut.frequency |
|
|
1312 | 1312 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
1313 | 1313 | paramInterval = dataOut.paramInterval |
|
|
1314 | 1314 | |
|
|
1315 | 1315 | nChannels = data_acf.shape[0] |
|
|
1316 | 1316 | nLags = data_acf.shape[1] |
|
|
1317 | 1317 | nProfiles = data_acf.shape[2] |
|
|
1318 | 1318 | nHeights = dataOut.nHeights |
|
|
1319 | 1319 | nCohInt = dataOut.nCohInt |
|
|
1320 | 1320 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
1321 | 1321 | heightList = dataOut.heightList |
|
|
1322 | 1322 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
1323 | 1323 | utctime = dataOut.utctime |
|
|
1324 | 1324 | |
|
|
1325 | 1325 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
1326 | 1326 | |
|
|
1327 | 1327 | #------------------------ SNR -------------------------------------- |
|
|
1328 | 1328 | power = data_acf[:,0,:,:].real |
|
|
1329 | 1329 | noise = numpy.zeros(nChannels) |
|
|
1330 | 1330 | SNR = numpy.zeros(power.shape) |
|
|
1331 | 1331 | for i in range(nChannels): |
|
|
1332 | 1332 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
1333 | 1333 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
1334 | 1334 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
1335 | 1335 | SNRdB = 10*numpy.log10(SNR) |
|
|
1336 | 1336 | |
|
|
1337 | 1337 | if mode == 'SA': |
|
|
1338 | 1338 | dataOut.groupList = dataOut.groupList[1] |
|
|
1339 | 1339 | nPairs = data_ccf.shape[0] |
|
|
1340 | 1340 | #---------------------- Coherence and Phase -------------------------- |
|
|
1341 | 1341 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
1342 | 1342 | # phase1 = numpy.copy(phase) |
|
|
1343 | 1343 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
1344 | 1344 | |
|
|
1345 | 1345 | for p in range(nPairs): |
|
|
1346 | 1346 | ch0 = pairsList[p][0] |
|
|
1347 | 1347 | ch1 = pairsList[p][1] |
|
|
1348 | 1348 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
1349 | 1349 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
1350 | 1350 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
1351 | 1351 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
1352 | 1352 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
1353 | 1353 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
1354 | 1354 | # struc = numpy.ones((5,1)) |
|
|
1355 | 1355 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
1356 | 1356 | #---------------------- Radial Velocity ---------------------------- |
|
|
1357 | 1357 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
1358 | 1358 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
1359 | 1359 | |
|
|
1360 | 1360 | if allData: |
|
|
1361 | 1361 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
1362 | 1362 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
1363 | 1363 | else: |
|
|
1364 | 1364 | #------------------------ Meteor mask --------------------------------- |
|
|
1365 | 1365 | # #SNR mask |
|
|
1366 | 1366 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
1367 | 1367 | # |
|
|
1368 | 1368 | # #Erase small objects |
|
|
1369 | 1369 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
1370 | 1370 | # |
|
|
1371 | 1371 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
1372 | 1372 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
1373 | 1373 | # indEEJ = numpy.where(indOver)[0] |
|
|
1374 | 1374 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
1375 | 1375 | # |
|
|
1376 | 1376 | # boolMetFin = boolMet1 |
|
|
1377 | 1377 | # |
|
|
1378 | 1378 | # if indEEJ.size > 0: |
|
|
1379 | 1379 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
1380 | 1380 | # |
|
|
1381 | 1381 | # boolMet2 = coh > cohThresh |
|
|
1382 | 1382 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
1383 | 1383 | # |
|
|
1384 | 1384 | # #Final Meteor mask |
|
|
1385 | 1385 | # boolMetFin = boolMet1|boolMet2 |
|
|
1386 | 1386 | |
|
|
1387 | 1387 | #Coherence mask |
|
|
1388 | 1388 | boolMet1 = coh > 0.75 |
|
|
1389 | 1389 | struc = numpy.ones((30,1)) |
|
|
1390 | 1390 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
1391 | 1391 | |
|
|
1392 | 1392 | #Derivative mask |
|
|
1393 | 1393 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
1394 | 1394 | boolMet2 = derPhase < 0.2 |
|
|
1395 | 1395 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
1396 | 1396 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
1397 | 1397 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
1398 | 1398 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
1399 | 1399 | # #Final mask |
|
|
1400 | 1400 | # boolMetFin = boolMet2 |
|
|
1401 | 1401 | boolMetFin = boolMet1&boolMet2 |
|
|
1402 | 1402 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
1403 | 1403 | #Creating data_param |
|
|
1404 | 1404 | coordMet = numpy.where(boolMetFin) |
|
|
1405 | 1405 | |
|
|
1406 | 1406 | tmet = coordMet[0] |
|
|
1407 | 1407 | hmet = coordMet[1] |
|
|
1408 | 1408 | |
|
|
1409 | 1409 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
1410 | 1410 | data_param[:,0] = utctime |
|
|
1411 | 1411 | data_param[:,1] = tmet |
|
|
1412 | 1412 | data_param[:,2] = hmet |
|
|
1413 | 1413 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
1414 | 1414 | data_param[:,4] = velRad[tmet,hmet] |
|
|
1415 | 1415 | data_param[:,5] = coh[tmet,hmet] |
|
|
1416 | 1416 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
1417 | 1417 | |
|
|
1418 | 1418 | elif mode == 'DBS': |
|
|
1419 | 1419 | dataOut.groupList = numpy.arange(nChannels) |
|
|
1420 | 1420 | |
|
|
1421 | 1421 | #Radial Velocities |
|
|
1422 | 1422 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
1423 | 1423 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
1424 | 1424 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
1425 | 1425 | |
|
|
1426 | 1426 | #Spectral width |
|
|
1427 | 1427 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
1428 | 1428 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
1429 | 1429 | acf1 = data_acf[:,1,:,:] |
|
|
1430 | 1430 | acf2 = data_acf[:,2,:,:] |
|
|
1431 | 1431 | |
|
|
1432 | 1432 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
1433 | 1433 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
1434 | 1434 | if allData: |
|
|
1435 | 1435 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
1436 | 1436 | else: |
|
|
1437 | 1437 | #SNR |
|
|
1438 | 1438 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
1439 | 1439 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
1440 | 1440 | |
|
|
1441 | 1441 | #Radial velocity |
|
|
1442 | 1442 | boolMet2 = numpy.abs(velRad) < 20 |
|
|
1443 | 1443 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
1444 | 1444 | |
|
|
1445 | 1445 | #Spectral Width |
|
|
1446 | 1446 | boolMet3 = spcWidth < 30 |
|
|
1447 | 1447 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
1448 | 1448 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
1449 | 1449 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
1450 | 1450 | |
|
|
1451 | 1451 | #Creating data_param |
|
|
1452 | 1452 | coordMet = numpy.where(boolMetFin) |
|
|
1453 | 1453 | |
|
|
1454 | 1454 | cmet = coordMet[0] |
|
|
1455 | 1455 | tmet = coordMet[1] |
|
|
1456 | 1456 | hmet = coordMet[2] |
|
|
1457 | 1457 | |
|
|
1458 | 1458 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
1459 | 1459 | data_param[:,0] = utctime |
|
|
1460 | 1460 | data_param[:,1] = cmet |
|
|
1461 | 1461 | data_param[:,2] = tmet |
|
|
1462 | 1462 | data_param[:,3] = hmet |
|
|
1463 | 1463 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
1464 | 1464 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
1465 | 1465 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
1466 | 1466 | |
|
|
1467 | 1467 | # self.dataOut.data_param = data_int |
|
|
1468 | 1468 | if len(data_param) == 0: |
|
|
1469 | 1469 | dataOut.flagNoData = True |
|
|
1470 | 1470 | else: |
|
|
1471 | 1471 | dataOut.data_param = data_param |
|
|
1472 | 1472 | |
|
|
1473 | 1473 | def __erase_small(self, binArray, threshX, threshY): |
|
|
1474 | 1474 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
1475 | 1475 | binArray1 = numpy.copy(binArray) |
|
|
1476 | 1476 | |
|
|
1477 | 1477 | for i in range(1,numfeat + 1): |
|
|
1478 | 1478 | auxBin = (labarray==i) |
|
|
1479 | 1479 | auxSize = auxBin.sum() |
|
|
1480 | 1480 | |
|
|
1481 | 1481 | x,y = numpy.where(auxBin) |
|
|
1482 | 1482 | widthX = x.max() - x.min() |
|
|
1483 | 1483 | widthY = y.max() - y.min() |
|
|
1484 | 1484 | |
|
|
1485 | 1485 | #width X: 3 seg -> 12.5*3 |
|
|
1486 | 1486 | #width Y: |
|
|
1487 | 1487 | |
|
|
1488 | 1488 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
1489 | 1489 | binArray1[auxBin] = False |
|
|
1490 | 1490 | |
|
|
1491 | 1491 | return binArray1 |
|
|
1492 | 1492 | |
|
|
1493 | 1493 | #--------------- Specular Meteor ---------------- |
|
|
1494 | 1494 | |
|
|
1495 | 1495 | class SMDetection(Operation): |
|
|
1496 | 1496 | ''' |
|
|
1497 | 1497 | Function DetectMeteors() |
|
|
1498 | 1498 | Project developed with paper: |
|
|
1499 | 1499 | HOLDSWORTH ET AL. 2004 |
|
|
1500 | 1500 | |
|
|
1501 | 1501 | Input: |
|
|
1502 | 1502 | self.dataOut.data_pre |
|
|
1503 | 1503 | |
|
|
1504 | 1504 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
1505 | 1505 | |
|
|
1506 | 1506 | hei_ref: Height reference for the Beacon signal extraction |
|
|
1507 | 1507 | tauindex: |
|
|
1508 | 1508 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
1509 | 1509 | |
|
|
1510 | 1510 | cohDetection: Whether to user Coherent detection or not |
|
|
1511 | 1511 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
1512 | 1512 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
1513 | 1513 | |
|
|
1514 | 1514 | noise_timeStep: Noise calculation time step |
|
|
1515 | 1515 | noise_multiple: Noise multiple to define signal threshold |
|
|
1516 | 1516 | |
|
|
1517 | 1517 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
1518 | 1518 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
1519 | 1519 | |
|
|
1520 | 1520 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
1521 | 1521 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
1522 | 1522 | |
|
|
1523 | 1523 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
1524 | 1524 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
1525 | 1525 | azimuth: Azimuth angle correction |
|
|
1526 | 1526 | |
|
|
1527 | 1527 | Affected: |
|
|
1528 | 1528 | self.dataOut.data_param |
|
|
1529 | 1529 | |
|
|
1530 | 1530 | Rejection Criteria (Errors): |
|
|
1531 | 1531 | 0: No error; analysis OK |
|
|
1532 | 1532 | 1: SNR < SNR threshold |
|
|
1533 | 1533 | 2: angle of arrival (AOA) ambiguously determined |
|
|
1534 | 1534 | 3: AOA estimate not feasible |
|
|
1535 | 1535 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1536 | 1536 | 5: echo at start or end of time series |
|
|
1537 | 1537 | 6: echo less than 5 examples long; too short for analysis |
|
|
1538 | 1538 | 7: echo rise exceeds 0.3s |
|
|
1539 | 1539 | 8: echo decay time less than twice rise time |
|
|
1540 | 1540 | 9: large power level before echo |
|
|
1541 | 1541 | 10: large power level after echo |
|
|
1542 | 1542 | 11: poor fit to amplitude for estimation of decay time |
|
|
1543 | 1543 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
1544 | 1544 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
1545 | 1545 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
1546 | 1546 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
1547 | 1547 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
1548 | 1548 | |
|
|
1549 | 1549 | 17: phase difference in meteor Reestimation |
|
|
1550 | 1550 | |
|
|
1551 | 1551 | Data Storage: |
|
|
1552 | 1552 | Meteors for Wind Estimation (8): |
|
|
1553 | 1553 | Utc Time | Range Height |
|
|
1554 | 1554 | Azimuth Zenith errorCosDir |
|
|
1555 | 1555 | VelRad errorVelRad |
|
|
1556 | 1556 | Phase0 Phase1 Phase2 Phase3 |
|
|
1557 | 1557 | TypeError |
|
|
1558 | 1558 | |
|
|
1559 | 1559 | ''' |
|
|
1560 | 1560 | |
|
|
1561 | 1561 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
1562 | 1562 | phaseOffsets = None, |
|
|
1563 | 1563 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
1564 | 1564 | noise_timeStep = 4, noise_multiple = 4, |
|
|
1565 | 1565 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
1566 | 1566 | phaseThresh = 20, SNRThresh = 5, |
|
|
1567 | 1567 | hmin = 50, hmax=150, azimuth = 0, |
|
|
1568 | 1568 | channelPositions = None) : |
|
|
1569 | 1569 | |
|
|
1570 | 1570 | |
|
|
1571 | 1571 | #Getting Pairslist |
|
|
1572 | 1572 | if channelPositions is None: |
|
|
1573 | 1573 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
1574 | 1574 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
1575 | 1575 | meteorOps = SMOperations() |
|
|
1576 | 1576 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
1577 | 1577 | heiRang = dataOut.getHeiRange() |
|
|
1578 | 1578 | #Get Beacon signal - No Beacon signal anymore |
|
|
1579 | 1579 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
1580 | 1580 | # |
|
|
1581 | 1581 | # if hei_ref != None: |
|
|
1582 | 1582 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
1583 | 1583 | # |
|
|
1584 | 1584 | |
|
|
1585 | 1585 | |
|
|
1586 | 1586 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
1587 | 1587 | # see if the user put in pre defined phase shifts |
|
|
1588 | 1588 | voltsPShift = dataOut.data_pre.copy() |
|
|
1589 | 1589 | |
|
|
1590 | 1590 | # if predefinedPhaseShifts != None: |
|
|
1591 | 1591 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
1592 | 1592 | # |
|
|
1593 | 1593 | # # elif beaconPhaseShifts: |
|
|
1594 | 1594 | # # #get hardware phase shifts using beacon signal |
|
|
1595 | 1595 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
1596 | 1596 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
1597 | 1597 | # |
|
|
1598 | 1598 | # else: |
|
|
1599 | 1599 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
1600 | 1600 | # |
|
|
1601 | 1601 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
1602 | 1602 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
1603 | 1603 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
1604 | 1604 | |
|
|
1605 | 1605 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
1606 | 1606 | |
|
|
1607 | 1607 | #Remove DC |
|
|
1608 | 1608 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
1609 | 1609 | voltsDC = numpy.mean(voltsDC,1) |
|
|
1610 | 1610 | for i in range(voltsDC.shape[0]): |
|
|
1611 | 1611 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
1612 | 1612 | |
|
|
1613 | 1613 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
1614 | 1614 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
1615 | 1615 | |
|
|
1616 | 1616 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
1617 | 1617 | #Coherent Detection |
|
|
1618 | 1618 | if cohDetection: |
|
|
1619 | 1619 | #use coherent detection to get the net power |
|
|
1620 | 1620 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
1621 | 1621 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
1622 | 1622 | |
|
|
1623 | 1623 | #Non-coherent detection! |
|
|
1624 | 1624 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
1625 | 1625 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
1626 | 1626 | |
|
|
1627 | 1627 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
1628 | 1628 | #Get noise |
|
|
1629 | 1629 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
1630 | 1630 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
1631 | 1631 | #Get signal threshold |
|
|
1632 | 1632 | signalThresh = noise_multiple*noise |
|
|
1633 | 1633 | #Meteor echoes detection |
|
|
1634 | 1634 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
1635 | 1635 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
1636 | 1636 | |
|
|
1637 | 1637 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
1638 | 1638 | #Parameters |
|
|
1639 | 1639 | heiRange = dataOut.getHeiRange() |
|
|
1640 | 1640 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
1641 | 1641 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
1642 | 1642 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
1643 | 1643 | #Multiple detection removals |
|
|
1644 | 1644 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
1645 | 1645 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
1646 | 1646 | |
|
|
1647 | 1647 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
1648 | 1648 | #Parameters |
|
|
1649 | 1649 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
1650 | 1650 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
1651 | 1651 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
1652 | 1652 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
1653 | 1653 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
1654 | 1654 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
1655 | 1655 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
1656 | 1656 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
1657 | 1657 | |
|
|
1658 | 1658 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
1659 | 1659 | #Calculating Radial Velocity (Error N 15) |
|
|
1660 | 1660 | radialStdThresh = 10 |
|
|
1661 | 1661 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
1662 | 1662 | |
|
|
1663 | 1663 | if len(listMeteors4) > 0: |
|
|
1664 | 1664 | #Setting New Array |
|
|
1665 | 1665 | date = dataOut.utctime |
|
|
1666 | 1666 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
1667 | 1667 | |
|
|
1668 | 1668 | #Correcting phase offset |
|
|
1669 | 1669 | if phaseOffsets != None: |
|
|
1670 | 1670 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
1671 | 1671 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
1672 | 1672 | |
|
|
1673 | 1673 | #Second Pairslist |
|
|
1674 | 1674 | pairsList = [] |
|
|
1675 | 1675 | pairx = (0,1) |
|
|
1676 | 1676 | pairy = (2,3) |
|
|
1677 | 1677 | pairsList.append(pairx) |
|
|
1678 | 1678 | pairsList.append(pairy) |
|
|
1679 | 1679 | |
|
|
1680 | 1680 | jph = numpy.array([0,0,0,0]) |
|
|
1681 | 1681 | h = (hmin,hmax) |
|
|
1682 | 1682 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
1683 | 1683 | |
|
|
1684 | 1684 | # #Calculate AOA (Error N 3, 4) |
|
|
1685 | 1685 | # #JONES ET AL. 1998 |
|
|
1686 | 1686 | # error = arrayParameters[:,-1] |
|
|
1687 | 1687 | # AOAthresh = numpy.pi/8 |
|
|
1688 | 1688 | # phases = -arrayParameters[:,9:13] |
|
|
1689 | 1689 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
1690 | 1690 | # |
|
|
1691 | 1691 | # #Calculate Heights (Error N 13 and 14) |
|
|
1692 | 1692 | # error = arrayParameters[:,-1] |
|
|
1693 | 1693 | # Ranges = arrayParameters[:,2] |
|
|
1694 | 1694 | # zenith = arrayParameters[:,5] |
|
|
1695 | 1695 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
1696 | 1696 | # error = arrayParameters[:,-1] |
|
|
1697 | 1697 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
1698 | 1698 | |
|
|
1699 | 1699 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
1700 | 1700 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
1701 | 1701 | dataOut.data_param = arrayParameters |
|
|
1702 | 1702 | |
|
|
1703 | 1703 | if arrayParameters is None: |
|
|
1704 | 1704 | dataOut.flagNoData = True |
|
|
1705 | 1705 | else: |
|
|
1706 | 1706 | dataOut.flagNoData = True |
|
|
1707 | 1707 | |
|
|
1708 | 1708 | return |
|
|
1709 | 1709 | |
|
|
1710 | 1710 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
1711 | 1711 | |
|
|
1712 | 1712 | minIndex = min(newheis[0]) |
|
|
1713 | 1713 | maxIndex = max(newheis[0]) |
|
|
1714 | 1714 | |
|
|
1715 | 1715 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
1716 | 1716 | nLength = voltage.shape[1]/n |
|
|
1717 | 1717 | nMin = 0 |
|
|
1718 | 1718 | nMax = 0 |
|
|
1719 | 1719 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
1720 | 1720 | |
|
|
1721 | 1721 | for i in range(n): |
|
|
1722 | 1722 | nMax += nLength |
|
|
1723 | 1723 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
1724 | 1724 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
1725 | 1725 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
1726 | 1726 | nMin = nMax |
|
|
1727 | 1727 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
1728 | 1728 | |
|
|
1729 | 1729 | #Remove Outliers |
|
|
1730 | 1730 | factor = 2 |
|
|
1731 | 1731 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
1732 | 1732 | dw = numpy.std(wt,axis = 1) |
|
|
1733 | 1733 | dw = dw.reshape((dw.size,1)) |
|
|
1734 | 1734 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
1735 | 1735 | phaseOffset[ind] = numpy.nan |
|
|
1736 | 1736 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
1737 | 1737 | |
|
|
1738 | 1738 | return phaseOffset |
|
|
1739 | 1739 | |
|
|
1740 | 1740 | def __shiftPhase(self, data, phaseShift): |
|
|
1741 | 1741 | #this will shift the phase of a complex number |
|
|
1742 | 1742 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
1743 | 1743 | return dataShifted |
|
|
1744 | 1744 | |
|
|
1745 | 1745 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
1746 | 1746 | nChannel = array.shape[0] |
|
|
1747 | 1747 | nHeights = array.shape[2] |
|
|
1748 | 1748 | numPairs = len(pairslist) |
|
|
1749 | 1749 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
1750 | 1750 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
1751 | 1751 | |
|
|
1752 | 1752 | #Correct phases |
|
|
1753 | 1753 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
1754 | 1754 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
1755 | 1755 | |
|
|
1756 | 1756 | if indDer[0].shape[0] > 0: |
|
|
1757 | 1757 | for i in range(indDer[0].shape[0]): |
|
|
1758 | 1758 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
1759 | 1759 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
1760 | 1760 | |
|
|
1761 | 1761 | # for j in range(numSides): |
|
|
1762 | 1762 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
1763 | 1763 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
1764 | 1764 | # |
|
|
1765 | 1765 | #Linear |
|
|
1766 | 1766 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
1767 | 1767 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
1768 | 1768 | for j in range(numPairs): |
|
|
1769 | 1769 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
1770 | 1770 | phaseInt[j] = fit[1] |
|
|
1771 | 1771 | #Phase Differences |
|
|
1772 | 1772 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
1773 | 1773 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
1774 | 1774 | |
|
|
1775 | 1775 | #Dealias |
|
|
1776 | 1776 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
1777 | 1777 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
1778 | 1778 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
1779 | 1779 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
1780 | 1780 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
1781 | 1781 | |
|
|
1782 | 1782 | return phaseDiff, phaseArrival |
|
|
1783 | 1783 | |
|
|
1784 | 1784 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
1785 | 1785 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
1786 | 1786 | #find the phase shifts of each channel over 1 second intervals |
|
|
1787 | 1787 | #only look at ranges below the beacon signal |
|
|
1788 | 1788 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
1789 | 1789 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
1790 | 1790 | numHeights = volts.shape[2] |
|
|
1791 | 1791 | nChannel = volts.shape[0] |
|
|
1792 | 1792 | voltsCohDet = volts.copy() |
|
|
1793 | 1793 | |
|
|
1794 | 1794 | pairsarray = numpy.array(pairslist) |
|
|
1795 | 1795 | indSides = pairsarray[:,1] |
|
|
1796 | 1796 | # indSides = numpy.array(range(nChannel)) |
|
|
1797 | 1797 | # indSides = numpy.delete(indSides, indCenter) |
|
|
1798 | 1798 | # |
|
|
1799 | 1799 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
1800 | 1800 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
1801 | 1801 | |
|
|
1802 | 1802 | startInd = 0 |
|
|
1803 | 1803 | endInd = 0 |
|
|
1804 | 1804 | |
|
|
1805 | 1805 | for i in range(numBlocks): |
|
|
1806 | 1806 | startInd = endInd |
|
|
1807 | 1807 | endInd = endInd + listBlocks[i].shape[1] |
|
|
1808 | 1808 | |
|
|
1809 | 1809 | arrayBlock = listBlocks[i] |
|
|
1810 | 1810 | # arrayBlockCenter = listCenter[i] |
|
|
1811 | 1811 | |
|
|
1812 | 1812 | #Estimate the Phase Difference |
|
|
1813 | 1813 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
1814 | 1814 | #Phase Difference RMS |
|
|
1815 | 1815 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
1816 | 1816 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
1817 | 1817 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
1818 | 1818 | #Shifting |
|
|
1819 | 1819 | if indPhase[0].shape[0] > 0: |
|
|
1820 | 1820 | for j in range(indSides.size): |
|
|
1821 | 1821 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
1822 | 1822 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
1823 | 1823 | |
|
|
1824 | 1824 | return voltsCohDet |
|
|
1825 | 1825 | |
|
|
1826 | 1826 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
1827 | 1827 | |
|
|
1828 | 1828 | nHeights = volts.shape[2] |
|
|
1829 | 1829 | nPoints = volts.shape[1] |
|
|
1830 | 1830 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
1831 | 1831 | |
|
|
1832 | 1832 | for i in range(len(pairslist)): |
|
|
1833 | 1833 | volts1 = volts[pairslist[i][0]] |
|
|
1834 | 1834 | volts2 = volts[pairslist[i][1]] |
|
|
1835 | 1835 | |
|
|
1836 | 1836 | for t in range(len(laglist)): |
|
|
1837 | 1837 | idxT = laglist[t] |
|
|
1838 | 1838 | if idxT >= 0: |
|
|
1839 | 1839 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
1840 | 1840 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
1841 | 1841 | else: |
|
|
1842 | 1842 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
1843 | 1843 | volts2[:(nPoints + idxT),:])) |
|
|
1844 | 1844 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
1845 | 1845 | |
|
|
1846 | 1846 | vStacked = None |
|
|
1847 | 1847 | return voltsCCF |
|
|
1848 | 1848 | |
|
|
1849 | 1849 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
1850 | 1850 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
1851 | 1851 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
1852 | 1852 | numHeights = power.shape[1] |
|
|
1853 | 1853 | |
|
|
1854 | 1854 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
1855 | 1855 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
1856 | 1856 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
1857 | 1857 | |
|
|
1858 | 1858 | startInd = 0 |
|
|
1859 | 1859 | endInd = 0 |
|
|
1860 | 1860 | |
|
|
1861 | 1861 | for i in range(numBlocks): #split por canal |
|
|
1862 | 1862 | startInd = endInd |
|
|
1863 | 1863 | endInd = endInd + listPower[i].shape[0] |
|
|
1864 | 1864 | |
|
|
1865 | 1865 | arrayBlock = listPower[i] |
|
|
1866 | 1866 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
1867 | 1867 | # noiseAux = numpy.median(noiseAux) |
|
|
1868 | 1868 | # noiseAux = numpy.mean(arrayBlock) |
|
|
1869 | 1869 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
1870 | 1870 | |
|
|
1871 | 1871 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
1872 | 1872 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
1873 | 1873 | |
|
|
1874 | 1874 | return noise, noise1 |
|
|
1875 | 1875 | |
|
|
1876 | 1876 | def __findMeteors(self, power, thresh): |
|
|
1877 | 1877 | nProf = power.shape[0] |
|
|
1878 | 1878 | nHeights = power.shape[1] |
|
|
1879 | 1879 | listMeteors = [] |
|
|
1880 | 1880 | |
|
|
1881 | 1881 | for i in range(nHeights): |
|
|
1882 | 1882 | powerAux = power[:,i] |
|
|
1883 | 1883 | threshAux = thresh[:,i] |
|
|
1884 | 1884 | |
|
|
1885 | 1885 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
1886 | 1886 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
1887 | 1887 | |
|
|
1888 | 1888 | j = 0 |
|
|
1889 | 1889 | |
|
|
1890 | 1890 | while (j < indUPthresh.size - 2): |
|
|
1891 | 1891 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
1892 | 1892 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
1893 | 1893 | indDNthresh = indDNthresh[indDNAux] |
|
|
1894 | 1894 | |
|
|
1895 | 1895 | if (indDNthresh.size > 0): |
|
|
1896 | 1896 | indEnd = indDNthresh[0] - 1 |
|
|
1897 | 1897 | indInit = indUPthresh[j] if isinstance(indUPthresh[j], (int, float)) else indUPthresh[j][0] ##CHECK!!!! |
|
|
1898 | 1898 | |
|
|
1899 | 1899 | meteor = powerAux[indInit:indEnd + 1] |
|
|
1900 | 1900 | indPeak = meteor.argmax() + indInit |
|
|
1901 | 1901 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
1902 | 1902 | |
|
|
1903 | 1903 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
1904 | 1904 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
1905 | 1905 | else: j+=1 |
|
|
1906 | 1906 | else: j+=1 |
|
|
1907 | 1907 | |
|
|
1908 | 1908 | return listMeteors |
|
|
1909 | 1909 | |
|
|
1910 | 1910 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
1911 | 1911 | |
|
|
1912 | 1912 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
1913 | 1913 | listMeteors1 = [] |
|
|
1914 | 1914 | |
|
|
1915 | 1915 | while arrayMeteors.shape[0] > 0: |
|
|
1916 | 1916 | FLAs = arrayMeteors[:,4] |
|
|
1917 | 1917 | maxFLA = FLAs.argmax() |
|
|
1918 | 1918 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
1919 | 1919 | |
|
|
1920 | 1920 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
1921 | 1921 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
1922 | 1922 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
1923 | 1923 | |
|
|
1924 | 1924 | #Check neighborhood |
|
|
1925 | 1925 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
1926 | 1926 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
1927 | 1927 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
1928 | 1928 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
1929 | 1929 | |
|
|
1930 | 1930 | #Check Heights |
|
|
1931 | 1931 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
1932 | 1932 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
1933 | 1933 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
1934 | 1934 | |
|
|
1935 | 1935 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
1936 | 1936 | |
|
|
1937 | 1937 | return listMeteors1 |
|
|
1938 | 1938 | |
|
|
1939 | 1939 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
1940 | 1940 | numHeights = volts.shape[2] |
|
|
1941 | 1941 | nChannel = volts.shape[0] |
|
|
1942 | 1942 | |
|
|
1943 | 1943 | thresholdPhase = thresh[0] |
|
|
1944 | 1944 | thresholdNoise = thresh[1] |
|
|
1945 | 1945 | thresholdDB = float(thresh[2]) |
|
|
1946 | 1946 | |
|
|
1947 | 1947 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
1948 | 1948 | pairsarray = numpy.array(pairslist) |
|
|
1949 | 1949 | indSides = pairsarray[:,1] |
|
|
1950 | 1950 | |
|
|
1951 | 1951 | pairslist1 = list(pairslist) |
|
|
1952 | 1952 | pairslist1.append((0,4)) |
|
|
1953 | 1953 | pairslist1.append((1,3)) |
|
|
1954 | 1954 | |
|
|
1955 | 1955 | listMeteors1 = [] |
|
|
1956 | 1956 | listPowerSeries = [] |
|
|
1957 | 1957 | listVoltageSeries = [] |
|
|
1958 | 1958 | #volts has the war data |
|
|
1959 | 1959 | |
|
|
1960 | 1960 | if frequency == 30.175e6: |
|
|
1961 | 1961 | timeLag = 45*10**-3 |
|
|
1962 | 1962 | else: |
|
|
1963 | 1963 | timeLag = 15*10**-3 |
|
|
1964 | 1964 | lag = int(numpy.ceil(timeLag/timeInterval)) |
|
|
1965 | 1965 | |
|
|
1966 | 1966 | for i in range(len(listMeteors)): |
|
|
1967 | 1967 | |
|
|
1968 | 1968 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
1969 | 1969 | meteorAux = numpy.zeros(16) |
|
|
1970 | 1970 | |
|
|
1971 | 1971 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
1972 | 1972 | mHeight = int(listMeteors[i][0]) |
|
|
1973 | 1973 | mStart = int(listMeteors[i][1]) |
|
|
1974 | 1974 | mPeak = int(listMeteors[i][2]) |
|
|
1975 | 1975 | mEnd = int(listMeteors[i][3]) |
|
|
1976 | 1976 | |
|
|
1977 | 1977 | #get the volt data between the start and end times of the meteor |
|
|
1978 | 1978 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
1979 | 1979 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
1980 | 1980 | |
|
|
1981 | 1981 | #3.6. Phase Difference estimation |
|
|
1982 | 1982 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
1983 | 1983 | |
|
|
1984 | 1984 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
1985 | 1985 | #meteorVolts0.- all Channels, all Profiles |
|
|
1986 | 1986 | meteorVolts0 = volts[:,:,mHeight] |
|
|
1987 | 1987 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
1988 | 1988 | meteorNoise = noise[:,mHeight] |
|
|
1989 | 1989 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
1990 | 1990 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
1991 | 1991 | |
|
|
1992 | 1992 | #Times reestimation |
|
|
1993 | 1993 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
1994 | 1994 | if mStart1.size > 0: |
|
|
1995 | 1995 | mStart1 = mStart1[-1] + 1 |
|
|
1996 | 1996 | |
|
|
1997 | 1997 | else: |
|
|
1998 | 1998 | mStart1 = mPeak |
|
|
1999 | 1999 | |
|
|
2000 | 2000 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
2001 | 2001 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
2002 | 2002 | if mEndDecayTime1.size == 0: |
|
|
2003 | 2003 | mEndDecayTime1 = powerNet0.size |
|
|
2004 | 2004 | else: |
|
|
2005 | 2005 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
2006 | 2006 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
2007 | 2007 | |
|
|
2008 | 2008 | #meteorVolts1.- all Channels, from start to end |
|
|
2009 | 2009 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
2010 | 2010 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
2011 | 2011 | if meteorVolts2.shape[1] == 0: |
|
|
2012 | 2012 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
2013 | 2013 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
2014 | 2014 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
2015 | 2015 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
2016 | 2016 | |
|
|
2017 | 2017 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
2018 | 2018 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
2019 | 2019 | if meteorVolts2.shape[1] > 0: |
|
|
2020 | 2020 | #Phase Difference re-estimation |
|
|
2021 | 2021 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
2022 | 2022 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
2023 | 2023 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
2024 | 2024 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
2025 | 2025 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
2026 | 2026 | |
|
|
2027 | 2027 | #Phase Difference RMS |
|
|
2028 | 2028 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
2029 | 2029 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
2030 | 2030 | #Data from Meteor |
|
|
2031 | 2031 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
2032 | 2032 | mPeakPower1 = powerNet1.max() |
|
|
2033 | 2033 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
2034 | 2034 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
2035 | 2035 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
2036 | 2036 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
2037 | 2037 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
2038 | 2038 | #Vectorize |
|
|
2039 | 2039 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
2040 | 2040 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
2041 | 2041 | |
|
|
2042 | 2042 | #Rejection Criterions |
|
|
2043 | 2043 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
2044 | 2044 | meteorAux[-1] = 17 |
|
|
2045 | 2045 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
2046 | 2046 | meteorAux[-1] = 1 |
|
|
2047 | 2047 | |
|
|
2048 | 2048 | |
|
|
2049 | 2049 | else: |
|
|
2050 | 2050 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
2051 | 2051 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
2052 | 2052 | PowerSeries = 0 |
|
|
2053 | 2053 | |
|
|
2054 | 2054 | listMeteors1.append(meteorAux) |
|
|
2055 | 2055 | listPowerSeries.append(PowerSeries) |
|
|
2056 | 2056 | listVoltageSeries.append(meteorVolts1) |
|
|
2057 | 2057 | |
|
|
2058 | 2058 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
2059 | 2059 | |
|
|
2060 | 2060 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
2061 | 2061 | |
|
|
2062 | 2062 | threshError = 10 |
|
|
2063 | 2063 | #Depending if it is 30 or 50 MHz |
|
|
2064 | 2064 | if frequency == 30.175e6: |
|
|
2065 | 2065 | timeLag = 45*10**-3 |
|
|
2066 | 2066 | else: |
|
|
2067 | 2067 | timeLag = 15*10**-3 |
|
|
2068 | 2068 | lag = int(numpy.ceil(timeLag/timeInterval)) |
|
|
2069 | 2069 | |
|
|
2070 | 2070 | listMeteors1 = [] |
|
|
2071 | 2071 | |
|
|
2072 | 2072 | for i in range(len(listMeteors)): |
|
|
2073 | 2073 | meteorPower = listPower[i] |
|
|
2074 | 2074 | meteorAux = listMeteors[i] |
|
|
2075 | 2075 | |
|
|
2076 | 2076 | if meteorAux[-1] == 0: |
|
|
2077 | 2077 | |
|
|
2078 | 2078 | try: |
|
|
2079 | 2079 | indmax = meteorPower.argmax() |
|
|
2080 | 2080 | indlag = indmax + lag |
|
|
2081 | 2081 | |
|
|
2082 | 2082 | y = meteorPower[indlag:] |
|
|
2083 | 2083 | x = numpy.arange(0, y.size)*timeLag |
|
|
2084 | 2084 | |
|
|
2085 | 2085 | #first guess |
|
|
2086 | 2086 | a = y[0] |
|
|
2087 | 2087 | tau = timeLag |
|
|
2088 | 2088 | #exponential fit |
|
|
2089 | 2089 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
2090 | 2090 | y1 = self.__exponential_function(x, *popt) |
|
|
2091 | 2091 | #error estimation |
|
|
2092 | 2092 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
2093 | 2093 | |
|
|
2094 | 2094 | decayTime = popt[1] |
|
|
2095 | 2095 | riseTime = indmax*timeInterval |
|
|
2096 | 2096 | meteorAux[11:13] = [decayTime, error] |
|
|
2097 | 2097 | |
|
|
2098 | 2098 | #Table items 7, 8 and 11 |
|
|
2099 | 2099 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
2100 | 2100 | meteorAux[-1] = 7 |
|
|
2101 | 2101 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
2102 | 2102 | meteorAux[-1] = 8 |
|
|
2103 | 2103 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
2104 | 2104 | meteorAux[-1] = 11 |
|
|
2105 | 2105 | |
|
|
2106 | 2106 | |
|
|
2107 | 2107 | except: |
|
|
2108 | 2108 | meteorAux[-1] = 11 |
|
|
2109 | 2109 | |
|
|
2110 | 2110 | |
|
|
2111 | 2111 | listMeteors1.append(meteorAux) |
|
|
2112 | 2112 | |
|
|
2113 | 2113 | return listMeteors1 |
|
|
2114 | 2114 | |
|
|
2115 | 2115 | #Exponential Function |
|
|
2116 | 2116 | |
|
|
2117 | 2117 | def __exponential_function(self, x, a, tau): |
|
|
2118 | 2118 | y = a*numpy.exp(-x/tau) |
|
|
2119 | 2119 | return y |
|
|
2120 | 2120 | |
|
|
2121 | 2121 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
2122 | 2122 | |
|
|
2123 | 2123 | pairslist1 = list(pairslist) |
|
|
2124 | 2124 | pairslist1.append((0,4)) |
|
|
2125 | 2125 | pairslist1.append((1,3)) |
|
|
2126 | 2126 | numPairs = len(pairslist1) |
|
|
2127 | 2127 | #Time Lag |
|
|
2128 | 2128 | timeLag = 45*10**-3 |
|
|
2129 | 2129 | c = 3e8 |
|
|
2130 | 2130 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
2131 | 2131 | freq = 30.175e6 |
|
|
2132 | 2132 | |
|
|
2133 | 2133 | listMeteors1 = [] |
|
|
2134 | 2134 | |
|
|
2135 | 2135 | for i in range(len(listMeteors)): |
|
|
2136 | 2136 | meteorAux = listMeteors[i] |
|
|
2137 | 2137 | if meteorAux[-1] == 0: |
|
|
2138 | 2138 | mStart = listMeteors[i][1] |
|
|
2139 | 2139 | mPeak = listMeteors[i][2] |
|
|
2140 | 2140 | mLag = mPeak - mStart + lag |
|
|
2141 | 2141 | |
|
|
2142 | 2142 | #get the volt data between the start and end times of the meteor |
|
|
2143 | 2143 | meteorVolts = listVolts[i] |
|
|
2144 | 2144 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
2145 | 2145 | |
|
|
2146 | 2146 | #Get CCF |
|
|
2147 | 2147 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
2148 | 2148 | |
|
|
2149 | 2149 | #Method 2 |
|
|
2150 | 2150 | slopes = numpy.zeros(numPairs) |
|
|
2151 | 2151 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
2152 | 2152 | angAllCCF = numpy.angle(allCCFs[:,[0,4,2,3],0]) |
|
|
2153 | 2153 | |
|
|
2154 | 2154 | #Correct phases |
|
|
2155 | 2155 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
2156 | 2156 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2157 | 2157 | |
|
|
2158 | 2158 | if indDer[0].shape[0] > 0: |
|
|
2159 | 2159 | for i in range(indDer[0].shape[0]): |
|
|
2160 | 2160 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
2161 | 2161 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
2162 | 2162 | |
|
|
2163 | 2163 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
2164 | 2164 | for j in range(numPairs): |
|
|
2165 | 2165 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
2166 | 2166 | slopes[j] = fit[0] |
|
|
2167 | 2167 | |
|
|
2168 | 2168 | #Remove Outlier |
|
|
2169 | 2169 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
2170 | 2170 | # slopes = numpy.delete(slopes,indOut) |
|
|
2171 | 2171 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
2172 | 2172 | # slopes = numpy.delete(slopes,indOut) |
|
|
2173 | 2173 | |
|
|
2174 | 2174 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
2175 | 2175 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
2176 | 2176 | meteorAux[-2] = radialError |
|
|
2177 | 2177 | meteorAux[-3] = radialVelocity |
|
|
2178 | 2178 | |
|
|
2179 | 2179 | #Setting Error |
|
|
2180 | 2180 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2181 | 2181 | if numpy.abs(radialVelocity) > 200: |
|
|
2182 | 2182 | meteorAux[-1] = 15 |
|
|
2183 | 2183 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
2184 | 2184 | elif radialError > radialStdThresh: |
|
|
2185 | 2185 | meteorAux[-1] = 12 |
|
|
2186 | 2186 | |
|
|
2187 | 2187 | listMeteors1.append(meteorAux) |
|
|
2188 | 2188 | return listMeteors1 |
|
|
2189 | 2189 | |
|
|
2190 | 2190 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
2191 | 2191 | |
|
|
2192 | 2192 | #New arrays |
|
|
2193 | 2193 | arrayMeteors = numpy.array(listMeteors) |
|
|
2194 | 2194 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
2195 | 2195 | |
|
|
2196 | 2196 | #Date inclusion |
|
|
2197 | 2197 | # date = re.findall(r'\((.*?)\)', date) |
|
|
2198 | 2198 | # date = date[0].split(',') |
|
|
2199 | 2199 | # date = map(int, date) |
|
|
2200 | 2200 | # |
|
|
2201 | 2201 | # if len(date)<6: |
|
|
2202 | 2202 | # date.append(0) |
|
|
2203 | 2203 | # |
|
|
2204 | 2204 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
2205 | 2205 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
2206 | 2206 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
2207 | 2207 | |
|
|
2208 | 2208 | #Meteor array |
|
|
2209 | 2209 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
2210 | 2210 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
2211 | 2211 | |
|
|
2212 | 2212 | #Parameters Array |
|
|
2213 | 2213 | arrayParameters[:,0] = arrayDate #Date |
|
|
2214 | 2214 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
2215 | 2215 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
2216 | 2216 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
2217 | 2217 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
2218 | 2218 | |
|
|
2219 | 2219 | |
|
|
2220 | 2220 | return arrayParameters |
|
|
2221 | 2221 | |
|
|
2222 | 2222 | class CorrectSMPhases(Operation): |
|
|
2223 | 2223 | |
|
|
2224 | 2224 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
2225 | 2225 | |
|
|
2226 | 2226 | arrayParameters = dataOut.data_param |
|
|
2227 | 2227 | pairsList = [] |
|
|
2228 | 2228 | pairx = (0,1) |
|
|
2229 | 2229 | pairy = (2,3) |
|
|
2230 | 2230 | pairsList.append(pairx) |
|
|
2231 | 2231 | pairsList.append(pairy) |
|
|
2232 | 2232 | jph = numpy.zeros(4) |
|
|
2233 | 2233 | |
|
|
2234 | 2234 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2235 | 2235 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2236 | 2236 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
2237 | 2237 | |
|
|
2238 | 2238 | meteorOps = SMOperations() |
|
|
2239 | 2239 | if channelPositions is None: |
|
|
2240 | 2240 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2241 | 2241 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2242 | 2242 | |
|
|
2243 | 2243 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2244 | 2244 | h = (hmin,hmax) |
|
|
2245 | 2245 | |
|
|
2246 | 2246 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2247 | 2247 | |
|
|
2248 | 2248 | dataOut.data_param = arrayParameters |
|
|
2249 | 2249 | return |
|
|
2250 | 2250 | |
|
|
2251 | 2251 | class SMPhaseCalibration(Operation): |
|
|
2252 | 2252 | |
|
|
2253 | 2253 | __buffer = None |
|
|
2254 | 2254 | |
|
|
2255 | 2255 | __initime = None |
|
|
2256 | 2256 | |
|
|
2257 | 2257 | __dataReady = False |
|
|
2258 | 2258 | |
|
|
2259 | 2259 | __isConfig = False |
|
|
2260 | 2260 | |
|
|
2261 | 2261 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
2262 | 2262 | |
|
|
2263 | 2263 | dataTime = currentTime + paramInterval |
|
|
2264 | 2264 | deltaTime = dataTime - initTime |
|
|
2265 | 2265 | |
|
|
2266 | 2266 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
2267 | 2267 | return True |
|
|
2268 | 2268 | |
|
|
2269 | 2269 | return False |
|
|
2270 | 2270 | |
|
|
2271 | 2271 | def __getGammas(self, pairs, d, phases): |
|
|
2272 | 2272 | gammas = numpy.zeros(2) |
|
|
2273 | 2273 | |
|
|
2274 | 2274 | for i in range(len(pairs)): |
|
|
2275 | 2275 | |
|
|
2276 | 2276 | pairi = pairs[i] |
|
|
2277 | 2277 | |
|
|
2278 | 2278 | phip3 = phases[:,pairi[0]] |
|
|
2279 | 2279 | d3 = d[pairi[0]] |
|
|
2280 | 2280 | phip2 = phases[:,pairi[1]] |
|
|
2281 | 2281 | d2 = d[pairi[1]] |
|
|
2282 | 2282 | #Calculating gamma |
|
|
2283 | 2283 | # jdcos = alp1/(k*d1) |
|
|
2284 | 2284 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
2285 | 2285 | jgamma = -phip2*d3/d2 - phip3 |
|
|
2286 | 2286 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
2287 | 2287 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
2288 | 2288 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
2289 | 2289 | |
|
|
2290 | 2290 | #Revised distribution |
|
|
2291 | 2291 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
2292 | 2292 | |
|
|
2293 | 2293 | #Histogram |
|
|
2294 |
nBins = 64 |
|
|
|
2294 | nBins = 64 | |
|
|
2295 | 2295 | rmin = -0.5*numpy.pi |
|
|
2296 | 2296 | rmax = 0.5*numpy.pi |
|
|
2297 | 2297 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
2298 | 2298 | |
|
|
2299 | 2299 | meteorsY = phaseHisto[0] |
|
|
2300 | 2300 | phasesX = phaseHisto[1][:-1] |
|
|
2301 | 2301 | width = phasesX[1] - phasesX[0] |
|
|
2302 | 2302 | phasesX += width/2 |
|
|
2303 | 2303 | |
|
|
2304 | 2304 | #Gaussian aproximation |
|
|
2305 | 2305 | bpeak = meteorsY.argmax() |
|
|
2306 | 2306 | peak = meteorsY.max() |
|
|
2307 | 2307 | jmin = bpeak - 5 |
|
|
2308 | 2308 | jmax = bpeak + 5 + 1 |
|
|
2309 | 2309 | |
|
|
2310 | 2310 | if jmin<0: |
|
|
2311 | 2311 | jmin = 0 |
|
|
2312 | 2312 | jmax = 6 |
|
|
2313 | 2313 | elif jmax > meteorsY.size: |
|
|
2314 | 2314 | jmin = meteorsY.size - 6 |
|
|
2315 | 2315 | jmax = meteorsY.size |
|
|
2316 | 2316 | |
|
|
2317 | 2317 | x0 = numpy.array([peak,bpeak,50]) |
|
|
2318 | 2318 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
2319 | 2319 | |
|
|
2320 | 2320 | #Gammas |
|
|
2321 | 2321 | gammas[i] = coeff[0][1] |
|
|
2322 | 2322 | |
|
|
2323 | 2323 | return gammas |
|
|
2324 | 2324 | |
|
|
2325 | 2325 | def __residualFunction(self, coeffs, y, t): |
|
|
2326 | 2326 | |
|
|
2327 | 2327 | return y - self.__gauss_function(t, coeffs) |
|
|
2328 | 2328 | |
|
|
2329 | 2329 | def __gauss_function(self, t, coeffs): |
|
|
2330 | 2330 | |
|
|
2331 | 2331 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
2332 | 2332 | |
|
|
2333 | 2333 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
2334 | 2334 | meteorOps = SMOperations() |
|
|
2335 | 2335 | nchan = 4 |
|
|
2336 | 2336 | pairx = pairsList[0] #x es 0 |
|
|
2337 | 2337 | pairy = pairsList[1] #y es 1 |
|
|
2338 | 2338 | center_xangle = 0 |
|
|
2339 | 2339 | center_yangle = 0 |
|
|
2340 | 2340 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
2341 | 2341 | ntimes = len(range_angle) |
|
|
2342 | 2342 | |
|
|
2343 |
nstepsx = 20 |
|
|
|
2344 |
nstepsy = 20 |
|
|
|
2343 | nstepsx = 20 | |
|
|
2344 | nstepsy = 20 | |
|
|
2345 | 2345 | |
|
|
2346 | 2346 | for iz in range(ntimes): |
|
|
2347 | 2347 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
2348 | 2348 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
2349 | 2349 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
2350 | 2350 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
2351 | 2351 | |
|
|
2352 | 2352 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
2353 | 2353 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
2354 | 2354 | |
|
|
2355 | 2355 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
2356 | 2356 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
2357 | 2357 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
2358 | 2358 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
2359 | 2359 | jph = numpy.zeros(nchan) |
|
|
2360 | 2360 | |
|
|
2361 | 2361 | # Iterations looking for the offset |
|
|
2362 | 2362 | for iy in range(int(nstepsy)): |
|
|
2363 | 2363 | for ix in range(int(nstepsx)): |
|
|
2364 | 2364 | d3 = d[pairsList[1][0]] |
|
|
2365 | 2365 | d2 = d[pairsList[1][1]] |
|
|
2366 | 2366 | d5 = d[pairsList[0][0]] |
|
|
2367 | 2367 | d4 = d[pairsList[0][1]] |
|
|
2368 | 2368 | |
|
|
2369 | 2369 | alp2 = alpha_y[iy] #gamma 1 |
|
|
2370 | 2370 | alp4 = alpha_x[ix] #gamma 0 |
|
|
2371 | 2371 | |
|
|
2372 | 2372 | alp3 = -alp2*d3/d2 - gammas[1] |
|
|
2373 | 2373 | alp5 = -alp4*d5/d4 - gammas[0] |
|
|
2374 | 2374 | # jph[pairy[1]] = alpha_y[iy] |
|
|
2375 | 2375 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
2376 | 2376 | |
|
|
2377 | 2377 | # jph[pairx[1]] = alpha_x[ix] |
|
|
2378 | 2378 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
2379 | 2379 | jph[pairsList[0][1]] = alp4 |
|
|
2380 | 2380 | jph[pairsList[0][0]] = alp5 |
|
|
2381 | 2381 | jph[pairsList[1][0]] = alp3 |
|
|
2382 | 2382 | jph[pairsList[1][1]] = alp2 |
|
|
2383 | 2383 | jph_array[:,ix,iy] = jph |
|
|
2384 | 2384 | # d = [2.0,2.5,2.5,2.0] |
|
|
2385 | 2385 | #falta chequear si va a leer bien los meteoros |
|
|
2386 | 2386 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
2387 | 2387 | error = meteorsArray1[:,-1] |
|
|
2388 | 2388 | ind1 = numpy.where(error==0)[0] |
|
|
2389 | 2389 | penalty[ix,iy] = ind1.size |
|
|
2390 | 2390 | |
|
|
2391 | 2391 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
2392 | 2392 | phOffset = jph_array[:,i,j] |
|
|
2393 | 2393 | |
|
|
2394 | 2394 | center_xangle = phOffset[pairx[1]] |
|
|
2395 | 2395 | center_yangle = phOffset[pairy[1]] |
|
|
2396 | 2396 | |
|
|
2397 | 2397 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
2398 | 2398 | phOffset = phOffset*180/numpy.pi |
|
|
2399 | 2399 | return phOffset |
|
|
2400 | 2400 | |
|
|
2401 | 2401 | |
|
|
2402 | 2402 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
2403 | 2403 | |
|
|
2404 | 2404 | dataOut.flagNoData = True |
|
|
2405 | 2405 | self.__dataReady = False |
|
|
2406 | 2406 | dataOut.outputInterval = nHours*3600 |
|
|
2407 | 2407 | |
|
|
2408 | 2408 | if self.__isConfig == False: |
|
|
2409 | 2409 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2410 | 2410 | #Get Initial LTC time |
|
|
2411 | 2411 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2412 | 2412 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2413 | 2413 | |
|
|
2414 | 2414 | self.__isConfig = True |
|
|
2415 | 2415 | |
|
|
2416 | 2416 | if self.__buffer is None: |
|
|
2417 | 2417 | self.__buffer = dataOut.data_param.copy() |
|
|
2418 | 2418 | |
|
|
2419 | 2419 | else: |
|
|
2420 | 2420 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2421 | 2421 | |
|
|
2422 | 2422 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2423 | 2423 | |
|
|
2424 | 2424 | if self.__dataReady: |
|
|
2425 | 2425 | dataOut.utctimeInit = self.__initime |
|
|
2426 | 2426 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2427 | 2427 | |
|
|
2428 | 2428 | freq = dataOut.frequency |
|
|
2429 | 2429 | c = dataOut.C #m/s |
|
|
2430 | 2430 | lamb = c/freq |
|
|
2431 | 2431 | k = 2*numpy.pi/lamb |
|
|
2432 | 2432 | azimuth = 0 |
|
|
2433 | 2433 | h = (hmin, hmax) |
|
|
2434 | 2434 | # pairs = ((0,1),(2,3)) #Estrella |
|
|
2435 | 2435 | # pairs = ((1,0),(2,3)) #T |
|
|
2436 | 2436 | |
|
|
2437 | 2437 | if channelPositions is None: |
|
|
2438 | 2438 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2439 | 2439 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2440 | 2440 | meteorOps = SMOperations() |
|
|
2441 | 2441 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2442 | 2442 | |
|
|
2443 | 2443 | #Checking correct order of pairs |
|
|
2444 | 2444 | pairs = [] |
|
|
2445 | 2445 | if distances[1] > distances[0]: |
|
|
2446 | 2446 | pairs.append((1,0)) |
|
|
2447 | 2447 | else: |
|
|
2448 | 2448 | pairs.append((0,1)) |
|
|
2449 | 2449 | |
|
|
2450 | 2450 | if distances[3] > distances[2]: |
|
|
2451 | 2451 | pairs.append((3,2)) |
|
|
2452 | 2452 | else: |
|
|
2453 | 2453 | pairs.append((2,3)) |
|
|
2454 | 2454 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
2455 | 2455 | |
|
|
2456 | 2456 | meteorsArray = self.__buffer |
|
|
2457 | 2457 | error = meteorsArray[:,-1] |
|
|
2458 | 2458 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
2459 | 2459 | ind1 = numpy.where(boolError)[0] |
|
|
2460 | 2460 | meteorsArray = meteorsArray[ind1,:] |
|
|
2461 | 2461 | meteorsArray[:,-1] = 0 |
|
|
2462 | 2462 | phases = meteorsArray[:,8:12] |
|
|
2463 | 2463 | |
|
|
2464 | 2464 | #Calculate Gammas |
|
|
2465 | 2465 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
2466 | 2466 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
2467 | 2467 | #Calculate Phases |
|
|
2468 | 2468 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
2469 | 2469 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
2470 | 2470 | dataOut.data_output = -phasesOff |
|
|
2471 | 2471 | dataOut.flagNoData = False |
|
|
2472 | 2472 | dataOut.channelList = pairslist0 |
|
|
2473 | 2473 | self.__buffer = None |
|
|
2474 | 2474 | |
|
|
2475 | 2475 | |
|
|
2476 | 2476 | return |
|
|
2477 | 2477 | |
|
|
2478 | 2478 | class SMOperations(): |
|
|
2479 | 2479 | |
|
|
2480 | 2480 | def __init__(self): |
|
|
2481 | 2481 | |
|
|
2482 | 2482 | return |
|
|
2483 | 2483 | |
|
|
2484 | 2484 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
2485 | 2485 | |
|
|
2486 | 2486 | arrayParameters = arrayParameters0.copy() |
|
|
2487 | 2487 | hmin = h[0] |
|
|
2488 | 2488 | hmax = h[1] |
|
|
2489 | 2489 | |
|
|
2490 | 2490 | #Calculate AOA (Error N 3, 4) |
|
|
2491 | 2491 | #JONES ET AL. 1998 |
|
|
2492 | 2492 | AOAthresh = numpy.pi/8 |
|
|
2493 | 2493 | error = arrayParameters[:,-1] |
|
|
2494 | 2494 | phases = -arrayParameters[:,8:12] + jph |
|
|
2495 | 2495 | # phases = numpy.unwrap(phases) |
|
|
2496 | 2496 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
2497 | 2497 | |
|
|
2498 | 2498 | #Calculate Heights (Error N 13 and 14) |
|
|
2499 | 2499 | error = arrayParameters[:,-1] |
|
|
2500 | 2500 | Ranges = arrayParameters[:,1] |
|
|
2501 | 2501 | zenith = arrayParameters[:,4] |
|
|
2502 | 2502 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2503 | 2503 | |
|
|
2504 | 2504 | #----------------------- Get Final data ------------------------------------ |
|
|
2505 | 2505 | # error = arrayParameters[:,-1] |
|
|
2506 | 2506 | # ind1 = numpy.where(error==0)[0] |
|
|
2507 | 2507 | # arrayParameters = arrayParameters[ind1,:] |
|
|
2508 | 2508 | |
|
|
2509 | 2509 | return arrayParameters |
|
|
2510 | 2510 | |
|
|
2511 | 2511 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
2512 | 2512 | |
|
|
2513 | 2513 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
2514 | 2514 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
2515 | 2515 | |
|
|
2516 | 2516 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
2517 | 2517 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
2518 | 2518 | arrayAOA[:,2] = cosDirError |
|
|
2519 | 2519 | |
|
|
2520 | 2520 | azimuthAngle = arrayAOA[:,0] |
|
|
2521 | 2521 | zenithAngle = arrayAOA[:,1] |
|
|
2522 | 2522 | |
|
|
2523 | 2523 | #Setting Error |
|
|
2524 | 2524 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
2525 | 2525 | error[indError] = 0 |
|
|
2526 | 2526 | #Number 3: AOA not fesible |
|
|
2527 | 2527 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
2528 | 2528 | error[indInvalid] = 3 |
|
|
2529 | 2529 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2530 | 2530 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
2531 | 2531 | error[indInvalid] = 4 |
|
|
2532 | 2532 | return arrayAOA, error |
|
|
2533 | 2533 | |
|
|
2534 | 2534 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
2535 | 2535 | |
|
|
2536 | 2536 | #Initializing some variables |
|
|
2537 | 2537 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
2538 | 2538 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
2539 | 2539 | |
|
|
2540 | 2540 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2541 | 2541 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2542 | 2542 | |
|
|
2543 | 2543 | |
|
|
2544 | 2544 | for i in range(2): |
|
|
2545 | 2545 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
2546 | 2546 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
2547 | 2547 | d0 = distances[pairsList[i][0]] |
|
|
2548 | 2548 | d1 = distances[pairsList[i][1]] |
|
|
2549 | 2549 | |
|
|
2550 | 2550 | ph0_aux = ph0 + ph1 |
|
|
2551 | 2551 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
2552 | 2552 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
2553 | 2553 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
2554 | 2554 | #First Estimation |
|
|
2555 | 2555 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
2556 | 2556 | |
|
|
2557 | 2557 | #Most-Accurate Second Estimation |
|
|
2558 | 2558 | phi1_aux = ph0 - ph1 |
|
|
2559 | 2559 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
2560 | 2560 | #Direction Cosine 1 |
|
|
2561 | 2561 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
2562 | 2562 | |
|
|
2563 | 2563 | #Searching the correct Direction Cosine |
|
|
2564 | 2564 | cosdir0_aux = cosdir0[:,i] |
|
|
2565 | 2565 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
2566 | 2566 | #Minimum Distance |
|
|
2567 | 2567 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
2568 | 2568 | indcos = cosDiff.argmin(axis = 1) |
|
|
2569 | 2569 | #Saving Value obtained |
|
|
2570 | 2570 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
2571 | 2571 | |
|
|
2572 | 2572 | return cosdir0, cosdir |
|
|
2573 | 2573 | |
|
|
2574 | 2574 | def __calculateAOA(self, cosdir, azimuth): |
|
|
2575 | 2575 | cosdirX = cosdir[:,0] |
|
|
2576 | 2576 | cosdirY = cosdir[:,1] |
|
|
2577 | 2577 | |
|
|
2578 | 2578 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
2579 | 2579 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
2580 | 2580 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
2581 | 2581 | |
|
|
2582 | 2582 | return angles |
|
|
2583 | 2583 | |
|
|
2584 | 2584 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
2585 | 2585 | |
|
|
2586 | 2586 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
2587 | 2587 | Re = 6371 #Earth Radius |
|
|
2588 | 2588 | heights = numpy.zeros(Ranges.shape) |
|
|
2589 | 2589 | |
|
|
2590 | 2590 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
2591 | 2591 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
2592 | 2592 | |
|
|
2593 | 2593 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
2594 | 2594 | |
|
|
2595 | 2595 | Ri = Ranges + R_aux |
|
|
2596 | 2596 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
2597 | 2597 | |
|
|
2598 | 2598 | #Check if there is a height between 70 and 110 km |
|
|
2599 | 2599 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
2600 | 2600 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
2601 | 2601 | |
|
|
2602 | 2602 | hCorr = hi[ind_h, :] |
|
|
2603 | 2603 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
2604 | 2604 | |
|
|
2605 | hCorr = hi[ind_hCorr] | |
|
|
2605 | hCorr = hi[ind_hCorr][:len(ind_h)] | |
|
|
2606 | 2606 | heights[ind_h] = hCorr |
|
|
2607 | 2607 | |
|
|
2608 | 2608 | #Setting Error |
|
|
2609 | 2609 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
2610 | 2610 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
2611 | 2611 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
2612 | 2612 | error[indError] = 0 |
|
|
2613 | 2613 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
2614 | 2614 | error[indInvalid2] = 14 |
|
|
2615 | 2615 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
2616 | 2616 | error[indInvalid1] = 13 |
|
|
2617 | 2617 | |
|
|
2618 | 2618 | return heights, error |
|
|
2619 | 2619 | |
|
|
2620 | 2620 | def getPhasePairs(self, channelPositions): |
|
|
2621 | 2621 | chanPos = numpy.array(channelPositions) |
|
|
2622 | 2622 | listOper = list(itertools.combinations(range(5),2)) |
|
|
2623 | 2623 | |
|
|
2624 | 2624 | distances = numpy.zeros(4) |
|
|
2625 | 2625 | axisX = [] |
|
|
2626 | 2626 | axisY = [] |
|
|
2627 | 2627 | distX = numpy.zeros(3) |
|
|
2628 | 2628 | distY = numpy.zeros(3) |
|
|
2629 | 2629 | ix = 0 |
|
|
2630 | 2630 | iy = 0 |
|
|
2631 | 2631 | |
|
|
2632 | 2632 | pairX = numpy.zeros((2,2)) |
|
|
2633 | 2633 | pairY = numpy.zeros((2,2)) |
|
|
2634 | 2634 | |
|
|
2635 | 2635 | for i in range(len(listOper)): |
|
|
2636 | 2636 | pairi = listOper[i] |
|
|
2637 | 2637 | |
|
|
2638 | 2638 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
2639 | 2639 | |
|
|
2640 | 2640 | if posDif[0] == 0: |
|
|
2641 | 2641 | axisY.append(pairi) |
|
|
2642 | 2642 | distY[iy] = posDif[1] |
|
|
2643 | 2643 | iy += 1 |
|
|
2644 | 2644 | elif posDif[1] == 0: |
|
|
2645 | 2645 | axisX.append(pairi) |
|
|
2646 | 2646 | distX[ix] = posDif[0] |
|
|
2647 | 2647 | ix += 1 |
|
|
2648 | 2648 | |
|
|
2649 | 2649 | for i in range(2): |
|
|
2650 | 2650 | if i==0: |
|
|
2651 | 2651 | dist0 = distX |
|
|
2652 | 2652 | axis0 = axisX |
|
|
2653 | 2653 | else: |
|
|
2654 | 2654 | dist0 = distY |
|
|
2655 | 2655 | axis0 = axisY |
|
|
2656 | 2656 | |
|
|
2657 | 2657 | side = numpy.argsort(dist0)[:-1] |
|
|
2658 | 2658 | axis0 = numpy.array(axis0)[side,:] |
|
|
2659 | 2659 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
2660 | 2660 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
2661 | 2661 | side = axis1[axis1 != chanC] |
|
|
2662 | 2662 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
2663 | 2663 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
2664 | 2664 | if diff1<0: |
|
|
2665 | 2665 | chan2 = side[0] |
|
|
2666 | 2666 | d2 = numpy.abs(diff1) |
|
|
2667 | 2667 | chan1 = side[1] |
|
|
2668 | 2668 | d1 = numpy.abs(diff2) |
|
|
2669 | 2669 | else: |
|
|
2670 | 2670 | chan2 = side[1] |
|
|
2671 | 2671 | d2 = numpy.abs(diff2) |
|
|
2672 | 2672 | chan1 = side[0] |
|
|
2673 | 2673 | d1 = numpy.abs(diff1) |
|
|
2674 | 2674 | |
|
|
2675 | 2675 | if i==0: |
|
|
2676 | 2676 | chanCX = chanC |
|
|
2677 | 2677 | chan1X = chan1 |
|
|
2678 | 2678 | chan2X = chan2 |
|
|
2679 | 2679 | distances[0:2] = numpy.array([d1,d2]) |
|
|
2680 | 2680 | else: |
|
|
2681 | 2681 | chanCY = chanC |
|
|
2682 | 2682 | chan1Y = chan1 |
|
|
2683 | 2683 | chan2Y = chan2 |
|
|
2684 | 2684 | distances[2:4] = numpy.array([d1,d2]) |
|
|
2685 | 2685 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
2686 | 2686 | # |
|
|
2687 | 2687 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
2688 | 2688 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
2689 | 2689 | # |
|
|
2690 | 2690 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
2691 | 2691 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
2692 | 2692 | # channel25X = int(pairX[0,ind25X]) |
|
|
2693 | 2693 | # channel20X = int(pairX[1,ind20X]) |
|
|
2694 | 2694 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
2695 | 2695 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
2696 | 2696 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
2697 | 2697 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
2698 | 2698 | |
|
|
2699 | 2699 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
2700 | 2700 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
2701 | 2701 | |
|
|
2702 | 2702 | return pairslist, distances |
|
|
2703 | 2703 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
2704 | 2704 | # |
|
|
2705 | 2705 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
2706 | 2706 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
2707 | 2707 | # |
|
|
2708 | 2708 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
2709 | 2709 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
2710 | 2710 | # arrayAOA[:,2] = cosDirError |
|
|
2711 | 2711 | # |
|
|
2712 | 2712 | # azimuthAngle = arrayAOA[:,0] |
|
|
2713 | 2713 | # zenithAngle = arrayAOA[:,1] |
|
|
2714 | 2714 | # |
|
|
2715 | 2715 | # #Setting Error |
|
|
2716 | 2716 | # #Number 3: AOA not fesible |
|
|
2717 | 2717 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
2718 | 2718 | # error[indInvalid] = 3 |
|
|
2719 | 2719 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2720 | 2720 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
2721 | 2721 | # error[indInvalid] = 4 |
|
|
2722 | 2722 | # return arrayAOA, error |
|
|
2723 | 2723 | # |
|
|
2724 | 2724 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
2725 | 2725 | # |
|
|
2726 | 2726 | # #Initializing some variables |
|
|
2727 | 2727 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
2728 | 2728 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
2729 | 2729 | # |
|
|
2730 | 2730 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2731 | 2731 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2732 | 2732 | # |
|
|
2733 | 2733 | # |
|
|
2734 | 2734 | # for i in range(2): |
|
|
2735 | 2735 | # #First Estimation |
|
|
2736 | 2736 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
2737 | 2737 | # #Dealias |
|
|
2738 | 2738 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
2739 | 2739 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
2740 | 2740 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
2741 | 2741 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
2742 | 2742 | # #Direction Cosine 0 |
|
|
2743 | 2743 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
2744 | 2744 | # |
|
|
2745 | 2745 | # #Most-Accurate Second Estimation |
|
|
2746 | 2746 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
2747 | 2747 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
2748 | 2748 | # #Direction Cosine 1 |
|
|
2749 | 2749 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
2750 | 2750 | # |
|
|
2751 | 2751 | # #Searching the correct Direction Cosine |
|
|
2752 | 2752 | # cosdir0_aux = cosdir0[:,i] |
|
|
2753 | 2753 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
2754 | 2754 | # #Minimum Distance |
|
|
2755 | 2755 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
2756 | 2756 | # indcos = cosDiff.argmin(axis = 1) |
|
|
2757 | 2757 | # #Saving Value obtained |
|
|
2758 | 2758 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
2759 | 2759 | # |
|
|
2760 | 2760 | # return cosdir0, cosdir |
|
|
2761 | 2761 | # |
|
|
2762 | 2762 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
2763 | 2763 | # cosdirX = cosdir[:,0] |
|
|
2764 | 2764 | # cosdirY = cosdir[:,1] |
|
|
2765 | 2765 | # |
|
|
2766 | 2766 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
2767 | 2767 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
2768 | 2768 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
2769 | 2769 | # |
|
|
2770 | 2770 | # return angles |
|
|
2771 | 2771 | # |
|
|
2772 | 2772 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
2773 | 2773 | # |
|
|
2774 | 2774 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
2775 | 2775 | # Re = 6371 #Earth Radius |
|
|
2776 | 2776 | # heights = numpy.zeros(Ranges.shape) |
|
|
2777 | 2777 | # |
|
|
2778 | 2778 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
2779 | 2779 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
2780 | 2780 | # |
|
|
2781 | 2781 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
2782 | 2782 | # |
|
|
2783 | 2783 | # Ri = Ranges + R_aux |
|
|
2784 | 2784 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
2785 | 2785 | # |
|
|
2786 | 2786 | # #Check if there is a height between 70 and 110 km |
|
|
2787 | 2787 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
2788 | 2788 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
2789 | 2789 | # |
|
|
2790 | 2790 | # hCorr = hi[ind_h, :] |
|
|
2791 | 2791 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
2792 | 2792 | # |
|
|
2793 | 2793 | # hCorr = hi[ind_hCorr] |
|
|
2794 | 2794 | # heights[ind_h] = hCorr |
|
|
2795 | 2795 | # |
|
|
2796 | 2796 | # #Setting Error |
|
|
2797 | 2797 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
2798 | 2798 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
2799 | 2799 | # |
|
|
2800 | 2800 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
2801 | 2801 | # error[indInvalid2] = 14 |
|
|
2802 | 2802 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
2803 | 2803 | # error[indInvalid1] = 13 |
|
|
2804 | 2804 | # |
|
|
2805 | 2805 | # return heights, error |
| @@ -1,1283 +1,1285 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | |
|
|
5 | 5 | from jroproc_base import ProcessingUnit, Operation |
|
|
6 | 6 | from schainpy.model.data.jrodata import Voltage |
|
|
7 | 7 | |
|
|
8 | 8 | class VoltageProc(ProcessingUnit): |
|
|
9 | 9 | |
|
|
10 | 10 | |
|
|
11 | 11 | def __init__(self, **kwargs): |
|
|
12 | 12 | |
|
|
13 | 13 | ProcessingUnit.__init__(self, **kwargs) |
|
|
14 | 14 | |
|
|
15 | 15 | # self.objectDict = {} |
|
|
16 | 16 | self.dataOut = Voltage() |
|
|
17 | 17 | self.flip = 1 |
|
|
18 | 18 | |
|
|
19 | 19 | def run(self): |
|
|
20 | 20 | if self.dataIn.type == 'AMISR': |
|
|
21 | 21 | self.__updateObjFromAmisrInput() |
|
|
22 | 22 | |
|
|
23 | 23 | if self.dataIn.type == 'Voltage': |
|
|
24 | 24 | self.dataOut.copy(self.dataIn) |
|
|
25 | 25 | |
|
|
26 | 26 | # self.dataOut.copy(self.dataIn) |
|
|
27 | 27 | |
|
|
28 | 28 | def __updateObjFromAmisrInput(self): |
|
|
29 | 29 | |
|
|
30 | 30 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
31 | 31 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
32 | 32 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
33 | 33 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
34 | 34 | |
|
|
35 | 35 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
36 | 36 | self.dataOut.data = self.dataIn.data |
|
|
37 | 37 | self.dataOut.utctime = self.dataIn.utctime |
|
|
38 | 38 | self.dataOut.channelList = self.dataIn.channelList |
|
|
39 | 39 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
40 | 40 | self.dataOut.heightList = self.dataIn.heightList |
|
|
41 | 41 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
42 | 42 | |
|
|
43 | 43 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
44 | 44 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
45 | 45 | self.dataOut.frequency = self.dataIn.frequency |
|
|
46 | 46 | |
|
|
47 | 47 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
48 | 48 | self.dataOut.zenith = self.dataIn.zenith |
|
|
49 | 49 | |
|
|
50 | 50 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
51 | 51 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
52 | 52 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
53 | 53 | # |
|
|
54 | 54 | # pass# |
|
|
55 | 55 | # |
|
|
56 | 56 | # def init(self): |
|
|
57 | 57 | # |
|
|
58 | 58 | # |
|
|
59 | 59 | # if self.dataIn.type == 'AMISR': |
|
|
60 | 60 | # self.__updateObjFromAmisrInput() |
|
|
61 | 61 | # |
|
|
62 | 62 | # if self.dataIn.type == 'Voltage': |
|
|
63 | 63 | # self.dataOut.copy(self.dataIn) |
|
|
64 | 64 | # # No necesita copiar en cada init() los atributos de dataIn |
|
|
65 | 65 | # # la copia deberia hacerse por cada nuevo bloque de datos |
|
|
66 | 66 | |
|
|
67 | 67 | def selectChannels(self, channelList): |
|
|
68 | 68 | |
|
|
69 | 69 | channelIndexList = [] |
|
|
70 | 70 | |
|
|
71 | 71 | for channel in channelList: |
|
|
72 | 72 | if channel not in self.dataOut.channelList: |
|
|
73 | 73 | raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList)) |
|
|
74 | 74 | |
|
|
75 | 75 | index = self.dataOut.channelList.index(channel) |
|
|
76 | 76 | channelIndexList.append(index) |
|
|
77 | 77 | |
|
|
78 | 78 | self.selectChannelsByIndex(channelIndexList) |
|
|
79 | 79 | |
|
|
80 | 80 | def selectChannelsByIndex(self, channelIndexList): |
|
|
81 | 81 | """ |
|
|
82 | 82 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
83 | 83 | |
|
|
84 | 84 | Input: |
|
|
85 | 85 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
86 | 86 | |
|
|
87 | 87 | Affected: |
|
|
88 | 88 | self.dataOut.data |
|
|
89 | 89 | self.dataOut.channelIndexList |
|
|
90 | 90 | self.dataOut.nChannels |
|
|
91 | 91 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
92 | 92 | self.dataOut.systemHeaderObj.numChannels |
|
|
93 | 93 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
94 | 94 | |
|
|
95 | 95 | Return: |
|
|
96 | 96 | None |
|
|
97 | 97 | """ |
|
|
98 | 98 | |
|
|
99 | 99 | for channelIndex in channelIndexList: |
|
|
100 | 100 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
101 | 101 | print channelIndexList |
|
|
102 | 102 | raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex |
|
|
103 | 103 | |
|
|
104 | 104 | if self.dataOut.flagDataAsBlock: |
|
|
105 | 105 | """ |
|
|
106 | 106 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
107 | 107 | """ |
|
|
108 | 108 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
109 | 109 | else: |
|
|
110 | 110 | data = self.dataOut.data[channelIndexList,:] |
|
|
111 | 111 | |
|
|
112 | 112 | self.dataOut.data = data |
|
|
113 | 113 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
114 | 114 | # self.dataOut.nChannels = nChannels |
|
|
115 | 115 | |
|
|
116 | 116 | return 1 |
|
|
117 | 117 | |
|
|
118 | 118 | def selectHeights(self, minHei=None, maxHei=None): |
|
|
119 | 119 | """ |
|
|
120 | 120 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
121 | 121 | minHei <= height <= maxHei |
|
|
122 | 122 | |
|
|
123 | 123 | Input: |
|
|
124 | 124 | minHei : valor minimo de altura a considerar |
|
|
125 | 125 | maxHei : valor maximo de altura a considerar |
|
|
126 | 126 | |
|
|
127 | 127 | Affected: |
|
|
128 | 128 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
129 | 129 | |
|
|
130 | 130 | Return: |
|
|
131 | 131 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
132 | 132 | """ |
|
|
133 | 133 | |
|
|
134 | 134 | if minHei == None: |
|
|
135 | 135 | minHei = self.dataOut.heightList[0] |
|
|
136 | 136 | |
|
|
137 | 137 | if maxHei == None: |
|
|
138 | 138 | maxHei = self.dataOut.heightList[-1] |
|
|
139 | 139 | |
|
|
140 | 140 | if (minHei < self.dataOut.heightList[0]): |
|
|
141 | 141 | minHei = self.dataOut.heightList[0] |
|
|
142 | 142 | |
|
|
143 | 143 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
144 | 144 | maxHei = self.dataOut.heightList[-1] |
|
|
145 | 145 | |
|
|
146 | 146 | minIndex = 0 |
|
|
147 | 147 | maxIndex = 0 |
|
|
148 | 148 | heights = self.dataOut.heightList |
|
|
149 | 149 | |
|
|
150 | 150 | inda = numpy.where(heights >= minHei) |
|
|
151 | 151 | indb = numpy.where(heights <= maxHei) |
|
|
152 | 152 | |
|
|
153 | 153 | try: |
|
|
154 | 154 | minIndex = inda[0][0] |
|
|
155 | 155 | except: |
|
|
156 | 156 | minIndex = 0 |
|
|
157 | 157 | |
|
|
158 | 158 | try: |
|
|
159 | 159 | maxIndex = indb[0][-1] |
|
|
160 | 160 | except: |
|
|
161 | 161 | maxIndex = len(heights) |
|
|
162 | 162 | |
|
|
163 | 163 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
164 | 164 | |
|
|
165 | 165 | return 1 |
|
|
166 | 166 | |
|
|
167 | 167 | |
|
|
168 | 168 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
169 | 169 | """ |
|
|
170 | 170 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
171 | 171 | minIndex <= index <= maxIndex |
|
|
172 | 172 | |
|
|
173 | 173 | Input: |
|
|
174 | 174 | minIndex : valor de indice minimo de altura a considerar |
|
|
175 | 175 | maxIndex : valor de indice maximo de altura a considerar |
|
|
176 | 176 | |
|
|
177 | 177 | Affected: |
|
|
178 | 178 | self.dataOut.data |
|
|
179 | 179 | self.dataOut.heightList |
|
|
180 | 180 | |
|
|
181 | 181 | Return: |
|
|
182 | 182 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
183 | 183 | """ |
|
|
184 | 184 | |
|
|
185 | 185 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
186 | 186 | raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
187 | 187 | |
|
|
188 | 188 | if (maxIndex >= self.dataOut.nHeights): |
|
|
189 | 189 | maxIndex = self.dataOut.nHeights |
|
|
190 | 190 | |
|
|
191 | 191 | #voltage |
|
|
192 | 192 | if self.dataOut.flagDataAsBlock: |
|
|
193 | 193 | """ |
|
|
194 | 194 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
195 | 195 | """ |
|
|
196 | 196 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
197 | 197 | else: |
|
|
198 | 198 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
199 | 199 | |
|
|
200 | 200 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
201 | 201 | |
|
|
202 | 202 | self.dataOut.data = data |
|
|
203 | 203 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
204 | 204 | |
|
|
205 | 205 | if self.dataOut.nHeights <= 1: |
|
|
206 | 206 | raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights) |
|
|
207 | 207 | |
|
|
208 | 208 | return 1 |
|
|
209 | 209 | |
|
|
210 | 210 | |
|
|
211 | 211 | def filterByHeights(self, window): |
|
|
212 | 212 | |
|
|
213 | 213 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
214 | 214 | |
|
|
215 | 215 | if window == None: |
|
|
216 | 216 | window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
217 | 217 | |
|
|
218 | 218 | newdelta = deltaHeight * window |
|
|
219 | 219 | r = self.dataOut.nHeights % window |
|
|
220 | 220 | newheights = (self.dataOut.nHeights-r)/window |
|
|
221 | 221 | |
|
|
222 | 222 | if newheights <= 1: |
|
|
223 | 223 | raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window) |
|
|
224 | 224 | |
|
|
225 | 225 | if self.dataOut.flagDataAsBlock: |
|
|
226 | 226 | """ |
|
|
227 | 227 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
228 | 228 | """ |
|
|
229 | 229 | buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r] |
|
|
230 | 230 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window) |
|
|
231 | 231 | buffer = numpy.sum(buffer,3) |
|
|
232 | 232 | |
|
|
233 | 233 | else: |
|
|
234 | 234 | buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r] |
|
|
235 | 235 | buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window) |
|
|
236 | 236 | buffer = numpy.sum(buffer,2) |
|
|
237 | 237 | |
|
|
238 | 238 | self.dataOut.data = buffer |
|
|
239 | 239 | self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
240 | 240 | self.dataOut.windowOfFilter = window |
|
|
241 | 241 | |
|
|
242 | 242 | def setH0(self, h0, deltaHeight = None): |
|
|
243 | 243 | |
|
|
244 | 244 | if not deltaHeight: |
|
|
245 | 245 | deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0] |
|
|
246 | 246 | |
|
|
247 | 247 | nHeights = self.dataOut.nHeights |
|
|
248 | 248 | |
|
|
249 | 249 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
250 | 250 | |
|
|
251 | 251 | self.dataOut.heightList = newHeiRange |
|
|
252 | 252 | |
|
|
253 | 253 | def deFlip(self, channelList = []): |
|
|
254 | 254 | |
|
|
255 | 255 | data = self.dataOut.data.copy() |
|
|
256 | 256 | |
|
|
257 | 257 | if self.dataOut.flagDataAsBlock: |
|
|
258 | 258 | flip = self.flip |
|
|
259 | 259 | profileList = range(self.dataOut.nProfiles) |
|
|
260 | 260 | |
|
|
261 | 261 | if not channelList: |
|
|
262 | 262 | for thisProfile in profileList: |
|
|
263 | 263 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
264 | 264 | flip *= -1.0 |
|
|
265 | 265 | else: |
|
|
266 | 266 | for thisChannel in channelList: |
|
|
267 | 267 | if thisChannel not in self.dataOut.channelList: |
|
|
268 | 268 | continue |
|
|
269 | 269 | |
|
|
270 | 270 | for thisProfile in profileList: |
|
|
271 | 271 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
272 | 272 | flip *= -1.0 |
|
|
273 | 273 | |
|
|
274 | 274 | self.flip = flip |
|
|
275 | 275 | |
|
|
276 | 276 | else: |
|
|
277 | 277 | if not channelList: |
|
|
278 | 278 | data[:,:] = data[:,:]*self.flip |
|
|
279 | 279 | else: |
|
|
280 | 280 | for thisChannel in channelList: |
|
|
281 | 281 | if thisChannel not in self.dataOut.channelList: |
|
|
282 | 282 | continue |
|
|
283 | 283 | |
|
|
284 | 284 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
285 | 285 | |
|
|
286 | 286 | self.flip *= -1. |
|
|
287 | 287 | |
|
|
288 | 288 | self.dataOut.data = data |
|
|
289 | 289 | |
|
|
290 | 290 | def setRadarFrequency(self, frequency=None): |
|
|
291 | 291 | |
|
|
292 | 292 | if frequency != None: |
|
|
293 | 293 | self.dataOut.frequency = frequency |
|
|
294 | 294 | |
|
|
295 | 295 | return 1 |
|
|
296 | 296 | |
|
|
297 | 297 | def interpolateHeights(self, topLim, botLim): |
|
|
298 | 298 | #69 al 72 para julia |
|
|
299 | 299 | #82-84 para meteoros |
|
|
300 | 300 | if len(numpy.shape(self.dataOut.data))==2: |
|
|
301 | 301 | sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2 |
|
|
302 | 302 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
303 | 303 | #self.dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
304 | 304 | self.dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
305 | 305 | else: |
|
|
306 | 306 | nHeights = self.dataOut.data.shape[2] |
|
|
307 | 307 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
308 | 308 | y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)] |
|
|
309 | 309 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
310 | 310 | xnew = numpy.arange(botLim,topLim+1) |
|
|
311 | 311 | ynew = f(xnew) |
|
|
312 | 312 | |
|
|
313 | 313 | self.dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
314 | 314 | |
|
|
315 | 315 | # import collections |
|
|
316 | 316 | |
|
|
317 | 317 | class CohInt(Operation): |
|
|
318 | 318 | |
|
|
319 | 319 | isConfig = False |
|
|
320 | 320 | |
|
|
321 | 321 | __profIndex = 0 |
|
|
322 | 322 | __withOverapping = False |
|
|
323 | 323 | |
|
|
324 | 324 | __byTime = False |
|
|
325 | 325 | __initime = None |
|
|
326 | 326 | __lastdatatime = None |
|
|
327 | 327 | __integrationtime = None |
|
|
328 | 328 | |
|
|
329 | 329 | __buffer = None |
|
|
330 | 330 | |
|
|
331 | 331 | __dataReady = False |
|
|
332 | 332 | |
|
|
333 | 333 | n = None |
|
|
334 | 334 | |
|
|
335 | 335 | |
|
|
336 | 336 | def __init__(self, **kwargs): |
|
|
337 | 337 | |
|
|
338 | 338 | Operation.__init__(self, **kwargs) |
|
|
339 | 339 | |
|
|
340 | 340 | # self.isConfig = False |
|
|
341 | 341 | |
|
|
342 | 342 | def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False): |
|
|
343 | 343 | """ |
|
|
344 | 344 | Set the parameters of the integration class. |
|
|
345 | 345 | |
|
|
346 | 346 | Inputs: |
|
|
347 | 347 | |
|
|
348 | 348 | n : Number of coherent integrations |
|
|
349 | 349 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
350 | 350 | overlapping : |
|
|
351 | 351 | |
|
|
352 | 352 | """ |
|
|
353 | 353 | |
|
|
354 | 354 | self.__initime = None |
|
|
355 | 355 | self.__lastdatatime = 0 |
|
|
356 | 356 | self.__buffer = None |
|
|
357 | 357 | self.__dataReady = False |
|
|
358 | 358 | self.byblock = byblock |
|
|
359 | 359 | |
|
|
360 | 360 | if n == None and timeInterval == None: |
|
|
361 | 361 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
362 | 362 | |
|
|
363 | 363 | if n != None: |
|
|
364 | 364 | self.n = n |
|
|
365 | 365 | self.__byTime = False |
|
|
366 | 366 | else: |
|
|
367 | 367 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
368 | 368 | self.n = 9999 |
|
|
369 | 369 | self.__byTime = True |
|
|
370 | 370 | |
|
|
371 | 371 | if overlapping: |
|
|
372 | 372 | self.__withOverapping = True |
|
|
373 | 373 | self.__buffer = None |
|
|
374 | 374 | else: |
|
|
375 | 375 | self.__withOverapping = False |
|
|
376 | 376 | self.__buffer = 0 |
|
|
377 | 377 | |
|
|
378 | 378 | self.__profIndex = 0 |
|
|
379 | 379 | |
|
|
380 | 380 | def putData(self, data): |
|
|
381 | 381 | |
|
|
382 | 382 | """ |
|
|
383 | 383 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
384 | 384 | |
|
|
385 | 385 | """ |
|
|
386 | 386 | |
|
|
387 | 387 | if not self.__withOverapping: |
|
|
388 | 388 | self.__buffer += data.copy() |
|
|
389 | 389 | self.__profIndex += 1 |
|
|
390 | 390 | return |
|
|
391 | 391 | |
|
|
392 | 392 | #Overlapping data |
|
|
393 | 393 | nChannels, nHeis = data.shape |
|
|
394 | 394 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
395 | 395 | |
|
|
396 | 396 | #If the buffer is empty then it takes the data value |
|
|
397 | 397 | if self.__buffer is None: |
|
|
398 | 398 | self.__buffer = data |
|
|
399 | 399 | self.__profIndex += 1 |
|
|
400 | 400 | return |
|
|
401 | 401 | |
|
|
402 | 402 | #If the buffer length is lower than n then stakcing the data value |
|
|
403 | 403 | if self.__profIndex < self.n: |
|
|
404 | 404 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
405 | 405 | self.__profIndex += 1 |
|
|
406 | 406 | return |
|
|
407 | 407 | |
|
|
408 | 408 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
409 | 409 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
410 | 410 | self.__buffer[self.n-1] = data |
|
|
411 | 411 | self.__profIndex = self.n |
|
|
412 | 412 | return |
|
|
413 | 413 | |
|
|
414 | 414 | |
|
|
415 | 415 | def pushData(self): |
|
|
416 | 416 | """ |
|
|
417 | 417 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
418 | 418 | |
|
|
419 | 419 | Affected: |
|
|
420 | 420 | |
|
|
421 | 421 | self.__profileIndex |
|
|
422 | 422 | |
|
|
423 | 423 | """ |
|
|
424 | 424 | |
|
|
425 | 425 | if not self.__withOverapping: |
|
|
426 | 426 | data = self.__buffer |
|
|
427 | 427 | n = self.__profIndex |
|
|
428 | 428 | |
|
|
429 | 429 | self.__buffer = 0 |
|
|
430 | 430 | self.__profIndex = 0 |
|
|
431 | 431 | |
|
|
432 | 432 | return data, n |
|
|
433 | 433 | |
|
|
434 | 434 | #Integration with Overlapping |
|
|
435 | 435 | data = numpy.sum(self.__buffer, axis=0) |
|
|
436 | 436 | n = self.__profIndex |
|
|
437 | 437 | |
|
|
438 | 438 | return data, n |
|
|
439 | 439 | |
|
|
440 | 440 | def byProfiles(self, data): |
|
|
441 | 441 | |
|
|
442 | 442 | self.__dataReady = False |
|
|
443 | 443 | avgdata = None |
|
|
444 | 444 | # n = None |
|
|
445 | 445 | |
|
|
446 | 446 | self.putData(data) |
|
|
447 | 447 | |
|
|
448 | 448 | if self.__profIndex == self.n: |
|
|
449 | 449 | |
|
|
450 | 450 | avgdata, n = self.pushData() |
|
|
451 | 451 | self.__dataReady = True |
|
|
452 | 452 | |
|
|
453 | 453 | return avgdata |
|
|
454 | 454 | |
|
|
455 | 455 | def byTime(self, data, datatime): |
|
|
456 | 456 | |
|
|
457 | 457 | self.__dataReady = False |
|
|
458 | 458 | avgdata = None |
|
|
459 | 459 | n = None |
|
|
460 | 460 | |
|
|
461 | 461 | self.putData(data) |
|
|
462 | 462 | |
|
|
463 | 463 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
464 | 464 | avgdata, n = self.pushData() |
|
|
465 | 465 | self.n = n |
|
|
466 | 466 | self.__dataReady = True |
|
|
467 | 467 | |
|
|
468 | 468 | return avgdata |
|
|
469 | 469 | |
|
|
470 | 470 | def integrate(self, data, datatime=None): |
|
|
471 | 471 | |
|
|
472 | 472 | if self.__initime == None: |
|
|
473 | 473 | self.__initime = datatime |
|
|
474 | 474 | |
|
|
475 | 475 | if self.__byTime: |
|
|
476 | 476 | avgdata = self.byTime(data, datatime) |
|
|
477 | 477 | else: |
|
|
478 | 478 | avgdata = self.byProfiles(data) |
|
|
479 | 479 | |
|
|
480 | 480 | |
|
|
481 | 481 | self.__lastdatatime = datatime |
|
|
482 | 482 | |
|
|
483 | 483 | if avgdata is None: |
|
|
484 | 484 | return None, None |
|
|
485 | 485 | |
|
|
486 | 486 | avgdatatime = self.__initime |
|
|
487 | 487 | |
|
|
488 | 488 | deltatime = datatime -self.__lastdatatime |
|
|
489 | 489 | |
|
|
490 | 490 | if not self.__withOverapping: |
|
|
491 | 491 | self.__initime = datatime |
|
|
492 | 492 | else: |
|
|
493 | 493 | self.__initime += deltatime |
|
|
494 | 494 | |
|
|
495 | 495 | return avgdata, avgdatatime |
|
|
496 | 496 | |
|
|
497 | 497 | def integrateByBlock(self, dataOut): |
|
|
498 | 498 | |
|
|
499 | 499 | times = int(dataOut.data.shape[1]/self.n) |
|
|
500 | 500 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
501 | 501 | |
|
|
502 | 502 | id_min = 0 |
|
|
503 | 503 | id_max = self.n |
|
|
504 | 504 | |
|
|
505 | 505 | for i in range(times): |
|
|
506 | 506 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
507 | 507 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
508 | 508 | id_min += self.n |
|
|
509 | 509 | id_max += self.n |
|
|
510 | 510 | |
|
|
511 | 511 | timeInterval = dataOut.ippSeconds*self.n |
|
|
512 | 512 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
513 | 513 | self.__dataReady = True |
|
|
514 | 514 | return avgdata, avgdatatime |
|
|
515 | 515 | |
|
|
516 | 516 | |
|
|
517 | 517 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False, byblock=False, **kwargs): |
|
|
518 | 518 | if not self.isConfig: |
|
|
519 | 519 | self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
520 | 520 | self.isConfig = True |
|
|
521 | 521 | |
|
|
522 | 522 | if dataOut.flagDataAsBlock: |
|
|
523 | 523 | """ |
|
|
524 | 524 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
525 | 525 | """ |
|
|
526 | 526 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
527 | 527 | dataOut.nProfiles /= self.n |
|
|
528 | 528 | else: |
|
|
529 | 529 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
530 | 530 | |
|
|
531 | 531 | # dataOut.timeInterval *= n |
|
|
532 | 532 | dataOut.flagNoData = True |
|
|
533 | 533 | |
|
|
534 | 534 | if self.__dataReady: |
|
|
535 | 535 | dataOut.data = avgdata |
|
|
536 | 536 | dataOut.nCohInt *= self.n |
|
|
537 | 537 | dataOut.utctime = avgdatatime |
|
|
538 | 538 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
539 | 539 | dataOut.flagNoData = False |
|
|
540 | 540 | |
|
|
541 | 541 | class Decoder(Operation): |
|
|
542 | 542 | |
|
|
543 | 543 | isConfig = False |
|
|
544 | 544 | __profIndex = 0 |
|
|
545 | 545 | |
|
|
546 | 546 | code = None |
|
|
547 | 547 | |
|
|
548 | 548 | nCode = None |
|
|
549 | 549 | nBaud = None |
|
|
550 | 550 | |
|
|
551 | 551 | |
|
|
552 | 552 | def __init__(self, **kwargs): |
|
|
553 | 553 | |
|
|
554 | 554 | Operation.__init__(self, **kwargs) |
|
|
555 | 555 | |
|
|
556 | 556 | self.times = None |
|
|
557 | 557 | self.osamp = None |
|
|
558 | 558 | # self.__setValues = False |
|
|
559 | 559 | self.isConfig = False |
|
|
560 | 560 | |
|
|
561 | 561 | def setup(self, code, osamp, dataOut): |
|
|
562 | 562 | |
|
|
563 | 563 | self.__profIndex = 0 |
|
|
564 | 564 | |
|
|
565 | 565 | self.code = code |
|
|
566 | 566 | |
|
|
567 | 567 | self.nCode = len(code) |
|
|
568 | 568 | self.nBaud = len(code[0]) |
|
|
569 | 569 | |
|
|
570 | 570 | if (osamp != None) and (osamp >1): |
|
|
571 | 571 | self.osamp = osamp |
|
|
572 | 572 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
573 | 573 | self.nBaud = self.nBaud*self.osamp |
|
|
574 | 574 | |
|
|
575 | 575 | self.__nChannels = dataOut.nChannels |
|
|
576 | 576 | self.__nProfiles = dataOut.nProfiles |
|
|
577 | 577 | self.__nHeis = dataOut.nHeights |
|
|
578 | 578 | |
|
|
579 | 579 | if self.__nHeis < self.nBaud: |
|
|
580 | 580 | raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud) |
|
|
581 | 581 | |
|
|
582 | 582 | #Frequency |
|
|
583 | 583 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
584 | 584 | |
|
|
585 | 585 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
586 | 586 | |
|
|
587 | 587 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
588 | 588 | |
|
|
589 | 589 | if dataOut.flagDataAsBlock: |
|
|
590 | 590 | |
|
|
591 | 591 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
592 | 592 | |
|
|
593 | 593 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
594 | 594 | |
|
|
595 | 595 | else: |
|
|
596 | 596 | |
|
|
597 | 597 | #Time |
|
|
598 | 598 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
599 | 599 | |
|
|
600 | 600 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
601 | 601 | |
|
|
602 | 602 | def __convolutionInFreq(self, data): |
|
|
603 | 603 | |
|
|
604 | 604 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
605 | 605 | |
|
|
606 | 606 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
607 | 607 | |
|
|
608 | 608 | conv = fft_data*fft_code |
|
|
609 | 609 | |
|
|
610 | 610 | data = numpy.fft.ifft(conv,axis=1) |
|
|
611 | 611 | |
|
|
612 | 612 | return data |
|
|
613 | 613 | |
|
|
614 | 614 | def __convolutionInFreqOpt(self, data): |
|
|
615 | 615 | |
|
|
616 | 616 | raise NotImplementedError |
|
|
617 | 617 | |
|
|
618 | 618 | def __convolutionInTime(self, data): |
|
|
619 | 619 | |
|
|
620 | 620 | code = self.code[self.__profIndex] |
|
|
621 | 621 | |
|
|
622 | 622 | for i in range(self.__nChannels): |
|
|
623 | 623 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
624 | 624 | |
|
|
625 | 625 | return self.datadecTime |
|
|
626 | 626 | |
|
|
627 | 627 | def __convolutionByBlockInTime(self, data): |
|
|
628 | 628 | |
|
|
629 | 629 | repetitions = self.__nProfiles / self.nCode |
|
|
630 | 630 | |
|
|
631 | 631 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
632 | 632 | junk = junk.flatten() |
|
|
633 | 633 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
634 | 634 | |
|
|
635 | 635 | for i in range(self.__nChannels): |
|
|
636 | 636 | for j in range(self.__nProfiles): |
|
|
637 | print self.datadecTime[i,j,:].shape | |
|
|
638 | print numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:].shape | |
|
|
637 | 639 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
638 | 640 | |
|
|
639 | 641 | return self.datadecTime |
|
|
640 | 642 | |
|
|
641 | 643 | def __convolutionByBlockInFreq(self, data): |
|
|
642 | 644 | |
|
|
643 | 645 | raise NotImplementedError, "Decoder by frequency fro Blocks not implemented" |
|
|
644 | 646 | |
|
|
645 | 647 | |
|
|
646 | 648 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
647 | 649 | |
|
|
648 | 650 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
649 | 651 | |
|
|
650 | 652 | conv = fft_data*fft_code |
|
|
651 | 653 | |
|
|
652 | 654 | data = numpy.fft.ifft(conv,axis=2) |
|
|
653 | 655 | |
|
|
654 | 656 | return data |
|
|
655 | 657 | |
|
|
656 | 658 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
657 | 659 | |
|
|
658 | 660 | if dataOut.flagDecodeData: |
|
|
659 | 661 | print "This data is already decoded, recoding again ..." |
|
|
660 | 662 | |
|
|
661 | 663 | if not self.isConfig: |
|
|
662 | 664 | |
|
|
663 | 665 | if code is None: |
|
|
664 | 666 | if dataOut.code is None: |
|
|
665 | 667 | raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type |
|
|
666 | 668 | |
|
|
667 | 669 | code = dataOut.code |
|
|
668 | 670 | else: |
|
|
669 | 671 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
670 | 672 | |
|
|
671 | 673 | self.setup(code, osamp, dataOut) |
|
|
672 | 674 | |
|
|
673 | 675 | self.isConfig = True |
|
|
674 | 676 | |
|
|
675 | 677 | if mode == 3: |
|
|
676 | 678 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
677 | 679 | |
|
|
678 | 680 | if times != None: |
|
|
679 | 681 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
680 | 682 | |
|
|
681 | 683 | if self.code is None: |
|
|
682 | 684 | print "Fail decoding: Code is not defined." |
|
|
683 | 685 | return |
|
|
684 | 686 | |
|
|
685 | 687 | datadec = None |
|
|
686 | 688 | if mode == 3: |
|
|
687 | 689 | mode = 0 |
|
|
688 | 690 | |
|
|
689 | 691 | if dataOut.flagDataAsBlock: |
|
|
690 | 692 | """ |
|
|
691 | 693 | Decoding when data have been read as block, |
|
|
692 | 694 | """ |
|
|
693 | 695 | |
|
|
694 | 696 | if mode == 0: |
|
|
695 | 697 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
696 | 698 | if mode == 1: |
|
|
697 | 699 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
698 | 700 | else: |
|
|
699 | 701 | """ |
|
|
700 | 702 | Decoding when data have been read profile by profile |
|
|
701 | 703 | """ |
|
|
702 | 704 | if mode == 0: |
|
|
703 | 705 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
704 | 706 | |
|
|
705 | 707 | if mode == 1: |
|
|
706 | 708 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
707 | 709 | |
|
|
708 | 710 | if mode == 2: |
|
|
709 | 711 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
710 | 712 | |
|
|
711 | 713 | if datadec is None: |
|
|
712 | 714 | raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode |
|
|
713 | 715 | |
|
|
714 | 716 | dataOut.code = self.code |
|
|
715 | 717 | dataOut.nCode = self.nCode |
|
|
716 | 718 | dataOut.nBaud = self.nBaud |
|
|
717 | 719 | |
|
|
718 | 720 | dataOut.data = datadec |
|
|
719 | 721 | |
|
|
720 | 722 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
721 | 723 | |
|
|
722 | 724 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
723 | 725 | |
|
|
724 | 726 | if self.__profIndex == self.nCode-1: |
|
|
725 | 727 | self.__profIndex = 0 |
|
|
726 | 728 | return 1 |
|
|
727 | 729 | |
|
|
728 | 730 | self.__profIndex += 1 |
|
|
729 | 731 | |
|
|
730 | 732 | return 1 |
|
|
731 | 733 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
732 | 734 | |
|
|
733 | 735 | |
|
|
734 | 736 | class ProfileConcat(Operation): |
|
|
735 | 737 | |
|
|
736 | 738 | isConfig = False |
|
|
737 | 739 | buffer = None |
|
|
738 | 740 | |
|
|
739 | 741 | def __init__(self, **kwargs): |
|
|
740 | 742 | |
|
|
741 | 743 | Operation.__init__(self, **kwargs) |
|
|
742 | 744 | self.profileIndex = 0 |
|
|
743 | 745 | |
|
|
744 | 746 | def reset(self): |
|
|
745 | 747 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
746 | 748 | self.start_index = 0 |
|
|
747 | 749 | self.times = 1 |
|
|
748 | 750 | |
|
|
749 | 751 | def setup(self, data, m, n=1): |
|
|
750 | 752 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
751 | 753 | self.nHeights = data.shape[1]#.nHeights |
|
|
752 | 754 | self.start_index = 0 |
|
|
753 | 755 | self.times = 1 |
|
|
754 | 756 | |
|
|
755 | 757 | def concat(self, data): |
|
|
756 | 758 | |
|
|
757 | 759 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
758 | 760 | self.start_index = self.start_index + self.nHeights |
|
|
759 | 761 | |
|
|
760 | 762 | def run(self, dataOut, m): |
|
|
761 | 763 | |
|
|
762 | 764 | dataOut.flagNoData = True |
|
|
763 | 765 | |
|
|
764 | 766 | if not self.isConfig: |
|
|
765 | 767 | self.setup(dataOut.data, m, 1) |
|
|
766 | 768 | self.isConfig = True |
|
|
767 | 769 | |
|
|
768 | 770 | if dataOut.flagDataAsBlock: |
|
|
769 | 771 | raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False" |
|
|
770 | 772 | |
|
|
771 | 773 | else: |
|
|
772 | 774 | self.concat(dataOut.data) |
|
|
773 | 775 | self.times += 1 |
|
|
774 | 776 | if self.times > m: |
|
|
775 | 777 | dataOut.data = self.buffer |
|
|
776 | 778 | self.reset() |
|
|
777 | 779 | dataOut.flagNoData = False |
|
|
778 | 780 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
779 | 781 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
780 | 782 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
781 | 783 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
782 | 784 | dataOut.ippSeconds *= m |
|
|
783 | 785 | |
|
|
784 | 786 | class ProfileSelector(Operation): |
|
|
785 | 787 | |
|
|
786 | 788 | profileIndex = None |
|
|
787 | 789 | # Tamanho total de los perfiles |
|
|
788 | 790 | nProfiles = None |
|
|
789 | 791 | |
|
|
790 | 792 | def __init__(self, **kwargs): |
|
|
791 | 793 | |
|
|
792 | 794 | Operation.__init__(self, **kwargs) |
|
|
793 | 795 | self.profileIndex = 0 |
|
|
794 | 796 | |
|
|
795 | 797 | def incProfileIndex(self): |
|
|
796 | 798 | |
|
|
797 | 799 | self.profileIndex += 1 |
|
|
798 | 800 | |
|
|
799 | 801 | if self.profileIndex >= self.nProfiles: |
|
|
800 | 802 | self.profileIndex = 0 |
|
|
801 | 803 | |
|
|
802 | 804 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
803 | 805 | |
|
|
804 | 806 | if profileIndex < minIndex: |
|
|
805 | 807 | return False |
|
|
806 | 808 | |
|
|
807 | 809 | if profileIndex > maxIndex: |
|
|
808 | 810 | return False |
|
|
809 | 811 | |
|
|
810 | 812 | return True |
|
|
811 | 813 | |
|
|
812 | 814 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
813 | 815 | |
|
|
814 | 816 | if profileIndex not in profileList: |
|
|
815 | 817 | return False |
|
|
816 | 818 | |
|
|
817 | 819 | return True |
|
|
818 | 820 | |
|
|
819 | 821 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
820 | 822 | |
|
|
821 | 823 | """ |
|
|
822 | 824 | ProfileSelector: |
|
|
823 | 825 | |
|
|
824 | 826 | Inputs: |
|
|
825 | 827 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
826 | 828 | |
|
|
827 | 829 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
828 | 830 | |
|
|
829 | 831 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
830 | 832 | |
|
|
831 | 833 | """ |
|
|
832 | 834 | |
|
|
833 | 835 | if rangeList is not None: |
|
|
834 | 836 | if type(rangeList[0]) not in (tuple, list): |
|
|
835 | 837 | rangeList = [rangeList] |
|
|
836 | 838 | |
|
|
837 | 839 | dataOut.flagNoData = True |
|
|
838 | 840 | |
|
|
839 | 841 | if dataOut.flagDataAsBlock: |
|
|
840 | 842 | """ |
|
|
841 | 843 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
842 | 844 | """ |
|
|
843 | 845 | if profileList != None: |
|
|
844 | 846 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
845 | 847 | |
|
|
846 | 848 | if profileRangeList != None: |
|
|
847 | 849 | minIndex = profileRangeList[0] |
|
|
848 | 850 | maxIndex = profileRangeList[1] |
|
|
849 | 851 | profileList = range(minIndex, maxIndex+1) |
|
|
850 | 852 | |
|
|
851 | 853 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
852 | 854 | |
|
|
853 | 855 | if rangeList != None: |
|
|
854 | 856 | |
|
|
855 | 857 | profileList = [] |
|
|
856 | 858 | |
|
|
857 | 859 | for thisRange in rangeList: |
|
|
858 | 860 | minIndex = thisRange[0] |
|
|
859 | 861 | maxIndex = thisRange[1] |
|
|
860 | 862 | |
|
|
861 | 863 | profileList.extend(range(minIndex, maxIndex+1)) |
|
|
862 | 864 | |
|
|
863 | 865 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
864 | 866 | |
|
|
865 | 867 | dataOut.nProfiles = len(profileList) |
|
|
866 | 868 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
867 | 869 | dataOut.flagNoData = False |
|
|
868 | 870 | |
|
|
869 | 871 | return True |
|
|
870 | 872 | |
|
|
871 | 873 | """ |
|
|
872 | 874 | data dimension = [nChannels, nHeis] |
|
|
873 | 875 | """ |
|
|
874 | 876 | |
|
|
875 | 877 | if profileList != None: |
|
|
876 | 878 | |
|
|
877 | 879 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
878 | 880 | |
|
|
879 | 881 | self.nProfiles = len(profileList) |
|
|
880 | 882 | dataOut.nProfiles = self.nProfiles |
|
|
881 | 883 | dataOut.profileIndex = self.profileIndex |
|
|
882 | 884 | dataOut.flagNoData = False |
|
|
883 | 885 | |
|
|
884 | 886 | self.incProfileIndex() |
|
|
885 | 887 | return True |
|
|
886 | 888 | |
|
|
887 | 889 | if profileRangeList != None: |
|
|
888 | 890 | |
|
|
889 | 891 | minIndex = profileRangeList[0] |
|
|
890 | 892 | maxIndex = profileRangeList[1] |
|
|
891 | 893 | |
|
|
892 | 894 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
893 | 895 | |
|
|
894 | 896 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
895 | 897 | dataOut.nProfiles = self.nProfiles |
|
|
896 | 898 | dataOut.profileIndex = self.profileIndex |
|
|
897 | 899 | dataOut.flagNoData = False |
|
|
898 | 900 | |
|
|
899 | 901 | self.incProfileIndex() |
|
|
900 | 902 | return True |
|
|
901 | 903 | |
|
|
902 | 904 | if rangeList != None: |
|
|
903 | 905 | |
|
|
904 | 906 | nProfiles = 0 |
|
|
905 | 907 | |
|
|
906 | 908 | for thisRange in rangeList: |
|
|
907 | 909 | minIndex = thisRange[0] |
|
|
908 | 910 | maxIndex = thisRange[1] |
|
|
909 | 911 | |
|
|
910 | 912 | nProfiles += maxIndex - minIndex + 1 |
|
|
911 | 913 | |
|
|
912 | 914 | for thisRange in rangeList: |
|
|
913 | 915 | |
|
|
914 | 916 | minIndex = thisRange[0] |
|
|
915 | 917 | maxIndex = thisRange[1] |
|
|
916 | 918 | |
|
|
917 | 919 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
918 | 920 | |
|
|
919 | 921 | self.nProfiles = nProfiles |
|
|
920 | 922 | dataOut.nProfiles = self.nProfiles |
|
|
921 | 923 | dataOut.profileIndex = self.profileIndex |
|
|
922 | 924 | dataOut.flagNoData = False |
|
|
923 | 925 | |
|
|
924 | 926 | self.incProfileIndex() |
|
|
925 | 927 | |
|
|
926 | 928 | break |
|
|
927 | 929 | |
|
|
928 | 930 | return True |
|
|
929 | 931 | |
|
|
930 | 932 | |
|
|
931 | 933 | if beam != None: #beam is only for AMISR data |
|
|
932 | 934 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
933 | 935 | dataOut.flagNoData = False |
|
|
934 | 936 | dataOut.profileIndex = self.profileIndex |
|
|
935 | 937 | |
|
|
936 | 938 | self.incProfileIndex() |
|
|
937 | 939 | |
|
|
938 | 940 | return True |
|
|
939 | 941 | |
|
|
940 | 942 | raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter" |
|
|
941 | 943 | |
|
|
942 | 944 | return False |
|
|
943 | 945 | |
|
|
944 | 946 | class Reshaper(Operation): |
|
|
945 | 947 | |
|
|
946 | 948 | def __init__(self, **kwargs): |
|
|
947 | 949 | |
|
|
948 | 950 | Operation.__init__(self, **kwargs) |
|
|
949 | 951 | |
|
|
950 | 952 | self.__buffer = None |
|
|
951 | 953 | self.__nitems = 0 |
|
|
952 | 954 | |
|
|
953 | 955 | def __appendProfile(self, dataOut, nTxs): |
|
|
954 | 956 | |
|
|
955 | 957 | if self.__buffer is None: |
|
|
956 | 958 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
957 | 959 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
958 | 960 | |
|
|
959 | 961 | ini = dataOut.nHeights * self.__nitems |
|
|
960 | 962 | end = ini + dataOut.nHeights |
|
|
961 | 963 | |
|
|
962 | 964 | self.__buffer[:, ini:end] = dataOut.data |
|
|
963 | 965 | |
|
|
964 | 966 | self.__nitems += 1 |
|
|
965 | 967 | |
|
|
966 | 968 | return int(self.__nitems*nTxs) |
|
|
967 | 969 | |
|
|
968 | 970 | def __getBuffer(self): |
|
|
969 | 971 | |
|
|
970 | 972 | if self.__nitems == int(1./self.__nTxs): |
|
|
971 | 973 | |
|
|
972 | 974 | self.__nitems = 0 |
|
|
973 | 975 | |
|
|
974 | 976 | return self.__buffer.copy() |
|
|
975 | 977 | |
|
|
976 | 978 | return None |
|
|
977 | 979 | |
|
|
978 | 980 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
979 | 981 | |
|
|
980 | 982 | if shape is None and nTxs is None: |
|
|
981 | 983 | raise ValueError, "Reshaper: shape of factor should be defined" |
|
|
982 | 984 | |
|
|
983 | 985 | if nTxs: |
|
|
984 | 986 | if nTxs < 0: |
|
|
985 | 987 | raise ValueError, "nTxs should be greater than 0" |
|
|
986 | 988 | |
|
|
987 | 989 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
988 | 990 | raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)) |
|
|
989 | 991 | |
|
|
990 | 992 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
991 | 993 | |
|
|
992 | 994 | return shape, nTxs |
|
|
993 | 995 | |
|
|
994 | 996 | if len(shape) != 2 and len(shape) != 3: |
|
|
995 | 997 | raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights) |
|
|
996 | 998 | |
|
|
997 | 999 | if len(shape) == 2: |
|
|
998 | 1000 | shape_tuple = [dataOut.nChannels] |
|
|
999 | 1001 | shape_tuple.extend(shape) |
|
|
1000 | 1002 | else: |
|
|
1001 | 1003 | shape_tuple = list(shape) |
|
|
1002 | 1004 | |
|
|
1003 | 1005 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1004 | 1006 | |
|
|
1005 | 1007 | return shape_tuple, nTxs |
|
|
1006 | 1008 | |
|
|
1007 | 1009 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1008 | 1010 | |
|
|
1009 | 1011 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1010 | 1012 | |
|
|
1011 | 1013 | dataOut.flagNoData = True |
|
|
1012 | 1014 | profileIndex = None |
|
|
1013 | 1015 | |
|
|
1014 | 1016 | if dataOut.flagDataAsBlock: |
|
|
1015 | 1017 | |
|
|
1016 | 1018 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1017 | 1019 | dataOut.flagNoData = False |
|
|
1018 | 1020 | |
|
|
1019 | 1021 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1020 | 1022 | |
|
|
1021 | 1023 | else: |
|
|
1022 | 1024 | |
|
|
1023 | 1025 | if self.__nTxs < 1: |
|
|
1024 | 1026 | |
|
|
1025 | 1027 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1026 | 1028 | new_data = self.__getBuffer() |
|
|
1027 | 1029 | |
|
|
1028 | 1030 | if new_data is not None: |
|
|
1029 | 1031 | dataOut.data = new_data |
|
|
1030 | 1032 | dataOut.flagNoData = False |
|
|
1031 | 1033 | |
|
|
1032 | 1034 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1033 | 1035 | |
|
|
1034 | 1036 | else: |
|
|
1035 | 1037 | raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)" |
|
|
1036 | 1038 | |
|
|
1037 | 1039 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1038 | 1040 | |
|
|
1039 | 1041 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1040 | 1042 | |
|
|
1041 | 1043 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1042 | 1044 | |
|
|
1043 | 1045 | dataOut.profileIndex = profileIndex |
|
|
1044 | 1046 | |
|
|
1045 | 1047 | dataOut.ippSeconds /= self.__nTxs |
|
|
1046 | 1048 | |
|
|
1047 | 1049 | class SplitProfiles(Operation): |
|
|
1048 | 1050 | |
|
|
1049 | 1051 | def __init__(self, **kwargs): |
|
|
1050 | 1052 | |
|
|
1051 | 1053 | Operation.__init__(self, **kwargs) |
|
|
1052 | 1054 | |
|
|
1053 | 1055 | def run(self, dataOut, n): |
|
|
1054 | 1056 | |
|
|
1055 | 1057 | dataOut.flagNoData = True |
|
|
1056 | 1058 | profileIndex = None |
|
|
1057 | 1059 | |
|
|
1058 | 1060 | if dataOut.flagDataAsBlock: |
|
|
1059 | 1061 | |
|
|
1060 | 1062 | #nchannels, nprofiles, nsamples |
|
|
1061 | 1063 | shape = dataOut.data.shape |
|
|
1062 | 1064 | |
|
|
1063 | 1065 | if shape[2] % n != 0: |
|
|
1064 | 1066 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]) |
|
|
1065 | 1067 | |
|
|
1066 | 1068 | new_shape = shape[0], shape[1]*n, shape[2]/n |
|
|
1067 | 1069 | |
|
|
1068 | 1070 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1069 | 1071 | dataOut.flagNoData = False |
|
|
1070 | 1072 | |
|
|
1071 | 1073 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1072 | 1074 | |
|
|
1073 | 1075 | else: |
|
|
1074 | 1076 | |
|
|
1075 | 1077 | raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)" |
|
|
1076 | 1078 | |
|
|
1077 | 1079 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1078 | 1080 | |
|
|
1079 | 1081 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1080 | 1082 | |
|
|
1081 | 1083 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1082 | 1084 | |
|
|
1083 | 1085 | dataOut.profileIndex = profileIndex |
|
|
1084 | 1086 | |
|
|
1085 | 1087 | dataOut.ippSeconds /= n |
|
|
1086 | 1088 | |
|
|
1087 | 1089 | class CombineProfiles(Operation): |
|
|
1088 | 1090 | |
|
|
1089 | 1091 | def __init__(self, **kwargs): |
|
|
1090 | 1092 | |
|
|
1091 | 1093 | Operation.__init__(self, **kwargs) |
|
|
1092 | 1094 | |
|
|
1093 | 1095 | self.__remData = None |
|
|
1094 | 1096 | self.__profileIndex = 0 |
|
|
1095 | 1097 | |
|
|
1096 | 1098 | def run(self, dataOut, n): |
|
|
1097 | 1099 | |
|
|
1098 | 1100 | dataOut.flagNoData = True |
|
|
1099 | 1101 | profileIndex = None |
|
|
1100 | 1102 | |
|
|
1101 | 1103 | if dataOut.flagDataAsBlock: |
|
|
1102 | 1104 | |
|
|
1103 | 1105 | #nchannels, nprofiles, nsamples |
|
|
1104 | 1106 | shape = dataOut.data.shape |
|
|
1105 | 1107 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1106 | 1108 | |
|
|
1107 | 1109 | if shape[1] % n != 0: |
|
|
1108 | 1110 | raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]) |
|
|
1109 | 1111 | |
|
|
1110 | 1112 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1111 | 1113 | dataOut.flagNoData = False |
|
|
1112 | 1114 | |
|
|
1113 | 1115 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1114 | 1116 | |
|
|
1115 | 1117 | else: |
|
|
1116 | 1118 | |
|
|
1117 | 1119 | #nchannels, nsamples |
|
|
1118 | 1120 | if self.__remData is None: |
|
|
1119 | 1121 | newData = dataOut.data |
|
|
1120 | 1122 | else: |
|
|
1121 | 1123 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1122 | 1124 | |
|
|
1123 | 1125 | self.__profileIndex += 1 |
|
|
1124 | 1126 | |
|
|
1125 | 1127 | if self.__profileIndex < n: |
|
|
1126 | 1128 | self.__remData = newData |
|
|
1127 | 1129 | #continue |
|
|
1128 | 1130 | return |
|
|
1129 | 1131 | |
|
|
1130 | 1132 | self.__profileIndex = 0 |
|
|
1131 | 1133 | self.__remData = None |
|
|
1132 | 1134 | |
|
|
1133 | 1135 | dataOut.data = newData |
|
|
1134 | 1136 | dataOut.flagNoData = False |
|
|
1135 | 1137 | |
|
|
1136 | 1138 | profileIndex = dataOut.profileIndex/n |
|
|
1137 | 1139 | |
|
|
1138 | 1140 | |
|
|
1139 | 1141 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1140 | 1142 | |
|
|
1141 | 1143 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1142 | 1144 | |
|
|
1143 | 1145 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1144 | 1146 | |
|
|
1145 | 1147 | dataOut.profileIndex = profileIndex |
|
|
1146 | 1148 | |
|
|
1147 | 1149 | dataOut.ippSeconds *= n |
|
|
1148 | 1150 | |
|
|
1149 | 1151 | # import collections |
|
|
1150 | 1152 | # from scipy.stats import mode |
|
|
1151 | 1153 | # |
|
|
1152 | 1154 | # class Synchronize(Operation): |
|
|
1153 | 1155 | # |
|
|
1154 | 1156 | # isConfig = False |
|
|
1155 | 1157 | # __profIndex = 0 |
|
|
1156 | 1158 | # |
|
|
1157 | 1159 | # def __init__(self, **kwargs): |
|
|
1158 | 1160 | # |
|
|
1159 | 1161 | # Operation.__init__(self, **kwargs) |
|
|
1160 | 1162 | # # self.isConfig = False |
|
|
1161 | 1163 | # self.__powBuffer = None |
|
|
1162 | 1164 | # self.__startIndex = 0 |
|
|
1163 | 1165 | # self.__pulseFound = False |
|
|
1164 | 1166 | # |
|
|
1165 | 1167 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1166 | 1168 | # |
|
|
1167 | 1169 | # #Read data |
|
|
1168 | 1170 | # |
|
|
1169 | 1171 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1170 | 1172 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1171 | 1173 | # |
|
|
1172 | 1174 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1173 | 1175 | # |
|
|
1174 | 1176 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1175 | 1177 | # |
|
|
1176 | 1178 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1177 | 1179 | # |
|
|
1178 | 1180 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1179 | 1181 | # |
|
|
1180 | 1182 | # if maxValue < noisedB + 10: |
|
|
1181 | 1183 | # #No se encuentra ningun pulso de transmision |
|
|
1182 | 1184 | # return None |
|
|
1183 | 1185 | # |
|
|
1184 | 1186 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1185 | 1187 | # |
|
|
1186 | 1188 | # if len(maxValuesIndex) < 2: |
|
|
1187 | 1189 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1188 | 1190 | # return None |
|
|
1189 | 1191 | # |
|
|
1190 | 1192 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1191 | 1193 | # |
|
|
1192 | 1194 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1193 | 1195 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1194 | 1196 | # |
|
|
1195 | 1197 | # if len(pulseIndex) < 2: |
|
|
1196 | 1198 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1197 | 1199 | # return None |
|
|
1198 | 1200 | # |
|
|
1199 | 1201 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1200 | 1202 | # |
|
|
1201 | 1203 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1202 | 1204 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1203 | 1205 | # |
|
|
1204 | 1206 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1205 | 1207 | # |
|
|
1206 | 1208 | # if len(realIndex) < 2: |
|
|
1207 | 1209 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1208 | 1210 | # return None |
|
|
1209 | 1211 | # |
|
|
1210 | 1212 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1211 | 1213 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1212 | 1214 | # |
|
|
1213 | 1215 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1214 | 1216 | # |
|
|
1215 | 1217 | # print "IPP = %d samples" %period |
|
|
1216 | 1218 | # |
|
|
1217 | 1219 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1218 | 1220 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1219 | 1221 | # |
|
|
1220 | 1222 | # return 1 |
|
|
1221 | 1223 | # |
|
|
1222 | 1224 | # |
|
|
1223 | 1225 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1224 | 1226 | # |
|
|
1225 | 1227 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1226 | 1228 | # maxlen = buffer_size*nSamples) |
|
|
1227 | 1229 | # |
|
|
1228 | 1230 | # bufferList = [] |
|
|
1229 | 1231 | # |
|
|
1230 | 1232 | # for i in range(nChannels): |
|
|
1231 | 1233 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1232 | 1234 | # maxlen = buffer_size*nSamples) |
|
|
1233 | 1235 | # |
|
|
1234 | 1236 | # bufferList.append(bufferByChannel) |
|
|
1235 | 1237 | # |
|
|
1236 | 1238 | # self.__nSamples = nSamples |
|
|
1237 | 1239 | # self.__nChannels = nChannels |
|
|
1238 | 1240 | # self.__bufferList = bufferList |
|
|
1239 | 1241 | # |
|
|
1240 | 1242 | # def run(self, dataOut, channel = 0): |
|
|
1241 | 1243 | # |
|
|
1242 | 1244 | # if not self.isConfig: |
|
|
1243 | 1245 | # nSamples = dataOut.nHeights |
|
|
1244 | 1246 | # nChannels = dataOut.nChannels |
|
|
1245 | 1247 | # self.setup(nSamples, nChannels) |
|
|
1246 | 1248 | # self.isConfig = True |
|
|
1247 | 1249 | # |
|
|
1248 | 1250 | # #Append new data to internal buffer |
|
|
1249 | 1251 | # for thisChannel in range(self.__nChannels): |
|
|
1250 | 1252 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1251 | 1253 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1252 | 1254 | # |
|
|
1253 | 1255 | # if self.__pulseFound: |
|
|
1254 | 1256 | # self.__startIndex -= self.__nSamples |
|
|
1255 | 1257 | # |
|
|
1256 | 1258 | # #Finding Tx Pulse |
|
|
1257 | 1259 | # if not self.__pulseFound: |
|
|
1258 | 1260 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1259 | 1261 | # |
|
|
1260 | 1262 | # if indexFound == None: |
|
|
1261 | 1263 | # dataOut.flagNoData = True |
|
|
1262 | 1264 | # return |
|
|
1263 | 1265 | # |
|
|
1264 | 1266 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1265 | 1267 | # self.__pulseFound = True |
|
|
1266 | 1268 | # self.__startIndex = indexFound |
|
|
1267 | 1269 | # |
|
|
1268 | 1270 | # #If pulse was found ... |
|
|
1269 | 1271 | # for thisChannel in range(self.__nChannels): |
|
|
1270 | 1272 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1271 | 1273 | # #print self.__startIndex |
|
|
1272 | 1274 | # x = numpy.array(bufferByChannel) |
|
|
1273 | 1275 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1274 | 1276 | # |
|
|
1275 | 1277 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1276 | 1278 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1277 | 1279 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1278 | 1280 | # |
|
|
1279 | 1281 | # dataOut.data = self.__arrayBuffer |
|
|
1280 | 1282 | # |
|
|
1281 | 1283 | # self.__startIndex += self.__newNSamples |
|
|
1282 | 1284 | # |
|
|
1283 | 1285 | # return |
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