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1 | 1 | ''' |
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2 | 2 | |
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3 | 3 | $Author: murco $ |
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4 | 4 | $Id: JROData.py 173 2012-11-20 15:06:21Z murco $ |
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5 | 5 | ''' |
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6 | 6 | |
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7 | 7 | import copy |
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8 | 8 | import numpy |
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9 | 9 | import datetime |
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10 | 10 | import json |
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11 | 11 | |
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12 | 12 | from schainpy.utils import log |
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13 | 13 | from .jroheaderIO import SystemHeader, RadarControllerHeader |
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14 | 14 | |
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15 | 15 | |
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16 | 16 | def getNumpyDtype(dataTypeCode): |
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17 | 17 | |
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18 | 18 | if dataTypeCode == 0: |
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19 | 19 | numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')]) |
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20 | 20 | elif dataTypeCode == 1: |
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21 | 21 | numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')]) |
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22 | 22 | elif dataTypeCode == 2: |
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23 | 23 | numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')]) |
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24 | 24 | elif dataTypeCode == 3: |
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25 | 25 | numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')]) |
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26 | 26 | elif dataTypeCode == 4: |
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27 | 27 | numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
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28 | 28 | elif dataTypeCode == 5: |
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29 | 29 | numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')]) |
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30 | 30 | else: |
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31 | 31 | raise ValueError('dataTypeCode was not defined') |
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32 | 32 | |
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33 | 33 | return numpyDtype |
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34 | 34 | |
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35 | 35 | |
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36 | 36 | def getDataTypeCode(numpyDtype): |
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37 | 37 | |
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38 | 38 | if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]): |
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39 | 39 | datatype = 0 |
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40 | 40 | elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]): |
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41 | 41 | datatype = 1 |
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42 | 42 | elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]): |
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43 | 43 | datatype = 2 |
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44 | 44 | elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]): |
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45 | 45 | datatype = 3 |
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46 | 46 | elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]): |
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47 | 47 | datatype = 4 |
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48 | 48 | elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]): |
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49 | 49 | datatype = 5 |
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50 | 50 | else: |
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51 | 51 | datatype = None |
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52 | 52 | |
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53 | 53 | return datatype |
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54 | 54 | |
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55 | 55 | |
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56 | 56 | def hildebrand_sekhon(data, navg): |
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57 | 57 | """ |
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58 | 58 | This method is for the objective determination of the noise level in Doppler spectra. This |
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59 | 59 | implementation technique is based on the fact that the standard deviation of the spectral |
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60 | 60 | densities is equal to the mean spectral density for white Gaussian noise |
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61 | 61 | |
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62 | 62 | Inputs: |
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63 | 63 | Data : heights |
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64 | 64 | navg : numbers of averages |
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65 | 65 | |
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66 | 66 | Return: |
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67 | 67 | mean : noise's level |
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68 | 68 | """ |
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69 | 69 | |
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70 | 70 | sortdata = numpy.sort(data, axis=None) |
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71 | 71 | lenOfData = len(sortdata) |
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72 | 72 | nums_min = lenOfData*0.2 |
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73 | 73 | |
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74 | 74 | if nums_min <= 5: |
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75 | 75 | |
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76 | 76 | nums_min = 5 |
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77 | 77 | |
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78 | 78 | sump = 0. |
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79 | 79 | sumq = 0. |
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80 | 80 | |
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81 | 81 | j = 0 |
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82 | 82 | cont = 1 |
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83 | 83 | |
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84 | 84 | while((cont == 1)and(j < lenOfData)): |
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85 | 85 | |
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86 | 86 | sump += sortdata[j] |
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87 | 87 | sumq += sortdata[j]**2 |
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88 | 88 | |
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89 | 89 | if j > nums_min: |
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90 | 90 | rtest = float(j)/(j-1) + 1.0/navg |
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91 | 91 | if ((sumq*j) > (rtest*sump**2)): |
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92 | 92 | j = j - 1 |
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93 | 93 | sump = sump - sortdata[j] |
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94 | 94 | sumq = sumq - sortdata[j]**2 |
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95 | 95 | cont = 0 |
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96 | 96 | |
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97 | 97 | j += 1 |
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98 | 98 | |
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99 | 99 | lnoise = sump / j |
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100 | 100 | |
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101 | 101 | return lnoise |
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102 | 102 | |
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103 | 103 | |
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104 | 104 | class Beam: |
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105 | 105 | |
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106 | 106 | def __init__(self): |
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107 | 107 | self.codeList = [] |
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108 | 108 | self.azimuthList = [] |
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109 | 109 | self.zenithList = [] |
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110 | 110 | |
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111 | 111 | |
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112 | 112 | class GenericData(object): |
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113 | 113 | |
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114 | 114 | flagNoData = True |
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115 | 115 | |
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116 | 116 | def copy(self, inputObj=None): |
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117 | 117 | |
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118 | 118 | if inputObj == None: |
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119 | 119 | return copy.deepcopy(self) |
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120 | 120 | |
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121 | 121 | for key in list(inputObj.__dict__.keys()): |
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122 | 122 | |
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123 | 123 | attribute = inputObj.__dict__[key] |
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124 | 124 | |
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125 | 125 | # If this attribute is a tuple or list |
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126 | 126 | if type(inputObj.__dict__[key]) in (tuple, list): |
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127 | 127 | self.__dict__[key] = attribute[:] |
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128 | 128 | continue |
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129 | 129 | |
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130 | 130 | # If this attribute is another object or instance |
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131 | 131 | if hasattr(attribute, '__dict__'): |
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132 | 132 | self.__dict__[key] = attribute.copy() |
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133 | 133 | continue |
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134 | 134 | |
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135 | 135 | self.__dict__[key] = inputObj.__dict__[key] |
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136 | 136 | |
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137 | 137 | def deepcopy(self): |
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138 | 138 | |
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139 | 139 | return copy.deepcopy(self) |
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140 | 140 | |
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141 | 141 | def isEmpty(self): |
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142 | 142 | |
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143 | 143 | return self.flagNoData |
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144 | 144 | |
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145 | 145 | |
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146 | 146 | class JROData(GenericData): |
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147 | 147 | |
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148 | 148 | # m_BasicHeader = BasicHeader() |
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149 | 149 | # m_ProcessingHeader = ProcessingHeader() |
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150 | 150 | |
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151 | 151 | systemHeaderObj = SystemHeader() |
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152 | 152 | radarControllerHeaderObj = RadarControllerHeader() |
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153 | 153 | # data = None |
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154 | 154 | type = None |
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155 | 155 | datatype = None # dtype but in string |
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156 | 156 | # dtype = None |
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157 | 157 | # nChannels = None |
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158 | 158 | # nHeights = None |
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159 | 159 | nProfiles = None |
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160 | 160 | heightList = None |
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161 | 161 | channelList = None |
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162 | 162 | flagDiscontinuousBlock = False |
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163 | 163 | useLocalTime = False |
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164 | 164 | utctime = None |
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165 | 165 | timeZone = None |
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166 | 166 | dstFlag = None |
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167 | 167 | errorCount = None |
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168 | 168 | blocksize = None |
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169 | 169 | # nCode = None |
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170 | 170 | # nBaud = None |
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171 | 171 | # code = None |
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172 | 172 | flagDecodeData = False # asumo q la data no esta decodificada |
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173 | 173 | flagDeflipData = False # asumo q la data no esta sin flip |
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174 | 174 | flagShiftFFT = False |
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175 | 175 | # ippSeconds = None |
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176 | 176 | # timeInterval = None |
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177 | 177 | nCohInt = None |
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178 | 178 | # noise = None |
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179 | 179 | windowOfFilter = 1 |
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180 | 180 | # Speed of ligth |
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181 | 181 | C = 3e8 |
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182 | 182 | frequency = 49.92e6 |
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183 | 183 | realtime = False |
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184 | 184 | beacon_heiIndexList = None |
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185 | 185 | last_block = None |
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186 | 186 | blocknow = None |
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187 | 187 | azimuth = None |
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188 | 188 | zenith = None |
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189 | 189 | beam = Beam() |
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190 | 190 | profileIndex = None |
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191 | 191 | error = None |
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192 | 192 | data = None |
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193 | 193 | nmodes = None |
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194 | 194 | |
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195 | 195 | def __str__(self): |
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196 | 196 | |
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197 | 197 | return '{} - {}'.format(self.type, self.getDatatime()) |
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198 | 198 | |
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199 | 199 | def getNoise(self): |
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200 | 200 | |
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201 | 201 | raise NotImplementedError |
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202 | 202 | |
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203 | 203 | def getNChannels(self): |
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204 | 204 | |
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205 | 205 | return len(self.channelList) |
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206 | 206 | |
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207 | 207 | def getChannelIndexList(self): |
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208 | 208 | |
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209 | 209 | return list(range(self.nChannels)) |
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210 | 210 | |
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211 | 211 | def getNHeights(self): |
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212 | 212 | |
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213 | 213 | return len(self.heightList) |
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214 | 214 | |
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215 | 215 | def getHeiRange(self, extrapoints=0): |
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216 | 216 | |
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217 | 217 | heis = self.heightList |
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218 | 218 | # deltah = self.heightList[1] - self.heightList[0] |
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219 | 219 | # |
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220 | 220 | # heis.append(self.heightList[-1]) |
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221 | 221 | |
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222 | 222 | return heis |
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223 | 223 | |
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224 | 224 | def getDeltaH(self): |
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225 | 225 | |
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226 | 226 | delta = self.heightList[1] - self.heightList[0] |
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227 | 227 | |
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228 | 228 | return delta |
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229 | 229 | |
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230 | 230 | def getltctime(self): |
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231 | 231 | |
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232 | 232 | if self.useLocalTime: |
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233 | 233 | return self.utctime - self.timeZone * 60 |
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234 | 234 | |
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235 | 235 | return self.utctime |
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236 | 236 | |
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237 | 237 | def getDatatime(self): |
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238 | 238 | |
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239 | 239 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
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240 | 240 | return datatimeValue |
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241 | 241 | |
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242 | 242 | def getTimeRange(self): |
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243 | 243 | |
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244 | 244 | datatime = [] |
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245 | 245 | |
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246 | 246 | datatime.append(self.ltctime) |
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247 | 247 | datatime.append(self.ltctime + self.timeInterval + 1) |
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248 | 248 | |
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249 | 249 | datatime = numpy.array(datatime) |
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250 | 250 | |
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251 | 251 | return datatime |
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252 | 252 | |
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253 | 253 | def getFmaxTimeResponse(self): |
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254 | 254 | |
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255 | 255 | period = (10**-6) * self.getDeltaH() / (0.15) |
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256 | 256 | |
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257 | 257 | PRF = 1. / (period * self.nCohInt) |
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258 | 258 | |
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259 | 259 | fmax = PRF |
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260 | 260 | |
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261 | 261 | return fmax |
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262 | 262 | |
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263 | 263 | def getFmax(self): |
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264 | 264 | PRF = 1. / (self.ippSeconds * self.nCohInt) |
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265 | 265 | |
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266 | 266 | fmax = PRF |
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267 | 267 | return fmax |
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268 | 268 | |
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269 | 269 | def getVmax(self): |
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270 | 270 | |
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271 | 271 | _lambda = self.C / self.frequency |
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272 | 272 | |
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273 | 273 | vmax = self.getFmax() * _lambda / 2 |
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274 | 274 | |
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275 | 275 | return vmax |
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276 | 276 | |
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277 | 277 | def get_ippSeconds(self): |
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278 | 278 | ''' |
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279 | 279 | ''' |
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280 | 280 | return self.radarControllerHeaderObj.ippSeconds |
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281 | 281 | |
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282 | 282 | def set_ippSeconds(self, ippSeconds): |
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283 | 283 | ''' |
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284 | 284 | ''' |
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285 | 285 | |
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286 | 286 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
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287 | 287 | |
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288 | 288 | return |
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289 | 289 | |
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290 | 290 | def get_dtype(self): |
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291 | 291 | ''' |
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292 | 292 | ''' |
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293 | 293 | return getNumpyDtype(self.datatype) |
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294 | 294 | |
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295 | 295 | def set_dtype(self, numpyDtype): |
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296 | 296 | ''' |
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297 | 297 | ''' |
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298 | 298 | |
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299 | 299 | self.datatype = getDataTypeCode(numpyDtype) |
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300 | 300 | |
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301 | 301 | def get_code(self): |
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302 | 302 | ''' |
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303 | 303 | ''' |
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304 | 304 | return self.radarControllerHeaderObj.code |
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305 | 305 | |
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306 | 306 | def set_code(self, code): |
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307 | 307 | ''' |
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308 | 308 | ''' |
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309 | 309 | self.radarControllerHeaderObj.code = code |
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310 | 310 | |
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311 | 311 | return |
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312 | 312 | |
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313 | 313 | def get_ncode(self): |
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314 | 314 | ''' |
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315 | 315 | ''' |
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316 | 316 | return self.radarControllerHeaderObj.nCode |
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317 | 317 | |
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318 | 318 | def set_ncode(self, nCode): |
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319 | 319 | ''' |
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320 | 320 | ''' |
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321 | 321 | self.radarControllerHeaderObj.nCode = nCode |
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322 | 322 | |
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323 | 323 | return |
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324 | 324 | |
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325 | 325 | def get_nbaud(self): |
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326 | 326 | ''' |
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327 | 327 | ''' |
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328 | 328 | return self.radarControllerHeaderObj.nBaud |
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329 | 329 | |
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330 | 330 | def set_nbaud(self, nBaud): |
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331 | 331 | ''' |
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332 | 332 | ''' |
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333 | 333 | self.radarControllerHeaderObj.nBaud = nBaud |
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334 | 334 | |
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335 | 335 | return |
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336 | 336 | |
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337 | 337 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
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338 | 338 | channelIndexList = property( |
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339 | 339 | getChannelIndexList, "I'm the 'channelIndexList' property.") |
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340 | 340 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
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341 | 341 | #noise = property(getNoise, "I'm the 'nHeights' property.") |
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342 | 342 | datatime = property(getDatatime, "I'm the 'datatime' property") |
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343 | 343 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
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344 | 344 | ippSeconds = property(get_ippSeconds, set_ippSeconds) |
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345 | 345 | dtype = property(get_dtype, set_dtype) |
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346 | 346 | # timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
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347 | 347 | code = property(get_code, set_code) |
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348 | 348 | nCode = property(get_ncode, set_ncode) |
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349 | 349 | nBaud = property(get_nbaud, set_nbaud) |
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350 | 350 | |
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351 | 351 | |
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352 | 352 | class Voltage(JROData): |
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353 | 353 | |
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354 | 354 | # data es un numpy array de 2 dmensiones (canales, alturas) |
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355 | 355 | data = None |
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356 | 356 | |
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357 | 357 | def __init__(self): |
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358 | 358 | ''' |
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359 | 359 | Constructor |
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360 | 360 | ''' |
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361 | 361 | |
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362 | 362 | self.useLocalTime = True |
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363 | 363 | self.radarControllerHeaderObj = RadarControllerHeader() |
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364 | 364 | self.systemHeaderObj = SystemHeader() |
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365 | 365 | self.type = "Voltage" |
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366 | 366 | self.data = None |
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367 | 367 | # self.dtype = None |
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368 | 368 | # self.nChannels = 0 |
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369 | 369 | # self.nHeights = 0 |
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370 | 370 | self.nProfiles = None |
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371 | 371 | self.heightList = None |
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372 | 372 | self.channelList = None |
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373 | 373 | # self.channelIndexList = None |
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374 | 374 | self.flagNoData = True |
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375 | 375 | self.flagDiscontinuousBlock = False |
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376 | 376 | self.utctime = None |
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377 | 377 | self.timeZone = None |
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378 | 378 | self.dstFlag = None |
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379 | 379 | self.errorCount = None |
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380 | 380 | self.nCohInt = None |
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381 | 381 | self.blocksize = None |
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382 | 382 | self.flagDecodeData = False # asumo q la data no esta decodificada |
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383 | 383 | self.flagDeflipData = False # asumo q la data no esta sin flip |
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384 | 384 | self.flagShiftFFT = False |
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385 | 385 | self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil |
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386 | 386 | self.profileIndex = 0 |
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387 | 387 | |
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388 | 388 | def getNoisebyHildebrand(self, channel=None): |
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389 | 389 | """ |
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390 | 390 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
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391 | 391 | |
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392 | 392 | Return: |
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393 | 393 | noiselevel |
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394 | 394 | """ |
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395 | 395 | |
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396 | 396 | if channel != None: |
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397 | 397 | data = self.data[channel] |
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398 | 398 | nChannels = 1 |
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399 | 399 | else: |
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400 | 400 | data = self.data |
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401 | 401 | nChannels = self.nChannels |
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402 | 402 | |
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403 | 403 | noise = numpy.zeros(nChannels) |
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404 | 404 | power = data * numpy.conjugate(data) |
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405 | 405 | |
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406 | 406 | for thisChannel in range(nChannels): |
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407 | 407 | if nChannels == 1: |
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408 | 408 | daux = power[:].real |
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409 | 409 | else: |
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410 | 410 | daux = power[thisChannel, :].real |
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411 | 411 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
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412 | 412 | |
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413 | 413 | return noise |
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414 | 414 | |
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415 | 415 | def getNoise(self, type=1, channel=None): |
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416 | 416 | |
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417 | 417 | if type == 1: |
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418 | 418 | noise = self.getNoisebyHildebrand(channel) |
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419 | 419 | |
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420 | 420 | return noise |
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421 | 421 | |
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422 | 422 | def getPower(self, channel=None): |
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423 | 423 | |
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424 | 424 | if channel != None: |
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425 | 425 | data = self.data[channel] |
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426 | 426 | else: |
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427 | 427 | data = self.data |
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428 | 428 | |
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429 | 429 | power = data * numpy.conjugate(data) |
|
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430 | 430 | powerdB = 10 * numpy.log10(power.real) |
|
|
431 | 431 | powerdB = numpy.squeeze(powerdB) |
|
|
432 | 432 | |
|
|
433 | 433 | return powerdB |
|
|
434 | 434 | |
|
|
435 | 435 | def getTimeInterval(self): |
|
|
436 | 436 | |
|
|
437 | 437 | timeInterval = self.ippSeconds * self.nCohInt |
|
|
438 | 438 | |
|
|
439 | 439 | return timeInterval |
|
|
440 | 440 | |
|
|
441 | 441 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
442 | 442 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
443 | 443 | |
|
|
444 | 444 | |
|
|
445 | 445 | class Spectra(JROData): |
|
|
446 | 446 | |
|
|
447 | 447 | # data spc es un numpy array de 2 dmensiones (canales, perfiles, alturas) |
|
|
448 | 448 | data_spc = None |
|
|
449 | 449 | # data cspc es un numpy array de 2 dmensiones (canales, pares, alturas) |
|
|
450 | 450 | data_cspc = None |
|
|
451 | 451 | # data dc es un numpy array de 2 dmensiones (canales, alturas) |
|
|
452 | 452 | data_dc = None |
|
|
453 | 453 | # data power |
|
|
454 | 454 | data_pwr = None |
|
|
455 | 455 | nFFTPoints = None |
|
|
456 | 456 | # nPairs = None |
|
|
457 | 457 | pairsList = None |
|
|
458 | 458 | nIncohInt = None |
|
|
459 | 459 | wavelength = None # Necesario para cacular el rango de velocidad desde la frecuencia |
|
|
460 | 460 | nCohInt = None # se requiere para determinar el valor de timeInterval |
|
|
461 | 461 | ippFactor = None |
|
|
462 | 462 | profileIndex = 0 |
|
|
463 | 463 | plotting = "spectra" |
|
|
464 | 464 | |
|
|
465 | 465 | def __init__(self): |
|
|
466 | 466 | ''' |
|
|
467 | 467 | Constructor |
|
|
468 | 468 | ''' |
|
|
469 | 469 | |
|
|
470 | 470 | self.useLocalTime = True |
|
|
471 | 471 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
472 | 472 | self.systemHeaderObj = SystemHeader() |
|
|
473 | 473 | self.type = "Spectra" |
|
|
474 | 474 | # self.data = None |
|
|
475 | 475 | # self.dtype = None |
|
|
476 | 476 | # self.nChannels = 0 |
|
|
477 | 477 | # self.nHeights = 0 |
|
|
478 | 478 | self.nProfiles = None |
|
|
479 | 479 | self.heightList = None |
|
|
480 | 480 | self.channelList = None |
|
|
481 | 481 | # self.channelIndexList = None |
|
|
482 | 482 | self.pairsList = None |
|
|
483 | 483 | self.flagNoData = True |
|
|
484 | 484 | self.flagDiscontinuousBlock = False |
|
|
485 | 485 | self.utctime = None |
|
|
486 | 486 | self.nCohInt = None |
|
|
487 | 487 | self.nIncohInt = None |
|
|
488 | 488 | self.blocksize = None |
|
|
489 | 489 | self.nFFTPoints = None |
|
|
490 | 490 | self.wavelength = None |
|
|
491 | 491 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
492 | 492 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
493 | 493 | self.flagShiftFFT = False |
|
|
494 | 494 | self.ippFactor = 1 |
|
|
495 | 495 | #self.noise = None |
|
|
496 | 496 | self.beacon_heiIndexList = [] |
|
|
497 | 497 | self.noise_estimation = None |
|
|
498 | 498 | |
|
|
499 | 499 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
500 | 500 | """ |
|
|
501 | 501 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
502 | 502 | |
|
|
503 | 503 | Return: |
|
|
504 | 504 | noiselevel |
|
|
505 | 505 | """ |
|
|
506 | 506 | |
|
|
507 | 507 | noise = numpy.zeros(self.nChannels) |
|
|
508 | 508 | |
|
|
509 | 509 | for channel in range(self.nChannels): |
|
|
510 | 510 | daux = self.data_spc[channel, |
|
|
511 | 511 | xmin_index:xmax_index, ymin_index:ymax_index] |
|
|
512 | 512 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
513 | 513 | |
|
|
514 | 514 | return noise |
|
|
515 | 515 | |
|
|
516 | 516 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
517 | 517 | |
|
|
518 | 518 | if self.noise_estimation is not None: |
|
|
519 | 519 | # this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
520 | 520 | return self.noise_estimation |
|
|
521 | 521 | else: |
|
|
522 | 522 | noise = self.getNoisebyHildebrand( |
|
|
523 | 523 | xmin_index, xmax_index, ymin_index, ymax_index) |
|
|
524 | 524 | return noise |
|
|
525 | 525 | |
|
|
526 | 526 | def getFreqRangeTimeResponse(self, extrapoints=0): |
|
|
527 | 527 | |
|
|
528 | 528 | deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor) |
|
|
529 | 529 | freqrange = deltafreq * \ |
|
|
530 | 530 | (numpy.arange(self.nFFTPoints + extrapoints) - |
|
|
531 | 531 | self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
532 | 532 | |
|
|
533 | 533 | return freqrange |
|
|
534 | 534 | |
|
|
535 | 535 | def getAcfRange(self, extrapoints=0): |
|
|
536 | 536 | |
|
|
537 | 537 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
538 | 538 | freqrange = deltafreq * \ |
|
|
539 | 539 | (numpy.arange(self.nFFTPoints + extrapoints) - |
|
|
540 | 540 | self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
541 | 541 | |
|
|
542 | 542 | return freqrange |
|
|
543 | 543 | |
|
|
544 | 544 | def getFreqRange(self, extrapoints=0): |
|
|
545 | 545 | |
|
|
546 | 546 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
547 | 547 | freqrange = deltafreq * \ |
|
|
548 | 548 | (numpy.arange(self.nFFTPoints + extrapoints) - |
|
|
549 | 549 | self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
550 | 550 | |
|
|
551 | 551 | return freqrange |
|
|
552 | 552 | |
|
|
553 | 553 | def getVelRange(self, extrapoints=0): |
|
|
554 | 554 | |
|
|
555 | 555 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
|
556 | 556 | velrange = deltav * (numpy.arange(self.nFFTPoints + |
|
|
557 | 557 | extrapoints) - self.nFFTPoints / 2.) |
|
|
558 | 558 | |
|
|
559 | 559 | if self.nmodes: |
|
|
560 | 560 | return velrange/self.nmodes |
|
|
561 | 561 | else: |
|
|
562 | 562 | return velrange |
|
|
563 | 563 | |
|
|
564 | 564 | def getNPairs(self): |
|
|
565 | 565 | |
|
|
566 | 566 | return len(self.pairsList) |
|
|
567 | 567 | |
|
|
568 | 568 | def getPairsIndexList(self): |
|
|
569 | 569 | |
|
|
570 | 570 | return list(range(self.nPairs)) |
|
|
571 | 571 | |
|
|
572 | 572 | def getNormFactor(self): |
|
|
573 | 573 | |
|
|
574 | 574 | pwcode = 1 |
|
|
575 | 575 | |
|
|
576 | 576 | if self.flagDecodeData: |
|
|
577 | 577 | pwcode = numpy.sum(self.code[0]**2) |
|
|
578 | 578 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
579 | 579 | normFactor = self.nProfiles * self.nIncohInt * \ |
|
|
580 | 580 | self.nCohInt * pwcode * self.windowOfFilter |
|
|
581 | 581 | |
|
|
582 | 582 | return normFactor |
|
|
583 | 583 | |
|
|
584 | 584 | def getFlagCspc(self): |
|
|
585 | 585 | |
|
|
586 | 586 | if self.data_cspc is None: |
|
|
587 | 587 | return True |
|
|
588 | 588 | |
|
|
589 | 589 | return False |
|
|
590 | 590 | |
|
|
591 | 591 | def getFlagDc(self): |
|
|
592 | 592 | |
|
|
593 | 593 | if self.data_dc is None: |
|
|
594 | 594 | return True |
|
|
595 | 595 | |
|
|
596 | 596 | return False |
|
|
597 | 597 | |
|
|
598 | 598 | def getTimeInterval(self): |
|
|
599 | 599 | |
|
|
600 | 600 | timeInterval = self.ippSeconds * self.nCohInt * \ |
|
|
601 | 601 | self.nIncohInt * self.nProfiles * self.ippFactor |
|
|
602 | 602 | |
|
|
603 | 603 | return timeInterval |
|
|
604 | 604 | |
|
|
605 | 605 | def getPower(self): |
|
|
606 | 606 | |
|
|
607 | 607 | factor = self.normFactor |
|
|
608 | 608 | z = self.data_spc / factor |
|
|
609 | 609 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
610 | 610 | avg = numpy.average(z, axis=1) |
|
|
611 | 611 | |
|
|
612 | 612 | return 10 * numpy.log10(avg) |
|
|
613 | 613 | |
|
|
614 | 614 | def getCoherence(self, pairsList=None, phase=False): |
|
|
615 | 615 | |
|
|
616 | 616 | z = [] |
|
|
617 | 617 | if pairsList is None: |
|
|
618 | 618 | pairsIndexList = self.pairsIndexList |
|
|
619 | 619 | else: |
|
|
620 | 620 | pairsIndexList = [] |
|
|
621 | 621 | for pair in pairsList: |
|
|
622 | 622 | if pair not in self.pairsList: |
|
|
623 | 623 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
|
624 | 624 | pair)) |
|
|
625 | 625 | pairsIndexList.append(self.pairsList.index(pair)) |
|
|
626 | 626 | for i in range(len(pairsIndexList)): |
|
|
627 | 627 | pair = self.pairsList[pairsIndexList[i]] |
|
|
628 | 628 | ccf = numpy.average( |
|
|
629 | 629 | self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
|
630 | 630 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
|
631 | 631 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
|
632 | 632 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
633 | 633 | if phase: |
|
|
634 | 634 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
635 | 635 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
636 | 636 | else: |
|
|
637 | 637 | data = numpy.abs(avgcoherenceComplex) |
|
|
638 | 638 | |
|
|
639 | 639 | z.append(data) |
|
|
640 | 640 | |
|
|
641 | 641 | return numpy.array(z) |
|
|
642 | 642 | |
|
|
643 | 643 | def setValue(self, value): |
|
|
644 | 644 | |
|
|
645 | 645 | print("This property should not be initialized") |
|
|
646 | 646 | |
|
|
647 | 647 | return |
|
|
648 | 648 | |
|
|
649 | 649 | nPairs = property(getNPairs, setValue, "I'm the 'nPairs' property.") |
|
|
650 | 650 | pairsIndexList = property( |
|
|
651 | 651 | getPairsIndexList, setValue, "I'm the 'pairsIndexList' property.") |
|
|
652 | 652 | normFactor = property(getNormFactor, setValue, |
|
|
653 | 653 | "I'm the 'getNormFactor' property.") |
|
|
654 | 654 | flag_cspc = property(getFlagCspc, setValue) |
|
|
655 | 655 | flag_dc = property(getFlagDc, setValue) |
|
|
656 | 656 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
|
657 | 657 | timeInterval = property(getTimeInterval, setValue, |
|
|
658 | 658 | "I'm the 'timeInterval' property") |
|
|
659 | 659 | |
|
|
660 | 660 | |
|
|
661 | 661 | class SpectraHeis(Spectra): |
|
|
662 | 662 | |
|
|
663 | 663 | data_spc = None |
|
|
664 | 664 | data_cspc = None |
|
|
665 | 665 | data_dc = None |
|
|
666 | 666 | nFFTPoints = None |
|
|
667 | 667 | # nPairs = None |
|
|
668 | 668 | pairsList = None |
|
|
669 | 669 | nCohInt = None |
|
|
670 | 670 | nIncohInt = None |
|
|
671 | 671 | |
|
|
672 | 672 | def __init__(self): |
|
|
673 | 673 | |
|
|
674 | 674 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
675 | 675 | |
|
|
676 | 676 | self.systemHeaderObj = SystemHeader() |
|
|
677 | 677 | |
|
|
678 | 678 | self.type = "SpectraHeis" |
|
|
679 | 679 | |
|
|
680 | 680 | # self.dtype = None |
|
|
681 | 681 | |
|
|
682 | 682 | # self.nChannels = 0 |
|
|
683 | 683 | |
|
|
684 | 684 | # self.nHeights = 0 |
|
|
685 | 685 | |
|
|
686 | 686 | self.nProfiles = None |
|
|
687 | 687 | |
|
|
688 | 688 | self.heightList = None |
|
|
689 | 689 | |
|
|
690 | 690 | self.channelList = None |
|
|
691 | 691 | |
|
|
692 | 692 | # self.channelIndexList = None |
|
|
693 | 693 | |
|
|
694 | 694 | self.flagNoData = True |
|
|
695 | 695 | |
|
|
696 | 696 | self.flagDiscontinuousBlock = False |
|
|
697 | 697 | |
|
|
698 | 698 | # self.nPairs = 0 |
|
|
699 | 699 | |
|
|
700 | 700 | self.utctime = None |
|
|
701 | 701 | |
|
|
702 | 702 | self.blocksize = None |
|
|
703 | 703 | |
|
|
704 | 704 | self.profileIndex = 0 |
|
|
705 | 705 | |
|
|
706 | 706 | self.nCohInt = 1 |
|
|
707 | 707 | |
|
|
708 | 708 | self.nIncohInt = 1 |
|
|
709 | 709 | |
|
|
710 | 710 | def getNormFactor(self): |
|
|
711 | 711 | pwcode = 1 |
|
|
712 | 712 | if self.flagDecodeData: |
|
|
713 | 713 | pwcode = numpy.sum(self.code[0]**2) |
|
|
714 | 714 | |
|
|
715 | 715 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
|
716 | 716 | |
|
|
717 | 717 | return normFactor |
|
|
718 | 718 | |
|
|
719 | 719 | def getTimeInterval(self): |
|
|
720 | 720 | |
|
|
721 | 721 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
722 | 722 | |
|
|
723 | 723 | return timeInterval |
|
|
724 | 724 | |
|
|
725 | 725 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
726 | 726 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
727 | 727 | |
|
|
728 | 728 | |
|
|
729 | 729 | class Fits(JROData): |
|
|
730 | 730 | |
|
|
731 | 731 | heightList = None |
|
|
732 | 732 | channelList = None |
|
|
733 | 733 | flagNoData = True |
|
|
734 | 734 | flagDiscontinuousBlock = False |
|
|
735 | 735 | useLocalTime = False |
|
|
736 | 736 | utctime = None |
|
|
737 | 737 | timeZone = None |
|
|
738 | 738 | # ippSeconds = None |
|
|
739 | 739 | # timeInterval = None |
|
|
740 | 740 | nCohInt = None |
|
|
741 | 741 | nIncohInt = None |
|
|
742 | 742 | noise = None |
|
|
743 | 743 | windowOfFilter = 1 |
|
|
744 | 744 | # Speed of ligth |
|
|
745 | 745 | C = 3e8 |
|
|
746 | 746 | frequency = 49.92e6 |
|
|
747 | 747 | realtime = False |
|
|
748 | 748 | |
|
|
749 | 749 | def __init__(self): |
|
|
750 | 750 | |
|
|
751 | 751 | self.type = "Fits" |
|
|
752 | 752 | |
|
|
753 | 753 | self.nProfiles = None |
|
|
754 | 754 | |
|
|
755 | 755 | self.heightList = None |
|
|
756 | 756 | |
|
|
757 | 757 | self.channelList = None |
|
|
758 | 758 | |
|
|
759 | 759 | # self.channelIndexList = None |
|
|
760 | 760 | |
|
|
761 | 761 | self.flagNoData = True |
|
|
762 | 762 | |
|
|
763 | 763 | self.utctime = None |
|
|
764 | 764 | |
|
|
765 | 765 | self.nCohInt = 1 |
|
|
766 | 766 | |
|
|
767 | 767 | self.nIncohInt = 1 |
|
|
768 | 768 | |
|
|
769 | 769 | self.useLocalTime = True |
|
|
770 | 770 | |
|
|
771 | 771 | self.profileIndex = 0 |
|
|
772 | 772 | |
|
|
773 | 773 | # self.utctime = None |
|
|
774 | 774 | # self.timeZone = None |
|
|
775 | 775 | # self.ltctime = None |
|
|
776 | 776 | # self.timeInterval = None |
|
|
777 | 777 | # self.header = None |
|
|
778 | 778 | # self.data_header = None |
|
|
779 | 779 | # self.data = None |
|
|
780 | 780 | # self.datatime = None |
|
|
781 | 781 | # self.flagNoData = False |
|
|
782 | 782 | # self.expName = '' |
|
|
783 | 783 | # self.nChannels = None |
|
|
784 | 784 | # self.nSamples = None |
|
|
785 | 785 | # self.dataBlocksPerFile = None |
|
|
786 | 786 | # self.comments = '' |
|
|
787 | 787 | # |
|
|
788 | 788 | |
|
|
789 | 789 | def getltctime(self): |
|
|
790 | 790 | |
|
|
791 | 791 | if self.useLocalTime: |
|
|
792 | 792 | return self.utctime - self.timeZone * 60 |
|
|
793 | 793 | |
|
|
794 | 794 | return self.utctime |
|
|
795 | 795 | |
|
|
796 | 796 | def getDatatime(self): |
|
|
797 | 797 | |
|
|
798 | 798 | datatime = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
799 | 799 | return datatime |
|
|
800 | 800 | |
|
|
801 | 801 | def getTimeRange(self): |
|
|
802 | 802 | |
|
|
803 | 803 | datatime = [] |
|
|
804 | 804 | |
|
|
805 | 805 | datatime.append(self.ltctime) |
|
|
806 | 806 | datatime.append(self.ltctime + self.timeInterval) |
|
|
807 | 807 | |
|
|
808 | 808 | datatime = numpy.array(datatime) |
|
|
809 | 809 | |
|
|
810 | 810 | return datatime |
|
|
811 | 811 | |
|
|
812 | 812 | def getHeiRange(self): |
|
|
813 | 813 | |
|
|
814 | 814 | heis = self.heightList |
|
|
815 | 815 | |
|
|
816 | 816 | return heis |
|
|
817 | 817 | |
|
|
818 | 818 | def getNHeights(self): |
|
|
819 | 819 | |
|
|
820 | 820 | return len(self.heightList) |
|
|
821 | 821 | |
|
|
822 | 822 | def getNChannels(self): |
|
|
823 | 823 | |
|
|
824 | 824 | return len(self.channelList) |
|
|
825 | 825 | |
|
|
826 | 826 | def getChannelIndexList(self): |
|
|
827 | 827 | |
|
|
828 | 828 | return list(range(self.nChannels)) |
|
|
829 | 829 | |
|
|
830 | 830 | def getNoise(self, type=1): |
|
|
831 | 831 | |
|
|
832 | 832 | #noise = numpy.zeros(self.nChannels) |
|
|
833 | 833 | |
|
|
834 | 834 | if type == 1: |
|
|
835 | 835 | noise = self.getNoisebyHildebrand() |
|
|
836 | 836 | |
|
|
837 | 837 | if type == 2: |
|
|
838 | 838 | noise = self.getNoisebySort() |
|
|
839 | 839 | |
|
|
840 | 840 | if type == 3: |
|
|
841 | 841 | noise = self.getNoisebyWindow() |
|
|
842 | 842 | |
|
|
843 | 843 | return noise |
|
|
844 | 844 | |
|
|
845 | 845 | def getTimeInterval(self): |
|
|
846 | 846 | |
|
|
847 | 847 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
848 | 848 | |
|
|
849 | 849 | return timeInterval |
|
|
850 | 850 | |
|
|
851 | 851 | def get_ippSeconds(self): |
|
|
852 | 852 | ''' |
|
|
853 | 853 | ''' |
|
|
854 | 854 | return self.ipp_sec |
|
|
855 | 855 | |
|
|
856 | 856 | |
|
|
857 | 857 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
858 | 858 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
859 | 859 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
860 | 860 | channelIndexList = property( |
|
|
861 | 861 | getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
862 | 862 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
863 | 863 | |
|
|
864 | 864 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
865 | 865 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
866 | 866 | ippSeconds = property(get_ippSeconds, '') |
|
|
867 | 867 | |
|
|
868 | 868 | class Correlation(JROData): |
|
|
869 | 869 | |
|
|
870 | 870 | noise = None |
|
|
871 | 871 | SNR = None |
|
|
872 | 872 | #-------------------------------------------------- |
|
|
873 | 873 | mode = None |
|
|
874 | 874 | split = False |
|
|
875 | 875 | data_cf = None |
|
|
876 | 876 | lags = None |
|
|
877 | 877 | lagRange = None |
|
|
878 | 878 | pairsList = None |
|
|
879 | 879 | normFactor = None |
|
|
880 | 880 | #-------------------------------------------------- |
|
|
881 | 881 | # calculateVelocity = None |
|
|
882 | 882 | nLags = None |
|
|
883 | 883 | nPairs = None |
|
|
884 | 884 | nAvg = None |
|
|
885 | 885 | |
|
|
886 | 886 | def __init__(self): |
|
|
887 | 887 | ''' |
|
|
888 | 888 | Constructor |
|
|
889 | 889 | ''' |
|
|
890 | 890 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
891 | 891 | |
|
|
892 | 892 | self.systemHeaderObj = SystemHeader() |
|
|
893 | 893 | |
|
|
894 | 894 | self.type = "Correlation" |
|
|
895 | 895 | |
|
|
896 | 896 | self.data = None |
|
|
897 | 897 | |
|
|
898 | 898 | self.dtype = None |
|
|
899 | 899 | |
|
|
900 | 900 | self.nProfiles = None |
|
|
901 | 901 | |
|
|
902 | 902 | self.heightList = None |
|
|
903 | 903 | |
|
|
904 | 904 | self.channelList = None |
|
|
905 | 905 | |
|
|
906 | 906 | self.flagNoData = True |
|
|
907 | 907 | |
|
|
908 | 908 | self.flagDiscontinuousBlock = False |
|
|
909 | 909 | |
|
|
910 | 910 | self.utctime = None |
|
|
911 | 911 | |
|
|
912 | 912 | self.timeZone = None |
|
|
913 | 913 | |
|
|
914 | 914 | self.dstFlag = None |
|
|
915 | 915 | |
|
|
916 | 916 | self.errorCount = None |
|
|
917 | 917 | |
|
|
918 | 918 | self.blocksize = None |
|
|
919 | 919 | |
|
|
920 | 920 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
921 | 921 | |
|
|
922 | 922 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
923 | 923 | |
|
|
924 | 924 | self.pairsList = None |
|
|
925 | 925 | |
|
|
926 | 926 | self.nPoints = None |
|
|
927 | 927 | |
|
|
928 | 928 | def getPairsList(self): |
|
|
929 | 929 | |
|
|
930 | 930 | return self.pairsList |
|
|
931 | 931 | |
|
|
932 | 932 | def getNoise(self, mode=2): |
|
|
933 | 933 | |
|
|
934 | 934 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
935 | 935 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
936 | 936 | |
|
|
937 | 937 | jspectra0 = self.data_corr[:, :, indR, :] |
|
|
938 | 938 | jspectra = copy.copy(jspectra0) |
|
|
939 | 939 | |
|
|
940 | 940 | num_chan = jspectra.shape[0] |
|
|
941 | 941 | num_hei = jspectra.shape[2] |
|
|
942 | 942 | |
|
|
943 | 943 | freq_dc = jspectra.shape[1] / 2 |
|
|
944 | 944 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
945 | 945 | |
|
|
946 | 946 | if ind_vel[0] < 0: |
|
|
947 | 947 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
|
948 | 948 | range(0, 1))] + self.num_prof |
|
|
949 | 949 | |
|
|
950 | 950 | if mode == 1: |
|
|
951 | 951 | jspectra[:, freq_dc, :] = ( |
|
|
952 | 952 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
953 | 953 | |
|
|
954 | 954 | if mode == 2: |
|
|
955 | 955 | |
|
|
956 | 956 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
957 | 957 | xx = numpy.zeros([4, 4]) |
|
|
958 | 958 | |
|
|
959 | 959 | for fil in range(4): |
|
|
960 | 960 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
961 | 961 | |
|
|
962 | 962 | xx_inv = numpy.linalg.inv(xx) |
|
|
963 | 963 | xx_aux = xx_inv[0, :] |
|
|
964 | 964 | |
|
|
965 | 965 | for ich in range(num_chan): |
|
|
966 | 966 | yy = jspectra[ich, ind_vel, :] |
|
|
967 | 967 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
968 | 968 | |
|
|
969 | 969 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
970 | 970 | cjunkid = sum(junkid) |
|
|
971 | 971 | |
|
|
972 | 972 | if cjunkid.any(): |
|
|
973 | 973 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
974 | 974 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
975 | 975 | |
|
|
976 | 976 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
|
977 | 977 | |
|
|
978 | 978 | return noise |
|
|
979 | 979 | |
|
|
980 | 980 | def getTimeInterval(self): |
|
|
981 | 981 | |
|
|
982 | 982 | timeInterval = self.ippSeconds * self.nCohInt * self.nProfiles |
|
|
983 | 983 | |
|
|
984 | 984 | return timeInterval |
|
|
985 | 985 | |
|
|
986 | 986 | def splitFunctions(self): |
|
|
987 | 987 | |
|
|
988 | 988 | pairsList = self.pairsList |
|
|
989 | 989 | ccf_pairs = [] |
|
|
990 | 990 | acf_pairs = [] |
|
|
991 | 991 | ccf_ind = [] |
|
|
992 | 992 | acf_ind = [] |
|
|
993 | 993 | for l in range(len(pairsList)): |
|
|
994 | 994 | chan0 = pairsList[l][0] |
|
|
995 | 995 | chan1 = pairsList[l][1] |
|
|
996 | 996 | |
|
|
997 | 997 | # Obteniendo pares de Autocorrelacion |
|
|
998 | 998 | if chan0 == chan1: |
|
|
999 | 999 | acf_pairs.append(chan0) |
|
|
1000 | 1000 | acf_ind.append(l) |
|
|
1001 | 1001 | else: |
|
|
1002 | 1002 | ccf_pairs.append(pairsList[l]) |
|
|
1003 | 1003 | ccf_ind.append(l) |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | data_acf = self.data_cf[acf_ind] |
|
|
1006 | 1006 | data_ccf = self.data_cf[ccf_ind] |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | def getNormFactor(self): |
|
|
1011 | 1011 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
|
1012 | 1012 | acf_pairs = numpy.array(acf_pairs) |
|
|
1013 | 1013 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
|
1014 | 1014 | |
|
|
1015 | 1015 | for p in range(self.nPairs): |
|
|
1016 | 1016 | pair = self.pairsList[p] |
|
|
1017 | 1017 | |
|
|
1018 | 1018 | ch0 = pair[0] |
|
|
1019 | 1019 | ch1 = pair[1] |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
|
1022 | 1022 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
|
1023 | 1023 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | return normFactor |
|
|
1026 | 1026 | |
|
|
1027 | 1027 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
1028 | 1028 | normFactor = property(getNormFactor, "I'm the 'normFactor property'") |
|
|
1029 | 1029 | |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | class Parameters(Spectra): |
|
|
1032 | 1032 | |
|
|
1033 | 1033 | experimentInfo = None # Information about the experiment |
|
|
1034 | 1034 | # Information from previous data |
|
|
1035 | 1035 | inputUnit = None # Type of data to be processed |
|
|
1036 | 1036 | operation = None # Type of operation to parametrize |
|
|
1037 | 1037 | # normFactor = None #Normalization Factor |
|
|
1038 | 1038 | groupList = None # List of Pairs, Groups, etc |
|
|
1039 | 1039 | # Parameters |
|
|
1040 | 1040 | data_param = None # Parameters obtained |
|
|
1041 | 1041 | data_pre = None # Data Pre Parametrization |
|
|
1042 | 1042 | data_SNR = None # Signal to Noise Ratio |
|
|
1043 | 1043 | # heightRange = None #Heights |
|
|
1044 | 1044 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
|
1045 | 1045 | # noise = None #Noise Potency |
|
|
1046 | 1046 | utctimeInit = None # Initial UTC time |
|
|
1047 | 1047 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
|
1048 | 1048 | useLocalTime = True |
|
|
1049 | 1049 | # Fitting |
|
|
1050 | 1050 | data_error = None # Error of the estimation |
|
|
1051 | 1051 | constants = None |
|
|
1052 | 1052 | library = None |
|
|
1053 | 1053 | # Output signal |
|
|
1054 | 1054 | outputInterval = None # Time interval to calculate output signal in seconds |
|
|
1055 | 1055 | data_output = None # Out signal |
|
|
1056 | 1056 | nAvg = None |
|
|
1057 | 1057 | noise_estimation = None |
|
|
1058 | 1058 | GauSPC = None # Fit gaussian SPC |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | def __init__(self): |
|
|
1061 | 1061 | ''' |
|
|
1062 | 1062 | Constructor |
|
|
1063 | 1063 | ''' |
|
|
1064 | 1064 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
1065 | 1065 | |
|
|
1066 | 1066 | self.systemHeaderObj = SystemHeader() |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | self.type = "Parameters" |
|
|
1069 | 1069 | |
|
|
1070 | 1070 | def getTimeRange1(self, interval): |
|
|
1071 | 1071 | |
|
|
1072 | 1072 | datatime = [] |
|
|
1073 | 1073 | |
|
|
1074 | 1074 | if self.useLocalTime: |
|
|
1075 | 1075 | time1 = self.utctimeInit - self.timeZone * 60 |
|
|
1076 | 1076 | else: |
|
|
1077 | 1077 | time1 = self.utctimeInit |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | datatime.append(time1) |
|
|
1080 | 1080 | datatime.append(time1 + interval) |
|
|
1081 | 1081 | datatime = numpy.array(datatime) |
|
|
1082 | 1082 | |
|
|
1083 | 1083 | return datatime |
|
|
1084 | 1084 | |
|
|
1085 | 1085 | def getTimeInterval(self): |
|
|
1086 | 1086 | |
|
|
1087 | 1087 | if hasattr(self, 'timeInterval1'): |
|
|
1088 | 1088 | return self.timeInterval1 |
|
|
1089 | 1089 | else: |
|
|
1090 | 1090 | return self.paramInterval |
|
|
1091 | 1091 | |
|
|
1092 | 1092 | def setValue(self, value): |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | print("This property should not be initialized") |
|
|
1095 | 1095 | |
|
|
1096 | 1096 | return |
|
|
1097 | 1097 | |
|
|
1098 | 1098 | def getNoise(self): |
|
|
1099 | 1099 | |
|
|
1100 | 1100 | return self.spc_noise |
|
|
1101 | 1101 | |
|
|
1102 | 1102 | timeInterval = property(getTimeInterval) |
|
|
1103 | 1103 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
|
1104 | 1104 | |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | class PlotterData(object): |
|
|
1107 | 1107 | ''' |
|
|
1108 | 1108 | Object to hold data to be plotted |
|
|
1109 | 1109 | ''' |
|
|
1110 | 1110 | |
|
|
1111 | 1111 | MAXNUMX = 100 |
|
|
1112 | 1112 | MAXNUMY = 100 |
|
|
1113 | 1113 | |
|
|
1114 | 1114 | def __init__(self, code, throttle_value, exp_code, buffering=True): |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | self.throttle = throttle_value |
|
|
1117 | 1117 | self.exp_code = exp_code |
|
|
1118 | 1118 | self.buffering = buffering |
|
|
1119 | 1119 | self.ready = False |
|
|
1120 | 1120 | self.localtime = False |
|
|
1121 | 1121 | self.data = {} |
|
|
1122 | 1122 | self.meta = {} |
|
|
1123 | 1123 | self.__times = [] |
|
|
1124 | 1124 | self.__heights = [] |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | if 'snr' in code: |
|
|
1127 | 1127 | self.plottypes = ['snr'] |
|
|
1128 | 1128 | elif code == 'spc': |
|
|
1129 | 1129 | self.plottypes = ['spc', 'noise', 'rti'] |
|
|
1130 | 1130 | elif code == 'rti': |
|
|
1131 | 1131 | self.plottypes = ['noise', 'rti'] |
|
|
1132 | 1132 | else: |
|
|
1133 | 1133 | self.plottypes = [code] |
|
|
1134 | 1134 | |
|
|
1135 | 1135 | for plot in self.plottypes: |
|
|
1136 | 1136 | self.data[plot] = {} |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | def __str__(self): |
|
|
1139 | 1139 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
1140 | 1140 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.__times)) |
|
|
1141 | 1141 | |
|
|
1142 | 1142 | def __len__(self): |
|
|
1143 | 1143 | return len(self.__times) |
|
|
1144 | 1144 | |
|
|
1145 | 1145 | def __getitem__(self, key): |
|
|
1146 | ||
|
|
1146 | ||
|
|
1147 | 1147 | if key not in self.data: |
|
|
1148 | 1148 | raise KeyError(log.error('Missing key: {}'.format(key))) |
|
|
1149 | 1149 | if 'spc' in key or not self.buffering: |
|
|
1150 | 1150 | ret = self.data[key] |
|
|
1151 | elif 'scope' in key: | |
|
|
1152 | ret = numpy.array(self.data[key][float(self.tm)]) | |
|
|
1151 | 1153 | else: |
|
|
1152 | 1154 | ret = numpy.array([self.data[key][x] for x in self.times]) |
|
|
1153 | 1155 | if ret.ndim > 1: |
|
|
1154 | 1156 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
1155 | 1157 | return ret |
|
|
1156 | 1158 | |
|
|
1157 | 1159 | def __contains__(self, key): |
|
|
1158 | 1160 | return key in self.data |
|
|
1159 | 1161 | |
|
|
1160 | 1162 | def setup(self): |
|
|
1161 | 1163 | ''' |
|
|
1162 | 1164 | Configure object |
|
|
1163 | 1165 | ''' |
|
|
1164 | 1166 | |
|
|
1165 | 1167 | self.type = '' |
|
|
1166 | 1168 | self.ready = False |
|
|
1167 | 1169 | self.data = {} |
|
|
1168 | 1170 | self.__times = [] |
|
|
1169 | 1171 | self.__heights = [] |
|
|
1170 | 1172 | self.__all_heights = set() |
|
|
1171 | 1173 | for plot in self.plottypes: |
|
|
1172 | 1174 | if 'snr' in plot: |
|
|
1173 | 1175 | plot = 'snr' |
|
|
1174 | 1176 | self.data[plot] = {} |
|
|
1175 | 1177 | |
|
|
1176 | 1178 | if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data: |
|
|
1177 | 1179 | self.data['noise'] = {} |
|
|
1178 | 1180 | if 'noise' not in self.plottypes: |
|
|
1179 | 1181 | self.plottypes.append('noise') |
|
|
1180 | 1182 | |
|
|
1181 | 1183 | def shape(self, key): |
|
|
1182 | 1184 | ''' |
|
|
1183 | 1185 | Get the shape of the one-element data for the given key |
|
|
1184 | 1186 | ''' |
|
|
1185 | 1187 | |
|
|
1186 | 1188 | if len(self.data[key]): |
|
|
1187 | 1189 | if 'spc' in key or not self.buffering: |
|
|
1188 | 1190 | return self.data[key].shape |
|
|
1189 | 1191 | return self.data[key][self.__times[0]].shape |
|
|
1190 | 1192 | return (0,) |
|
|
1191 | 1193 | |
|
|
1192 | 1194 | def update(self, dataOut, tm): |
|
|
1193 | 1195 | ''' |
|
|
1194 | 1196 | Update data object with new dataOut |
|
|
1195 | 1197 | ''' |
|
|
1196 | 1198 | |
|
|
1197 | 1199 | if tm in self.__times: |
|
|
1198 | 1200 | return |
|
|
1199 | ||
|
|
1201 | self.profileIndex = dataOut.profileIndex | |
|
|
1202 | self.tm = tm | |
|
|
1200 | 1203 | self.type = dataOut.type |
|
|
1201 | 1204 | self.parameters = getattr(dataOut, 'parameters', []) |
|
|
1202 | 1205 | if hasattr(dataOut, 'pairsList'): |
|
|
1203 | 1206 | self.pairs = dataOut.pairsList |
|
|
1204 | 1207 | if hasattr(dataOut, 'meta'): |
|
|
1205 | 1208 | self.meta = dataOut.meta |
|
|
1206 | 1209 | self.channels = dataOut.channelList |
|
|
1207 | 1210 | self.interval = dataOut.getTimeInterval() |
|
|
1208 | 1211 | self.localtime = dataOut.useLocalTime |
|
|
1209 | 1212 | if 'spc' in self.plottypes or 'cspc' in self.plottypes: |
|
|
1210 | 1213 | self.xrange = (dataOut.getFreqRange(1)/1000., |
|
|
1211 | 1214 | dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
1212 | 1215 | self.factor = dataOut.normFactor |
|
|
1213 | 1216 | self.__heights.append(dataOut.heightList) |
|
|
1214 | 1217 | self.__all_heights.update(dataOut.heightList) |
|
|
1215 | 1218 | self.__times.append(tm) |
|
|
1216 | 1219 | |
|
|
1217 | 1220 | for plot in self.plottypes: |
|
|
1218 | 1221 | if plot == 'spc': |
|
|
1219 | 1222 | z = dataOut.data_spc/dataOut.normFactor |
|
|
1220 | 1223 | buffer = 10*numpy.log10(z) |
|
|
1221 | 1224 | if plot == 'cspc': |
|
|
1222 | 1225 | z = dataOut.data_spc/dataOut.normFactor |
|
|
1223 | 1226 | buffer = (dataOut.data_spc, dataOut.data_cspc) |
|
|
1224 | 1227 | if plot == 'noise': |
|
|
1225 | 1228 | buffer = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
1226 | 1229 | if plot == 'rti': |
|
|
1227 | 1230 | buffer = dataOut.getPower() |
|
|
1228 | 1231 | if plot == 'snr_db': |
|
|
1229 | 1232 | buffer = dataOut.data_SNR |
|
|
1230 | 1233 | if plot == 'snr': |
|
|
1231 | 1234 | buffer = 10*numpy.log10(dataOut.data_SNR) |
|
|
1232 | 1235 | if plot == 'dop': |
|
|
1233 | 1236 | buffer = 10*numpy.log10(dataOut.data_DOP) |
|
|
1234 | 1237 | if plot == 'mean': |
|
|
1235 | 1238 | buffer = dataOut.data_MEAN |
|
|
1236 | 1239 | if plot == 'std': |
|
|
1237 | 1240 | buffer = dataOut.data_STD |
|
|
1238 | 1241 | if plot == 'coh': |
|
|
1239 | 1242 | buffer = dataOut.getCoherence() |
|
|
1240 | 1243 | if plot == 'phase': |
|
|
1241 | 1244 | buffer = dataOut.getCoherence(phase=True) |
|
|
1242 | 1245 | if plot == 'output': |
|
|
1243 | 1246 | buffer = dataOut.data_output |
|
|
1244 | 1247 | if plot == 'param': |
|
|
1245 | 1248 | buffer = dataOut.data_param |
|
|
1246 | ||
|
|
1249 | if plot == 'scope': | |
|
|
1250 | buffer = dataOut.data | |
|
|
1251 | self.flagDataAsBlock = dataOut.flagDataAsBlock | |
|
|
1252 | self.nProfiles = dataOut.nProfiles | |
|
|
1253 | ||
|
|
1247 | 1254 | if plot == 'spc': |
|
|
1248 | 1255 | self.data[plot] = buffer |
|
|
1249 | 1256 | elif plot == 'cspc': |
|
|
1250 | 1257 | self.data['spc'] = buffer[0] |
|
|
1251 | 1258 | self.data['cspc'] = buffer[1] |
|
|
1252 | 1259 | else: |
|
|
1253 | 1260 | if self.buffering: |
|
|
1254 | 1261 | self.data[plot][tm] = buffer |
|
|
1255 | 1262 | else: |
|
|
1256 | 1263 | self.data[plot] = buffer |
|
|
1257 | 1264 | |
|
|
1258 | 1265 | def normalize_heights(self): |
|
|
1259 | 1266 | ''' |
|
|
1260 | 1267 | Ensure same-dimension of the data for different heighList |
|
|
1261 | 1268 | ''' |
|
|
1262 | 1269 | |
|
|
1263 | 1270 | H = numpy.array(list(self.__all_heights)) |
|
|
1264 | 1271 | H.sort() |
|
|
1265 | 1272 | for key in self.data: |
|
|
1266 | 1273 | shape = self.shape(key)[:-1] + H.shape |
|
|
1267 | 1274 | for tm, obj in list(self.data[key].items()): |
|
|
1268 | 1275 | h = self.__heights[self.__times.index(tm)] |
|
|
1269 | 1276 | if H.size == h.size: |
|
|
1270 | 1277 | continue |
|
|
1271 | 1278 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
1272 | 1279 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
1273 | 1280 | if len(shape) == 2: |
|
|
1274 | 1281 | dummy[:, index] = obj |
|
|
1275 | 1282 | else: |
|
|
1276 | 1283 | dummy[index] = obj |
|
|
1277 | 1284 | self.data[key][tm] = dummy |
|
|
1278 | 1285 | |
|
|
1279 | 1286 | self.__heights = [H for tm in self.__times] |
|
|
1280 | 1287 | |
|
|
1281 | 1288 | def jsonify(self, decimate=False): |
|
|
1282 | 1289 | ''' |
|
|
1283 | 1290 | Convert data to json |
|
|
1284 | 1291 | ''' |
|
|
1285 | 1292 | |
|
|
1286 | 1293 | data = {} |
|
|
1287 | 1294 | tm = self.times[-1] |
|
|
1288 | 1295 | dy = int(self.heights.size/self.MAXNUMY) + 1 |
|
|
1289 | 1296 | for key in self.data: |
|
|
1290 | 1297 | if key in ('spc', 'cspc') or not self.buffering: |
|
|
1291 | 1298 | dx = int(self.data[key].shape[1]/self.MAXNUMX) + 1 |
|
|
1292 | 1299 | data[key] = self.roundFloats( |
|
|
1293 | 1300 | self.data[key][::, ::dx, ::dy].tolist()) |
|
|
1294 | 1301 | else: |
|
|
1295 | 1302 | data[key] = self.roundFloats(self.data[key][tm].tolist()) |
|
|
1296 | 1303 | |
|
|
1297 | 1304 | ret = {'data': data} |
|
|
1298 | 1305 | ret['exp_code'] = self.exp_code |
|
|
1299 | 1306 | ret['time'] = float(tm) |
|
|
1300 | 1307 | ret['interval'] = float(self.interval) |
|
|
1301 | 1308 | ret['localtime'] = self.localtime |
|
|
1302 | 1309 | ret['yrange'] = self.roundFloats(self.heights[::dy].tolist()) |
|
|
1303 | 1310 | if 'spc' in self.data or 'cspc' in self.data: |
|
|
1304 | 1311 | ret['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
|
1305 | 1312 | else: |
|
|
1306 | 1313 | ret['xrange'] = [] |
|
|
1307 | 1314 | if hasattr(self, 'pairs'): |
|
|
1308 | 1315 | ret['pairs'] = [(int(p[0]), int(p[1])) for p in self.pairs] |
|
|
1309 | 1316 | else: |
|
|
1310 | 1317 | ret['pairs'] = [] |
|
|
1311 | 1318 | |
|
|
1312 | 1319 | for key, value in list(self.meta.items()): |
|
|
1313 | 1320 | ret[key] = value |
|
|
1314 | 1321 | |
|
|
1315 | 1322 | return json.dumps(ret) |
|
|
1316 | 1323 | |
|
|
1317 | 1324 | @property |
|
|
1318 | 1325 | def times(self): |
|
|
1319 | 1326 | ''' |
|
|
1320 | 1327 | Return the list of times of the current data |
|
|
1321 | 1328 | ''' |
|
|
1322 | 1329 | |
|
|
1323 | 1330 | ret = numpy.array(self.__times) |
|
|
1324 | 1331 | ret.sort() |
|
|
1325 | 1332 | return ret |
|
|
1326 | 1333 | |
|
|
1327 | 1334 | @property |
|
|
1328 | 1335 | def min_time(self): |
|
|
1329 | 1336 | ''' |
|
|
1330 | 1337 | Return the minimun time value |
|
|
1331 | 1338 | ''' |
|
|
1332 | 1339 | |
|
|
1333 | 1340 | return self.times[0] |
|
|
1334 | 1341 | |
|
|
1335 | 1342 | @property |
|
|
1336 | 1343 | def max_time(self): |
|
|
1337 | 1344 | ''' |
|
|
1338 | 1345 | Return the maximun time value |
|
|
1339 | 1346 | ''' |
|
|
1340 | 1347 | |
|
|
1341 | 1348 | return self.times[-1] |
|
|
1342 | 1349 | |
|
|
1343 | 1350 | @property |
|
|
1344 | 1351 | def heights(self): |
|
|
1345 | 1352 | ''' |
|
|
1346 | 1353 | Return the list of heights of the current data |
|
|
1347 | 1354 | ''' |
|
|
1348 | 1355 | |
|
|
1349 | 1356 | return numpy.array(self.__heights[-1]) |
|
|
1350 | 1357 | |
|
|
1351 | 1358 | @staticmethod |
|
|
1352 | 1359 | def roundFloats(obj): |
|
|
1353 | 1360 | if isinstance(obj, list): |
|
|
1354 | 1361 | return list(map(PlotterData.roundFloats, obj)) |
|
|
1355 | 1362 | elif isinstance(obj, float): |
|
|
1356 | 1363 | return round(obj, 2) |
| @@ -1,799 +1,801 | |||
|
|
1 | 1 | |
|
|
2 | 2 | import os |
|
|
3 | 3 | import sys |
|
|
4 | 4 | import zmq |
|
|
5 | 5 | import time |
|
|
6 | 6 | import datetime |
|
|
7 | 7 | from functools import wraps |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | import matplotlib |
|
|
10 | 10 | |
|
|
11 | 11 | if 'BACKEND' in os.environ: |
|
|
12 | 12 | matplotlib.use(os.environ['BACKEND']) |
|
|
13 | 13 | elif 'linux' in sys.platform: |
|
|
14 | 14 | matplotlib.use("TkAgg") |
|
|
15 | 15 | elif 'darwin' in sys.platform: |
|
|
16 | 16 | matplotlib.use('TkAgg') |
|
|
17 | 17 | else: |
|
|
18 | 18 | from schainpy.utils import log |
|
|
19 | 19 | log.warning('Using default Backend="Agg"', 'INFO') |
|
|
20 | 20 | matplotlib.use('Agg') |
|
|
21 | 21 | |
|
|
22 | 22 | import matplotlib.pyplot as plt |
|
|
23 | 23 | from matplotlib.patches import Polygon |
|
|
24 | 24 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
|
|
25 | 25 | from matplotlib.ticker import FuncFormatter, LinearLocator, MultipleLocator |
|
|
26 | 26 | |
|
|
27 | 27 | from schainpy.model.data.jrodata import PlotterData |
|
|
28 | 28 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
29 | 29 | from schainpy.utils import log |
|
|
30 | 30 | |
|
|
31 | 31 | jet_values = matplotlib.pyplot.get_cmap('jet', 100)(numpy.arange(100))[10:90] |
|
|
32 | 32 | blu_values = matplotlib.pyplot.get_cmap( |
|
|
33 | 33 | 'seismic_r', 20)(numpy.arange(20))[10:15] |
|
|
34 | 34 | ncmap = matplotlib.colors.LinearSegmentedColormap.from_list( |
|
|
35 | 35 | 'jro', numpy.vstack((blu_values, jet_values))) |
|
|
36 | 36 | matplotlib.pyplot.register_cmap(cmap=ncmap) |
|
|
37 | 37 | |
|
|
38 | 38 | CMAPS = [plt.get_cmap(s) for s in ('jro', 'jet', 'viridis', |
|
|
39 | 39 | 'plasma', 'inferno', 'Greys', 'seismic', 'bwr', 'coolwarm')] |
|
|
40 | 40 | |
|
|
41 | 41 | EARTH_RADIUS = 6.3710e3 |
|
|
42 | 42 | |
|
|
43 | 43 | |
|
|
44 | 44 | def ll2xy(lat1, lon1, lat2, lon2): |
|
|
45 | 45 | |
|
|
46 | 46 | p = 0.017453292519943295 |
|
|
47 | 47 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
|
48 | 48 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
|
49 | 49 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
|
50 | 50 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
|
51 | 51 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
|
52 | 52 | theta = -theta + numpy.pi/2 |
|
|
53 | 53 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
54 | 54 | |
|
|
55 | 55 | |
|
|
56 | 56 | def km2deg(km): |
|
|
57 | 57 | ''' |
|
|
58 | 58 | Convert distance in km to degrees |
|
|
59 | 59 | ''' |
|
|
60 | 60 | |
|
|
61 | 61 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
|
62 | 62 | |
|
|
63 | 63 | |
|
|
64 | 64 | def figpause(interval): |
|
|
65 | 65 | backend = plt.rcParams['backend'] |
|
|
66 | 66 | if backend in matplotlib.rcsetup.interactive_bk: |
|
|
67 | 67 | figManager = matplotlib._pylab_helpers.Gcf.get_active() |
|
|
68 | 68 | if figManager is not None: |
|
|
69 | 69 | canvas = figManager.canvas |
|
|
70 | 70 | if canvas.figure.stale: |
|
|
71 | 71 | canvas.draw() |
|
|
72 | 72 | try: |
|
|
73 | 73 | canvas.start_event_loop(interval) |
|
|
74 | 74 | except: |
|
|
75 | 75 | pass |
|
|
76 | 76 | return |
|
|
77 | 77 | |
|
|
78 | 78 | |
|
|
79 | 79 | def popup(message): |
|
|
80 | 80 | ''' |
|
|
81 | 81 | ''' |
|
|
82 | 82 | |
|
|
83 | 83 | fig = plt.figure(figsize=(12, 8), facecolor='r') |
|
|
84 | 84 | text = '\n'.join([s.strip() for s in message.split(':')]) |
|
|
85 | 85 | fig.text(0.01, 0.5, text, ha='left', va='center', |
|
|
86 | 86 | size='20', weight='heavy', color='w') |
|
|
87 | 87 | fig.show() |
|
|
88 | 88 | figpause(1000) |
|
|
89 | 89 | |
|
|
90 | 90 | |
|
|
91 | 91 | class Throttle(object): |
|
|
92 | 92 | ''' |
|
|
93 | 93 | Decorator that prevents a function from being called more than once every |
|
|
94 | 94 | time period. |
|
|
95 | 95 | To create a function that cannot be called more than once a minute, but |
|
|
96 | 96 | will sleep until it can be called: |
|
|
97 | 97 | @Throttle(minutes=1) |
|
|
98 | 98 | def foo(): |
|
|
99 | 99 | pass |
|
|
100 | 100 | |
|
|
101 | 101 | for i in range(10): |
|
|
102 | 102 | foo() |
|
|
103 | 103 | print "This function has run %s times." % i |
|
|
104 | 104 | ''' |
|
|
105 | 105 | |
|
|
106 | 106 | def __init__(self, seconds=0, minutes=0, hours=0): |
|
|
107 | 107 | self.throttle_period = datetime.timedelta( |
|
|
108 | 108 | seconds=seconds, minutes=minutes, hours=hours |
|
|
109 | 109 | ) |
|
|
110 | 110 | |
|
|
111 | 111 | self.time_of_last_call = datetime.datetime.min |
|
|
112 | 112 | |
|
|
113 | 113 | def __call__(self, fn): |
|
|
114 | 114 | @wraps(fn) |
|
|
115 | 115 | def wrapper(*args, **kwargs): |
|
|
116 | 116 | coerce = kwargs.pop('coerce', None) |
|
|
117 | 117 | if coerce: |
|
|
118 | 118 | self.time_of_last_call = datetime.datetime.now() |
|
|
119 | 119 | return fn(*args, **kwargs) |
|
|
120 | 120 | else: |
|
|
121 | 121 | now = datetime.datetime.now() |
|
|
122 | 122 | time_since_last_call = now - self.time_of_last_call |
|
|
123 | 123 | time_left = self.throttle_period - time_since_last_call |
|
|
124 | 124 | |
|
|
125 | 125 | if time_left > datetime.timedelta(seconds=0): |
|
|
126 | 126 | return |
|
|
127 | 127 | |
|
|
128 | 128 | self.time_of_last_call = datetime.datetime.now() |
|
|
129 | 129 | return fn(*args, **kwargs) |
|
|
130 | 130 | |
|
|
131 | 131 | return wrapper |
|
|
132 | 132 | |
|
|
133 | 133 | def apply_throttle(value): |
|
|
134 | 134 | |
|
|
135 | 135 | @Throttle(seconds=value) |
|
|
136 | 136 | def fnThrottled(fn): |
|
|
137 | 137 | fn() |
|
|
138 | 138 | |
|
|
139 | 139 | return fnThrottled |
|
|
140 | 140 | |
|
|
141 | 141 | @MPDecorator |
|
|
142 | 142 | class Plotter(ProcessingUnit): |
|
|
143 | 143 | ''' |
|
|
144 | 144 | Proccessing unit to handle plot operations |
|
|
145 | 145 | ''' |
|
|
146 | 146 | |
|
|
147 | 147 | def __init__(self): |
|
|
148 | 148 | |
|
|
149 | 149 | ProcessingUnit.__init__(self) |
|
|
150 | 150 | |
|
|
151 | 151 | def setup(self, **kwargs): |
|
|
152 | 152 | |
|
|
153 | 153 | self.connections = 0 |
|
|
154 | 154 | self.web_address = kwargs.get('web_server', False) |
|
|
155 | 155 | self.realtime = kwargs.get('realtime', False) |
|
|
156 | 156 | self.localtime = kwargs.get('localtime', True) |
|
|
157 | 157 | self.buffering = kwargs.get('buffering', True) |
|
|
158 | 158 | self.throttle = kwargs.get('throttle', 2) |
|
|
159 | 159 | self.exp_code = kwargs.get('exp_code', None) |
|
|
160 | 160 | self.set_ready = apply_throttle(self.throttle) |
|
|
161 | 161 | self.dates = [] |
|
|
162 | 162 | self.data = PlotterData( |
|
|
163 | 163 | self.plots, self.throttle, self.exp_code, self.buffering) |
|
|
164 | 164 | self.isConfig = True |
|
|
165 | 165 | |
|
|
166 | 166 | def ready(self): |
|
|
167 | 167 | ''' |
|
|
168 | 168 | Set dataOut ready |
|
|
169 | 169 | ''' |
|
|
170 | 170 | |
|
|
171 | 171 | self.data.ready = True |
|
|
172 | 172 | self.dataOut.data_plt = self.data |
|
|
173 | 173 | |
|
|
174 | 174 | def run(self, realtime=True, localtime=True, buffering=True, |
|
|
175 | 175 | throttle=2, exp_code=None, web_server=None): |
|
|
176 | 176 | |
|
|
177 | 177 | if not self.isConfig: |
|
|
178 | 178 | self.setup(realtime=realtime, localtime=localtime, |
|
|
179 | 179 | buffering=buffering, throttle=throttle, exp_code=exp_code, |
|
|
180 | 180 | web_server=web_server) |
|
|
181 | 181 | |
|
|
182 | 182 | if self.web_address: |
|
|
183 | 183 | log.success( |
|
|
184 | 184 | 'Sending to web: {}'.format(self.web_address), |
|
|
185 | 185 | self.name |
|
|
186 | 186 | ) |
|
|
187 | 187 | self.context = zmq.Context() |
|
|
188 | 188 | self.sender_web = self.context.socket(zmq.REQ) |
|
|
189 | 189 | self.sender_web.connect(self.web_address) |
|
|
190 | 190 | self.poll = zmq.Poller() |
|
|
191 | 191 | self.poll.register(self.sender_web, zmq.POLLIN) |
|
|
192 | 192 | time.sleep(1) |
|
|
193 | 193 | |
|
|
194 | 194 | # t = Thread(target=self.event_monitor, args=(monitor,)) |
|
|
195 | 195 | # t.start() |
|
|
196 | 196 | |
|
|
197 | 197 | self.dataOut = self.dataIn |
|
|
198 | 198 | self.data.ready = False |
|
|
199 | 199 | |
|
|
200 | 200 | if self.dataOut.flagNoData: |
|
|
201 | 201 | coerce = True |
|
|
202 | 202 | else: |
|
|
203 | 203 | coerce = False |
|
|
204 | 204 | |
|
|
205 | 205 | if self.dataOut.type == 'Parameters': |
|
|
206 | 206 | tm = self.dataOut.utctimeInit |
|
|
207 | 207 | else: |
|
|
208 | 208 | tm = self.dataOut.utctime |
|
|
209 | 209 | if self.dataOut.useLocalTime: |
|
|
210 | 210 | if not self.localtime: |
|
|
211 | 211 | tm += time.timezone |
|
|
212 | 212 | dt = datetime.datetime.fromtimestamp(tm).date() |
|
|
213 | 213 | else: |
|
|
214 | 214 | if self.localtime: |
|
|
215 | 215 | tm -= time.timezone |
|
|
216 | 216 | dt = datetime.datetime.utcfromtimestamp(tm).date() |
|
|
217 | 217 | if dt not in self.dates: |
|
|
218 | 218 | if self.data: |
|
|
219 | 219 | self.ready() |
|
|
220 | 220 | self.data.setup() |
|
|
221 | 221 | self.dates.append(dt) |
|
|
222 | 222 | |
|
|
223 | 223 | self.data.update(self.dataOut, tm) |
|
|
224 | 224 | |
|
|
225 | 225 | if False: # TODO check when publishers ends |
|
|
226 | 226 | self.connections -= 1 |
|
|
227 | 227 | if self.connections == 0 and dt in self.dates: |
|
|
228 | 228 | self.data.ended = True |
|
|
229 | 229 | self.ready() |
|
|
230 | 230 | time.sleep(1) |
|
|
231 | 231 | else: |
|
|
232 | 232 | if self.realtime: |
|
|
233 | 233 | self.ready() |
|
|
234 | 234 | if self.web_address: |
|
|
235 | 235 | retries = 5 |
|
|
236 | 236 | while True: |
|
|
237 | 237 | self.sender_web.send(self.data.jsonify()) |
|
|
238 | 238 | socks = dict(self.poll.poll(5000)) |
|
|
239 | 239 | if socks.get(self.sender_web) == zmq.POLLIN: |
|
|
240 | 240 | reply = self.sender_web.recv_string() |
|
|
241 | 241 | if reply == 'ok': |
|
|
242 | 242 | log.log("Response from server ok", self.name) |
|
|
243 | 243 | break |
|
|
244 | 244 | else: |
|
|
245 | 245 | log.warning( |
|
|
246 | 246 | "Malformed reply from server: {}".format(reply), self.name) |
|
|
247 | 247 | |
|
|
248 | 248 | else: |
|
|
249 | 249 | log.warning( |
|
|
250 | 250 | "No response from server, retrying...", self.name) |
|
|
251 | 251 | self.sender_web.setsockopt(zmq.LINGER, 0) |
|
|
252 | 252 | self.sender_web.close() |
|
|
253 | 253 | self.poll.unregister(self.sender_web) |
|
|
254 | 254 | retries -= 1 |
|
|
255 | 255 | if retries == 0: |
|
|
256 | 256 | log.error( |
|
|
257 | 257 | "Server seems to be offline, abandoning", self.name) |
|
|
258 | 258 | self.sender_web = self.context.socket(zmq.REQ) |
|
|
259 | 259 | self.sender_web.connect(self.web_address) |
|
|
260 | 260 | self.poll.register(self.sender_web, zmq.POLLIN) |
|
|
261 | 261 | time.sleep(1) |
|
|
262 | 262 | break |
|
|
263 | 263 | self.sender_web = self.context.socket(zmq.REQ) |
|
|
264 | 264 | self.sender_web.connect(self.web_address) |
|
|
265 | 265 | self.poll.register(self.sender_web, zmq.POLLIN) |
|
|
266 | 266 | time.sleep(1) |
|
|
267 | 267 | else: |
|
|
268 | 268 | self.set_ready(self.ready, coerce=coerce) |
|
|
269 | 269 | |
|
|
270 | 270 | return |
|
|
271 | 271 | |
|
|
272 | 272 | def close(self): |
|
|
273 | 273 | pass |
|
|
274 | 274 | |
|
|
275 | 275 | |
|
|
276 | 276 | @MPDecorator |
|
|
277 | 277 | class Plot(Operation): |
|
|
278 | 278 | ''' |
|
|
279 | 279 | Base class for Schain plotting operations |
|
|
280 | 280 | ''' |
|
|
281 | 281 | |
|
|
282 | 282 | CODE = 'Figure' |
|
|
283 | 283 | colormap = 'jro' |
|
|
284 | 284 | bgcolor = 'white' |
|
|
285 | 285 | __missing = 1E30 |
|
|
286 | 286 | |
|
|
287 | 287 | __attrs__ = ['show', 'save', 'xmin', 'xmax', 'ymin', 'ymax', 'zmin', 'zmax', |
|
|
288 | 288 | 'zlimits', 'xlabel', 'ylabel', 'xaxis', 'cb_label', 'title', |
|
|
289 | 289 | 'colorbar', 'bgcolor', 'width', 'height', 'localtime', 'oneFigure', |
|
|
290 | 290 | 'showprofile', 'decimation', 'pause'] |
|
|
291 | 291 | |
|
|
292 | 292 | def __init__(self): |
|
|
293 | 293 | |
|
|
294 | 294 | Operation.__init__(self) |
|
|
295 | 295 | self.isConfig = False |
|
|
296 | 296 | self.isPlotConfig = False |
|
|
297 | 297 | |
|
|
298 | 298 | def __fmtTime(self, x, pos): |
|
|
299 | 299 | ''' |
|
|
300 | 300 | ''' |
|
|
301 | 301 | |
|
|
302 | 302 | return '{}'.format(self.getDateTime(x).strftime('%H:%M')) |
|
|
303 | 303 | |
|
|
304 | 304 | def __setup(self, **kwargs): |
|
|
305 | 305 | ''' |
|
|
306 | 306 | Initialize variables |
|
|
307 | 307 | ''' |
|
|
308 | 308 | |
|
|
309 | 309 | self.figures = [] |
|
|
310 | 310 | self.axes = [] |
|
|
311 | 311 | self.cb_axes = [] |
|
|
312 | 312 | self.localtime = kwargs.pop('localtime', True) |
|
|
313 | 313 | self.show = kwargs.get('show', True) |
|
|
314 | 314 | self.save = kwargs.get('save', False) |
|
|
315 | 315 | self.ftp = kwargs.get('ftp', False) |
|
|
316 | 316 | self.colormap = kwargs.get('colormap', self.colormap) |
|
|
317 | 317 | self.colormap_coh = kwargs.get('colormap_coh', 'jet') |
|
|
318 | 318 | self.colormap_phase = kwargs.get('colormap_phase', 'RdBu_r') |
|
|
319 | 319 | self.colormaps = kwargs.get('colormaps', None) |
|
|
320 | 320 | self.bgcolor = kwargs.get('bgcolor', self.bgcolor) |
|
|
321 | 321 | self.showprofile = kwargs.get('showprofile', False) |
|
|
322 | 322 | self.title = kwargs.get('wintitle', self.CODE.upper()) |
|
|
323 | 323 | self.cb_label = kwargs.get('cb_label', None) |
|
|
324 | 324 | self.cb_labels = kwargs.get('cb_labels', None) |
|
|
325 | 325 | self.labels = kwargs.get('labels', None) |
|
|
326 | 326 | self.xaxis = kwargs.get('xaxis', 'frequency') |
|
|
327 | 327 | self.zmin = kwargs.get('zmin', None) |
|
|
328 | 328 | self.zmax = kwargs.get('zmax', None) |
|
|
329 | 329 | self.zlimits = kwargs.get('zlimits', None) |
|
|
330 | 330 | self.xmin = kwargs.get('xmin', None) |
|
|
331 | 331 | self.xmax = kwargs.get('xmax', None) |
|
|
332 | 332 | self.xrange = kwargs.get('xrange', 12) |
|
|
333 | 333 | self.xscale = kwargs.get('xscale', None) |
|
|
334 | 334 | self.ymin = kwargs.get('ymin', None) |
|
|
335 | 335 | self.ymax = kwargs.get('ymax', None) |
|
|
336 | 336 | self.yscale = kwargs.get('yscale', None) |
|
|
337 | 337 | self.xlabel = kwargs.get('xlabel', None) |
|
|
338 | 338 | self.decimation = kwargs.get('decimation', None) |
|
|
339 | 339 | self.showSNR = kwargs.get('showSNR', False) |
|
|
340 | 340 | self.oneFigure = kwargs.get('oneFigure', True) |
|
|
341 | 341 | self.width = kwargs.get('width', None) |
|
|
342 | 342 | self.height = kwargs.get('height', None) |
|
|
343 | 343 | self.colorbar = kwargs.get('colorbar', True) |
|
|
344 | 344 | self.factors = kwargs.get('factors', [1, 1, 1, 1, 1, 1, 1, 1]) |
|
|
345 | 345 | self.channels = kwargs.get('channels', None) |
|
|
346 | 346 | self.titles = kwargs.get('titles', []) |
|
|
347 | 347 | self.polar = False |
|
|
348 | self.type = kwargs.get('type', 'iq') | |
|
|
348 | 349 | self.grid = kwargs.get('grid', False) |
|
|
349 | 350 | self.pause = kwargs.get('pause', False) |
|
|
350 | 351 | self.save_labels = kwargs.get('save_labels', None) |
|
|
351 | 352 | self.realtime = kwargs.get('realtime', True) |
|
|
352 | 353 | self.buffering = kwargs.get('buffering', True) |
|
|
353 | 354 | self.throttle = kwargs.get('throttle', 2) |
|
|
354 | 355 | self.exp_code = kwargs.get('exp_code', None) |
|
|
355 | 356 | self.__throttle_plot = apply_throttle(self.throttle) |
|
|
356 | 357 | self.data = PlotterData( |
|
|
357 | 358 | self.CODE, self.throttle, self.exp_code, self.buffering) |
|
|
358 | 359 | |
|
|
359 | 360 | def __setup_plot(self): |
|
|
360 | 361 | ''' |
|
|
361 | 362 | Common setup for all figures, here figures and axes are created |
|
|
362 | 363 | ''' |
|
|
363 | 364 | |
|
|
364 | 365 | self.setup() |
|
|
365 | 366 | |
|
|
366 | 367 | self.time_label = 'LT' if self.localtime else 'UTC' |
|
|
367 | 368 | if self.data.localtime: |
|
|
368 | 369 | self.getDateTime = datetime.datetime.fromtimestamp |
|
|
369 | 370 | else: |
|
|
370 | 371 | self.getDateTime = datetime.datetime.utcfromtimestamp |
|
|
371 | 372 | |
|
|
372 | 373 | if self.width is None: |
|
|
373 | 374 | self.width = 8 |
|
|
374 | 375 | |
|
|
375 | 376 | self.figures = [] |
|
|
376 | 377 | self.axes = [] |
|
|
377 | 378 | self.cb_axes = [] |
|
|
378 | 379 | self.pf_axes = [] |
|
|
379 | 380 | self.cmaps = [] |
|
|
380 | 381 | |
|
|
381 | 382 | size = '15%' if self.ncols == 1 else '30%' |
|
|
382 | 383 | pad = '4%' if self.ncols == 1 else '8%' |
|
|
383 | 384 | |
|
|
384 | 385 | if self.oneFigure: |
|
|
385 | 386 | if self.height is None: |
|
|
386 | 387 | self.height = 1.4 * self.nrows + 1 |
|
|
387 | 388 | fig = plt.figure(figsize=(self.width, self.height), |
|
|
388 | 389 | edgecolor='k', |
|
|
389 | 390 | facecolor='w') |
|
|
390 | 391 | self.figures.append(fig) |
|
|
391 | 392 | for n in range(self.nplots): |
|
|
392 | 393 | ax = fig.add_subplot(self.nrows, self.ncols, |
|
|
393 | 394 | n + 1, polar=self.polar) |
|
|
394 | 395 | ax.tick_params(labelsize=8) |
|
|
395 | 396 | ax.firsttime = True |
|
|
396 | 397 | ax.index = 0 |
|
|
397 | 398 | ax.press = None |
|
|
398 | 399 | self.axes.append(ax) |
|
|
399 | 400 | if self.showprofile: |
|
|
400 | 401 | cax = self.__add_axes(ax, size=size, pad=pad) |
|
|
401 | 402 | cax.tick_params(labelsize=8) |
|
|
402 | 403 | self.pf_axes.append(cax) |
|
|
403 | 404 | else: |
|
|
404 | 405 | if self.height is None: |
|
|
405 | 406 | self.height = 3 |
|
|
406 | 407 | for n in range(self.nplots): |
|
|
407 | 408 | fig = plt.figure(figsize=(self.width, self.height), |
|
|
408 | 409 | edgecolor='k', |
|
|
409 | 410 | facecolor='w') |
|
|
410 | 411 | ax = fig.add_subplot(1, 1, 1, polar=self.polar) |
|
|
411 | 412 | ax.tick_params(labelsize=8) |
|
|
412 | 413 | ax.firsttime = True |
|
|
413 | 414 | ax.index = 0 |
|
|
414 | 415 | ax.press = None |
|
|
415 | 416 | self.figures.append(fig) |
|
|
416 | 417 | self.axes.append(ax) |
|
|
417 | 418 | if self.showprofile: |
|
|
418 | 419 | cax = self.__add_axes(ax, size=size, pad=pad) |
|
|
419 | 420 | cax.tick_params(labelsize=8) |
|
|
420 | 421 | self.pf_axes.append(cax) |
|
|
421 | 422 | |
|
|
422 | 423 | for n in range(self.nrows): |
|
|
423 | 424 | if self.colormaps is not None: |
|
|
424 | 425 | cmap = plt.get_cmap(self.colormaps[n]) |
|
|
425 | 426 | else: |
|
|
426 | 427 | cmap = plt.get_cmap(self.colormap) |
|
|
427 | 428 | cmap.set_bad(self.bgcolor, 1.) |
|
|
428 | 429 | self.cmaps.append(cmap) |
|
|
429 | 430 | |
|
|
430 | 431 | for fig in self.figures: |
|
|
431 | 432 | fig.canvas.mpl_connect('key_press_event', self.OnKeyPress) |
|
|
432 | 433 | fig.canvas.mpl_connect('scroll_event', self.OnBtnScroll) |
|
|
433 | 434 | fig.canvas.mpl_connect('button_press_event', self.onBtnPress) |
|
|
434 | 435 | fig.canvas.mpl_connect('motion_notify_event', self.onMotion) |
|
|
435 | 436 | fig.canvas.mpl_connect('button_release_event', self.onBtnRelease) |
|
|
436 | 437 | if self.show: |
|
|
437 | 438 | fig.show() |
|
|
438 | 439 | |
|
|
439 | 440 | def OnKeyPress(self, event): |
|
|
440 | 441 | ''' |
|
|
441 | 442 | Event for pressing keys (up, down) change colormap |
|
|
442 | 443 | ''' |
|
|
443 | 444 | ax = event.inaxes |
|
|
444 | 445 | if ax in self.axes: |
|
|
445 | 446 | if event.key == 'down': |
|
|
446 | 447 | ax.index += 1 |
|
|
447 | 448 | elif event.key == 'up': |
|
|
448 | 449 | ax.index -= 1 |
|
|
449 | 450 | if ax.index < 0: |
|
|
450 | 451 | ax.index = len(CMAPS) - 1 |
|
|
451 | 452 | elif ax.index == len(CMAPS): |
|
|
452 | 453 | ax.index = 0 |
|
|
453 | 454 | cmap = CMAPS[ax.index] |
|
|
454 | 455 | ax.cbar.set_cmap(cmap) |
|
|
455 | 456 | ax.cbar.draw_all() |
|
|
456 | 457 | ax.plt.set_cmap(cmap) |
|
|
457 | 458 | ax.cbar.patch.figure.canvas.draw() |
|
|
458 | 459 | self.colormap = cmap.name |
|
|
459 | 460 | |
|
|
460 | 461 | def OnBtnScroll(self, event): |
|
|
461 | 462 | ''' |
|
|
462 | 463 | Event for scrolling, scale figure |
|
|
463 | 464 | ''' |
|
|
464 | 465 | cb_ax = event.inaxes |
|
|
465 | 466 | if cb_ax in [ax.cbar.ax for ax in self.axes if ax.cbar]: |
|
|
466 | 467 | ax = [ax for ax in self.axes if cb_ax == ax.cbar.ax][0] |
|
|
467 | 468 | pt = ax.cbar.ax.bbox.get_points()[:, 1] |
|
|
468 | 469 | nrm = ax.cbar.norm |
|
|
469 | 470 | vmin, vmax, p0, p1, pS = ( |
|
|
470 | 471 | nrm.vmin, nrm.vmax, pt[0], pt[1], event.y) |
|
|
471 | 472 | scale = 2 if event.step == 1 else 0.5 |
|
|
472 | 473 | point = vmin + (vmax - vmin) / (p1 - p0) * (pS - p0) |
|
|
473 | 474 | ax.cbar.norm.vmin = point - scale * (point - vmin) |
|
|
474 | 475 | ax.cbar.norm.vmax = point - scale * (point - vmax) |
|
|
475 | 476 | ax.plt.set_norm(ax.cbar.norm) |
|
|
476 | 477 | ax.cbar.draw_all() |
|
|
477 | 478 | ax.cbar.patch.figure.canvas.draw() |
|
|
478 | 479 | |
|
|
479 | 480 | def onBtnPress(self, event): |
|
|
480 | 481 | ''' |
|
|
481 | 482 | Event for mouse button press |
|
|
482 | 483 | ''' |
|
|
483 | 484 | cb_ax = event.inaxes |
|
|
484 | 485 | if cb_ax is None: |
|
|
485 | 486 | return |
|
|
486 | 487 | |
|
|
487 | 488 | if cb_ax in [ax.cbar.ax for ax in self.axes if ax.cbar]: |
|
|
488 | 489 | cb_ax.press = event.x, event.y |
|
|
489 | 490 | else: |
|
|
490 | 491 | cb_ax.press = None |
|
|
491 | 492 | |
|
|
492 | 493 | def onMotion(self, event): |
|
|
493 | 494 | ''' |
|
|
494 | 495 | Event for move inside colorbar |
|
|
495 | 496 | ''' |
|
|
496 | 497 | cb_ax = event.inaxes |
|
|
497 | 498 | if cb_ax is None: |
|
|
498 | 499 | return |
|
|
499 | 500 | if cb_ax not in [ax.cbar.ax for ax in self.axes if ax.cbar]: |
|
|
500 | 501 | return |
|
|
501 | 502 | if cb_ax.press is None: |
|
|
502 | 503 | return |
|
|
503 | 504 | |
|
|
504 | 505 | ax = [ax for ax in self.axes if cb_ax == ax.cbar.ax][0] |
|
|
505 | 506 | xprev, yprev = cb_ax.press |
|
|
506 | 507 | dx = event.x - xprev |
|
|
507 | 508 | dy = event.y - yprev |
|
|
508 | 509 | cb_ax.press = event.x, event.y |
|
|
509 | 510 | scale = ax.cbar.norm.vmax - ax.cbar.norm.vmin |
|
|
510 | 511 | perc = 0.03 |
|
|
511 | 512 | |
|
|
512 | 513 | if event.button == 1: |
|
|
513 | 514 | ax.cbar.norm.vmin -= (perc * scale) * numpy.sign(dy) |
|
|
514 | 515 | ax.cbar.norm.vmax -= (perc * scale) * numpy.sign(dy) |
|
|
515 | 516 | elif event.button == 3: |
|
|
516 | 517 | ax.cbar.norm.vmin -= (perc * scale) * numpy.sign(dy) |
|
|
517 | 518 | ax.cbar.norm.vmax += (perc * scale) * numpy.sign(dy) |
|
|
518 | 519 | |
|
|
519 | 520 | ax.cbar.draw_all() |
|
|
520 | 521 | ax.plt.set_norm(ax.cbar.norm) |
|
|
521 | 522 | ax.cbar.patch.figure.canvas.draw() |
|
|
522 | 523 | |
|
|
523 | 524 | def onBtnRelease(self, event): |
|
|
524 | 525 | ''' |
|
|
525 | 526 | Event for mouse button release |
|
|
526 | 527 | ''' |
|
|
527 | 528 | cb_ax = event.inaxes |
|
|
528 | 529 | if cb_ax is not None: |
|
|
529 | 530 | cb_ax.press = None |
|
|
530 | 531 | |
|
|
531 | 532 | def __add_axes(self, ax, size='30%', pad='8%'): |
|
|
532 | 533 | ''' |
|
|
533 | 534 | Add new axes to the given figure |
|
|
534 | 535 | ''' |
|
|
535 | 536 | divider = make_axes_locatable(ax) |
|
|
536 | 537 | nax = divider.new_horizontal(size=size, pad=pad) |
|
|
537 | 538 | ax.figure.add_axes(nax) |
|
|
538 | 539 | return nax |
|
|
539 | 540 | |
|
|
540 | 541 | def setup(self): |
|
|
541 | 542 | ''' |
|
|
542 | 543 | This method should be implemented in the child class, the following |
|
|
543 | 544 | attributes should be set: |
|
|
544 | 545 | |
|
|
545 | 546 | self.nrows: number of rows |
|
|
546 | 547 | self.ncols: number of cols |
|
|
547 | 548 | self.nplots: number of plots (channels or pairs) |
|
|
548 | 549 | self.ylabel: label for Y axes |
|
|
549 | 550 | self.titles: list of axes title |
|
|
550 | 551 | |
|
|
551 | 552 | ''' |
|
|
552 | 553 | raise NotImplementedError |
|
|
553 | 554 | |
|
|
554 | 555 | def fill_gaps(self, x_buffer, y_buffer, z_buffer): |
|
|
555 | 556 | ''' |
|
|
556 | 557 | Create a masked array for missing data |
|
|
557 | 558 | ''' |
|
|
558 | 559 | if x_buffer.shape[0] < 2: |
|
|
559 | 560 | return x_buffer, y_buffer, z_buffer |
|
|
560 | 561 | |
|
|
561 | 562 | deltas = x_buffer[1:] - x_buffer[0:-1] |
|
|
562 | 563 | x_median = numpy.median(deltas) |
|
|
563 | 564 | |
|
|
564 | 565 | index = numpy.where(deltas > 5 * x_median) |
|
|
565 | 566 | |
|
|
566 | 567 | if len(index[0]) != 0: |
|
|
567 | 568 | z_buffer[::, index[0], ::] = self.__missing |
|
|
568 | 569 | z_buffer = numpy.ma.masked_inside(z_buffer, |
|
|
569 | 570 | 0.99 * self.__missing, |
|
|
570 | 571 | 1.01 * self.__missing) |
|
|
571 | 572 | |
|
|
572 | 573 | return x_buffer, y_buffer, z_buffer |
|
|
573 | 574 | |
|
|
574 | 575 | def decimate(self): |
|
|
575 | 576 | |
|
|
576 | 577 | # dx = int(len(self.x)/self.__MAXNUMX) + 1 |
|
|
577 | 578 | dy = int(len(self.y) / self.decimation) + 1 |
|
|
578 | 579 | |
|
|
579 | 580 | # x = self.x[::dx] |
|
|
580 | 581 | x = self.x |
|
|
581 | 582 | y = self.y[::dy] |
|
|
582 | 583 | z = self.z[::, ::, ::dy] |
|
|
583 | 584 | |
|
|
584 | 585 | return x, y, z |
|
|
585 | 586 | |
|
|
586 | 587 | def format(self): |
|
|
587 | 588 | ''' |
|
|
588 | 589 | Set min and max values, labels, ticks and titles |
|
|
589 | 590 | ''' |
|
|
590 | 591 | |
|
|
591 | 592 | if self.xmin is None: |
|
|
592 | 593 | xmin = self.data.min_time |
|
|
593 | 594 | else: |
|
|
594 | 595 | if self.xaxis is 'time': |
|
|
595 | 596 | dt = self.getDateTime(self.data.min_time) |
|
|
596 | 597 | xmin = (dt.replace(hour=int(self.xmin), minute=0, second=0) - |
|
|
597 | 598 | datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
598 | 599 | if self.data.localtime: |
|
|
599 | 600 | xmin += time.timezone |
|
|
600 | 601 | else: |
|
|
601 | 602 | xmin = self.xmin |
|
|
602 | 603 | |
|
|
603 | 604 | if self.xmax is None: |
|
|
604 | 605 | xmax = xmin + self.xrange * 60 * 60 |
|
|
605 | 606 | else: |
|
|
606 | 607 | if self.xaxis is 'time': |
|
|
607 | 608 | dt = self.getDateTime(self.data.max_time) |
|
|
608 | 609 | xmax = (dt.replace(hour=int(self.xmax), minute=59, second=59) - |
|
|
609 | 610 | datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=1)).total_seconds() |
|
|
610 | 611 | if self.data.localtime: |
|
|
611 | 612 | xmax += time.timezone |
|
|
612 | 613 | else: |
|
|
613 | 614 | xmax = self.xmax |
|
|
614 | ||
|
|
615 | ||
|
|
615 | 616 | ymin = self.ymin if self.ymin else numpy.nanmin(self.y) |
|
|
616 | 617 | ymax = self.ymax if self.ymax else numpy.nanmax(self.y) |
|
|
617 | Y = numpy.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000]) | |
|
|
618 | i = 1 if numpy.where( | |
|
|
619 | abs(ymax-ymin) <= Y)[0][0] < 0 else numpy.where(abs(ymax-ymin) <= Y)[0][0] | |
|
|
620 | ystep = Y[i] / 10. | |
|
|
621 | ||
|
|
618 | Y = numpy.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000]) | |
|
|
619 | #i = 1 if numpy.where( | |
|
|
620 | # abs(ymax-ymin) <= Y)[0][0] < 0 else numpy.where(abs(ymax-ymin) <= Y)[0][0] | |
|
|
621 | #ystep = Y[i] / 10. | |
|
|
622 | ystep = round(ymax,-1)//5 | |
|
|
622 | 623 | if self.xaxis is not 'time': |
|
|
623 | 624 | X = numpy.array([0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, |
|
|
624 | 200, 500, 1000, 2000, 5000])/2. | |
|
|
625 | 200, 500, 1000, 2000, 5000, 10000, 20000, 50000])/2. | |
|
|
626 | ||
|
|
625 | 627 | i = 1 if numpy.where( |
|
|
626 | 628 | abs(xmax-xmin) <= X)[0][0] < 0 else numpy.where(abs(xmax-xmin) <= X)[0][0] |
|
|
627 | 629 | xstep = X[i] / 5. |
|
|
628 | 630 | |
|
|
629 | 631 | for n, ax in enumerate(self.axes): |
|
|
630 | 632 | if ax.firsttime: |
|
|
631 | 633 | ax.set_facecolor(self.bgcolor) |
|
|
632 | 634 | ax.yaxis.set_major_locator(MultipleLocator(ystep)) |
|
|
633 | 635 | if self.xscale: |
|
|
634 | 636 | ax.xaxis.set_major_formatter(FuncFormatter( |
|
|
635 | 637 | lambda x, pos: '{0:g}'.format(x*self.xscale))) |
|
|
636 | 638 | if self.xscale: |
|
|
637 | 639 | ax.yaxis.set_major_formatter(FuncFormatter( |
|
|
638 | 640 | lambda x, pos: '{0:g}'.format(x*self.yscale))) |
|
|
639 | 641 | if self.xaxis is 'time': |
|
|
640 | 642 | ax.xaxis.set_major_formatter(FuncFormatter(self.__fmtTime)) |
|
|
641 | 643 | ax.xaxis.set_major_locator(LinearLocator(9)) |
|
|
642 | 644 | else: |
|
|
643 | 645 | ax.xaxis.set_major_locator(MultipleLocator(xstep)) |
|
|
644 | 646 | if self.xlabel is not None: |
|
|
645 | 647 | ax.set_xlabel(self.xlabel) |
|
|
646 | 648 | ax.set_ylabel(self.ylabel) |
|
|
647 | 649 | ax.firsttime = False |
|
|
648 | 650 | if self.showprofile: |
|
|
649 | 651 | self.pf_axes[n].set_ylim(ymin, ymax) |
|
|
650 | 652 | self.pf_axes[n].set_xlim(self.zmin, self.zmax) |
|
|
651 | 653 | self.pf_axes[n].set_xlabel('dB') |
|
|
652 | 654 | self.pf_axes[n].grid(b=True, axis='x') |
|
|
653 | 655 | [tick.set_visible(False) |
|
|
654 | 656 | for tick in self.pf_axes[n].get_yticklabels()] |
|
|
655 | 657 | if self.colorbar: |
|
|
656 | 658 | ax.cbar = plt.colorbar( |
|
|
657 | 659 | ax.plt, ax=ax, fraction=0.05, pad=0.02, aspect=10) |
|
|
658 | 660 | ax.cbar.ax.tick_params(labelsize=8) |
|
|
659 | 661 | ax.cbar.ax.press = None |
|
|
660 | 662 | if self.cb_label: |
|
|
661 | 663 | ax.cbar.set_label(self.cb_label, size=8) |
|
|
662 | 664 | elif self.cb_labels: |
|
|
663 | 665 | ax.cbar.set_label(self.cb_labels[n], size=8) |
|
|
664 | 666 | else: |
|
|
665 | 667 | ax.cbar = None |
|
|
666 | 668 | if self.grid: |
|
|
667 | 669 | ax.grid(True) |
|
|
668 | 670 | |
|
|
669 | 671 | if not self.polar: |
|
|
670 | 672 | ax.set_xlim(xmin, xmax) |
|
|
671 | 673 | ax.set_ylim(ymin, ymax) |
|
|
672 | 674 | ax.set_title('{} {} {}'.format( |
|
|
673 | 675 | self.titles[n], |
|
|
674 | 676 | self.getDateTime(self.data.max_time).strftime( |
|
|
675 | 677 | '%H:%M:%S'), |
|
|
676 | 678 | self.time_label), |
|
|
677 | 679 | size=8) |
|
|
678 | 680 | else: |
|
|
679 | 681 | ax.set_title('{}'.format(self.titles[n]), size=8) |
|
|
680 | 682 | ax.set_ylim(0, 90) |
|
|
681 | 683 | ax.set_yticks(numpy.arange(0, 90, 20)) |
|
|
682 | 684 | ax.yaxis.labelpad = 40 |
|
|
683 | 685 | |
|
|
684 | 686 | def clear_figures(self): |
|
|
685 | 687 | ''' |
|
|
686 | 688 | Reset axes for redraw plots |
|
|
687 | 689 | ''' |
|
|
688 | 690 | |
|
|
689 | 691 | for ax in self.axes: |
|
|
690 | 692 | ax.clear() |
|
|
691 | 693 | ax.firsttime = True |
|
|
692 | 694 | if ax.cbar: |
|
|
693 | 695 | ax.cbar.remove() |
|
|
694 | 696 | |
|
|
695 | 697 | def __plot(self): |
|
|
696 | 698 | ''' |
|
|
697 | 699 | Main function to plot, format and save figures |
|
|
698 | 700 | ''' |
|
|
699 | 701 | |
|
|
700 | 702 | #try: |
|
|
701 | 703 | self.plot() |
|
|
702 | 704 | self.format() |
|
|
703 | 705 | #except Exception as e: |
|
|
704 | 706 | # log.warning('{} Plot could not be updated... check data'.format( |
|
|
705 | 707 | # self.CODE), self.name) |
|
|
706 | 708 | # log.error(str(e), '') |
|
|
707 | 709 | # return |
|
|
708 | 710 | |
|
|
709 | 711 | for n, fig in enumerate(self.figures): |
|
|
710 | 712 | if self.nrows == 0 or self.nplots == 0: |
|
|
711 | 713 | log.warning('No data', self.name) |
|
|
712 | 714 | fig.text(0.5, 0.5, 'No Data', fontsize='large', ha='center') |
|
|
713 | 715 | fig.canvas.manager.set_window_title(self.CODE) |
|
|
714 | 716 | continue |
|
|
715 | 717 | |
|
|
716 | 718 | fig.tight_layout() |
|
|
717 | 719 | fig.canvas.manager.set_window_title('{} - {}'.format(self.title, |
|
|
718 | 720 | self.getDateTime(self.data.max_time).strftime('%Y/%m/%d'))) |
|
|
719 | 721 | fig.canvas.draw() |
|
|
720 | 722 | |
|
|
721 | 723 | if self.save: |
|
|
722 | 724 | |
|
|
723 | 725 | if self.save_labels: |
|
|
724 | 726 | labels = self.save_labels |
|
|
725 | 727 | else: |
|
|
726 | 728 | labels = list(range(self.nrows)) |
|
|
727 | 729 | |
|
|
728 | 730 | if self.oneFigure: |
|
|
729 | 731 | label = '' |
|
|
730 | 732 | else: |
|
|
731 | 733 | label = '-{}'.format(labels[n]) |
|
|
732 | 734 | figname = os.path.join( |
|
|
733 | 735 | self.save, |
|
|
734 | 736 | self.CODE, |
|
|
735 | 737 | '{}{}_{}.png'.format( |
|
|
736 | 738 | self.CODE, |
|
|
737 | 739 | label, |
|
|
738 | 740 | self.getDateTime(self.data.max_time).strftime( |
|
|
739 | 741 | '%Y%m%d_%H%M%S'), |
|
|
740 | 742 | ) |
|
|
741 | 743 | ) |
|
|
742 | 744 | log.log('Saving figure: {}'.format(figname), self.name) |
|
|
743 | 745 | if not os.path.isdir(os.path.dirname(figname)): |
|
|
744 | 746 | os.makedirs(os.path.dirname(figname)) |
|
|
745 | 747 | fig.savefig(figname) |
|
|
746 | 748 | |
|
|
747 | 749 | def plot(self): |
|
|
748 | 750 | ''' |
|
|
749 | 751 | Must be defined in the child class |
|
|
750 | 752 | ''' |
|
|
751 | 753 | raise NotImplementedError |
|
|
752 | 754 | |
|
|
753 | 755 | def run(self, dataOut, **kwargs): |
|
|
754 | 756 | |
|
|
755 | 757 | if dataOut.error: |
|
|
756 | 758 | coerce = True |
|
|
757 | 759 | else: |
|
|
758 | 760 | coerce = False |
|
|
759 | 761 | |
|
|
760 | 762 | if self.isConfig is False: |
|
|
761 | 763 | self.__setup(**kwargs) |
|
|
762 | 764 | self.data.setup() |
|
|
763 | 765 | self.isConfig = True |
|
|
764 | 766 | |
|
|
765 | 767 | if dataOut.type == 'Parameters': |
|
|
766 | 768 | tm = dataOut.utctimeInit |
|
|
767 | 769 | else: |
|
|
768 | 770 | tm = dataOut.utctime |
|
|
769 | 771 | |
|
|
770 | 772 | if dataOut.useLocalTime: |
|
|
771 | 773 | if not self.localtime: |
|
|
772 | 774 | tm += time.timezone |
|
|
773 | 775 | else: |
|
|
774 | 776 | if self.localtime: |
|
|
775 | 777 | tm -= time.timezone |
|
|
776 | 778 | |
|
|
777 | 779 | if self.data and (tm - self.data.min_time) >= self.xrange*60*60: |
|
|
778 | 780 | self.__plot() |
|
|
779 | 781 | self.data.setup() |
|
|
780 | 782 | self.clear_figures() |
|
|
781 | 783 | |
|
|
782 | 784 | self.data.update(dataOut, tm) |
|
|
783 | 785 | |
|
|
784 | 786 | if self.isPlotConfig is False: |
|
|
785 | 787 | self.__setup_plot() |
|
|
786 | 788 | self.isPlotConfig = True |
|
|
787 | 789 | |
|
|
788 | 790 | if self.realtime: |
|
|
789 | 791 | self.__plot() |
|
|
790 | 792 | else: |
|
|
791 | 793 | self.__throttle_plot(self.__plot, coerce=coerce) |
|
|
792 | 794 | |
|
|
793 | 795 | figpause(0.001) |
|
|
794 | 796 | |
|
|
795 | 797 | def close(self): |
|
|
796 | 798 | |
|
|
797 | 799 | if self.data and self.pause: |
|
|
798 | 800 | figpause(10) |
|
|
799 | 801 | |
| @@ -1,616 +1,748 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | New Plots Operations |
|
|
3 | 3 | |
|
|
4 | 4 | @author: juan.espinoza@jro.igp.gob.pe |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | |
|
|
8 | 8 | import time |
|
|
9 | 9 | import datetime |
|
|
10 | 10 | import numpy |
|
|
11 | 11 | |
|
|
12 | 12 | from schainpy.model.graphics.jroplot_base import Plot, plt |
|
|
13 | 13 | from schainpy.utils import log |
|
|
14 | 14 | |
|
|
15 | 15 | EARTH_RADIUS = 6.3710e3 |
|
|
16 | 16 | |
|
|
17 | 17 | |
|
|
18 | 18 | def ll2xy(lat1, lon1, lat2, lon2): |
|
|
19 | 19 | |
|
|
20 | 20 | p = 0.017453292519943295 |
|
|
21 | 21 | a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \ |
|
|
22 | 22 | numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2 |
|
|
23 | 23 | r = 12742 * numpy.arcsin(numpy.sqrt(a)) |
|
|
24 | 24 | theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p) |
|
|
25 | 25 | * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p)) |
|
|
26 | 26 | theta = -theta + numpy.pi/2 |
|
|
27 | 27 | return r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
28 | 28 | |
|
|
29 | 29 | |
|
|
30 | 30 | def km2deg(km): |
|
|
31 | 31 | ''' |
|
|
32 | 32 | Convert distance in km to degrees |
|
|
33 | 33 | ''' |
|
|
34 | 34 | |
|
|
35 | 35 | return numpy.rad2deg(km/EARTH_RADIUS) |
|
|
36 | 36 | |
|
|
37 | 37 | |
|
|
38 | 38 | class SpectraPlot(Plot): |
|
|
39 | 39 | ''' |
|
|
40 | 40 | Plot for Spectra data |
|
|
41 | 41 | ''' |
|
|
42 | 42 | |
|
|
43 | 43 | CODE = 'spc' |
|
|
44 | 44 | colormap = 'jro' |
|
|
45 | 45 | |
|
|
46 | 46 | def setup(self): |
|
|
47 | 47 | self.nplots = len(self.data.channels) |
|
|
48 | 48 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
49 | 49 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
50 | 50 | self.width = 3.4 * self.ncols |
|
|
51 | 51 | self.height = 3 * self.nrows |
|
|
52 | 52 | self.cb_label = 'dB' |
|
|
53 | 53 | if self.showprofile: |
|
|
54 | 54 | self.width += 0.8 * self.ncols |
|
|
55 | 55 | |
|
|
56 | 56 | self.ylabel = 'Range [km]' |
|
|
57 | 57 | |
|
|
58 | 58 | def plot(self): |
|
|
59 | 59 | if self.xaxis == "frequency": |
|
|
60 | 60 | x = self.data.xrange[0] |
|
|
61 | 61 | self.xlabel = "Frequency (kHz)" |
|
|
62 | 62 | elif self.xaxis == "time": |
|
|
63 | 63 | x = self.data.xrange[1] |
|
|
64 | 64 | self.xlabel = "Time (ms)" |
|
|
65 | 65 | else: |
|
|
66 | 66 | x = self.data.xrange[2] |
|
|
67 | 67 | self.xlabel = "Velocity (m/s)" |
|
|
68 | 68 | |
|
|
69 | 69 | if self.CODE == 'spc_mean': |
|
|
70 | 70 | x = self.data.xrange[2] |
|
|
71 | 71 | self.xlabel = "Velocity (m/s)" |
|
|
72 | 72 | |
|
|
73 | 73 | self.titles = [] |
|
|
74 | 74 | |
|
|
75 | 75 | y = self.data.heights |
|
|
76 | 76 | self.y = y |
|
|
77 | 77 | z = self.data['spc'] |
|
|
78 | 78 | |
|
|
79 | 79 | for n, ax in enumerate(self.axes): |
|
|
80 | 80 | noise = self.data['noise'][n][-1] |
|
|
81 | 81 | if self.CODE == 'spc_mean': |
|
|
82 | 82 | mean = self.data['mean'][n][-1] |
|
|
83 | 83 | if ax.firsttime: |
|
|
84 | 84 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
85 | 85 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
86 | 86 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
87 | 87 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
88 | 88 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
89 | 89 | vmin=self.zmin, |
|
|
90 | 90 | vmax=self.zmax, |
|
|
91 | 91 | cmap=plt.get_cmap(self.colormap) |
|
|
92 | 92 | ) |
|
|
93 | 93 | |
|
|
94 | 94 | if self.showprofile: |
|
|
95 | 95 | ax.plt_profile = self.pf_axes[n].plot( |
|
|
96 | 96 | self.data['rti'][n][-1], y)[0] |
|
|
97 | 97 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
|
|
98 | 98 | color="k", linestyle="dashed", lw=1)[0] |
|
|
99 | 99 | if self.CODE == 'spc_mean': |
|
|
100 | 100 | ax.plt_mean = ax.plot(mean, y, color='k')[0] |
|
|
101 | 101 | else: |
|
|
102 | 102 | ax.plt.set_array(z[n].T.ravel()) |
|
|
103 | 103 | if self.showprofile: |
|
|
104 | 104 | ax.plt_profile.set_data(self.data['rti'][n][-1], y) |
|
|
105 | 105 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
|
|
106 | 106 | if self.CODE == 'spc_mean': |
|
|
107 | 107 | ax.plt_mean.set_data(mean, y) |
|
|
108 | 108 | |
|
|
109 | 109 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
|
110 | 110 | |
|
|
111 | 111 | |
|
|
112 | 112 | class CrossSpectraPlot(Plot): |
|
|
113 | 113 | |
|
|
114 | 114 | CODE = 'cspc' |
|
|
115 | 115 | colormap = 'jet' |
|
|
116 | 116 | zmin_coh = None |
|
|
117 | 117 | zmax_coh = None |
|
|
118 | 118 | zmin_phase = None |
|
|
119 | 119 | zmax_phase = None |
|
|
120 | 120 | |
|
|
121 | 121 | def setup(self): |
|
|
122 | 122 | |
|
|
123 | 123 | self.ncols = 4 |
|
|
124 | 124 | self.nrows = len(self.data.pairs) |
|
|
125 | 125 | self.nplots = self.nrows * 4 |
|
|
126 | 126 | self.width = 3.4 * self.ncols |
|
|
127 | 127 | self.height = 3 * self.nrows |
|
|
128 | 128 | self.ylabel = 'Range [km]' |
|
|
129 | 129 | self.showprofile = False |
|
|
130 | 130 | |
|
|
131 | 131 | def plot(self): |
|
|
132 | 132 | |
|
|
133 | 133 | if self.xaxis == "frequency": |
|
|
134 | 134 | x = self.data.xrange[0] |
|
|
135 | 135 | self.xlabel = "Frequency (kHz)" |
|
|
136 | 136 | elif self.xaxis == "time": |
|
|
137 | 137 | x = self.data.xrange[1] |
|
|
138 | 138 | self.xlabel = "Time (ms)" |
|
|
139 | 139 | else: |
|
|
140 | 140 | x = self.data.xrange[2] |
|
|
141 | 141 | self.xlabel = "Velocity (m/s)" |
|
|
142 | 142 | |
|
|
143 | 143 | self.titles = [] |
|
|
144 | 144 | |
|
|
145 | 145 | y = self.data.heights |
|
|
146 | 146 | self.y = y |
|
|
147 | 147 | spc = self.data['spc'] |
|
|
148 | 148 | cspc = self.data['cspc'] |
|
|
149 | 149 | |
|
|
150 | 150 | for n in range(self.nrows): |
|
|
151 | 151 | noise = self.data['noise'][n][-1] |
|
|
152 | 152 | pair = self.data.pairs[n] |
|
|
153 | 153 | ax = self.axes[4 * n] |
|
|
154 | 154 | spc0 = 10.*numpy.log10(spc[pair[0]]/self.data.factor) |
|
|
155 | 155 | if ax.firsttime: |
|
|
156 | 156 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
157 | 157 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
158 | 158 | self.zmin = self.zmin if self.zmin else numpy.nanmin(spc) |
|
|
159 | 159 | self.zmax = self.zmax if self.zmax else numpy.nanmax(spc) |
|
|
160 | 160 | ax.plt = ax.pcolormesh(x , y , spc0.T, |
|
|
161 | 161 | vmin=self.zmin, |
|
|
162 | 162 | vmax=self.zmax, |
|
|
163 | 163 | cmap=plt.get_cmap(self.colormap) |
|
|
164 | 164 | ) |
|
|
165 | 165 | else: |
|
|
166 | 166 | ax.plt.set_array(spc0.T.ravel()) |
|
|
167 | 167 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise)) |
|
|
168 | 168 | |
|
|
169 | 169 | ax = self.axes[4 * n + 1] |
|
|
170 | 170 | spc1 = 10.*numpy.log10(spc[pair[1]]/self.data.factor) |
|
|
171 | 171 | if ax.firsttime: |
|
|
172 | 172 | ax.plt = ax.pcolormesh(x , y, spc1.T, |
|
|
173 | 173 | vmin=self.zmin, |
|
|
174 | 174 | vmax=self.zmax, |
|
|
175 | 175 | cmap=plt.get_cmap(self.colormap) |
|
|
176 | 176 | ) |
|
|
177 | 177 | else: |
|
|
178 | 178 | ax.plt.set_array(spc1.T.ravel()) |
|
|
179 | 179 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise)) |
|
|
180 | 180 | |
|
|
181 | 181 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
|
|
182 | 182 | coh = numpy.abs(out) |
|
|
183 | 183 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
|
|
184 | 184 | |
|
|
185 | 185 | ax = self.axes[4 * n + 2] |
|
|
186 | 186 | if ax.firsttime: |
|
|
187 | 187 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
188 | 188 | vmin=0, |
|
|
189 | 189 | vmax=1, |
|
|
190 | 190 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
191 | 191 | ) |
|
|
192 | 192 | else: |
|
|
193 | 193 | ax.plt.set_array(coh.T.ravel()) |
|
|
194 | 194 | self.titles.append( |
|
|
195 | 195 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
196 | 196 | |
|
|
197 | 197 | ax = self.axes[4 * n + 3] |
|
|
198 | 198 | if ax.firsttime: |
|
|
199 | 199 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
200 | 200 | vmin=-180, |
|
|
201 | 201 | vmax=180, |
|
|
202 | 202 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
203 | 203 | ) |
|
|
204 | 204 | else: |
|
|
205 | 205 | ax.plt.set_array(phase.T.ravel()) |
|
|
206 | 206 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
207 | 207 | |
|
|
208 | 208 | |
|
|
209 | 209 | class SpectraMeanPlot(SpectraPlot): |
|
|
210 | 210 | ''' |
|
|
211 | 211 | Plot for Spectra and Mean |
|
|
212 | 212 | ''' |
|
|
213 | 213 | CODE = 'spc_mean' |
|
|
214 | 214 | colormap = 'jro' |
|
|
215 | 215 | |
|
|
216 | 216 | |
|
|
217 | 217 | class RTIPlot(Plot): |
|
|
218 | 218 | ''' |
|
|
219 | 219 | Plot for RTI data |
|
|
220 | 220 | ''' |
|
|
221 | 221 | |
|
|
222 | 222 | CODE = 'rti' |
|
|
223 | 223 | colormap = 'jro' |
|
|
224 | 224 | |
|
|
225 | 225 | def setup(self): |
|
|
226 | 226 | self.xaxis = 'time' |
|
|
227 | 227 | self.ncols = 1 |
|
|
228 | 228 | self.nrows = len(self.data.channels) |
|
|
229 | 229 | self.nplots = len(self.data.channels) |
|
|
230 | 230 | self.ylabel = 'Range [km]' |
|
|
231 | 231 | self.cb_label = 'dB' |
|
|
232 | 232 | self.titles = ['{} Channel {}'.format( |
|
|
233 | 233 | self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
234 | 234 | |
|
|
235 | 235 | def plot(self): |
|
|
236 | 236 | self.x = self.data.times |
|
|
237 | 237 | self.y = self.data.heights |
|
|
238 | 238 | self.z = self.data[self.CODE] |
|
|
239 | 239 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
240 | 240 | |
|
|
241 | 241 | if self.decimation is None: |
|
|
242 | 242 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
243 | 243 | else: |
|
|
244 | 244 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
245 | 245 | |
|
|
246 | 246 | for n, ax in enumerate(self.axes): |
|
|
247 | 247 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
248 | 248 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
249 | 249 | if ax.firsttime: |
|
|
250 | 250 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
251 | 251 | vmin=self.zmin, |
|
|
252 | 252 | vmax=self.zmax, |
|
|
253 | 253 | cmap=plt.get_cmap(self.colormap) |
|
|
254 | 254 | ) |
|
|
255 | 255 | if self.showprofile: |
|
|
256 | 256 | ax.plot_profile = self.pf_axes[n].plot( |
|
|
257 | 257 | self.data['rti'][n][-1], self.y)[0] |
|
|
258 | 258 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y, |
|
|
259 | 259 | color="k", linestyle="dashed", lw=1)[0] |
|
|
260 | 260 | else: |
|
|
261 | 261 | ax.collections.remove(ax.collections[0]) |
|
|
262 | 262 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
263 | 263 | vmin=self.zmin, |
|
|
264 | 264 | vmax=self.zmax, |
|
|
265 | 265 | cmap=plt.get_cmap(self.colormap) |
|
|
266 | 266 | ) |
|
|
267 | 267 | if self.showprofile: |
|
|
268 | 268 | ax.plot_profile.set_data(self.data['rti'][n][-1], self.y) |
|
|
269 | 269 | ax.plot_noise.set_data(numpy.repeat( |
|
|
270 | 270 | self.data['noise'][n][-1], len(self.y)), self.y) |
|
|
271 | 271 | |
|
|
272 | 272 | |
|
|
273 | 273 | class CoherencePlot(RTIPlot): |
|
|
274 | 274 | ''' |
|
|
275 | 275 | Plot for Coherence data |
|
|
276 | 276 | ''' |
|
|
277 | 277 | |
|
|
278 | 278 | CODE = 'coh' |
|
|
279 | 279 | |
|
|
280 | 280 | def setup(self): |
|
|
281 | 281 | self.xaxis = 'time' |
|
|
282 | 282 | self.ncols = 1 |
|
|
283 | 283 | self.nrows = len(self.data.pairs) |
|
|
284 | 284 | self.nplots = len(self.data.pairs) |
|
|
285 | 285 | self.ylabel = 'Range [km]' |
|
|
286 | 286 | if self.CODE == 'coh': |
|
|
287 | 287 | self.cb_label = '' |
|
|
288 | 288 | self.titles = [ |
|
|
289 | 289 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
290 | 290 | else: |
|
|
291 | 291 | self.cb_label = 'Degrees' |
|
|
292 | 292 | self.titles = [ |
|
|
293 | 293 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
294 | 294 | |
|
|
295 | 295 | |
|
|
296 | 296 | class PhasePlot(CoherencePlot): |
|
|
297 | 297 | ''' |
|
|
298 | 298 | Plot for Phase map data |
|
|
299 | 299 | ''' |
|
|
300 | 300 | |
|
|
301 | 301 | CODE = 'phase' |
|
|
302 | 302 | colormap = 'seismic' |
|
|
303 | 303 | |
|
|
304 | 304 | |
|
|
305 | 305 | class NoisePlot(Plot): |
|
|
306 | 306 | ''' |
|
|
307 | 307 | Plot for noise |
|
|
308 | 308 | ''' |
|
|
309 | 309 | |
|
|
310 | 310 | CODE = 'noise' |
|
|
311 | 311 | |
|
|
312 | 312 | def setup(self): |
|
|
313 | 313 | self.xaxis = 'time' |
|
|
314 | 314 | self.ncols = 1 |
|
|
315 | 315 | self.nrows = 1 |
|
|
316 | 316 | self.nplots = 1 |
|
|
317 | 317 | self.ylabel = 'Intensity [dB]' |
|
|
318 | 318 | self.titles = ['Noise'] |
|
|
319 | 319 | self.colorbar = False |
|
|
320 | 320 | |
|
|
321 | 321 | def plot(self): |
|
|
322 | 322 | |
|
|
323 | 323 | x = self.data.times |
|
|
324 | 324 | xmin = self.data.min_time |
|
|
325 | 325 | xmax = xmin + self.xrange * 60 * 60 |
|
|
326 | 326 | Y = self.data[self.CODE] |
|
|
327 | 327 | |
|
|
328 | 328 | if self.axes[0].firsttime: |
|
|
329 | 329 | for ch in self.data.channels: |
|
|
330 | 330 | y = Y[ch] |
|
|
331 | 331 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
332 | 332 | plt.legend() |
|
|
333 | 333 | else: |
|
|
334 | 334 | for ch in self.data.channels: |
|
|
335 | 335 | y = Y[ch] |
|
|
336 | 336 | self.axes[0].lines[ch].set_data(x, y) |
|
|
337 | 337 | |
|
|
338 | 338 | self.ymin = numpy.nanmin(Y) - 5 |
|
|
339 | 339 | self.ymax = numpy.nanmax(Y) + 5 |
|
|
340 | 340 | |
|
|
341 | 341 | |
|
|
342 | 342 | class SnrPlot(RTIPlot): |
|
|
343 | 343 | ''' |
|
|
344 | 344 | Plot for SNR Data |
|
|
345 | 345 | ''' |
|
|
346 | 346 | |
|
|
347 | 347 | CODE = 'snr' |
|
|
348 | 348 | colormap = 'jet' |
|
|
349 | 349 | |
|
|
350 | 350 | |
|
|
351 | 351 | class DopplerPlot(RTIPlot): |
|
|
352 | 352 | ''' |
|
|
353 | 353 | Plot for DOPPLER Data |
|
|
354 | 354 | ''' |
|
|
355 | 355 | |
|
|
356 | 356 | CODE = 'dop' |
|
|
357 | 357 | colormap = 'jet' |
|
|
358 | 358 | |
|
|
359 | 359 | |
|
|
360 | 360 | class SkyMapPlot(Plot): |
|
|
361 | 361 | ''' |
|
|
362 | 362 | Plot for meteors detection data |
|
|
363 | 363 | ''' |
|
|
364 | 364 | |
|
|
365 | 365 | CODE = 'param' |
|
|
366 | 366 | |
|
|
367 | 367 | def setup(self): |
|
|
368 | 368 | |
|
|
369 | 369 | self.ncols = 1 |
|
|
370 | 370 | self.nrows = 1 |
|
|
371 | 371 | self.width = 7.2 |
|
|
372 | 372 | self.height = 7.2 |
|
|
373 | 373 | self.nplots = 1 |
|
|
374 | 374 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
375 | 375 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
376 | 376 | self.polar = True |
|
|
377 | 377 | self.ymin = -180 |
|
|
378 | 378 | self.ymax = 180 |
|
|
379 | 379 | self.colorbar = False |
|
|
380 | 380 | |
|
|
381 | 381 | def plot(self): |
|
|
382 | 382 | |
|
|
383 | 383 | arrayParameters = numpy.concatenate(self.data['param']) |
|
|
384 | 384 | error = arrayParameters[:, -1] |
|
|
385 | 385 | indValid = numpy.where(error == 0)[0] |
|
|
386 | 386 | finalMeteor = arrayParameters[indValid, :] |
|
|
387 | 387 | finalAzimuth = finalMeteor[:, 3] |
|
|
388 | 388 | finalZenith = finalMeteor[:, 4] |
|
|
389 | 389 | |
|
|
390 | 390 | x = finalAzimuth * numpy.pi / 180 |
|
|
391 | 391 | y = finalZenith |
|
|
392 | 392 | |
|
|
393 | 393 | ax = self.axes[0] |
|
|
394 | 394 | |
|
|
395 | 395 | if ax.firsttime: |
|
|
396 | 396 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
397 | 397 | else: |
|
|
398 | 398 | ax.plot.set_data(x, y) |
|
|
399 | 399 | |
|
|
400 | 400 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
401 | 401 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
402 | 402 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
403 | 403 | dt2, |
|
|
404 | 404 | len(x)) |
|
|
405 | 405 | self.titles[0] = title |
|
|
406 | 406 | |
|
|
407 | 407 | |
|
|
408 | 408 | class ParametersPlot(RTIPlot): |
|
|
409 | 409 | ''' |
|
|
410 | 410 | Plot for data_param object |
|
|
411 | 411 | ''' |
|
|
412 | 412 | |
|
|
413 | 413 | CODE = 'param' |
|
|
414 | 414 | colormap = 'seismic' |
|
|
415 | 415 | |
|
|
416 | 416 | def setup(self): |
|
|
417 | 417 | self.xaxis = 'time' |
|
|
418 | 418 | self.ncols = 1 |
|
|
419 | 419 | self.nrows = self.data.shape(self.CODE)[0] |
|
|
420 | 420 | self.nplots = self.nrows |
|
|
421 | 421 | if self.showSNR: |
|
|
422 | 422 | self.nrows += 1 |
|
|
423 | 423 | self.nplots += 1 |
|
|
424 | 424 | |
|
|
425 | 425 | self.ylabel = 'Height [km]' |
|
|
426 | 426 | if not self.titles: |
|
|
427 | 427 | self.titles = self.data.parameters \ |
|
|
428 | 428 | if self.data.parameters else ['Param {}'.format(x) for x in range(self.nrows)] |
|
|
429 | 429 | if self.showSNR: |
|
|
430 | 430 | self.titles.append('SNR') |
|
|
431 | 431 | |
|
|
432 | 432 | def plot(self): |
|
|
433 | 433 | self.data.normalize_heights() |
|
|
434 | 434 | self.x = self.data.times |
|
|
435 | 435 | self.y = self.data.heights |
|
|
436 | 436 | if self.showSNR: |
|
|
437 | 437 | self.z = numpy.concatenate( |
|
|
438 | 438 | (self.data[self.CODE], self.data['snr']) |
|
|
439 | 439 | ) |
|
|
440 | 440 | else: |
|
|
441 | 441 | self.z = self.data[self.CODE] |
|
|
442 | 442 | |
|
|
443 | 443 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
444 | 444 | |
|
|
445 | 445 | if self.decimation is None: |
|
|
446 | 446 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
447 | 447 | else: |
|
|
448 | 448 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
449 | 449 | |
|
|
450 | 450 | for n, ax in enumerate(self.axes): |
|
|
451 | 451 | |
|
|
452 | 452 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
453 | 453 | self.z[n]) |
|
|
454 | 454 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
455 | 455 | self.z[n]) |
|
|
456 | 456 | |
|
|
457 | 457 | if ax.firsttime: |
|
|
458 | 458 | if self.zlimits is not None: |
|
|
459 | 459 | self.zmin, self.zmax = self.zlimits[n] |
|
|
460 | 460 | |
|
|
461 | 461 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
462 | 462 | vmin=self.zmin, |
|
|
463 | 463 | vmax=self.zmax, |
|
|
464 | 464 | cmap=self.cmaps[n] |
|
|
465 | 465 | ) |
|
|
466 | 466 | else: |
|
|
467 | 467 | if self.zlimits is not None: |
|
|
468 | 468 | self.zmin, self.zmax = self.zlimits[n] |
|
|
469 | 469 | ax.collections.remove(ax.collections[0]) |
|
|
470 | 470 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
471 | 471 | vmin=self.zmin, |
|
|
472 | 472 | vmax=self.zmax, |
|
|
473 | 473 | cmap=self.cmaps[n] |
|
|
474 | 474 | ) |
|
|
475 | 475 | |
|
|
476 | 476 | |
|
|
477 | 477 | class OutputPlot(ParametersPlot): |
|
|
478 | 478 | ''' |
|
|
479 | 479 | Plot data_output object |
|
|
480 | 480 | ''' |
|
|
481 | 481 | |
|
|
482 | 482 | CODE = 'output' |
|
|
483 | 483 | colormap = 'seismic' |
|
|
484 | 484 | |
|
|
485 | 485 | |
|
|
486 | 486 | class PolarMapPlot(Plot): |
|
|
487 | 487 | ''' |
|
|
488 | 488 | Plot for weather radar |
|
|
489 | 489 | ''' |
|
|
490 | 490 | |
|
|
491 | 491 | CODE = 'param' |
|
|
492 | 492 | colormap = 'seismic' |
|
|
493 | 493 | |
|
|
494 | 494 | def setup(self): |
|
|
495 | 495 | self.ncols = 1 |
|
|
496 | 496 | self.nrows = 1 |
|
|
497 | 497 | self.width = 9 |
|
|
498 | 498 | self.height = 8 |
|
|
499 | 499 | self.mode = self.data.meta['mode'] |
|
|
500 | 500 | if self.channels is not None: |
|
|
501 | 501 | self.nplots = len(self.channels) |
|
|
502 | 502 | self.nrows = len(self.channels) |
|
|
503 | 503 | else: |
|
|
504 | 504 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
505 | 505 | self.nrows = self.nplots |
|
|
506 | 506 | self.channels = list(range(self.nplots)) |
|
|
507 | 507 | if self.mode == 'E': |
|
|
508 | 508 | self.xlabel = 'Longitude' |
|
|
509 | 509 | self.ylabel = 'Latitude' |
|
|
510 | 510 | else: |
|
|
511 | 511 | self.xlabel = 'Range (km)' |
|
|
512 | 512 | self.ylabel = 'Height (km)' |
|
|
513 | 513 | self.bgcolor = 'white' |
|
|
514 | 514 | self.cb_labels = self.data.meta['units'] |
|
|
515 | 515 | self.lat = self.data.meta['latitude'] |
|
|
516 | 516 | self.lon = self.data.meta['longitude'] |
|
|
517 | 517 | self.xmin, self.xmax = float( |
|
|
518 | 518 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
|
|
519 | 519 | self.ymin, self.ymax = float( |
|
|
520 | 520 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
|
|
521 | 521 | # self.polar = True |
|
|
522 | 522 | |
|
|
523 | 523 | def plot(self): |
|
|
524 | 524 | |
|
|
525 | 525 | for n, ax in enumerate(self.axes): |
|
|
526 | 526 | data = self.data['param'][self.channels[n]] |
|
|
527 | 527 | |
|
|
528 | 528 | zeniths = numpy.linspace( |
|
|
529 | 529 | 0, self.data.meta['max_range'], data.shape[1]) |
|
|
530 | 530 | if self.mode == 'E': |
|
|
531 | 531 | azimuths = -numpy.radians(self.data.heights)+numpy.pi/2 |
|
|
532 | 532 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
533 | 533 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
|
|
534 | 534 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
|
|
535 | 535 | x = km2deg(x) + self.lon |
|
|
536 | 536 | y = km2deg(y) + self.lat |
|
|
537 | 537 | else: |
|
|
538 | 538 | azimuths = numpy.radians(self.data.heights) |
|
|
539 | 539 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
540 | 540 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
541 | 541 | self.y = zeniths |
|
|
542 | 542 | |
|
|
543 | 543 | if ax.firsttime: |
|
|
544 | 544 | if self.zlimits is not None: |
|
|
545 | 545 | self.zmin, self.zmax = self.zlimits[n] |
|
|
546 | 546 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
547 | 547 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
548 | 548 | vmin=self.zmin, |
|
|
549 | 549 | vmax=self.zmax, |
|
|
550 | 550 | cmap=self.cmaps[n]) |
|
|
551 | 551 | else: |
|
|
552 | 552 | if self.zlimits is not None: |
|
|
553 | 553 | self.zmin, self.zmax = self.zlimits[n] |
|
|
554 | 554 | ax.collections.remove(ax.collections[0]) |
|
|
555 | 555 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
556 | 556 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
557 | 557 | vmin=self.zmin, |
|
|
558 | 558 | vmax=self.zmax, |
|
|
559 | 559 | cmap=self.cmaps[n]) |
|
|
560 | 560 | |
|
|
561 | 561 | if self.mode == 'A': |
|
|
562 | 562 | continue |
|
|
563 | 563 | |
|
|
564 | 564 | # plot district names |
|
|
565 | 565 | f = open('/data/workspace/schain_scripts/distrito.csv') |
|
|
566 | 566 | for line in f: |
|
|
567 | 567 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
|
|
568 | 568 | lat = float(lat) |
|
|
569 | 569 | lon = float(lon) |
|
|
570 | 570 | # ax.plot(lon, lat, '.b', ms=2) |
|
|
571 | 571 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
|
|
572 | 572 | va='bottom', size='8', color='black') |
|
|
573 | 573 | |
|
|
574 | 574 | # plot limites |
|
|
575 | 575 | limites = [] |
|
|
576 | 576 | tmp = [] |
|
|
577 | 577 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
|
|
578 | 578 | if '#' in line: |
|
|
579 | 579 | if tmp: |
|
|
580 | 580 | limites.append(tmp) |
|
|
581 | 581 | tmp = [] |
|
|
582 | 582 | continue |
|
|
583 | 583 | values = line.strip().split(',') |
|
|
584 | 584 | tmp.append((float(values[0]), float(values[1]))) |
|
|
585 | 585 | for points in limites: |
|
|
586 | 586 | ax.add_patch( |
|
|
587 | 587 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
|
|
588 | 588 | |
|
|
589 | 589 | # plot Cuencas |
|
|
590 | 590 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
|
|
591 | 591 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
|
|
592 | 592 | values = [line.strip().split(',') for line in f] |
|
|
593 | 593 | points = [(float(s[0]), float(s[1])) for s in values] |
|
|
594 | 594 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
|
|
595 | 595 | |
|
|
596 | 596 | # plot grid |
|
|
597 | 597 | for r in (15, 30, 45, 60): |
|
|
598 | 598 | ax.add_artist(plt.Circle((self.lon, self.lat), |
|
|
599 | 599 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
|
|
600 | 600 | ax.text( |
|
|
601 | 601 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
|
|
602 | 602 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
|
|
603 | 603 | '{}km'.format(r), |
|
|
604 | 604 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
|
|
605 | 605 | |
|
|
606 | 606 | if self.mode == 'E': |
|
|
607 | 607 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
|
608 | 608 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
|
609 | 609 | else: |
|
|
610 | 610 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
|
|
611 | 611 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
|
|
612 | 612 | |
|
|
613 | 613 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
|
|
614 | 614 | self.titles = ['{} {}'.format( |
|
|
615 | 615 | self.data.parameters[x], title) for x in self.channels] |
|
|
616 | No newline at end of file | |
|
|
616 | ||
|
|
617 | class ScopePlot(Plot): | |
|
|
618 | ||
|
|
619 | ''' | |
|
|
620 | Plot for Scope | |
|
|
621 | ''' | |
|
|
622 | ||
|
|
623 | CODE = 'scope' | |
|
|
624 | ||
|
|
625 | def setup(self): | |
|
|
626 | ||
|
|
627 | self.xaxis = 'Range (Km)' | |
|
|
628 | self.ncols = 1 | |
|
|
629 | self.nrows = 1 | |
|
|
630 | self.nplots = 1 | |
|
|
631 | self.ylabel = 'Intensity [dB]' | |
|
|
632 | self.titles = ['Scope'] | |
|
|
633 | self.colorbar = False | |
|
|
634 | colspan = 3 | |
|
|
635 | rowspan = 1 | |
|
|
636 | ||
|
|
637 | def plot_iq(self, x, y, channelIndexList, thisDatetime, wintitle): | |
|
|
638 | ||
|
|
639 | yreal = y[channelIndexList,:].real | |
|
|
640 | yimag = y[channelIndexList,:].imag | |
|
|
641 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) | |
|
|
642 | self.xlabel = "Range (Km)" | |
|
|
643 | self.ylabel = "Intensity - IQ" | |
|
|
644 | ||
|
|
645 | self.y = yreal | |
|
|
646 | self.x = x | |
|
|
647 | self.xmin = min(x) | |
|
|
648 | self.xmax = max(x) | |
|
|
649 | ||
|
|
650 | ||
|
|
651 | self.titles[0] = title | |
|
|
652 | ||
|
|
653 | for i,ax in enumerate(self.axes): | |
|
|
654 | title = "Channel %d" %(i) | |
|
|
655 | if ax.firsttime: | |
|
|
656 | ax.plt_r = ax.plot(x, yreal[i,:], color='b')[0] | |
|
|
657 | ax.plt_i = ax.plot(x, yimag[i,:], color='r')[0] | |
|
|
658 | else: | |
|
|
659 | #pass | |
|
|
660 | ax.plt_r.set_data(x, yreal[i,:]) | |
|
|
661 | ax.plt_i.set_data(x, yimag[i,:]) | |
|
|
662 | ||
|
|
663 | def plot_power(self, x, y, channelIndexList, thisDatetime, wintitle): | |
|
|
664 | y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:]) | |
|
|
665 | yreal = y.real | |
|
|
666 | self.y = yreal | |
|
|
667 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) | |
|
|
668 | self.xlabel = "Range (Km)" | |
|
|
669 | self.ylabel = "Intensity" | |
|
|
670 | self.xmin = min(x) | |
|
|
671 | self.xmax = max(x) | |
|
|
672 | ||
|
|
673 | ||
|
|
674 | self.titles[0] = title | |
|
|
675 | ||
|
|
676 | for i,ax in enumerate(self.axes): | |
|
|
677 | title = "Channel %d" %(i) | |
|
|
678 | ||
|
|
679 | ychannel = yreal[i,:] | |
|
|
680 | ||
|
|
681 | if ax.firsttime: | |
|
|
682 | ax.plt_r = ax.plot(x, ychannel)[0] | |
|
|
683 | else: | |
|
|
684 | #pass | |
|
|
685 | ax.plt_r.set_data(x, ychannel) | |
|
|
686 | ||
|
|
687 | ||
|
|
688 | def plot(self): | |
|
|
689 | ||
|
|
690 | if self.channels: | |
|
|
691 | channels = self.channels | |
|
|
692 | else: | |
|
|
693 | channels = self.data.channels | |
|
|
694 | ||
|
|
695 | ||
|
|
696 | ||
|
|
697 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]) | |
|
|
698 | ||
|
|
699 | scope = self.data['scope'] | |
|
|
700 | ||
|
|
701 | ||
|
|
702 | if self.data.flagDataAsBlock: | |
|
|
703 | ||
|
|
704 | for i in range(self.data.nProfiles): | |
|
|
705 | ||
|
|
706 | wintitle1 = " [Profile = %d] " %i | |
|
|
707 | ||
|
|
708 | if self.type == "power": | |
|
|
709 | self.plot_power(self.data.heights, | |
|
|
710 | scope[:,i,:], | |
|
|
711 | channels, | |
|
|
712 | thisDatetime, | |
|
|
713 | wintitle1 | |
|
|
714 | ) | |
|
|
715 | ||
|
|
716 | if self.type == "iq": | |
|
|
717 | self.plot_iq(self.data.heights, | |
|
|
718 | scope[:,i,:], | |
|
|
719 | channels, | |
|
|
720 | thisDatetime, | |
|
|
721 | wintitle1 | |
|
|
722 | ) | |
|
|
723 | ||
|
|
724 | ||
|
|
725 | ||
|
|
726 | ||
|
|
727 | ||
|
|
728 | else: | |
|
|
729 | wintitle = " [Profile = %d] " %self.data.profileIndex | |
|
|
730 | ||
|
|
731 | if self.type == "power": | |
|
|
732 | self.plot_power(self.data.heights, | |
|
|
733 | scope, | |
|
|
734 | channels, | |
|
|
735 | thisDatetime, | |
|
|
736 | wintitle | |
|
|
737 | ) | |
|
|
738 | ||
|
|
739 | if self.type == "iq": | |
|
|
740 | self.plot_iq(self.data.heights, | |
|
|
741 | scope, | |
|
|
742 | channels, | |
|
|
743 | thisDatetime, | |
|
|
744 | wintitle | |
|
|
745 | ) | |
|
|
746 | ||
|
|
747 | ||
|
|
748 | No newline at end of file | |
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