@@ -1,1069 +1,1074 | |||
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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """Definition of diferent Data objects for different types of data |
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6 | 6 | |
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7 | 7 | Here you will find the diferent data objects for the different types |
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8 | 8 | of data, this data objects must be used as dataIn or dataOut objects in |
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9 | 9 | processing units and operations. Currently the supported data objects are: |
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10 | 10 | Voltage, Spectra, SpectraHeis, Fits, Correlation and Parameters |
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11 | 11 | """ |
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12 | 12 | |
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13 | 13 | import copy |
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14 | 14 | import numpy |
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15 | 15 | import datetime |
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16 | 16 | import json |
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17 | 17 | |
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18 | 18 | import schainpy.admin |
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19 | 19 | from schainpy.utils import log |
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20 | 20 | from .jroheaderIO import SystemHeader, RadarControllerHeader |
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21 | 21 | from schainpy.model.data import _noise |
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22 | 22 | |
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23 | 23 | |
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24 | 24 | def getNumpyDtype(dataTypeCode): |
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25 | 25 | |
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26 | 26 | if dataTypeCode == 0: |
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27 | 27 | numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
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28 | 28 | elif dataTypeCode == 1: |
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29 | 29 | numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
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30 | 30 | elif dataTypeCode == 2: |
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31 | 31 | numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
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32 | 32 | elif dataTypeCode == 3: |
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33 | 33 | numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
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34 | 34 | elif dataTypeCode == 4: |
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35 | 35 | numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
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36 | 36 | elif dataTypeCode == 5: |
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37 | 37 | numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
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38 | 38 | else: |
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39 | 39 | raise ValueError('dataTypeCode was not defined') |
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40 | 40 | |
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41 | 41 | return numpyDtype |
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42 | 42 | |
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43 | 43 | |
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44 | 44 | def getDataTypeCode(numpyDtype): |
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45 | 45 | |
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46 | 46 | if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]): |
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47 | 47 | datatype = 0 |
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48 | 48 | elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]): |
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49 | 49 | datatype = 1 |
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50 | 50 | elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]): |
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51 | 51 | datatype = 2 |
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52 | 52 | elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]): |
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53 | 53 | datatype = 3 |
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54 | 54 | elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]): |
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55 | 55 | datatype = 4 |
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56 | 56 | elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]): |
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57 | 57 | datatype = 5 |
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58 | 58 | else: |
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59 | 59 | datatype = None |
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60 | 60 | |
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61 | 61 | return datatype |
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62 | 62 | |
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63 | 63 | |
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64 | 64 | def hildebrand_sekhon(data, navg): |
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65 | 65 | """ |
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66 | 66 | This method is for the objective determination of the noise level in Doppler spectra. This |
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67 | 67 | implementation technique is based on the fact that the standard deviation of the spectral |
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68 | 68 | densities is equal to the mean spectral density for white Gaussian noise |
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69 | 69 | |
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70 | 70 | Inputs: |
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71 | 71 | Data : heights |
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72 | 72 | navg : numbers of averages |
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73 | 73 | |
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74 | 74 | Return: |
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75 | 75 | mean : noise's level |
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76 | 76 | """ |
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77 | 77 | |
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78 | 78 | sortdata = numpy.sort(data, axis=None) |
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79 | 79 | ''' |
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80 | 80 | lenOfData = len(sortdata) |
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81 | 81 | nums_min = lenOfData*0.2 |
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82 | 82 | |
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83 | 83 | if nums_min <= 5: |
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84 | 84 | |
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85 | 85 | nums_min = 5 |
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86 | 86 | |
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87 | 87 | sump = 0. |
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88 | 88 | sumq = 0. |
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89 | 89 | |
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90 | 90 | j = 0 |
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91 | 91 | cont = 1 |
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92 | 92 | |
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93 | 93 | while((cont == 1)and(j < lenOfData)): |
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94 | 94 | |
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95 | 95 | sump += sortdata[j] |
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96 | 96 | sumq += sortdata[j]**2 |
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97 | 97 | |
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98 | 98 | if j > nums_min: |
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99 | 99 | rtest = float(j)/(j-1) + 1.0/navg |
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100 | 100 | if ((sumq*j) > (rtest*sump**2)): |
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101 | 101 | j = j - 1 |
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102 | 102 | sump = sump - sortdata[j] |
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103 | 103 | sumq = sumq - sortdata[j]**2 |
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104 | 104 | cont = 0 |
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105 | 105 | |
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106 | 106 | j += 1 |
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107 | 107 | |
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108 | 108 | lnoise = sump / j |
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109 | 109 | ''' |
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110 | 110 | return _noise.hildebrand_sekhon(sortdata, navg) |
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111 | 111 | |
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112 | 112 | |
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113 | 113 | class Beam: |
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114 | 114 | |
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115 | 115 | def __init__(self): |
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116 | 116 | self.codeList = [] |
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117 | 117 | self.azimuthList = [] |
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118 | 118 | self.zenithList = [] |
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119 | 119 | |
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120 | 120 | |
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121 | 121 | class GenericData(object): |
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122 | 122 | |
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123 | 123 | flagNoData = True |
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124 | 124 | |
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125 | 125 | def copy(self, inputObj=None): |
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126 | 126 | |
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127 | 127 | if inputObj == None: |
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128 | 128 | return copy.deepcopy(self) |
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129 | 129 | |
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130 | 130 | for key in list(inputObj.__dict__.keys()): |
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131 | 131 | |
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132 | 132 | attribute = inputObj.__dict__[key] |
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133 | 133 | |
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134 | 134 | # If this attribute is a tuple or list |
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135 | 135 | if type(inputObj.__dict__[key]) in (tuple, list): |
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136 | 136 | self.__dict__[key] = attribute[:] |
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137 | 137 | continue |
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138 | 138 | |
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139 | 139 | # If this attribute is another object or instance |
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140 | 140 | if hasattr(attribute, '__dict__'): |
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141 | 141 | self.__dict__[key] = attribute.copy() |
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142 | 142 | continue |
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143 | 143 | |
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144 | 144 | self.__dict__[key] = inputObj.__dict__[key] |
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145 | 145 | |
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146 | 146 | def deepcopy(self): |
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147 | 147 | |
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148 | 148 | return copy.deepcopy(self) |
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149 | 149 | |
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150 | 150 | def isEmpty(self): |
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151 | 151 | |
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152 | 152 | return self.flagNoData |
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153 | 153 | |
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154 | 154 | def isReady(self): |
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155 | 155 | |
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156 | 156 | return not self.flagNoData |
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157 | 157 | |
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158 | 158 | |
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159 | 159 | class JROData(GenericData): |
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160 | 160 | |
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161 | 161 | systemHeaderObj = SystemHeader() |
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162 | 162 | radarControllerHeaderObj = RadarControllerHeader() |
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163 | 163 | type = None |
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164 | 164 | datatype = None # dtype but in string |
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165 | 165 | nProfiles = None |
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166 | 166 | heightList = None |
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167 | 167 | channelList = None |
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168 | 168 | flagDiscontinuousBlock = False |
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169 | 169 | useLocalTime = False |
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170 | 170 | utctime = None |
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171 | 171 | timeZone = None |
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172 | 172 | dstFlag = None |
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173 | 173 | errorCount = None |
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174 | 174 | blocksize = None |
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175 | 175 | flagDecodeData = False # asumo q la data no esta decodificada |
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176 | 176 | flagDeflipData = False # asumo q la data no esta sin flip |
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177 | 177 | flagShiftFFT = False |
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178 | 178 | nCohInt = None |
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179 | 179 | windowOfFilter = 1 |
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180 | 180 | C = 3e8 |
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181 | 181 | frequency = 49.92e6 |
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182 | 182 | realtime = False |
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183 | 183 | beacon_heiIndexList = None |
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184 | 184 | last_block = None |
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185 | 185 | blocknow = None |
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186 | 186 | azimuth = None |
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187 | 187 | zenith = None |
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188 | 188 | beam = Beam() |
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189 | 189 | profileIndex = None |
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190 | 190 | error = None |
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191 | 191 | data = None |
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192 | 192 | nmodes = None |
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193 | 193 | h0 = 0 |
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194 | 194 | metadata_list = ['heightList', 'timeZone', 'type'] |
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195 | 195 | |
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196 | 196 | def __str__(self): |
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197 | 197 | |
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198 | 198 | return '{} - {}'.format(self.type, self.datatime()) |
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199 | 199 | |
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200 | 200 | def getNoise(self): |
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201 | 201 | |
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202 | 202 | raise NotImplementedError |
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203 | 203 | |
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204 | 204 | @property |
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205 | 205 | def nChannels(self): |
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206 | 206 | |
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207 | 207 | return len(self.channelList) |
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208 | 208 | |
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209 | 209 | @property |
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210 | 210 | def channelIndexList(self): |
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211 | 211 | |
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212 | 212 | return list(range(self.nChannels)) |
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213 | 213 | |
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214 | 214 | @property |
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215 | 215 | def nHeights(self): |
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216 | 216 | |
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217 | 217 | return len(self.heightList) |
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218 | 218 | |
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219 | 219 | def getDeltaH(self): |
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220 | 220 | |
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221 | 221 | return self.heightList[1] - self.heightList[0] |
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222 | 222 | |
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223 | 223 | @property |
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224 | 224 | def ltctime(self): |
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225 | 225 | |
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226 | 226 | if self.useLocalTime: |
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227 | 227 | return self.utctime - self.timeZone * 60 |
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228 | 228 | |
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229 | 229 | return self.utctime |
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230 | 230 | |
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231 | 231 | @property |
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232 | 232 | def datatime(self): |
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233 | 233 | |
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234 | 234 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
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235 | 235 | return datatimeValue |
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236 | 236 | |
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237 | 237 | def getTimeRange(self): |
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238 | 238 | |
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239 | 239 | datatime = [] |
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240 | 240 | |
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241 | 241 | datatime.append(self.ltctime) |
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242 | 242 | datatime.append(self.ltctime + self.timeInterval + 1) |
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243 | 243 | |
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244 | 244 | datatime = numpy.array(datatime) |
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245 | 245 | |
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246 | 246 | return datatime |
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247 | 247 | |
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248 | 248 | def getFmaxTimeResponse(self): |
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249 | 249 | |
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250 | 250 | period = (10**-6) * self.getDeltaH() / (0.15) |
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251 | 251 | |
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252 | 252 | PRF = 1. / (period * self.nCohInt) |
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253 | 253 | |
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254 | 254 | fmax = PRF |
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255 | 255 | |
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256 | 256 | return fmax |
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257 | 257 | |
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258 | 258 | def getFmax(self): |
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259 | 259 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
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260 | 260 | |
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261 | 261 | fmax = PRF |
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262 | 262 | return fmax |
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263 | 263 | |
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264 | 264 | def getVmax(self): |
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265 | 265 | |
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266 | 266 | _lambda = self.C / self.frequency |
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267 | 267 | |
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268 | 268 | vmax = self.getFmax() * _lambda / 2 |
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269 | 269 | |
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270 | 270 | return vmax |
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271 | 271 | |
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272 | 272 | @property |
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273 | 273 | def ippSeconds(self): |
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274 | 274 | ''' |
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275 | 275 | ''' |
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276 | 276 | return self.radarControllerHeaderObj.ippSeconds |
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277 | 277 | |
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278 | 278 | @ippSeconds.setter |
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279 | 279 | def ippSeconds(self, ippSeconds): |
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280 | 280 | ''' |
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281 | 281 | ''' |
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282 | 282 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
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283 | 283 | |
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284 | 284 | @property |
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285 | 285 | def code(self): |
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286 | 286 | ''' |
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287 | 287 | ''' |
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288 | 288 | return self.radarControllerHeaderObj.code |
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289 | 289 | |
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290 | 290 | @code.setter |
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291 | 291 | def code(self, code): |
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292 | 292 | ''' |
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293 | 293 | ''' |
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294 | 294 | self.radarControllerHeaderObj.code = code |
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295 | 295 | |
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296 | 296 | @property |
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297 | 297 | def nCode(self): |
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298 | 298 | ''' |
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299 | 299 | ''' |
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300 | 300 | return self.radarControllerHeaderObj.nCode |
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301 | 301 | |
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302 | 302 | @nCode.setter |
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303 | 303 | def nCode(self, ncode): |
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304 | 304 | ''' |
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305 | 305 | ''' |
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306 | 306 | self.radarControllerHeaderObj.nCode = ncode |
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307 | 307 | |
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308 | 308 | @property |
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309 | 309 | def nBaud(self): |
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310 | 310 | ''' |
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311 | 311 | ''' |
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312 | 312 | return self.radarControllerHeaderObj.nBaud |
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313 | 313 | |
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314 | 314 | @nBaud.setter |
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315 | 315 | def nBaud(self, nbaud): |
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316 | 316 | ''' |
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317 | 317 | ''' |
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318 | 318 | self.radarControllerHeaderObj.nBaud = nbaud |
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319 | 319 | |
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320 | 320 | @property |
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321 | 321 | def ipp(self): |
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322 | 322 | ''' |
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323 | 323 | ''' |
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324 | 324 | return self.radarControllerHeaderObj.ipp |
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325 | 325 | |
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326 | 326 | @ipp.setter |
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327 | 327 | def ipp(self, ipp): |
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328 | 328 | ''' |
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329 | 329 | ''' |
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330 | 330 | self.radarControllerHeaderObj.ipp = ipp |
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331 | 331 | |
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332 | 332 | @property |
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333 | 333 | def metadata(self): |
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334 | 334 | ''' |
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335 | 335 | ''' |
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336 | 336 | |
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337 | 337 | return {attr: getattr(self, attr) for attr in self.metadata_list} |
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338 | 338 | |
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339 | 339 | |
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340 | 340 | class Voltage(JROData): |
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341 | 341 | |
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342 | 342 | dataPP_POW = None |
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343 | 343 | dataPP_DOP = None |
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344 | 344 | dataPP_WIDTH = None |
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345 | 345 | dataPP_SNR = None |
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346 | 346 | |
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347 | 347 | def __init__(self): |
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348 | 348 | ''' |
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349 | 349 | Constructor |
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350 | 350 | ''' |
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351 | 351 | |
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352 | 352 | self.useLocalTime = True |
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353 | 353 | self.radarControllerHeaderObj = RadarControllerHeader() |
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354 | 354 | self.systemHeaderObj = SystemHeader() |
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355 | 355 | self.type = "Voltage" |
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356 | 356 | self.data = None |
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357 | 357 | self.nProfiles = None |
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358 | 358 | self.heightList = None |
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359 | 359 | self.channelList = None |
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360 | 360 | self.flagNoData = True |
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361 | 361 | self.flagDiscontinuousBlock = False |
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362 | 362 | self.utctime = None |
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363 | 363 | self.timeZone = 0 |
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364 | 364 | self.dstFlag = None |
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365 | 365 | self.errorCount = None |
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366 | 366 | self.nCohInt = None |
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367 | 367 | self.blocksize = None |
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368 | 368 | self.flagCohInt = False |
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369 | 369 | self.flagDecodeData = False # asumo q la data no esta decodificada |
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370 | 370 | self.flagDeflipData = False # asumo q la data no esta sin flip |
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371 | 371 | self.flagShiftFFT = False |
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372 | 372 | self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil |
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373 | 373 | self.profileIndex = 0 |
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374 | 374 | self.metadata_list = ['type', 'heightList', 'timeZone', 'nProfiles', 'channelList', 'nCohInt', |
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375 | 375 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp'] |
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376 | 376 | |
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377 | 377 | def getNoisebyHildebrand(self, channel=None): |
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378 | 378 | """ |
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379 | 379 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
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380 | 380 | |
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381 | 381 | Return: |
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382 | 382 | noiselevel |
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383 | 383 | """ |
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384 | 384 | |
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385 | 385 | if channel != None: |
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386 | 386 | data = self.data[channel] |
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387 | 387 | nChannels = 1 |
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388 | 388 | else: |
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389 | 389 | data = self.data |
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390 | 390 | nChannels = self.nChannels |
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391 | 391 | |
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392 | 392 | noise = numpy.zeros(nChannels) |
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393 | 393 | power = data * numpy.conjugate(data) |
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394 | 394 | |
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395 | 395 | for thisChannel in range(nChannels): |
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396 | 396 | if nChannels == 1: |
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397 | 397 | daux = power[:].real |
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398 | 398 | else: |
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399 | 399 | daux = power[thisChannel, :].real |
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400 | 400 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
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401 | 401 | |
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402 | 402 | return noise |
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403 | 403 | |
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404 | 404 | def getNoise(self, type=1, channel=None): |
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405 | 405 | |
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406 | 406 | if type == 1: |
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407 | 407 | noise = self.getNoisebyHildebrand(channel) |
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408 | 408 | |
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409 | 409 | return noise |
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410 | 410 | |
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411 | 411 | def getPower(self, channel=None): |
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412 | 412 | |
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413 | 413 | if channel != None: |
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414 | 414 | data = self.data[channel] |
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415 | 415 | else: |
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416 | 416 | data = self.data |
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417 | 417 | |
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418 | 418 | power = data * numpy.conjugate(data) |
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419 | 419 | powerdB = 10 * numpy.log10(power.real) |
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420 | 420 | powerdB = numpy.squeeze(powerdB) |
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421 | 421 | |
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422 | 422 | return powerdB |
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423 | 423 | |
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424 | 424 | @property |
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425 | 425 | def timeInterval(self): |
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426 | 426 | |
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427 | 427 | return self.ippSeconds * self.nCohInt |
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428 | 428 | |
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429 | 429 | noise = property(getNoise, "I'm the 'nHeights' property.") |
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430 | 430 | |
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431 | 431 | |
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432 | 432 | class Spectra(JROData): |
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433 | 433 | |
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434 | 434 | def __init__(self): |
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435 | 435 | ''' |
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436 | 436 | Constructor |
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437 | 437 | ''' |
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438 | 438 | |
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439 | 439 | self.data_dc = None |
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440 | 440 | self.data_spc = None |
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441 | 441 | self.data_cspc = None |
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442 | 442 | self.useLocalTime = True |
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443 | 443 | self.radarControllerHeaderObj = RadarControllerHeader() |
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444 | 444 | self.systemHeaderObj = SystemHeader() |
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445 | 445 | self.type = "Spectra" |
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446 | 446 | self.timeZone = 0 |
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447 | 447 | self.nProfiles = None |
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448 | 448 | self.heightList = None |
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449 | 449 | self.channelList = None |
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450 | 450 | self.pairsList = None |
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451 | 451 | self.flagNoData = True |
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452 | 452 | self.flagDiscontinuousBlock = False |
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453 | 453 | self.utctime = None |
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454 | 454 | self.nCohInt = None |
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455 | 455 | self.nIncohInt = None |
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456 | 456 | self.blocksize = None |
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457 | 457 | self.nFFTPoints = None |
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458 | 458 | self.wavelength = None |
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459 | 459 | self.flagDecodeData = False # asumo q la data no esta decodificada |
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460 | 460 | self.flagDeflipData = False # asumo q la data no esta sin flip |
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461 | 461 | self.flagShiftFFT = False |
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462 | 462 | self.ippFactor = 1 |
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463 | 463 | self.beacon_heiIndexList = [] |
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464 | 464 | self.noise_estimation = None |
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465 | 465 | self.metadata_list = ['type', 'heightList', 'timeZone', 'pairsList', 'channelList', 'nCohInt', |
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466 | 466 | 'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp','nIncohInt', 'nFFTPoints', 'nProfiles'] |
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467 | 467 | |
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468 | 468 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
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469 | 469 | """ |
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470 | 470 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
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471 | 471 | |
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472 | 472 | Return: |
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473 | 473 | noiselevel |
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474 | 474 | """ |
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475 | 475 | |
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476 | 476 | noise = numpy.zeros(self.nChannels) |
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477 | 477 | |
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478 | 478 | for channel in range(self.nChannels): |
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479 | 479 | daux = self.data_spc[channel, |
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480 | 480 | xmin_index:xmax_index, ymin_index:ymax_index] |
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481 | 481 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
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482 | 482 | |
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483 | 483 | return noise |
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484 | 484 | |
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485 | 485 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
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486 | 486 | |
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487 | 487 | if self.noise_estimation is not None: |
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488 | 488 | # this was estimated by getNoise Operation defined in jroproc_spectra.py |
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489 | 489 | return self.noise_estimation |
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490 | 490 | else: |
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491 | 491 | noise = self.getNoisebyHildebrand( |
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492 | 492 | xmin_index, xmax_index, ymin_index, ymax_index) |
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493 | 493 | return noise |
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494 | 494 | |
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495 | 495 | def getFreqRangeTimeResponse(self, extrapoints=0): |
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496 | 496 | |
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497 | 497 | deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor) |
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498 | 498 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2 |
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499 | 499 | |
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500 | 500 | return freqrange |
|
501 | 501 | |
|
502 | 502 | def getAcfRange(self, extrapoints=0): |
|
503 | 503 | |
|
504 | 504 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
505 | 505 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
506 | 506 | |
|
507 | 507 | return freqrange |
|
508 | 508 | |
|
509 | 509 | def getFreqRange(self, extrapoints=0): |
|
510 | 510 | |
|
511 | 511 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
512 | 512 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
513 | 513 | |
|
514 | 514 | return freqrange |
|
515 | 515 | |
|
516 | 516 | def getVelRange(self, extrapoints=0): |
|
517 | 517 | |
|
518 | 518 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
519 | 519 | velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) |
|
520 | 520 | |
|
521 | 521 | if self.nmodes: |
|
522 | 522 | return velrange/self.nmodes |
|
523 | 523 | else: |
|
524 | 524 | return velrange |
|
525 | 525 | |
|
526 | 526 | @property |
|
527 | 527 | def nPairs(self): |
|
528 | 528 | |
|
529 | 529 | return len(self.pairsList) |
|
530 | 530 | |
|
531 | 531 | @property |
|
532 | 532 | def pairsIndexList(self): |
|
533 | 533 | |
|
534 | 534 | return list(range(self.nPairs)) |
|
535 | 535 | |
|
536 | 536 | @property |
|
537 | 537 | def normFactor(self): |
|
538 | 538 | |
|
539 | 539 | pwcode = 1 |
|
540 | 540 | |
|
541 | 541 | if self.flagDecodeData: |
|
542 | 542 | pwcode = numpy.sum(self.code[0]**2) |
|
543 | 543 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
544 | 544 | normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter |
|
545 | 545 | |
|
546 | 546 | return normFactor |
|
547 | 547 | |
|
548 | 548 | @property |
|
549 | 549 | def flag_cspc(self): |
|
550 | 550 | |
|
551 | 551 | if self.data_cspc is None: |
|
552 | 552 | return True |
|
553 | 553 | |
|
554 | 554 | return False |
|
555 | 555 | |
|
556 | 556 | @property |
|
557 | 557 | def flag_dc(self): |
|
558 | 558 | |
|
559 | 559 | if self.data_dc is None: |
|
560 | 560 | return True |
|
561 | 561 | |
|
562 | 562 | return False |
|
563 | 563 | |
|
564 | 564 | @property |
|
565 | 565 | def timeInterval(self): |
|
566 | 566 | |
|
567 | 567 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor |
|
568 | 568 | if self.nmodes: |
|
569 | 569 | return self.nmodes*timeInterval |
|
570 | 570 | else: |
|
571 | 571 | return timeInterval |
|
572 | 572 | |
|
573 | 573 | def getPower(self): |
|
574 | 574 | |
|
575 | 575 | factor = self.normFactor |
|
576 | 576 | z = self.data_spc / factor |
|
577 | 577 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
578 | 578 | avg = numpy.average(z, axis=1) |
|
579 | 579 | return 10 * numpy.log10(avg) |
|
580 | 580 | |
|
581 | 581 | def getCoherence(self, pairsList=None, phase=False): |
|
582 | 582 | |
|
583 | 583 | z = [] |
|
584 | 584 | if pairsList is None: |
|
585 | 585 | pairsIndexList = self.pairsIndexList |
|
586 | 586 | else: |
|
587 | 587 | pairsIndexList = [] |
|
588 | 588 | for pair in pairsList: |
|
589 | 589 | if pair not in self.pairsList: |
|
590 | 590 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
591 | 591 | pair)) |
|
592 | 592 | pairsIndexList.append(self.pairsList.index(pair)) |
|
593 | 593 | for i in range(len(pairsIndexList)): |
|
594 | 594 | pair = self.pairsList[pairsIndexList[i]] |
|
595 | 595 | ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
596 | 596 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
597 | 597 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
598 | 598 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
599 | 599 | if phase: |
|
600 | 600 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
601 | 601 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
602 | 602 | else: |
|
603 | 603 | data = numpy.abs(avgcoherenceComplex) |
|
604 | 604 | |
|
605 | 605 | z.append(data) |
|
606 | 606 | |
|
607 | 607 | return numpy.array(z) |
|
608 | 608 | |
|
609 | 609 | def setValue(self, value): |
|
610 | 610 | |
|
611 | 611 | print("This property should not be initialized") |
|
612 | 612 | |
|
613 | 613 | return |
|
614 | 614 | |
|
615 | 615 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
616 | 616 | |
|
617 | 617 | |
|
618 | 618 | class SpectraHeis(Spectra): |
|
619 | 619 | |
|
620 | 620 | def __init__(self): |
|
621 | 621 | |
|
622 | 622 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
623 | 623 | self.systemHeaderObj = SystemHeader() |
|
624 | 624 | self.type = "SpectraHeis" |
|
625 | 625 | self.nProfiles = None |
|
626 | 626 | self.heightList = None |
|
627 | 627 | self.channelList = None |
|
628 | 628 | self.flagNoData = True |
|
629 | 629 | self.flagDiscontinuousBlock = False |
|
630 | 630 | self.utctime = None |
|
631 | 631 | self.blocksize = None |
|
632 | 632 | self.profileIndex = 0 |
|
633 | 633 | self.nCohInt = 1 |
|
634 | 634 | self.nIncohInt = 1 |
|
635 | 635 | |
|
636 | 636 | @property |
|
637 | 637 | def normFactor(self): |
|
638 | 638 | pwcode = 1 |
|
639 | 639 | if self.flagDecodeData: |
|
640 | 640 | pwcode = numpy.sum(self.code[0]**2) |
|
641 | 641 | |
|
642 | 642 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
643 | 643 | |
|
644 | 644 | return normFactor |
|
645 | 645 | |
|
646 | 646 | @property |
|
647 | 647 | def timeInterval(self): |
|
648 | 648 | |
|
649 | 649 | return self.ippSeconds * self.nCohInt * self.nIncohInt |
|
650 | 650 | |
|
651 | 651 | |
|
652 | 652 | class Fits(JROData): |
|
653 | 653 | |
|
654 | 654 | def __init__(self): |
|
655 | 655 | |
|
656 | 656 | self.type = "Fits" |
|
657 | 657 | self.nProfiles = None |
|
658 | 658 | self.heightList = None |
|
659 | 659 | self.channelList = None |
|
660 | 660 | self.flagNoData = True |
|
661 | 661 | self.utctime = None |
|
662 | 662 | self.nCohInt = 1 |
|
663 | 663 | self.nIncohInt = 1 |
|
664 | 664 | self.useLocalTime = True |
|
665 | 665 | self.profileIndex = 0 |
|
666 | 666 | self.timeZone = 0 |
|
667 | 667 | |
|
668 | 668 | def getTimeRange(self): |
|
669 | 669 | |
|
670 | 670 | datatime = [] |
|
671 | 671 | |
|
672 | 672 | datatime.append(self.ltctime) |
|
673 | 673 | datatime.append(self.ltctime + self.timeInterval) |
|
674 | 674 | |
|
675 | 675 | datatime = numpy.array(datatime) |
|
676 | 676 | |
|
677 | 677 | return datatime |
|
678 | 678 | |
|
679 | 679 | def getChannelIndexList(self): |
|
680 | 680 | |
|
681 | 681 | return list(range(self.nChannels)) |
|
682 | 682 | |
|
683 | 683 | def getNoise(self, type=1): |
|
684 | 684 | |
|
685 | 685 | |
|
686 | 686 | if type == 1: |
|
687 | 687 | noise = self.getNoisebyHildebrand() |
|
688 | 688 | |
|
689 | 689 | if type == 2: |
|
690 | 690 | noise = self.getNoisebySort() |
|
691 | 691 | |
|
692 | 692 | if type == 3: |
|
693 | 693 | noise = self.getNoisebyWindow() |
|
694 | 694 | |
|
695 | 695 | return noise |
|
696 | 696 | |
|
697 | 697 | @property |
|
698 | 698 | def timeInterval(self): |
|
699 | 699 | |
|
700 | 700 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
701 | 701 | |
|
702 | 702 | return timeInterval |
|
703 | 703 | |
|
704 | 704 | @property |
|
705 | 705 | def ippSeconds(self): |
|
706 | 706 | ''' |
|
707 | 707 | ''' |
|
708 | 708 | return self.ipp_sec |
|
709 | 709 | |
|
710 | 710 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
711 | 711 | |
|
712 | 712 | |
|
713 | 713 | class Correlation(JROData): |
|
714 | 714 | |
|
715 | 715 | def __init__(self): |
|
716 | 716 | ''' |
|
717 | 717 | Constructor |
|
718 | 718 | ''' |
|
719 | 719 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
720 | 720 | self.systemHeaderObj = SystemHeader() |
|
721 | 721 | self.type = "Correlation" |
|
722 | 722 | self.data = None |
|
723 | 723 | self.dtype = None |
|
724 | 724 | self.nProfiles = None |
|
725 | 725 | self.heightList = None |
|
726 | 726 | self.channelList = None |
|
727 | 727 | self.flagNoData = True |
|
728 | 728 | self.flagDiscontinuousBlock = False |
|
729 | 729 | self.utctime = None |
|
730 | 730 | self.timeZone = 0 |
|
731 | 731 | self.dstFlag = None |
|
732 | 732 | self.errorCount = None |
|
733 | 733 | self.blocksize = None |
|
734 | 734 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
735 | 735 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
736 | 736 | self.pairsList = None |
|
737 | 737 | self.nPoints = None |
|
738 | 738 | |
|
739 | 739 | def getPairsList(self): |
|
740 | 740 | |
|
741 | 741 | return self.pairsList |
|
742 | 742 | |
|
743 | 743 | def getNoise(self, mode=2): |
|
744 | 744 | |
|
745 | 745 | indR = numpy.where(self.lagR == 0)[0][0] |
|
746 | 746 | indT = numpy.where(self.lagT == 0)[0][0] |
|
747 | 747 | |
|
748 | 748 | jspectra0 = self.data_corr[:, :, indR, :] |
|
749 | 749 | jspectra = copy.copy(jspectra0) |
|
750 | 750 | |
|
751 | 751 | num_chan = jspectra.shape[0] |
|
752 | 752 | num_hei = jspectra.shape[2] |
|
753 | 753 | |
|
754 | 754 | freq_dc = jspectra.shape[1] / 2 |
|
755 | 755 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
756 | 756 | |
|
757 | 757 | if ind_vel[0] < 0: |
|
758 | 758 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
759 | 759 | range(0, 1))] + self.num_prof |
|
760 | 760 | |
|
761 | 761 | if mode == 1: |
|
762 | 762 | jspectra[:, freq_dc, :] = ( |
|
763 | 763 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
764 | 764 | |
|
765 | 765 | if mode == 2: |
|
766 | 766 | |
|
767 | 767 | vel = numpy.array([-2, -1, 1, 2]) |
|
768 | 768 | xx = numpy.zeros([4, 4]) |
|
769 | 769 | |
|
770 | 770 | for fil in range(4): |
|
771 | 771 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
772 | 772 | |
|
773 | 773 | xx_inv = numpy.linalg.inv(xx) |
|
774 | 774 | xx_aux = xx_inv[0, :] |
|
775 | 775 | |
|
776 | 776 | for ich in range(num_chan): |
|
777 | 777 | yy = jspectra[ich, ind_vel, :] |
|
778 | 778 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
779 | 779 | |
|
780 | 780 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
781 | 781 | cjunkid = sum(junkid) |
|
782 | 782 | |
|
783 | 783 | if cjunkid.any(): |
|
784 | 784 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
785 | 785 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
786 | 786 | |
|
787 | 787 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
788 | 788 | |
|
789 | 789 | return noise |
|
790 | 790 | |
|
791 | 791 | @property |
|
792 | 792 | def timeInterval(self): |
|
793 | 793 | |
|
794 | 794 | return self.ippSeconds * self.nCohInt * self.nProfiles |
|
795 | 795 | |
|
796 | 796 | def splitFunctions(self): |
|
797 | 797 | |
|
798 | 798 | pairsList = self.pairsList |
|
799 | 799 | ccf_pairs = [] |
|
800 | 800 | acf_pairs = [] |
|
801 | 801 | ccf_ind = [] |
|
802 | 802 | acf_ind = [] |
|
803 | 803 | for l in range(len(pairsList)): |
|
804 | 804 | chan0 = pairsList[l][0] |
|
805 | 805 | chan1 = pairsList[l][1] |
|
806 | 806 | |
|
807 | 807 | # Obteniendo pares de Autocorrelacion |
|
808 | 808 | if chan0 == chan1: |
|
809 | 809 | acf_pairs.append(chan0) |
|
810 | 810 | acf_ind.append(l) |
|
811 | 811 | else: |
|
812 | 812 | ccf_pairs.append(pairsList[l]) |
|
813 | 813 | ccf_ind.append(l) |
|
814 | 814 | |
|
815 | 815 | data_acf = self.data_cf[acf_ind] |
|
816 | 816 | data_ccf = self.data_cf[ccf_ind] |
|
817 | 817 | |
|
818 | 818 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
819 | 819 | |
|
820 | 820 | @property |
|
821 | 821 | def normFactor(self): |
|
822 | 822 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
823 | 823 | acf_pairs = numpy.array(acf_pairs) |
|
824 | 824 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
825 | 825 | |
|
826 | 826 | for p in range(self.nPairs): |
|
827 | 827 | pair = self.pairsList[p] |
|
828 | 828 | |
|
829 | 829 | ch0 = pair[0] |
|
830 | 830 | ch1 = pair[1] |
|
831 | 831 | |
|
832 | 832 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
833 | 833 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
834 | 834 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
835 | 835 | |
|
836 | 836 | return normFactor |
|
837 | 837 | |
|
838 | 838 | |
|
839 | 839 | class Parameters(Spectra): |
|
840 | 840 | |
|
841 | 841 | groupList = None # List of Pairs, Groups, etc |
|
842 | 842 | data_param = None # Parameters obtained |
|
843 | 843 | data_pre = None # Data Pre Parametrization |
|
844 | 844 | data_SNR = None # Signal to Noise Ratio |
|
845 | 845 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
846 | 846 | utctimeInit = None # Initial UTC time |
|
847 | 847 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
848 | 848 | useLocalTime = True |
|
849 | 849 | # Fitting |
|
850 | 850 | data_error = None # Error of the estimation |
|
851 | 851 | constants = None |
|
852 | 852 | library = None |
|
853 | 853 | # Output signal |
|
854 | 854 | outputInterval = None # Time interval to calculate output signal in seconds |
|
855 | 855 | data_output = None # Out signal |
|
856 | 856 | nAvg = None |
|
857 | 857 | noise_estimation = None |
|
858 | 858 | GauSPC = None # Fit gaussian SPC |
|
859 | 859 | |
|
860 | 860 | def __init__(self): |
|
861 | 861 | ''' |
|
862 | 862 | Constructor |
|
863 | 863 | ''' |
|
864 | 864 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
865 | 865 | self.systemHeaderObj = SystemHeader() |
|
866 | 866 | self.type = "Parameters" |
|
867 | 867 | self.timeZone = 0 |
|
868 | 868 | |
|
869 | 869 | def getTimeRange1(self, interval): |
|
870 | 870 | |
|
871 | 871 | datatime = [] |
|
872 | 872 | |
|
873 | 873 | if self.useLocalTime: |
|
874 | 874 | time1 = self.utctimeInit - self.timeZone * 60 |
|
875 | 875 | else: |
|
876 | 876 | time1 = self.utctimeInit |
|
877 | 877 | |
|
878 | 878 | datatime.append(time1) |
|
879 | 879 | datatime.append(time1 + interval) |
|
880 | 880 | datatime = numpy.array(datatime) |
|
881 | 881 | |
|
882 | 882 | return datatime |
|
883 | 883 | |
|
884 | 884 | @property |
|
885 | 885 | def timeInterval(self): |
|
886 | 886 | |
|
887 | 887 | if hasattr(self, 'timeInterval1'): |
|
888 | 888 | return self.timeInterval1 |
|
889 | 889 | else: |
|
890 | 890 | return self.paramInterval |
|
891 | 891 | |
|
892 | 892 | def setValue(self, value): |
|
893 | 893 | |
|
894 | 894 | print("This property should not be initialized") |
|
895 | 895 | |
|
896 | 896 | return |
|
897 | 897 | |
|
898 | 898 | def getNoise(self): |
|
899 | 899 | |
|
900 | 900 | return self.spc_noise |
|
901 | 901 | |
|
902 | 902 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
903 | 903 | |
|
904 | 904 | |
|
905 | 905 | class PlotterData(object): |
|
906 | 906 | ''' |
|
907 | 907 | Object to hold data to be plotted |
|
908 | 908 | ''' |
|
909 | 909 | |
|
910 |
MAXNUMX = |
|
|
911 |
MAXNUMY = |
|
|
910 | MAXNUMX = 1000 | |
|
911 | MAXNUMY = 1000 | |
|
912 | 912 | |
|
913 | 913 | def __init__(self, code, exp_code, localtime=True): |
|
914 | 914 | |
|
915 | 915 | self.key = code |
|
916 | 916 | self.exp_code = exp_code |
|
917 | 917 | self.ready = False |
|
918 | 918 | self.flagNoData = False |
|
919 | 919 | self.localtime = localtime |
|
920 | 920 | self.data = {} |
|
921 | 921 | self.meta = {} |
|
922 | 922 | self.__heights = [] |
|
923 | 923 | |
|
924 | 924 | def __str__(self): |
|
925 | 925 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
926 | 926 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.times)) |
|
927 | 927 | |
|
928 | 928 | def __len__(self): |
|
929 | 929 | return len(self.data) |
|
930 | 930 | |
|
931 | 931 | def __getitem__(self, key): |
|
932 | 932 | if isinstance(key, int): |
|
933 | 933 | return self.data[self.times[key]] |
|
934 | 934 | elif isinstance(key, str): |
|
935 | 935 | ret = numpy.array([self.data[x][key] for x in self.times]) |
|
936 | 936 | if ret.ndim > 1: |
|
937 | 937 | ret = numpy.swapaxes(ret, 0, 1) |
|
938 | 938 | return ret |
|
939 | 939 | |
|
940 | 940 | def __contains__(self, key): |
|
941 | 941 | return key in self.data[self.min_time] |
|
942 | 942 | |
|
943 | 943 | def setup(self): |
|
944 | 944 | ''' |
|
945 | 945 | Configure object |
|
946 | 946 | ''' |
|
947 | 947 | self.type = '' |
|
948 | 948 | self.ready = False |
|
949 | 949 | del self.data |
|
950 | 950 | self.data = {} |
|
951 | 951 | self.__heights = [] |
|
952 | 952 | self.__all_heights = set() |
|
953 | 953 | |
|
954 | 954 | def shape(self, key): |
|
955 | 955 | ''' |
|
956 | 956 | Get the shape of the one-element data for the given key |
|
957 | 957 | ''' |
|
958 | 958 | |
|
959 | 959 | if len(self.data[self.min_time][key]): |
|
960 | 960 | return self.data[self.min_time][key].shape |
|
961 | 961 | return (0,) |
|
962 | 962 | |
|
963 | 963 | def update(self, data, tm, meta={}): |
|
964 | 964 | ''' |
|
965 | 965 | Update data object with new dataOut |
|
966 | 966 | ''' |
|
967 | 967 | |
|
968 | 968 | self.data[tm] = data |
|
969 | 969 | |
|
970 | 970 | for key, value in meta.items(): |
|
971 | 971 | setattr(self, key, value) |
|
972 | 972 | |
|
973 | 973 | def normalize_heights(self): |
|
974 | 974 | ''' |
|
975 | 975 | Ensure same-dimension of the data for different heighList |
|
976 | 976 | ''' |
|
977 | 977 | |
|
978 | 978 | H = numpy.array(list(self.__all_heights)) |
|
979 | 979 | H.sort() |
|
980 | 980 | for key in self.data: |
|
981 | 981 | shape = self.shape(key)[:-1] + H.shape |
|
982 | 982 | for tm, obj in list(self.data[key].items()): |
|
983 | 983 | h = self.__heights[self.times.tolist().index(tm)] |
|
984 | 984 | if H.size == h.size: |
|
985 | 985 | continue |
|
986 | 986 | index = numpy.where(numpy.in1d(H, h))[0] |
|
987 | 987 | dummy = numpy.zeros(shape) + numpy.nan |
|
988 | 988 | if len(shape) == 2: |
|
989 | 989 | dummy[:, index] = obj |
|
990 | 990 | else: |
|
991 | 991 | dummy[index] = obj |
|
992 | 992 | self.data[key][tm] = dummy |
|
993 | 993 | |
|
994 | 994 | self.__heights = [H for tm in self.times] |
|
995 | 995 | |
|
996 | def jsonify(self, tm, plot_name, plot_type, decimate=False): | |
|
996 | def jsonify(self, tm, plot_name, plot_type, key=None, decimate=False): | |
|
997 | 997 | ''' |
|
998 | 998 | Convert data to json |
|
999 | 999 | ''' |
|
1000 | 1000 | |
|
1001 | if key is None: | |
|
1002 | key = self.key | |
|
1003 | ||
|
1001 | 1004 | meta = {} |
|
1002 | 1005 | meta['xrange'] = [] |
|
1003 | 1006 | dy = int(len(self.yrange)/self.MAXNUMY) + 1 |
|
1004 |
tmp = self.data[tm][ |
|
|
1007 | tmp = self.data[tm][key] | |
|
1005 | 1008 | shape = tmp.shape |
|
1006 | 1009 | if len(shape) == 2: |
|
1007 |
data = self.roundFloats(self.data[tm][ |
|
|
1010 | data = self.roundFloats(self.data[tm][key][::, ::dy].tolist()) | |
|
1008 | 1011 | elif len(shape) == 3: |
|
1009 |
dx = int(self.data[tm][ |
|
|
1012 | dx = int(self.data[tm][key].shape[1]/self.MAXNUMX) + 1 | |
|
1010 | 1013 | data = self.roundFloats( |
|
1011 |
self.data[tm][ |
|
|
1014 | self.data[tm][key][::, ::dx, ::dy].tolist()) | |
|
1012 | 1015 | meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
1013 | 1016 | else: |
|
1014 |
data = self.roundFloats(self.data[tm][ |
|
|
1017 | data = self.roundFloats(self.data[tm][key].tolist()) | |
|
1015 | 1018 | |
|
1016 | 1019 | ret = { |
|
1017 | 1020 | 'plot': plot_name, |
|
1018 | 1021 | 'code': self.exp_code, |
|
1019 | 1022 | 'time': float(tm), |
|
1020 | 1023 | 'data': data, |
|
1021 | 1024 | } |
|
1022 | 1025 | meta['type'] = plot_type |
|
1023 | 1026 | meta['interval'] = float(self.interval) |
|
1024 | 1027 | meta['localtime'] = self.localtime |
|
1025 | meta['yrange'] = self.roundFloats(self.yrange[::dy].tolist()) | |
|
1028 | #meta['yrange'] = self.roundFloats(self.yrange[::dy].tolist()) | |
|
1029 | meta['yrange'] = self.roundFloats(self.lat[::dy].tolist()) | |
|
1030 | meta['xrange'] = self.roundFloats(self.lon[::dy].tolist()) | |
|
1026 | 1031 | meta.update(self.meta) |
|
1027 | 1032 | ret['metadata'] = meta |
|
1028 | 1033 | return json.dumps(ret) |
|
1029 | 1034 | |
|
1030 | 1035 | @property |
|
1031 | 1036 | def times(self): |
|
1032 | 1037 | ''' |
|
1033 | 1038 | Return the list of times of the current data |
|
1034 | 1039 | ''' |
|
1035 | 1040 | |
|
1036 | 1041 | ret = [t for t in self.data] |
|
1037 | 1042 | ret.sort() |
|
1038 | 1043 | return numpy.array(ret) |
|
1039 | 1044 | |
|
1040 | 1045 | @property |
|
1041 | 1046 | def min_time(self): |
|
1042 | 1047 | ''' |
|
1043 | 1048 | Return the minimun time value |
|
1044 | 1049 | ''' |
|
1045 | 1050 | |
|
1046 | 1051 | return self.times[0] |
|
1047 | 1052 | |
|
1048 | 1053 | @property |
|
1049 | 1054 | def max_time(self): |
|
1050 | 1055 | ''' |
|
1051 | 1056 | Return the maximun time value |
|
1052 | 1057 | ''' |
|
1053 | 1058 | |
|
1054 | 1059 | return self.times[-1] |
|
1055 | 1060 | |
|
1056 | 1061 | # @property |
|
1057 | 1062 | # def heights(self): |
|
1058 | 1063 | # ''' |
|
1059 | 1064 | # Return the list of heights of the current data |
|
1060 | 1065 | # ''' |
|
1061 | 1066 | |
|
1062 | 1067 | # return numpy.array(self.__heights[-1]) |
|
1063 | 1068 | |
|
1064 | 1069 | @staticmethod |
|
1065 | 1070 | def roundFloats(obj): |
|
1066 | 1071 | if isinstance(obj, list): |
|
1067 | 1072 | return list(map(PlotterData.roundFloats, obj)) |
|
1068 | 1073 | elif isinstance(obj, float): |
|
1069 |
return round(obj, |
|
|
1074 | return round(obj, 4) |
@@ -1,732 +1,732 | |||
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
2 | 2 | # All rights reserved. |
|
3 | 3 | # |
|
4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
5 | 5 | """Base class to create plot operations |
|
6 | 6 | |
|
7 | 7 | """ |
|
8 | 8 | |
|
9 | 9 | import os |
|
10 | 10 | import sys |
|
11 | 11 | import zmq |
|
12 | 12 | import time |
|
13 | 13 | import numpy |
|
14 | 14 | import datetime |
|
15 | 15 | from collections import deque |
|
16 | 16 | from functools import wraps |
|
17 | 17 | from threading import Thread |
|
18 | 18 | import matplotlib,re |
|
19 | 19 | |
|
20 | 20 | if 'BACKEND' in os.environ: |
|
21 | 21 | matplotlib.use(os.environ['BACKEND']) |
|
22 | 22 | elif 'linux' in sys.platform: |
|
23 | 23 | matplotlib.use("TkAgg") |
|
24 | 24 | elif 'darwin' in sys.platform: |
|
25 | 25 | matplotlib.use('MacOSX') |
|
26 | 26 | else: |
|
27 | 27 | from schainpy.utils import log |
|
28 | 28 | log.warning('Using default Backend="Agg"', 'INFO') |
|
29 | 29 | matplotlib.use('Agg') |
|
30 | 30 | |
|
31 | 31 | import matplotlib.pyplot as plt |
|
32 | 32 | from matplotlib.patches import Polygon |
|
33 | 33 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
|
34 | 34 | from matplotlib.ticker import FuncFormatter, LinearLocator, MultipleLocator |
|
35 | 35 | |
|
36 | 36 | from .plotting_codes import * |
|
37 | 37 | |
|
38 | 38 | from schainpy.model.data.jrodata import PlotterData |
|
39 | 39 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
40 | 40 | from schainpy.utils import log |
|
41 | 41 | |
|
42 | 42 | for name, cb_table in sophy_cb_tables: |
|
43 | 43 | ncmap = matplotlib.colors.ListedColormap(cb_table, name=name) |
|
44 | 44 | matplotlib.pyplot.register_cmap(cmap=ncmap) |
|
45 | 45 | |
|
46 | 46 | EARTH_RADIUS = 6.3710e3 |
|
47 | 47 | |
|
48 | 48 | def ll2xy(lat1, lon1, lat2, lon2): |
|
49 | 49 | |
|
50 | 50 | p = 0.017453292519943295 |
|
51 | 51 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
52 | 52 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
53 | 53 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
54 | 54 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
55 | 55 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
56 | 56 | theta = -theta + numpy.pi/2 |
|
57 | 57 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
58 | 58 | |
|
59 | 59 | |
|
60 | 60 | def km2deg(km): |
|
61 | 61 | ''' |
|
62 | 62 | Convert distance in km to degrees |
|
63 | 63 | ''' |
|
64 | 64 | |
|
65 | 65 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
66 | 66 | |
|
67 | 67 | |
|
68 | 68 | def figpause(interval): |
|
69 | 69 | backend = plt.rcParams['backend'] |
|
70 | 70 | if backend in matplotlib.rcsetup.interactive_bk: |
|
71 | 71 | figManager = matplotlib._pylab_helpers.Gcf.get_active() |
|
72 | 72 | if figManager is not None: |
|
73 | 73 | canvas = figManager.canvas |
|
74 | 74 | if canvas.figure.stale: |
|
75 | 75 | canvas.draw() |
|
76 | 76 | try: |
|
77 | 77 | canvas.start_event_loop(interval) |
|
78 | 78 | except: |
|
79 | 79 | pass |
|
80 | 80 | return |
|
81 | 81 | |
|
82 | 82 | def popup(message): |
|
83 | 83 | ''' |
|
84 | 84 | ''' |
|
85 | 85 | |
|
86 | 86 | fig = plt.figure(figsize=(12, 8), facecolor='r') |
|
87 | 87 | text = '\n'.join([s.strip() for s in message.split(':')]) |
|
88 | 88 | fig.text(0.01, 0.5, text, ha='left', va='center', |
|
89 | 89 | size='20', weight='heavy', color='w') |
|
90 | 90 | fig.show() |
|
91 | 91 | figpause(1000) |
|
92 | 92 | |
|
93 | 93 | |
|
94 | 94 | class Throttle(object): |
|
95 | 95 | ''' |
|
96 | 96 | Decorator that prevents a function from being called more than once every |
|
97 | 97 | time period. |
|
98 | 98 | To create a function that cannot be called more than once a minute, but |
|
99 | 99 | will sleep until it can be called: |
|
100 | 100 | @Throttle(minutes=1) |
|
101 | 101 | def foo(): |
|
102 | 102 | pass |
|
103 | 103 | |
|
104 | 104 | for i in range(10): |
|
105 | 105 | foo() |
|
106 | 106 | print "This function has run %s times." % i |
|
107 | 107 | ''' |
|
108 | 108 | |
|
109 | 109 | def __init__(self, seconds=0, minutes=0, hours=0): |
|
110 | 110 | self.throttle_period = datetime.timedelta( |
|
111 | 111 | seconds=seconds, minutes=minutes, hours=hours |
|
112 | 112 | ) |
|
113 | 113 | |
|
114 | 114 | self.time_of_last_call = datetime.datetime.min |
|
115 | 115 | |
|
116 | 116 | def __call__(self, fn): |
|
117 | 117 | @wraps(fn) |
|
118 | 118 | def wrapper(*args, **kwargs): |
|
119 | 119 | coerce = kwargs.pop('coerce', None) |
|
120 | 120 | if coerce: |
|
121 | 121 | self.time_of_last_call = datetime.datetime.now() |
|
122 | 122 | return fn(*args, **kwargs) |
|
123 | 123 | else: |
|
124 | 124 | now = datetime.datetime.now() |
|
125 | 125 | time_since_last_call = now - self.time_of_last_call |
|
126 | 126 | time_left = self.throttle_period - time_since_last_call |
|
127 | 127 | |
|
128 | 128 | if time_left > datetime.timedelta(seconds=0): |
|
129 | 129 | return |
|
130 | 130 | |
|
131 | 131 | self.time_of_last_call = datetime.datetime.now() |
|
132 | 132 | return fn(*args, **kwargs) |
|
133 | 133 | |
|
134 | 134 | return wrapper |
|
135 | 135 | |
|
136 | 136 | def apply_throttle(value): |
|
137 | 137 | |
|
138 | 138 | @Throttle(seconds=value) |
|
139 | 139 | def fnThrottled(fn): |
|
140 | 140 | fn() |
|
141 | 141 | |
|
142 | 142 | return fnThrottled |
|
143 | 143 | |
|
144 | 144 | |
|
145 | 145 | @MPDecorator |
|
146 | 146 | class Plot(Operation): |
|
147 | 147 | """Base class for Schain plotting operations |
|
148 | 148 | |
|
149 | 149 | This class should never be use directtly you must subclass a new operation, |
|
150 | 150 | children classes must be defined as follow: |
|
151 | 151 | |
|
152 | 152 | ExamplePlot(Plot): |
|
153 | 153 | |
|
154 | 154 | CODE = 'code' |
|
155 | 155 | colormap = 'jet' |
|
156 | 156 | plot_type = 'pcolor' # options are ('pcolor', 'pcolorbuffer', 'scatter', 'scatterbuffer') |
|
157 | 157 | |
|
158 | 158 | def setup(self): |
|
159 | 159 | pass |
|
160 | 160 | |
|
161 | 161 | def plot(self): |
|
162 | 162 | pass |
|
163 | 163 | |
|
164 | 164 | """ |
|
165 | 165 | |
|
166 | 166 | CODE = 'Figure' |
|
167 | 167 | colormap = 'jet' |
|
168 | 168 | bgcolor = 'white' |
|
169 | 169 | buffering = True |
|
170 | 170 | __missing = 1E30 |
|
171 | 171 | |
|
172 | 172 | __attrs__ = ['show', 'save', 'ymin', 'ymax', 'zmin', 'zmax', 'title', |
|
173 | 173 | 'showprofile'] |
|
174 | 174 | |
|
175 | 175 | def __init__(self): |
|
176 | 176 | |
|
177 | 177 | Operation.__init__(self) |
|
178 | 178 | self.isConfig = False |
|
179 | 179 | self.isPlotConfig = False |
|
180 | 180 | self.save_time = 0 |
|
181 | 181 | self.sender_time = 0 |
|
182 | 182 | self.data = None |
|
183 | 183 | self.firsttime = True |
|
184 | 184 | self.sender_queue = deque(maxlen=10) |
|
185 | 185 | self.plots_adjust = {'left': 0.125, 'right': 0.9, 'bottom': 0.15, 'top': 0.9, 'wspace': 0.2, 'hspace': 0.2} |
|
186 | 186 | |
|
187 | 187 | def __fmtTime(self, x, pos): |
|
188 | 188 | ''' |
|
189 | 189 | ''' |
|
190 | 190 | |
|
191 | 191 | return '{}'.format(self.getDateTime(x).strftime('%H:%M')) |
|
192 | 192 | |
|
193 | 193 | def __setup(self, **kwargs): |
|
194 | 194 | ''' |
|
195 | 195 | Initialize variables |
|
196 | 196 | ''' |
|
197 | 197 | |
|
198 | 198 | self.figures = [] |
|
199 | 199 | self.axes = [] |
|
200 | 200 | self.cb_axes = [] |
|
201 | 201 | self.localtime = kwargs.pop('localtime', True) |
|
202 | 202 | self.show = kwargs.get('show', True) |
|
203 | 203 | self.save = kwargs.get('save', False) |
|
204 | 204 | self.save_period = kwargs.get('save_period', 0) |
|
205 | 205 | self.colormap = kwargs.get('colormap', self.colormap) |
|
206 | 206 | self.colormap_coh = kwargs.get('colormap_coh', 'jet') |
|
207 | 207 | self.colormap_phase = kwargs.get('colormap_phase', 'RdBu_r') |
|
208 | 208 | self.colormaps = kwargs.get('colormaps', None) |
|
209 | 209 | self.bgcolor = kwargs.get('bgcolor', self.bgcolor) |
|
210 | 210 | self.showprofile = kwargs.get('showprofile', False) |
|
211 | 211 | self.title = kwargs.get('wintitle', self.CODE.upper()) |
|
212 | 212 | self.cb_label = kwargs.get('cb_label', None) |
|
213 | 213 | self.cb_labels = kwargs.get('cb_labels', None) |
|
214 | 214 | self.labels = kwargs.get('labels', None) |
|
215 | 215 | self.xaxis = kwargs.get('xaxis', 'frequency') |
|
216 | 216 | self.zmin = kwargs.get('zmin', None) |
|
217 | 217 | self.zmax = kwargs.get('zmax', None) |
|
218 | 218 | self.zlimits = kwargs.get('zlimits', None) |
|
219 | 219 | self.xmin = kwargs.get('xmin', None) |
|
220 | 220 | self.xmax = kwargs.get('xmax', None) |
|
221 | 221 | self.xrange = kwargs.get('xrange', 12) |
|
222 | 222 | self.xscale = kwargs.get('xscale', None) |
|
223 | 223 | self.ymin = kwargs.get('ymin', None) |
|
224 | 224 | self.ymax = kwargs.get('ymax', None) |
|
225 | 225 | self.yscale = kwargs.get('yscale', None) |
|
226 | 226 | self.xlabel = kwargs.get('xlabel', None) |
|
227 | 227 | self.attr_time = kwargs.get('attr_time', 'utctime') |
|
228 | 228 | self.attr_data = kwargs.get('attr_data', 'data_param') |
|
229 | 229 | self.decimation = kwargs.get('decimation', None) |
|
230 | 230 | self.oneFigure = kwargs.get('oneFigure', True) |
|
231 | 231 | self.width = kwargs.get('width', None) |
|
232 | 232 | self.height = kwargs.get('height', None) |
|
233 | 233 | self.colorbar = kwargs.get('colorbar', True) |
|
234 | 234 | self.factors = kwargs.get('factors', [1, 1, 1, 1, 1, 1, 1, 1]) |
|
235 | 235 | self.channels = kwargs.get('channels', None) |
|
236 | 236 | self.titles = kwargs.get('titles', []) |
|
237 | 237 | self.polar = False |
|
238 | 238 | self.type = kwargs.get('type', 'iq') |
|
239 | 239 | self.grid = kwargs.get('grid', False) |
|
240 | 240 | self.pause = kwargs.get('pause', False) |
|
241 | 241 | self.save_code = kwargs.get('save_code', self.CODE) |
|
242 | 242 | self.throttle = kwargs.get('throttle', 0) |
|
243 | 243 | self.exp_code = kwargs.get('exp_code', None) |
|
244 | 244 | self.server = kwargs.get('server', False) |
|
245 | 245 | self.sender_period = kwargs.get('sender_period', 60) |
|
246 | 246 | self.tag = kwargs.get('tag', '') |
|
247 | 247 | self.height_index = kwargs.get('height_index', None) |
|
248 | 248 | self.__throttle_plot = apply_throttle(self.throttle) |
|
249 | 249 | code = self.attr_data if self.attr_data else self.CODE |
|
250 | 250 | self.data = PlotterData(self.CODE, self.exp_code, self.localtime) |
|
251 | 251 | self.ang_min = kwargs.get('ang_min', None) |
|
252 | 252 | self.ang_max = kwargs.get('ang_max', None) |
|
253 | 253 | self.mode = kwargs.get('mode', None) |
|
254 | 254 | self.snr_threshold = kwargs.get('snr_threshold', 0) |
|
255 | 255 | |
|
256 | 256 | |
|
257 | 257 | if self.server: |
|
258 | 258 | if not self.server.startswith('tcp://'): |
|
259 | 259 | self.server = 'tcp://{}'.format(self.server) |
|
260 | 260 | log.success( |
|
261 | 261 | 'Sending to server: {}'.format(self.server), |
|
262 | 262 | self.name |
|
263 | 263 | ) |
|
264 | 264 | |
|
265 | 265 | if isinstance(self.attr_data, str): |
|
266 | 266 | self.attr_data = [self.attr_data] |
|
267 | 267 | |
|
268 | 268 | def __setup_plot(self): |
|
269 | 269 | ''' |
|
270 | 270 | Common setup for all figures, here figures and axes are created |
|
271 | 271 | ''' |
|
272 | 272 | |
|
273 | 273 | self.setup() |
|
274 | 274 | |
|
275 | 275 | self.time_label = 'LT' if self.localtime else 'UTC' |
|
276 | 276 | |
|
277 | 277 | if self.width is None: |
|
278 | 278 | self.width = 8 |
|
279 | 279 | |
|
280 | 280 | self.figures = [] |
|
281 | 281 | self.axes = [] |
|
282 | 282 | self.cb_axes = [] |
|
283 | 283 | self.pf_axes = [] |
|
284 | 284 | self.cmaps = [] |
|
285 | 285 | |
|
286 | 286 | size = '15%' if self.ncols == 1 else '30%' |
|
287 | 287 | pad = '4%' if self.ncols == 1 else '8%' |
|
288 | 288 | |
|
289 | 289 | if self.oneFigure: |
|
290 | 290 | if self.height is None: |
|
291 | 291 | self.height = 1.4 * self.nrows + 1 |
|
292 | 292 | fig = plt.figure(figsize=(self.width, self.height), |
|
293 | 293 | edgecolor='k', |
|
294 | 294 | facecolor='w') |
|
295 | 295 | self.figures.append(fig) |
|
296 | 296 | for n in range(self.nplots): |
|
297 | 297 | ax = fig.add_subplot(self.nrows, self.ncols, |
|
298 | 298 | n + 1, polar=self.polar) |
|
299 | 299 | ax.tick_params(labelsize=8) |
|
300 | 300 | ax.firsttime = True |
|
301 | 301 | ax.index = 0 |
|
302 | 302 | ax.press = None |
|
303 | 303 | self.axes.append(ax) |
|
304 | 304 | if self.showprofile: |
|
305 | 305 | cax = self.__add_axes(ax, size=size, pad=pad) |
|
306 | 306 | cax.tick_params(labelsize=8) |
|
307 | 307 | self.pf_axes.append(cax) |
|
308 | 308 | else: |
|
309 | 309 | if self.height is None: |
|
310 | 310 | self.height = 3 |
|
311 | 311 | for n in range(self.nplots): |
|
312 | 312 | fig = plt.figure(figsize=(self.width, self.height), |
|
313 | 313 | edgecolor='k', |
|
314 | 314 | facecolor='w') |
|
315 | 315 | ax = fig.add_subplot(1, 1, 1, polar=self.polar) |
|
316 | 316 | ax.tick_params(labelsize=8) |
|
317 | 317 | ax.firsttime = True |
|
318 | 318 | ax.index = 0 |
|
319 | 319 | ax.press = None |
|
320 | 320 | self.figures.append(fig) |
|
321 | 321 | self.axes.append(ax) |
|
322 | 322 | if self.showprofile: |
|
323 | 323 | cax = self.__add_axes(ax, size=size, pad=pad) |
|
324 | 324 | cax.tick_params(labelsize=8) |
|
325 | 325 | self.pf_axes.append(cax) |
|
326 | 326 | |
|
327 | 327 | for n in range(self.nrows): |
|
328 | 328 | if self.colormaps is not None: |
|
329 | 329 | cmap = plt.get_cmap(self.colormaps[n]) |
|
330 | 330 | else: |
|
331 | 331 | cmap = plt.get_cmap(self.colormap) |
|
332 | 332 | cmap.set_bad(self.bgcolor, 1.) |
|
333 | 333 | self.cmaps.append(cmap) |
|
334 | 334 | |
|
335 | 335 | def __add_axes(self, ax, size='30%', pad='8%'): |
|
336 | 336 | ''' |
|
337 | 337 | Add new axes to the given figure |
|
338 | 338 | ''' |
|
339 | 339 | divider = make_axes_locatable(ax) |
|
340 | 340 | nax = divider.new_horizontal(size=size, pad=pad) |
|
341 | 341 | ax.figure.add_axes(nax) |
|
342 | 342 | return nax |
|
343 | 343 | |
|
344 | 344 | def fill_gaps(self, x_buffer, y_buffer, z_buffer): |
|
345 | 345 | ''' |
|
346 | 346 | Create a masked array for missing data |
|
347 | 347 | ''' |
|
348 | 348 | if x_buffer.shape[0] < 2: |
|
349 | 349 | return x_buffer, y_buffer, z_buffer |
|
350 | 350 | |
|
351 | 351 | deltas = x_buffer[1:] - x_buffer[0:-1] |
|
352 | 352 | x_median = numpy.median(deltas) |
|
353 | 353 | |
|
354 | 354 | index = numpy.where(deltas > 5 * x_median) |
|
355 | 355 | |
|
356 | 356 | if len(index[0]) != 0: |
|
357 | 357 | z_buffer[::, index[0], ::] = self.__missing |
|
358 | 358 | z_buffer = numpy.ma.masked_inside(z_buffer, |
|
359 | 359 | 0.99 * self.__missing, |
|
360 | 360 | 1.01 * self.__missing) |
|
361 | 361 | |
|
362 | 362 | return x_buffer, y_buffer, z_buffer |
|
363 | 363 | |
|
364 | 364 | def decimate(self): |
|
365 | 365 | |
|
366 | 366 | # dx = int(len(self.x)/self.__MAXNUMX) + 1 |
|
367 | 367 | dy = int(len(self.y) / self.decimation) + 1 |
|
368 | 368 | |
|
369 | 369 | # x = self.x[::dx] |
|
370 | 370 | x = self.x |
|
371 | 371 | y = self.y[::dy] |
|
372 | 372 | z = self.z[::, ::, ::dy] |
|
373 | 373 | |
|
374 | 374 | return x, y, z |
|
375 | 375 | |
|
376 | 376 | def format(self): |
|
377 | 377 | ''' |
|
378 | 378 | Set min and max values, labels, ticks and titles |
|
379 | 379 | ''' |
|
380 | 380 | |
|
381 | 381 | for n, ax in enumerate(self.axes): |
|
382 | 382 | if ax.firsttime: |
|
383 | 383 | if self.xaxis != 'time': |
|
384 | 384 | xmin = self.xmin |
|
385 | 385 | xmax = self.xmax |
|
386 | 386 | else: |
|
387 | 387 | xmin = self.tmin |
|
388 | 388 | xmax = self.tmin + self.xrange*60*60 |
|
389 | 389 | ax.xaxis.set_major_formatter(FuncFormatter(self.__fmtTime)) |
|
390 | 390 | ax.xaxis.set_major_locator(LinearLocator(9)) |
|
391 | 391 | ymin = self.ymin if self.ymin is not None else numpy.nanmin(self.y[numpy.isfinite(self.y)]) |
|
392 | 392 | ymax = self.ymax if self.ymax is not None else numpy.nanmax(self.y[numpy.isfinite(self.y)]) |
|
393 | 393 | ax.set_facecolor(self.bgcolor) |
|
394 | 394 | if self.xscale: |
|
395 | 395 | ax.xaxis.set_major_formatter(FuncFormatter( |
|
396 | 396 | lambda x, pos: '{0:g}'.format(x*self.xscale))) |
|
397 | 397 | if self.yscale: |
|
398 | 398 | ax.yaxis.set_major_formatter(FuncFormatter( |
|
399 | 399 | lambda x, pos: '{0:g}'.format(x*self.yscale))) |
|
400 | 400 | if self.xlabel is not None: |
|
401 | 401 | ax.set_xlabel(self.xlabel) |
|
402 | 402 | if self.ylabel is not None: |
|
403 | 403 | ax.set_ylabel(self.ylabel) |
|
404 | 404 | if self.showprofile: |
|
405 | 405 | self.pf_axes[n].set_ylim(ymin, ymax) |
|
406 | 406 | self.pf_axes[n].set_xlim(self.zmin, self.zmax) |
|
407 | 407 | self.pf_axes[n].set_xlabel('dB') |
|
408 | 408 | self.pf_axes[n].grid(b=True, axis='x') |
|
409 | 409 | [tick.set_visible(False) |
|
410 | 410 | for tick in self.pf_axes[n].get_yticklabels()] |
|
411 | 411 | if self.colorbar: |
|
412 | 412 | ax.cbar = plt.colorbar( |
|
413 | 413 | ax.plt, ax=ax, fraction=0.05, pad=0.06, aspect=10) |
|
414 | 414 | ax.cbar.ax.tick_params(labelsize=8) |
|
415 | 415 | ax.cbar.ax.press = None |
|
416 | 416 | if self.cb_label: |
|
417 | 417 | ax.cbar.set_label(self.cb_label, size=8) |
|
418 | 418 | elif self.cb_labels: |
|
419 | 419 | ax.cbar.set_label(self.cb_labels[n], size=8) |
|
420 | 420 | else: |
|
421 | 421 | ax.cbar = None |
|
422 | 422 | ax.set_xlim(xmin, xmax) |
|
423 | 423 | ax.set_ylim(ymin, ymax) |
|
424 | 424 | ax.firsttime = False |
|
425 | 425 | if self.grid: |
|
426 | 426 | ax.grid(True) |
|
427 | 427 | if not self.polar: |
|
428 | 428 | ax.set_title('{} {} {}'.format( |
|
429 | 429 | self.titles[n], |
|
430 | 430 | self.getDateTime(self.data.max_time).strftime( |
|
431 | 431 | '%Y-%m-%d %H:%M:%S'), |
|
432 | 432 | self.time_label), |
|
433 | 433 | size=8) |
|
434 | 434 | else: |
|
435 | 435 | #ax.set_title('{}'.format(self.titles[n]), size=8) |
|
436 | 436 | ax.set_title('{} {} {}'.format( |
|
437 | 437 | self.titles[n], |
|
438 | 438 | self.getDateTime(self.data.max_time).strftime( |
|
439 | 439 | '%Y-%m-%d %H:%M:%S'), |
|
440 | 440 | self.time_label), |
|
441 | 441 | size=8) |
|
442 | 442 | ax.set_ylim(0, self.ymax) |
|
443 | 443 | ax.set_yticks(ax.get_yticks(), labels=ax.get_yticks(), color='white') |
|
444 | 444 | ax.yaxis.labelpad = 28 |
|
445 | 445 | |
|
446 | 446 | if self.firsttime: |
|
447 | 447 | for n, fig in enumerate(self.figures): |
|
448 | 448 | fig.subplots_adjust(**self.plots_adjust) |
|
449 | 449 | self.firsttime = False |
|
450 | 450 | |
|
451 | 451 | def clear_figures(self): |
|
452 | 452 | ''' |
|
453 | 453 | Reset axes for redraw plots |
|
454 | 454 | ''' |
|
455 | 455 | |
|
456 | 456 | for ax in self.axes+self.pf_axes+self.cb_axes: |
|
457 | 457 | ax.clear() |
|
458 | 458 | ax.firsttime = True |
|
459 | 459 | if hasattr(ax, 'cbar') and ax.cbar: |
|
460 | 460 | ax.cbar.remove() |
|
461 | 461 | |
|
462 | 462 | def __plot(self): |
|
463 | 463 | ''' |
|
464 | 464 | Main function to plot, format and save figures |
|
465 | 465 | ''' |
|
466 | 466 | |
|
467 | 467 | self.plot() |
|
468 | 468 | self.format() |
|
469 | 469 | |
|
470 | 470 | for n, fig in enumerate(self.figures): |
|
471 | 471 | if self.nrows == 0 or self.nplots == 0: |
|
472 | 472 | log.warning('No data', self.name) |
|
473 | 473 | fig.text(0.5, 0.5, 'No Data', fontsize='large', ha='center') |
|
474 | 474 | fig.canvas.manager.set_window_title(self.CODE) |
|
475 | 475 | continue |
|
476 | 476 | |
|
477 | 477 | fig.canvas.manager.set_window_title('{} - {}'.format(self.title, |
|
478 | 478 | self.getDateTime(self.data.max_time).strftime('%Y/%m/%d'))) |
|
479 | 479 | fig.canvas.draw() |
|
480 | 480 | if self.show: |
|
481 | 481 | fig.show() |
|
482 | 482 | figpause(0.01) |
|
483 | 483 | |
|
484 | 484 | if self.save: |
|
485 | 485 | if self.CODE=="PPI" or self.CODE=="RHI": |
|
486 | 486 | self.save_figure(n,stitle =self.titles) |
|
487 | 487 | else: |
|
488 | 488 | self.save_figure(n) |
|
489 | 489 | |
|
490 | 490 | if self.server: |
|
491 | 491 | self.send_to_server() |
|
492 | 492 | |
|
493 | 493 | def __update(self, dataOut, timestamp): |
|
494 | 494 | ''' |
|
495 | 495 | ''' |
|
496 | 496 | |
|
497 | 497 | metadata = { |
|
498 | 498 | 'yrange': dataOut.heightList, |
|
499 | 499 | 'interval': dataOut.timeInterval, |
|
500 | 500 | 'channels': dataOut.channelList |
|
501 | 501 | } |
|
502 | 502 | |
|
503 | 503 | data, meta = self.update(dataOut) |
|
504 | 504 | metadata.update(meta) |
|
505 | 505 | self.data.update(data, timestamp, metadata) |
|
506 | 506 | |
|
507 | 507 | def save_figure(self, n,stitle=None): |
|
508 | 508 | ''' |
|
509 | 509 | ''' |
|
510 | 510 | if stitle is not None: |
|
511 | 511 | s_string = re.sub(r"[^A-Z0-9.]","",str(stitle)) |
|
512 | 512 | new_string=s_string[:3]+"_"+s_string[4:6]+"_"+s_string[6:] |
|
513 | 513 | |
|
514 | 514 | if self.oneFigure: |
|
515 | 515 | if (self.data.max_time - self.save_time) <= self.save_period: |
|
516 | 516 | return |
|
517 | 517 | |
|
518 | 518 | self.save_time = self.data.max_time |
|
519 | 519 | |
|
520 | 520 | fig = self.figures[n] |
|
521 | 521 | |
|
522 | 522 | if self.throttle == 0: |
|
523 | 523 | if self.oneFigure: |
|
524 | 524 | if stitle is not None: |
|
525 | 525 | figname = os.path.join( |
|
526 | 526 | self.save, |
|
527 | 527 | self.save_code + '_' + new_string, |
|
528 | 528 | '{}_{}_{}.png'.format( |
|
529 | 529 | self.save_code, |
|
530 | 530 | new_string, |
|
531 | 531 | self.getDateTime(self.data.max_time).strftime( |
|
532 | 532 | '%Y%m%d_%H%M%S', |
|
533 | 533 | ), |
|
534 | 534 | ) |
|
535 | 535 | ) |
|
536 | 536 | else: |
|
537 | 537 | figname = os.path.join( |
|
538 | 538 | self.save, |
|
539 | 539 | self.save_code, |
|
540 | 540 | '{}_{}.png'.format( |
|
541 | 541 | self.save_code, |
|
542 | 542 | self.getDateTime(self.data.max_time).strftime( |
|
543 | 543 | '%Y%m%d_%H%M%S' |
|
544 | 544 | ), |
|
545 | 545 | ) |
|
546 | 546 | ) |
|
547 | 547 | else: |
|
548 | 548 | figname = os.path.join( |
|
549 | 549 | self.save, |
|
550 | 550 | self.save_code, |
|
551 | 551 | '{}_ch{}_{}.png'.format( |
|
552 | 552 | self.save_code,n, |
|
553 | 553 | self.getDateTime(self.data.max_time).strftime( |
|
554 | 554 | '%Y%m%d_%H%M%S' |
|
555 | 555 | ), |
|
556 | 556 | ) |
|
557 | 557 | ) |
|
558 | 558 | log.log('Saving figure: {}'.format(figname), self.name) |
|
559 | 559 | if not os.path.isdir(os.path.dirname(figname)): |
|
560 | 560 | os.makedirs(os.path.dirname(figname)) |
|
561 | 561 | fig.savefig(figname) |
|
562 | 562 | |
|
563 | 563 | figname = os.path.join( |
|
564 | 564 | self.save, |
|
565 | 565 | '{}_{}.png'.format( |
|
566 | 566 | self.save_code, |
|
567 | 567 | self.getDateTime(self.data.min_time).strftime( |
|
568 | 568 | '%Y%m%d' |
|
569 | 569 | ), |
|
570 | 570 | ) |
|
571 | 571 | ) |
|
572 | 572 | |
|
573 | 573 | log.log('Saving figure: {}'.format(figname), self.name) |
|
574 | 574 | if not os.path.isdir(os.path.dirname(figname)): |
|
575 | 575 | os.makedirs(os.path.dirname(figname)) |
|
576 | 576 | fig.savefig(figname) |
|
577 | 577 | |
|
578 | 578 | def send_to_server(self): |
|
579 | 579 | ''' |
|
580 | 580 | ''' |
|
581 | 581 | |
|
582 | 582 | if self.exp_code == None: |
|
583 | 583 | log.warning('Missing `exp_code` skipping sending to server...') |
|
584 | 584 | |
|
585 | 585 | last_time = self.data.max_time |
|
586 | 586 | interval = last_time - self.sender_time |
|
587 | 587 | if interval < self.sender_period: |
|
588 | 588 | return |
|
589 | 589 | |
|
590 | 590 | self.sender_time = last_time |
|
591 | 591 | |
|
592 | 592 | attrs = ['titles', 'zmin', 'zmax', 'tag', 'ymin', 'ymax'] |
|
593 | 593 | for attr in attrs: |
|
594 | 594 | value = getattr(self, attr) |
|
595 | 595 | if value: |
|
596 | 596 | if isinstance(value, (numpy.float32, numpy.float64)): |
|
597 | 597 | value = round(float(value), 2) |
|
598 | 598 | self.data.meta[attr] = value |
|
599 | if self.colormap == 'jet': | |
|
599 | if self.colormap == 'jet' or self.colormap == 'sophy_w': | |
|
600 | 600 | self.data.meta['colormap'] = 'Jet' |
|
601 |
elif ' |
|
|
601 | elif 'sophy_v' in self.colormap: | |
|
602 | 602 | self.data.meta['colormap'] = 'RdBu' |
|
603 | 603 | else: |
|
604 | 604 | self.data.meta['colormap'] = 'Viridis' |
|
605 | 605 | self.data.meta['interval'] = int(interval) |
|
606 | 606 | |
|
607 | 607 | self.sender_queue.append(last_time) |
|
608 | 608 | |
|
609 | 609 | while True: |
|
610 | 610 | try: |
|
611 | 611 | tm = self.sender_queue.popleft() |
|
612 | 612 | except IndexError: |
|
613 | 613 | break |
|
614 | msg = self.data.jsonify(tm, self.save_code, self.plot_type) | |
|
614 | msg = self.data.jsonify(tm, self.save_code, self.plot_type, key='var') | |
|
615 | 615 | self.socket.send_string(msg) |
|
616 | 616 | socks = dict(self.poll.poll(2000)) |
|
617 | 617 | if socks.get(self.socket) == zmq.POLLIN: |
|
618 | 618 | reply = self.socket.recv_string() |
|
619 | 619 | if reply == 'ok': |
|
620 | 620 | log.log("Response from server ok", self.name) |
|
621 | 621 | time.sleep(0.1) |
|
622 | 622 | continue |
|
623 | 623 | else: |
|
624 | 624 | log.warning( |
|
625 | 625 | "Malformed reply from server: {}".format(reply), self.name) |
|
626 | 626 | else: |
|
627 | 627 | log.warning( |
|
628 | 628 | "No response from server, retrying...", self.name) |
|
629 | 629 | self.sender_queue.appendleft(tm) |
|
630 | 630 | self.socket.setsockopt(zmq.LINGER, 0) |
|
631 | 631 | self.socket.close() |
|
632 | 632 | self.poll.unregister(self.socket) |
|
633 | 633 | self.socket = self.context.socket(zmq.REQ) |
|
634 | 634 | self.socket.connect(self.server) |
|
635 | 635 | self.poll.register(self.socket, zmq.POLLIN) |
|
636 | 636 | break |
|
637 | 637 | |
|
638 | 638 | def setup(self): |
|
639 | 639 | ''' |
|
640 | 640 | This method should be implemented in the child class, the following |
|
641 | 641 | attributes should be set: |
|
642 | 642 | |
|
643 | 643 | self.nrows: number of rows |
|
644 | 644 | self.ncols: number of cols |
|
645 | 645 | self.nplots: number of plots (channels or pairs) |
|
646 | 646 | self.ylabel: label for Y axes |
|
647 | 647 | self.titles: list of axes title |
|
648 | 648 | |
|
649 | 649 | ''' |
|
650 | 650 | raise NotImplementedError |
|
651 | 651 | |
|
652 | 652 | def plot(self): |
|
653 | 653 | ''' |
|
654 | 654 | Must be defined in the child class, the actual plotting method |
|
655 | 655 | ''' |
|
656 | 656 | raise NotImplementedError |
|
657 | 657 | |
|
658 | 658 | def update(self, dataOut): |
|
659 | 659 | ''' |
|
660 | 660 | Must be defined in the child class, update self.data with new data |
|
661 | 661 | ''' |
|
662 | 662 | |
|
663 | 663 | data = { |
|
664 | 664 | self.CODE: getattr(dataOut, 'data_{}'.format(self.CODE)) |
|
665 | 665 | } |
|
666 | 666 | meta = {} |
|
667 | 667 | |
|
668 | 668 | return data, meta |
|
669 | 669 | |
|
670 | 670 | def run(self, dataOut, **kwargs): |
|
671 | 671 | ''' |
|
672 | 672 | Main plotting routine |
|
673 | 673 | ''' |
|
674 | 674 | |
|
675 | 675 | if self.isConfig is False: |
|
676 | 676 | self.__setup(**kwargs) |
|
677 | 677 | |
|
678 | 678 | if self.localtime: |
|
679 | 679 | self.getDateTime = datetime.datetime.fromtimestamp |
|
680 | 680 | else: |
|
681 | 681 | self.getDateTime = datetime.datetime.utcfromtimestamp |
|
682 | 682 | |
|
683 | 683 | self.data.setup() |
|
684 | 684 | self.isConfig = True |
|
685 | 685 | if self.server: |
|
686 | 686 | self.context = zmq.Context() |
|
687 | 687 | self.socket = self.context.socket(zmq.REQ) |
|
688 | 688 | self.socket.connect(self.server) |
|
689 | 689 | self.poll = zmq.Poller() |
|
690 | 690 | self.poll.register(self.socket, zmq.POLLIN) |
|
691 | 691 | |
|
692 | 692 | tm = getattr(dataOut, self.attr_time) |
|
693 | 693 | |
|
694 | 694 | if self.data and 'time' in self.xaxis and (tm - self.tmin) >= self.xrange*60*60: |
|
695 | 695 | self.save_time = tm |
|
696 | 696 | self.__plot() |
|
697 | 697 | self.tmin += self.xrange*60*60 |
|
698 | 698 | self.data.setup() |
|
699 | 699 | self.clear_figures() |
|
700 | 700 | |
|
701 | 701 | self.__update(dataOut, tm) |
|
702 | 702 | |
|
703 | 703 | if self.isPlotConfig is False: |
|
704 | 704 | self.__setup_plot() |
|
705 | 705 | self.isPlotConfig = True |
|
706 | 706 | if self.xaxis == 'time': |
|
707 | 707 | dt = self.getDateTime(tm) |
|
708 | 708 | if self.xmin is None: |
|
709 | 709 | self.tmin = tm |
|
710 | 710 | self.xmin = dt.hour |
|
711 | 711 | minutes = (self.xmin-int(self.xmin)) * 60 |
|
712 | 712 | seconds = (minutes - int(minutes)) * 60 |
|
713 | 713 | self.tmin = (dt.replace(hour=int(self.xmin), minute=int(minutes), second=int(seconds)) - |
|
714 | 714 | datetime.datetime(1970, 1, 1)).total_seconds() |
|
715 | 715 | if self.localtime: |
|
716 | 716 | self.tmin += time.timezone |
|
717 | 717 | |
|
718 | 718 | if self.xmin is not None and self.xmax is not None: |
|
719 | 719 | self.xrange = self.xmax - self.xmin |
|
720 | 720 | |
|
721 | 721 | if self.throttle == 0: |
|
722 | 722 | self.__plot() |
|
723 | 723 | else: |
|
724 | 724 | self.__throttle_plot(self.__plot)#, coerce=coerce) |
|
725 | 725 | |
|
726 | 726 | def close(self): |
|
727 | 727 | |
|
728 | 728 | if self.data and not self.data.flagNoData: |
|
729 | 729 | self.save_time = 0 |
|
730 | 730 | self.__plot() |
|
731 | 731 | if self.data and not self.data.flagNoData and self.pause: |
|
732 | 732 | figpause(10) |
@@ -1,512 +1,519 | |||
|
1 | 1 | import os |
|
2 | 2 | import datetime |
|
3 | 3 | import numpy |
|
4 | 4 | from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter |
|
5 | 5 | |
|
6 | 6 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
7 | 7 | from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot |
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8 | 8 | from schainpy.utils import log |
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9 | # libreria wradlib | |
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10 |
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9 | ||
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10 | import wradlib.georef as georef | |
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11 | 11 | |
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12 | 12 | EARTH_RADIUS = 6.3710e3 |
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13 | 13 | |
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14 | 14 | |
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15 | 15 | def ll2xy(lat1, lon1, lat2, lon2): |
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16 | 16 | |
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17 | 17 | p = 0.017453292519943295 |
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18 | 18 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
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19 | 19 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
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20 | 20 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
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21 | 21 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
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22 | 22 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
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23 | 23 | theta = -theta + numpy.pi/2 |
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24 | 24 | return r*numpy.cos(theta), r*numpy.sin(theta) |
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25 | 25 | |
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26 | 26 | |
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27 | 27 | def km2deg(km): |
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28 | 28 | ''' |
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29 | 29 | Convert distance in km to degrees |
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30 | 30 | ''' |
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31 | 31 | |
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32 | 32 | return numpy.rad2deg(km/EARTH_RADIUS) |
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33 | 33 | |
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34 | 34 | |
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35 | 35 | |
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36 | 36 | class SpectralMomentsPlot(SpectraPlot): |
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37 | 37 | ''' |
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38 | 38 | Plot for Spectral Moments |
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39 | 39 | ''' |
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40 | 40 | CODE = 'spc_moments' |
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41 | 41 | # colormap = 'jet' |
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42 | 42 | # plot_type = 'pcolor' |
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43 | 43 | |
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44 | 44 | class DobleGaussianPlot(SpectraPlot): |
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45 | 45 | ''' |
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46 | 46 | Plot for Double Gaussian Plot |
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47 | 47 | ''' |
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48 | 48 | CODE = 'gaussian_fit' |
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49 | 49 | # colormap = 'jet' |
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50 | 50 | # plot_type = 'pcolor' |
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51 | 51 | |
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52 | 52 | class DoubleGaussianSpectraCutPlot(SpectraCutPlot): |
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53 | 53 | ''' |
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54 | 54 | Plot SpectraCut with Double Gaussian Fit |
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55 | 55 | ''' |
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56 | 56 | CODE = 'cut_gaussian_fit' |
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57 | 57 | |
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58 | 58 | class SnrPlot(RTIPlot): |
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59 | 59 | ''' |
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60 | 60 | Plot for SNR Data |
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61 | 61 | ''' |
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62 | 62 | |
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63 | 63 | CODE = 'snr' |
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64 | 64 | colormap = 'jet' |
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65 | 65 | |
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66 | 66 | def update(self, dataOut): |
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67 | 67 | |
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68 | 68 | data = { |
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69 | 69 | 'snr': 10*numpy.log10(dataOut.data_snr) |
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70 | 70 | } |
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71 | 71 | |
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72 | 72 | return data, {} |
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73 | 73 | |
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74 | 74 | class DopplerPlot(RTIPlot): |
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75 | 75 | ''' |
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76 | 76 | Plot for DOPPLER Data (1st moment) |
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77 | 77 | ''' |
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78 | 78 | |
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79 | 79 | CODE = 'dop' |
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80 | 80 | colormap = 'jet' |
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81 | 81 | |
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82 | 82 | def update(self, dataOut): |
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83 | 83 | |
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84 | 84 | data = { |
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85 | 85 | 'dop': 10*numpy.log10(dataOut.data_dop) |
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86 | 86 | } |
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87 | 87 | |
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88 | 88 | return data, {} |
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89 | 89 | |
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90 | 90 | class PowerPlot(RTIPlot): |
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91 | 91 | ''' |
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92 | 92 | Plot for Power Data (0 moment) |
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93 | 93 | ''' |
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94 | 94 | |
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95 | 95 | CODE = 'pow' |
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96 | 96 | colormap = 'jet' |
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97 | 97 | |
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98 | 98 | def update(self, dataOut): |
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99 | 99 | data = { |
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100 | 100 | 'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor) |
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101 | 101 | } |
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102 | 102 | return data, {} |
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103 | 103 | |
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104 | 104 | class SpectralWidthPlot(RTIPlot): |
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105 | 105 | ''' |
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106 | 106 | Plot for Spectral Width Data (2nd moment) |
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107 | 107 | ''' |
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108 | 108 | |
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109 | 109 | CODE = 'width' |
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110 | 110 | colormap = 'jet' |
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111 | 111 | |
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112 | 112 | def update(self, dataOut): |
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113 | 113 | |
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114 | 114 | data = { |
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115 | 115 | 'width': dataOut.data_width |
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116 | 116 | } |
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117 | 117 | |
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118 | 118 | return data, {} |
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119 | 119 | |
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120 | 120 | class SkyMapPlot(Plot): |
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121 | 121 | ''' |
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122 | 122 | Plot for meteors detection data |
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123 | 123 | ''' |
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124 | 124 | |
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125 | 125 | CODE = 'param' |
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126 | 126 | |
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127 | 127 | def setup(self): |
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128 | 128 | |
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129 | 129 | self.ncols = 1 |
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130 | 130 | self.nrows = 1 |
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131 | 131 | self.width = 7.2 |
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132 | 132 | self.height = 7.2 |
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133 | 133 | self.nplots = 1 |
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134 | 134 | self.xlabel = 'Zonal Zenith Angle (deg)' |
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135 | 135 | self.ylabel = 'Meridional Zenith Angle (deg)' |
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136 | 136 | self.polar = True |
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137 | 137 | self.ymin = -180 |
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138 | 138 | self.ymax = 180 |
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139 | 139 | self.colorbar = False |
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140 | 140 | |
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141 | 141 | def plot(self): |
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142 | 142 | |
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143 | 143 | arrayParameters = numpy.concatenate(self.data['param']) |
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144 | 144 | error = arrayParameters[:, -1] |
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145 | 145 | indValid = numpy.where(error == 0)[0] |
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146 | 146 | finalMeteor = arrayParameters[indValid, :] |
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147 | 147 | finalAzimuth = finalMeteor[:, 3] |
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148 | 148 | finalZenith = finalMeteor[:, 4] |
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149 | 149 | |
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150 | 150 | x = finalAzimuth * numpy.pi / 180 |
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151 | 151 | y = finalZenith |
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152 | 152 | |
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153 | 153 | ax = self.axes[0] |
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154 | 154 | |
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155 | 155 | if ax.firsttime: |
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156 | 156 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
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157 | 157 | else: |
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158 | 158 | ax.plot.set_data(x, y) |
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159 | 159 | |
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160 | 160 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
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161 | 161 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
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162 | 162 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
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163 | 163 | dt2, |
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164 | 164 | len(x)) |
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165 | 165 | self.titles[0] = title |
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166 | 166 | |
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167 | 167 | |
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168 | 168 | class GenericRTIPlot(Plot): |
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169 | 169 | ''' |
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170 | 170 | Plot for data_xxxx object |
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171 | 171 | ''' |
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172 | 172 | |
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173 | 173 | CODE = 'param' |
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174 | 174 | colormap = 'viridis' |
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175 | 175 | plot_type = 'pcolorbuffer' |
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176 | 176 | |
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177 | 177 | def setup(self): |
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178 | 178 | self.xaxis = 'time' |
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179 | 179 | self.ncols = 1 |
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180 | 180 | self.nrows = self.data.shape('param')[0] |
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181 | 181 | self.nplots = self.nrows |
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182 | 182 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95}) |
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183 | 183 | |
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184 | 184 | if not self.xlabel: |
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185 | 185 | self.xlabel = 'Time' |
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186 | 186 | |
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187 | 187 | self.ylabel = 'Range [km]' |
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188 | 188 | if not self.titles: |
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189 | 189 | self.titles = ['Param {}'.format(x) for x in range(self.nrows)] |
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190 | 190 | |
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191 | 191 | def update(self, dataOut): |
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192 | 192 | |
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193 | 193 | data = { |
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194 | 194 | 'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0) |
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195 | 195 | } |
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196 | 196 | |
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197 | 197 | meta = {} |
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198 | 198 | |
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199 | 199 | return data, meta |
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200 | 200 | |
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201 | 201 | def plot(self): |
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202 | 202 | # self.data.normalize_heights() |
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203 | 203 | self.x = self.data.times |
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204 | 204 | self.y = self.data.yrange |
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205 | 205 | self.z = self.data['param'] |
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206 | 206 | self.z = 10*numpy.log10(self.z) |
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207 | 207 | self.z = numpy.ma.masked_invalid(self.z) |
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208 | 208 | |
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209 | 209 | if self.decimation is None: |
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210 | 210 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
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211 | 211 | else: |
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212 | 212 | x, y, z = self.fill_gaps(*self.decimate()) |
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213 | 213 | |
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214 | 214 | for n, ax in enumerate(self.axes): |
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215 | 215 | |
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216 | 216 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
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217 | 217 | self.z[n]) |
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218 | 218 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
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219 | 219 | self.z[n]) |
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220 | 220 | |
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221 | 221 | if ax.firsttime: |
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222 | 222 | if self.zlimits is not None: |
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223 | 223 | self.zmin, self.zmax = self.zlimits[n] |
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224 | 224 | |
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225 | 225 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
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226 | 226 | vmin=self.zmin, |
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227 | 227 | vmax=self.zmax, |
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228 | 228 | cmap=self.cmaps[n] |
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229 | 229 | ) |
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230 | 230 | else: |
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231 | 231 | if self.zlimits is not None: |
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232 | 232 | self.zmin, self.zmax = self.zlimits[n] |
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233 | 233 | ax.collections.remove(ax.collections[0]) |
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234 | 234 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
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235 | 235 | vmin=self.zmin, |
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236 | 236 | vmax=self.zmax, |
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237 | 237 | cmap=self.cmaps[n] |
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238 | 238 | ) |
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239 | 239 | |
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240 | 240 | |
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241 | 241 | class PolarMapPlot(Plot): |
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242 | 242 | ''' |
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243 | 243 | Plot for weather radar |
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244 | 244 | ''' |
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245 | 245 | |
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246 | 246 | CODE = 'param' |
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247 | 247 | colormap = 'seismic' |
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248 | 248 | |
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249 | 249 | def setup(self): |
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250 | 250 | self.ncols = 1 |
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251 | 251 | self.nrows = 1 |
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252 | 252 | self.width = 9 |
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253 | 253 | self.height = 8 |
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254 | 254 | self.mode = self.data.meta['mode'] |
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255 | 255 | if self.channels is not None: |
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256 | 256 | self.nplots = len(self.channels) |
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257 | 257 | self.nrows = len(self.channels) |
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258 | 258 | else: |
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259 | 259 | self.nplots = self.data.shape(self.CODE)[0] |
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260 | 260 | self.nrows = self.nplots |
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261 | 261 | self.channels = list(range(self.nplots)) |
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262 | 262 | if self.mode == 'E': |
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263 | 263 | self.xlabel = 'Longitude' |
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264 | 264 | self.ylabel = 'Latitude' |
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265 | 265 | else: |
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266 | 266 | self.xlabel = 'Range (km)' |
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267 | 267 | self.ylabel = 'Height (km)' |
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268 | 268 | self.bgcolor = 'white' |
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269 | 269 | self.cb_labels = self.data.meta['units'] |
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270 | 270 | self.lat = self.data.meta['latitude'] |
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271 | 271 | self.lon = self.data.meta['longitude'] |
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272 | 272 | self.xmin, self.xmax = float( |
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273 | 273 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
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274 | 274 | self.ymin, self.ymax = float( |
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275 | 275 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
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276 | 276 | # self.polar = True |
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277 | 277 | |
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278 | 278 | def plot(self): |
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279 | 279 | |
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280 | 280 | for n, ax in enumerate(self.axes): |
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281 | 281 | data = self.data['param'][self.channels[n]] |
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282 | 282 | |
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283 | 283 | zeniths = numpy.linspace( |
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284 | 284 | 0, self.data.meta['max_range'], data.shape[1]) |
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285 | 285 | if self.mode == 'E': |
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286 | 286 | azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2 |
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287 | 287 | r, theta = numpy.meshgrid(zeniths, azimuths) |
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288 | 288 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
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289 | 289 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
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290 | 290 | x = km2deg(x) + self.lon |
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291 | 291 | y = km2deg(y) + self.lat |
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292 | 292 | else: |
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293 | 293 | azimuths = numpy.radians(self.data.yrange) |
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294 | 294 | r, theta = numpy.meshgrid(zeniths, azimuths) |
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295 | 295 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
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296 | 296 | self.y = zeniths |
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297 | 297 | |
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298 | 298 | if ax.firsttime: |
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299 | 299 | if self.zlimits is not None: |
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300 | 300 | self.zmin, self.zmax = self.zlimits[n] |
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301 | 301 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
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302 | 302 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
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303 | 303 | vmin=self.zmin, |
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304 | 304 | vmax=self.zmax, |
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305 | 305 | cmap=self.cmaps[n]) |
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306 | 306 | else: |
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307 | 307 | if self.zlimits is not None: |
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308 | 308 | self.zmin, self.zmax = self.zlimits[n] |
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309 | 309 | ax.collections.remove(ax.collections[0]) |
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310 | 310 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
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311 | 311 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
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312 | 312 | vmin=self.zmin, |
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313 | 313 | vmax=self.zmax, |
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314 | 314 | cmap=self.cmaps[n]) |
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315 | 315 | |
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316 | 316 | if self.mode == 'A': |
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317 | 317 | continue |
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318 | 318 | |
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319 | 319 | # plot district names |
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320 | 320 | f = open('/data/workspace/schain_scripts/distrito.csv') |
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321 | 321 | for line in f: |
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322 | 322 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
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323 | 323 | lat = float(lat) |
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324 | 324 | lon = float(lon) |
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325 | 325 | # ax.plot(lon, lat, '.b', ms=2) |
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326 | 326 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
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327 | 327 | va='bottom', size='8', color='black') |
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328 | 328 | |
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329 | 329 | # plot limites |
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330 | 330 | limites = [] |
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331 | 331 | tmp = [] |
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332 | 332 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
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333 | 333 | if '#' in line: |
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334 | 334 | if tmp: |
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335 | 335 | limites.append(tmp) |
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336 | 336 | tmp = [] |
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337 | 337 | continue |
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338 | 338 | values = line.strip().split(',') |
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339 | 339 | tmp.append((float(values[0]), float(values[1]))) |
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340 | 340 | for points in limites: |
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341 | 341 | ax.add_patch( |
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342 | 342 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
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343 | 343 | |
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344 | 344 | # plot Cuencas |
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345 | 345 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
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346 | 346 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
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347 | 347 | values = [line.strip().split(',') for line in f] |
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348 | 348 | points = [(float(s[0]), float(s[1])) for s in values] |
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349 | 349 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
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350 | 350 | |
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351 | 351 | # plot grid |
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352 | 352 | for r in (15, 30, 45, 60): |
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353 | 353 | ax.add_artist(plt.Circle((self.lon, self.lat), |
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354 | 354 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
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355 | 355 | ax.text( |
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356 | 356 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
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357 | 357 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
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358 | 358 | '{}km'.format(r), |
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359 | 359 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
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360 | 360 | |
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361 | 361 | if self.mode == 'E': |
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362 | 362 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
363 | 363 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
364 | 364 | else: |
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365 | 365 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
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366 | 366 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
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367 | 367 | |
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368 | 368 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
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369 | 369 | self.titles = ['{} {}'.format( |
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370 | 370 | self.data.parameters[x], title) for x in self.channels] |
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371 | 371 | |
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372 | 372 | class WeatherParamsPlot(Plot): |
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373 | 373 | #CODE = 'RHI' |
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374 | 374 | #plot_name = 'RHI' |
|
375 |
|
|
|
375 | plot_type = 'scattermap' | |
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376 | 376 | buffering = False |
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377 | 377 | |
|
378 | 378 | def setup(self): |
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379 | 379 | |
|
380 | 380 | self.ncols = 1 |
|
381 | 381 | self.nrows = 1 |
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382 | 382 | self.nplots= 1 |
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383 | 383 | self.ylabel= 'Range [km]' |
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384 | 384 | self.xlabel= 'Range [km]' |
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385 | 385 | self.polar = True |
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386 | 386 | self.grid = True |
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387 | 387 | if self.channels is not None: |
|
388 | 388 | self.nplots = len(self.channels) |
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389 | 389 | self.nrows = len(self.channels) |
|
390 | 390 | else: |
|
391 | 391 | self.nplots = self.data.shape(self.CODE)[0] |
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392 | 392 | self.nrows = self.nplots |
|
393 | 393 | self.channels = list(range(self.nplots)) |
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394 | 394 | |
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395 | 395 | self.colorbar=True |
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396 | 396 | self.width =8 |
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397 | 397 | self.height =8 |
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398 | 398 | self.ini =0 |
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399 | 399 | self.len_azi =0 |
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400 | 400 | self.buffer_ini = None |
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401 | 401 | self.buffer_ele = None |
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402 | 402 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
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403 | 403 | self.flag =0 |
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404 | 404 | self.indicador= 0 |
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405 | 405 | self.last_data_ele = None |
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406 | 406 | self.val_mean = None |
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407 | 407 | |
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408 | 408 | def update(self, dataOut): |
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409 | 409 | |
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410 | 410 | data = {} |
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411 | 411 | meta = {} |
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412 | 412 | if hasattr(dataOut, 'dataPP_POWER'): |
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413 | 413 | factor = 1 |
|
414 | 414 | if hasattr(dataOut, 'nFFTPoints'): |
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415 | 415 | factor = dataOut.normFactor |
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416 | 416 | |
|
417 | 417 | mask = dataOut.data_snr<self.snr_threshold |
|
418 | 418 | |
|
419 | 419 | if 'pow' in self.attr_data[0].lower(): |
|
420 | 420 | # data['data'] = 10*numpy.log10(getattr(dataOut, self.attr_data[0])/(factor)) |
|
421 |
|
|
|
421 | tmp = numpy.ma.masked_array(10*numpy.log10(getattr(dataOut, self.attr_data[0])/(factor)), mask=mask) | |
|
422 | 422 | else: |
|
423 |
|
|
|
424 |
# |
|
|
423 | tmp = numpy.ma.masked_array(getattr(dataOut, self.attr_data[0]), mask=mask) | |
|
424 | # tmp = getattr(dataOut, self.attr_data[0]) | |
|
425 | ||
|
426 | r = dataOut.heightList | |
|
427 | delta_height = r[1]-r[0] | |
|
428 | valid = numpy.where(r>=0)[0] | |
|
429 | data['r'] = numpy.arange(len(valid))*delta_height | |
|
430 | ||
|
431 | try: | |
|
432 | data['data'] = tmp[self.channels[0]][:,valid] | |
|
433 | except: | |
|
434 | data['data'] = tmp[0][:,valid] | |
|
425 | 435 | |
|
426 | 436 | if dataOut.mode_op == 'PPI': |
|
427 | 437 | self.CODE = 'PPI' |
|
428 | 438 | self.title = self.CODE |
|
429 | 439 | elif dataOut.mode_op == 'RHI': |
|
430 | 440 | self.CODE = 'RHI' |
|
431 | 441 | self.title = self.CODE |
|
432 | 442 | |
|
433 |
data['azi'] |
|
|
434 |
data['ele'] |
|
|
443 | data['azi'] = dataOut.data_azi | |
|
444 | data['ele'] = dataOut.data_ele | |
|
435 | 445 | data['mode_op'] = dataOut.mode_op |
|
436 | ||
|
446 | var = data['data'].flatten() | |
|
447 | r = numpy.tile(data['r'], data['data'].shape[0]).reshape(data['data'].shape)*1000 | |
|
448 | lla = georef.spherical_to_proj(r, data['azi'], data['ele'], (-75.295893, -12.040436, 3379.2147)) | |
|
449 | meta['lat'] = lla[:,:,1].flatten()[var.mask==False] | |
|
450 | meta['lon'] = lla[:,:,0].flatten()[var.mask==False] | |
|
451 | data['var'] = numpy.array([var[var.mask==False]]) | |
|
452 | ||
|
437 | 453 | return data, meta |
|
438 | 454 | |
|
439 | 455 | def plot(self): |
|
440 |
data |
|
|
441 | r = self.data.yrange | |
|
442 | delta_height = r[1]-r[0] | |
|
443 | r_mask = numpy.where(r>=0)[0] | |
|
444 | r = numpy.arange(len(r_mask))*delta_height | |
|
445 | self.y = 2*r | |
|
446 | ||
|
447 | try: | |
|
448 | z = data['data'][self.channels[0]][:,r_mask] | |
|
449 | except: | |
|
450 | z = data['data'][0][:,r_mask] | |
|
451 | ||
|
456 | data = self.data[-1] | |
|
457 | z = data['data'] | |
|
458 | r = data['r'] | |
|
452 | 459 | self.titles = [] |
|
453 | 460 | |
|
454 | 461 | self.ymax = self.ymax if self.ymax else numpy.nanmax(r) |
|
455 | 462 | self.ymin = self.ymin if self.ymin else numpy.nanmin(r) |
|
456 | 463 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
457 | 464 | self.zmin = self.zmin if self.zmin is not None else numpy.nanmin(z) |
|
458 | 465 | |
|
459 | 466 | if data['mode_op'] == 'RHI': |
|
460 | 467 | try: |
|
461 | 468 | if self.data['mode_op'][-2] == 'PPI': |
|
462 | 469 | self.ang_min = None |
|
463 | 470 | self.ang_max = None |
|
464 | 471 | except: |
|
465 | 472 | pass |
|
466 | 473 | self.ang_min = self.ang_min if self.ang_min else 0 |
|
467 | 474 | self.ang_max = self.ang_max if self.ang_max else 90 |
|
468 | 475 | r, theta = numpy.meshgrid(r, numpy.radians(data['ele']) ) |
|
469 | 476 | elif data['mode_op'] == 'PPI': |
|
470 | 477 | try: |
|
471 | 478 | if self.data['mode_op'][-2] == 'RHI': |
|
472 | 479 | self.ang_min = None |
|
473 | 480 | self.ang_max = None |
|
474 | 481 | except: |
|
475 | 482 | pass |
|
476 | 483 | self.ang_min = self.ang_min if self.ang_min else 0 |
|
477 | 484 | self.ang_max = self.ang_max if self.ang_max else 360 |
|
478 | 485 | r, theta = numpy.meshgrid(r, numpy.radians(data['azi']) ) |
|
479 | 486 | |
|
480 | 487 | self.clear_figures() |
|
481 | 488 | |
|
482 | 489 | for i,ax in enumerate(self.axes): |
|
483 | 490 | |
|
484 | 491 | if ax.firsttime: |
|
485 | 492 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
486 | 493 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
487 | 494 | if data['mode_op'] == 'PPI': |
|
488 | 495 | ax.set_theta_direction(-1) |
|
489 | 496 | ax.set_theta_offset(numpy.pi/2) |
|
490 | 497 | |
|
491 | 498 | else: |
|
492 | 499 | ax.set_xlim(numpy.radians(self.ang_min),numpy.radians(self.ang_max)) |
|
493 | 500 | ax.plt = ax.pcolormesh(theta, r, z, cmap=self.colormap, vmin=self.zmin, vmax=self.zmax) |
|
494 | 501 | if data['mode_op'] == 'PPI': |
|
495 | 502 | ax.set_theta_direction(-1) |
|
496 | 503 | ax.set_theta_offset(numpy.pi/2) |
|
497 | 504 | |
|
498 | 505 | ax.grid(True) |
|
499 | 506 | if data['mode_op'] == 'RHI': |
|
500 | 507 | len_aux = int(data['azi'].shape[0]/4) |
|
501 | 508 | mean = numpy.mean(data['azi'][len_aux:-len_aux]) |
|
502 | 509 | if len(self.channels) !=1: |
|
503 | 510 | self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)] |
|
504 | 511 | else: |
|
505 | 512 | self.titles = ['RHI {} at AZ: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] |
|
506 | 513 | elif data['mode_op'] == 'PPI': |
|
507 | 514 | len_aux = int(data['ele'].shape[0]/4) |
|
508 | 515 | mean = numpy.mean(data['ele'][len_aux:-len_aux]) |
|
509 | 516 | if len(self.channels) !=1: |
|
510 | 517 | self.titles = ['PPI {} at EL: {} CH {}'.format(self.self.labels[x], str(round(mean,1)), x) for x in range(self.nrows)] |
|
511 | 518 | else: |
|
512 | 519 | self.titles = ['PPI {} at EL: {} CH {}'.format(self.labels[0], str(round(mean,1)), self.channels[0])] |
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