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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 * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
530 | 530 | |
|
|
531 | 531 | return freqrange |
|
|
532 | 532 | |
|
|
533 | 533 | def getAcfRange(self, extrapoints=0): |
|
|
534 | 534 | |
|
|
535 | 535 | deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor)) |
|
|
536 | 536 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
537 | 537 | |
|
|
538 | 538 | return freqrange |
|
|
539 | 539 | |
|
|
540 | 540 | def getFreqRange(self, extrapoints=0): |
|
|
541 | 541 | |
|
|
542 | 542 | deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor) |
|
|
543 | 543 | freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2 |
|
|
544 | 544 | |
|
|
545 | 545 | return freqrange |
|
|
546 | 546 | |
|
|
547 | 547 | def getVelRange(self, extrapoints=0): |
|
|
548 | 548 | |
|
|
549 | 549 | deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor) |
|
|
550 | 550 | velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) |
|
|
551 | 551 | |
|
|
552 | 552 | if self.nmodes: |
|
|
553 | 553 | return velrange/self.nmodes |
|
|
554 | 554 | else: |
|
|
555 | 555 | return velrange |
|
|
556 | 556 | |
|
|
557 | 557 | def getNPairs(self): |
|
|
558 | 558 | |
|
|
559 | 559 | return len(self.pairsList) |
|
|
560 | 560 | |
|
|
561 | 561 | def getPairsIndexList(self): |
|
|
562 | 562 | |
|
|
563 | 563 | return list(range(self.nPairs)) |
|
|
564 | 564 | |
|
|
565 | 565 | def getNormFactor(self): |
|
|
566 | 566 | |
|
|
567 | 567 | pwcode = 1 |
|
|
568 | 568 | |
|
|
569 | 569 | if self.flagDecodeData: |
|
|
570 | 570 | pwcode = numpy.sum(self.code[0]**2) |
|
|
571 | 571 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
572 | 572 | normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter |
|
|
573 | 573 | |
|
|
574 | 574 | return normFactor |
|
|
575 | 575 | |
|
|
576 | 576 | def getFlagCspc(self): |
|
|
577 | 577 | |
|
|
578 | 578 | if self.data_cspc is None: |
|
|
579 | 579 | return True |
|
|
580 | 580 | |
|
|
581 | 581 | return False |
|
|
582 | 582 | |
|
|
583 | 583 | def getFlagDc(self): |
|
|
584 | 584 | |
|
|
585 | 585 | if self.data_dc is None: |
|
|
586 | 586 | return True |
|
|
587 | 587 | |
|
|
588 | 588 | return False |
|
|
589 | 589 | |
|
|
590 | 590 | def getTimeInterval(self): |
|
|
591 | 591 | |
|
|
592 | 592 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor |
|
|
593 | 593 | |
|
|
594 | 594 | return timeInterval |
|
|
595 | 595 | |
|
|
596 | 596 | def getPower(self): |
|
|
597 | 597 | |
|
|
598 | 598 | factor = self.normFactor |
|
|
599 | 599 | z = self.data_spc / factor |
|
|
600 | 600 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
601 | 601 | avg = numpy.average(z, axis=1) |
|
|
602 | 602 | |
|
|
603 | 603 | return 10 * numpy.log10(avg) |
|
|
604 | 604 | |
|
|
605 | 605 | def getCoherence(self, pairsList=None, phase=False): |
|
|
606 | 606 | |
|
|
607 | 607 | z = [] |
|
|
608 | 608 | if pairsList is None: |
|
|
609 | 609 | pairsIndexList = self.pairsIndexList |
|
|
610 | 610 | else: |
|
|
611 | 611 | pairsIndexList = [] |
|
|
612 | 612 | for pair in pairsList: |
|
|
613 | 613 | if pair not in self.pairsList: |
|
|
614 | 614 | raise ValueError("Pair %s is not in dataOut.pairsList" % ( |
|
|
615 | 615 | pair)) |
|
|
616 | 616 | pairsIndexList.append(self.pairsList.index(pair)) |
|
|
617 | 617 | for i in range(len(pairsIndexList)): |
|
|
618 | 618 | pair = self.pairsList[pairsIndexList[i]] |
|
|
619 | 619 | ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0) |
|
|
620 | 620 | powa = numpy.average(self.data_spc[pair[0], :, :], axis=0) |
|
|
621 | 621 | powb = numpy.average(self.data_spc[pair[1], :, :], axis=0) |
|
|
622 | 622 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) |
|
|
623 | 623 | if phase: |
|
|
624 | 624 | data = numpy.arctan2(avgcoherenceComplex.imag, |
|
|
625 | 625 | avgcoherenceComplex.real) * 180 / numpy.pi |
|
|
626 | 626 | else: |
|
|
627 | 627 | data = numpy.abs(avgcoherenceComplex) |
|
|
628 | 628 | |
|
|
629 | 629 | z.append(data) |
|
|
630 | 630 | |
|
|
631 | 631 | return numpy.array(z) |
|
|
632 | 632 | |
|
|
633 | 633 | def setValue(self, value): |
|
|
634 | 634 | |
|
|
635 | 635 | print("This property should not be initialized") |
|
|
636 | 636 | |
|
|
637 | 637 | return |
|
|
638 | 638 | |
|
|
639 | 639 | nPairs = property(getNPairs, setValue, "I'm the 'nPairs' property.") |
|
|
640 | 640 | pairsIndexList = property( |
|
|
641 | 641 | getPairsIndexList, setValue, "I'm the 'pairsIndexList' property.") |
|
|
642 | 642 | normFactor = property(getNormFactor, setValue, |
|
|
643 | 643 | "I'm the 'getNormFactor' property.") |
|
|
644 | 644 | flag_cspc = property(getFlagCspc, setValue) |
|
|
645 | 645 | flag_dc = property(getFlagDc, setValue) |
|
|
646 | 646 | noise = property(getNoise, setValue, "I'm the 'nHeights' property.") |
|
|
647 | 647 | timeInterval = property(getTimeInterval, setValue, |
|
|
648 | 648 | "I'm the 'timeInterval' property") |
|
|
649 | 649 | |
|
|
650 | 650 | |
|
|
651 | 651 | class SpectraHeis(Spectra): |
|
|
652 | 652 | |
|
|
653 | 653 | data_spc = None |
|
|
654 | 654 | data_cspc = None |
|
|
655 | 655 | data_dc = None |
|
|
656 | 656 | nFFTPoints = None |
|
|
657 | 657 | # nPairs = None |
|
|
658 | 658 | pairsList = None |
|
|
659 | 659 | nCohInt = None |
|
|
660 | 660 | nIncohInt = None |
|
|
661 | 661 | |
|
|
662 | 662 | def __init__(self): |
|
|
663 | 663 | |
|
|
664 | 664 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
665 | 665 | |
|
|
666 | 666 | self.systemHeaderObj = SystemHeader() |
|
|
667 | 667 | |
|
|
668 | 668 | self.type = "SpectraHeis" |
|
|
669 | 669 | |
|
|
670 | 670 | # self.dtype = None |
|
|
671 | 671 | |
|
|
672 | 672 | # self.nChannels = 0 |
|
|
673 | 673 | |
|
|
674 | 674 | # self.nHeights = 0 |
|
|
675 | 675 | |
|
|
676 | 676 | self.nProfiles = None |
|
|
677 | 677 | |
|
|
678 | 678 | self.heightList = None |
|
|
679 | 679 | |
|
|
680 | 680 | self.channelList = None |
|
|
681 | 681 | |
|
|
682 | 682 | # self.channelIndexList = None |
|
|
683 | 683 | |
|
|
684 | 684 | self.flagNoData = True |
|
|
685 | 685 | |
|
|
686 | 686 | self.flagDiscontinuousBlock = False |
|
|
687 | 687 | |
|
|
688 | 688 | # self.nPairs = 0 |
|
|
689 | 689 | |
|
|
690 | 690 | self.utctime = None |
|
|
691 | 691 | |
|
|
692 | 692 | self.blocksize = None |
|
|
693 | 693 | |
|
|
694 | 694 | self.profileIndex = 0 |
|
|
695 | 695 | |
|
|
696 | 696 | self.nCohInt = 1 |
|
|
697 | 697 | |
|
|
698 | 698 | self.nIncohInt = 1 |
|
|
699 | 699 | |
|
|
700 | 700 | def getNormFactor(self): |
|
|
701 | 701 | pwcode = 1 |
|
|
702 | 702 | if self.flagDecodeData: |
|
|
703 | 703 | pwcode = numpy.sum(self.code[0]**2) |
|
|
704 | 704 | |
|
|
705 | 705 | normFactor = self.nIncohInt * self.nCohInt * pwcode |
|
|
706 | 706 | |
|
|
707 | 707 | return normFactor |
|
|
708 | 708 | |
|
|
709 | 709 | def getTimeInterval(self): |
|
|
710 | 710 | |
|
|
711 | 711 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
712 | 712 | |
|
|
713 | 713 | return timeInterval |
|
|
714 | 714 | |
|
|
715 | 715 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
716 | 716 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
717 | 717 | |
|
|
718 | 718 | |
|
|
719 | 719 | class Fits(JROData): |
|
|
720 | 720 | |
|
|
721 | 721 | heightList = None |
|
|
722 | 722 | channelList = None |
|
|
723 | 723 | flagNoData = True |
|
|
724 | 724 | flagDiscontinuousBlock = False |
|
|
725 | 725 | useLocalTime = False |
|
|
726 | 726 | utctime = None |
|
|
727 | 727 | timeZone = None |
|
|
728 | 728 | # ippSeconds = None |
|
|
729 | 729 | # timeInterval = None |
|
|
730 | 730 | nCohInt = None |
|
|
731 | 731 | nIncohInt = None |
|
|
732 | 732 | noise = None |
|
|
733 | 733 | windowOfFilter = 1 |
|
|
734 | 734 | # Speed of ligth |
|
|
735 | 735 | C = 3e8 |
|
|
736 | 736 | frequency = 49.92e6 |
|
|
737 | 737 | realtime = False |
|
|
738 | 738 | |
|
|
739 | 739 | def __init__(self): |
|
|
740 | 740 | |
|
|
741 | 741 | self.type = "Fits" |
|
|
742 | 742 | |
|
|
743 | 743 | self.nProfiles = None |
|
|
744 | 744 | |
|
|
745 | 745 | self.heightList = None |
|
|
746 | 746 | |
|
|
747 | 747 | self.channelList = None |
|
|
748 | 748 | |
|
|
749 | 749 | # self.channelIndexList = None |
|
|
750 | 750 | |
|
|
751 | 751 | self.flagNoData = True |
|
|
752 | 752 | |
|
|
753 | 753 | self.utctime = None |
|
|
754 | 754 | |
|
|
755 | 755 | self.nCohInt = 1 |
|
|
756 | 756 | |
|
|
757 | 757 | self.nIncohInt = 1 |
|
|
758 | 758 | |
|
|
759 | 759 | self.useLocalTime = True |
|
|
760 | 760 | |
|
|
761 | 761 | self.profileIndex = 0 |
|
|
762 | 762 | |
|
|
763 | 763 | # self.utctime = None |
|
|
764 | 764 | # self.timeZone = None |
|
|
765 | 765 | # self.ltctime = None |
|
|
766 | 766 | # self.timeInterval = None |
|
|
767 | 767 | # self.header = None |
|
|
768 | 768 | # self.data_header = None |
|
|
769 | 769 | # self.data = None |
|
|
770 | 770 | # self.datatime = None |
|
|
771 | 771 | # self.flagNoData = False |
|
|
772 | 772 | # self.expName = '' |
|
|
773 | 773 | # self.nChannels = None |
|
|
774 | 774 | # self.nSamples = None |
|
|
775 | 775 | # self.dataBlocksPerFile = None |
|
|
776 | 776 | # self.comments = '' |
|
|
777 | 777 | # |
|
|
778 | 778 | |
|
|
779 | 779 | def getltctime(self): |
|
|
780 | 780 | |
|
|
781 | 781 | if self.useLocalTime: |
|
|
782 | 782 | return self.utctime - self.timeZone * 60 |
|
|
783 | 783 | |
|
|
784 | 784 | return self.utctime |
|
|
785 | 785 | |
|
|
786 | 786 | def getDatatime(self): |
|
|
787 | 787 | |
|
|
788 | 788 | datatime = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
789 | 789 | return datatime |
|
|
790 | 790 | |
|
|
791 | 791 | def getTimeRange(self): |
|
|
792 | 792 | |
|
|
793 | 793 | datatime = [] |
|
|
794 | 794 | |
|
|
795 | 795 | datatime.append(self.ltctime) |
|
|
796 | 796 | datatime.append(self.ltctime + self.timeInterval) |
|
|
797 | 797 | |
|
|
798 | 798 | datatime = numpy.array(datatime) |
|
|
799 | 799 | |
|
|
800 | 800 | return datatime |
|
|
801 | 801 | |
|
|
802 | 802 | def getHeiRange(self): |
|
|
803 | 803 | |
|
|
804 | 804 | heis = self.heightList |
|
|
805 | 805 | |
|
|
806 | 806 | return heis |
|
|
807 | 807 | |
|
|
808 | 808 | def getNHeights(self): |
|
|
809 | 809 | |
|
|
810 | 810 | return len(self.heightList) |
|
|
811 | 811 | |
|
|
812 | 812 | def getNChannels(self): |
|
|
813 | 813 | |
|
|
814 | 814 | return len(self.channelList) |
|
|
815 | 815 | |
|
|
816 | 816 | def getChannelIndexList(self): |
|
|
817 | 817 | |
|
|
818 | 818 | return list(range(self.nChannels)) |
|
|
819 | 819 | |
|
|
820 | 820 | def getNoise(self, type=1): |
|
|
821 | 821 | |
|
|
822 | 822 | #noise = numpy.zeros(self.nChannels) |
|
|
823 | 823 | |
|
|
824 | 824 | if type == 1: |
|
|
825 | 825 | noise = self.getNoisebyHildebrand() |
|
|
826 | 826 | |
|
|
827 | 827 | if type == 2: |
|
|
828 | 828 | noise = self.getNoisebySort() |
|
|
829 | 829 | |
|
|
830 | 830 | if type == 3: |
|
|
831 | 831 | noise = self.getNoisebyWindow() |
|
|
832 | 832 | |
|
|
833 | 833 | return noise |
|
|
834 | 834 | |
|
|
835 | 835 | def getTimeInterval(self): |
|
|
836 | 836 | |
|
|
837 | 837 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
838 | 838 | |
|
|
839 | 839 | return timeInterval |
|
|
840 | 840 | |
|
|
841 | 841 | def get_ippSeconds(self): |
|
|
842 | 842 | ''' |
|
|
843 | 843 | ''' |
|
|
844 | 844 | return self.ipp_sec |
|
|
845 | 845 | |
|
|
846 | 846 | |
|
|
847 | 847 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
848 | 848 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
849 | 849 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
850 | 850 | channelIndexList = property( |
|
|
851 | 851 | getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
852 | 852 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
853 | 853 | |
|
|
854 | 854 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
855 | 855 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
856 | 856 | ippSeconds = property(get_ippSeconds, '') |
|
|
857 | 857 | |
|
|
858 | 858 | class Correlation(JROData): |
|
|
859 | 859 | |
|
|
860 | 860 | noise = None |
|
|
861 | 861 | SNR = None |
|
|
862 | 862 | #-------------------------------------------------- |
|
|
863 | 863 | mode = None |
|
|
864 | 864 | split = False |
|
|
865 | 865 | data_cf = None |
|
|
866 | 866 | lags = None |
|
|
867 | 867 | lagRange = None |
|
|
868 | 868 | pairsList = None |
|
|
869 | 869 | normFactor = None |
|
|
870 | 870 | #-------------------------------------------------- |
|
|
871 | 871 | # calculateVelocity = None |
|
|
872 | 872 | nLags = None |
|
|
873 | 873 | nPairs = None |
|
|
874 | 874 | nAvg = None |
|
|
875 | 875 | |
|
|
876 | 876 | def __init__(self): |
|
|
877 | 877 | ''' |
|
|
878 | 878 | Constructor |
|
|
879 | 879 | ''' |
|
|
880 | 880 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
881 | 881 | |
|
|
882 | 882 | self.systemHeaderObj = SystemHeader() |
|
|
883 | 883 | |
|
|
884 | 884 | self.type = "Correlation" |
|
|
885 | 885 | |
|
|
886 | 886 | self.data = None |
|
|
887 | 887 | |
|
|
888 | 888 | self.dtype = None |
|
|
889 | 889 | |
|
|
890 | 890 | self.nProfiles = None |
|
|
891 | 891 | |
|
|
892 | 892 | self.heightList = None |
|
|
893 | 893 | |
|
|
894 | 894 | self.channelList = None |
|
|
895 | 895 | |
|
|
896 | 896 | self.flagNoData = True |
|
|
897 | 897 | |
|
|
898 | 898 | self.flagDiscontinuousBlock = False |
|
|
899 | 899 | |
|
|
900 | 900 | self.utctime = None |
|
|
901 | 901 | |
|
|
902 | 902 | self.timeZone = None |
|
|
903 | 903 | |
|
|
904 | 904 | self.dstFlag = None |
|
|
905 | 905 | |
|
|
906 | 906 | self.errorCount = None |
|
|
907 | 907 | |
|
|
908 | 908 | self.blocksize = None |
|
|
909 | 909 | |
|
|
910 | 910 | self.flagDecodeData = False # asumo q la data no esta decodificada |
|
|
911 | 911 | |
|
|
912 | 912 | self.flagDeflipData = False # asumo q la data no esta sin flip |
|
|
913 | 913 | |
|
|
914 | 914 | self.pairsList = None |
|
|
915 | 915 | |
|
|
916 | 916 | self.nPoints = None |
|
|
917 | 917 | |
|
|
918 | 918 | def getPairsList(self): |
|
|
919 | 919 | |
|
|
920 | 920 | return self.pairsList |
|
|
921 | 921 | |
|
|
922 | 922 | def getNoise(self, mode=2): |
|
|
923 | 923 | |
|
|
924 | 924 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
925 | 925 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
926 | 926 | |
|
|
927 | 927 | jspectra0 = self.data_corr[:, :, indR, :] |
|
|
928 | 928 | jspectra = copy.copy(jspectra0) |
|
|
929 | 929 | |
|
|
930 | 930 | num_chan = jspectra.shape[0] |
|
|
931 | 931 | num_hei = jspectra.shape[2] |
|
|
932 | 932 | |
|
|
933 | 933 | freq_dc = jspectra.shape[1] / 2 |
|
|
934 | 934 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
935 | 935 | |
|
|
936 | 936 | if ind_vel[0] < 0: |
|
|
937 | 937 | ind_vel[list(range(0, 1))] = ind_vel[list( |
|
|
938 | 938 | range(0, 1))] + self.num_prof |
|
|
939 | 939 | |
|
|
940 | 940 | if mode == 1: |
|
|
941 | 941 | jspectra[:, freq_dc, :] = ( |
|
|
942 | 942 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
943 | 943 | |
|
|
944 | 944 | if mode == 2: |
|
|
945 | 945 | |
|
|
946 | 946 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
947 | 947 | xx = numpy.zeros([4, 4]) |
|
|
948 | 948 | |
|
|
949 | 949 | for fil in range(4): |
|
|
950 | 950 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
951 | 951 | |
|
|
952 | 952 | xx_inv = numpy.linalg.inv(xx) |
|
|
953 | 953 | xx_aux = xx_inv[0, :] |
|
|
954 | 954 | |
|
|
955 | 955 | for ich in range(num_chan): |
|
|
956 | 956 | yy = jspectra[ich, ind_vel, :] |
|
|
957 | 957 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
958 | 958 | |
|
|
959 | 959 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
960 | 960 | cjunkid = sum(junkid) |
|
|
961 | 961 | |
|
|
962 | 962 | if cjunkid.any(): |
|
|
963 | 963 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
964 | 964 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
965 | 965 | |
|
|
966 | 966 | noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :] |
|
|
967 | 967 | |
|
|
968 | 968 | return noise |
|
|
969 | 969 | |
|
|
970 | 970 | def getTimeInterval(self): |
|
|
971 | 971 | |
|
|
972 | 972 | timeInterval = self.ippSeconds * self.nCohInt * self.nProfiles |
|
|
973 | 973 | |
|
|
974 | 974 | return timeInterval |
|
|
975 | 975 | |
|
|
976 | 976 | def splitFunctions(self): |
|
|
977 | 977 | |
|
|
978 | 978 | pairsList = self.pairsList |
|
|
979 | 979 | ccf_pairs = [] |
|
|
980 | 980 | acf_pairs = [] |
|
|
981 | 981 | ccf_ind = [] |
|
|
982 | 982 | acf_ind = [] |
|
|
983 | 983 | for l in range(len(pairsList)): |
|
|
984 | 984 | chan0 = pairsList[l][0] |
|
|
985 | 985 | chan1 = pairsList[l][1] |
|
|
986 | 986 | |
|
|
987 | 987 | # Obteniendo pares de Autocorrelacion |
|
|
988 | 988 | if chan0 == chan1: |
|
|
989 | 989 | acf_pairs.append(chan0) |
|
|
990 | 990 | acf_ind.append(l) |
|
|
991 | 991 | else: |
|
|
992 | 992 | ccf_pairs.append(pairsList[l]) |
|
|
993 | 993 | ccf_ind.append(l) |
|
|
994 | 994 | |
|
|
995 | 995 | data_acf = self.data_cf[acf_ind] |
|
|
996 | 996 | data_ccf = self.data_cf[ccf_ind] |
|
|
997 | 997 | |
|
|
998 | 998 | return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf |
|
|
999 | 999 | |
|
|
1000 | 1000 | def getNormFactor(self): |
|
|
1001 | 1001 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions() |
|
|
1002 | 1002 | acf_pairs = numpy.array(acf_pairs) |
|
|
1003 | 1003 | normFactor = numpy.zeros((self.nPairs, self.nHeights)) |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | for p in range(self.nPairs): |
|
|
1006 | 1006 | pair = self.pairsList[p] |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | ch0 = pair[0] |
|
|
1009 | 1009 | ch1 = pair[1] |
|
|
1010 | 1010 | |
|
|
1011 | 1011 | ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1) |
|
|
1012 | 1012 | ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1) |
|
|
1013 | 1013 | normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max) |
|
|
1014 | 1014 | |
|
|
1015 | 1015 | return normFactor |
|
|
1016 | 1016 | |
|
|
1017 | 1017 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
1018 | 1018 | normFactor = property(getNormFactor, "I'm the 'normFactor property'") |
|
|
1019 | 1019 | |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | class Parameters(Spectra): |
|
|
1022 | 1022 | |
|
|
1023 | 1023 | experimentInfo = None # Information about the experiment |
|
|
1024 | 1024 | # Information from previous data |
|
|
1025 | 1025 | inputUnit = None # Type of data to be processed |
|
|
1026 | 1026 | operation = None # Type of operation to parametrize |
|
|
1027 | 1027 | # normFactor = None #Normalization Factor |
|
|
1028 | 1028 | groupList = None # List of Pairs, Groups, etc |
|
|
1029 | 1029 | # Parameters |
|
|
1030 | 1030 | data_param = None # Parameters obtained |
|
|
1031 | 1031 | data_pre = None # Data Pre Parametrization |
|
|
1032 | 1032 | data_SNR = None # Signal to Noise Ratio |
|
|
1033 | 1033 | # heightRange = None #Heights |
|
|
1034 | 1034 | abscissaList = None # Abscissa, can be velocities, lags or time |
|
|
1035 | 1035 | # noise = None #Noise Potency |
|
|
1036 | 1036 | utctimeInit = None # Initial UTC time |
|
|
1037 | 1037 | paramInterval = None # Time interval to calculate Parameters in seconds |
|
|
1038 | 1038 | useLocalTime = True |
|
|
1039 | 1039 | # Fitting |
|
|
1040 | 1040 | data_error = None # Error of the estimation |
|
|
1041 | 1041 | constants = None |
|
|
1042 | 1042 | library = None |
|
|
1043 | 1043 | # Output signal |
|
|
1044 | 1044 | outputInterval = None # Time interval to calculate output signal in seconds |
|
|
1045 | 1045 | data_output = None # Out signal |
|
|
1046 | 1046 | nAvg = None |
|
|
1047 | 1047 | noise_estimation = None |
|
|
1048 | 1048 | GauSPC = None # Fit gaussian SPC |
|
|
1049 | 1049 | |
|
|
1050 | 1050 | def __init__(self): |
|
|
1051 | 1051 | ''' |
|
|
1052 | 1052 | Constructor |
|
|
1053 | 1053 | ''' |
|
|
1054 | 1054 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | self.systemHeaderObj = SystemHeader() |
|
|
1057 | 1057 | |
|
|
1058 | 1058 | self.type = "Parameters" |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | def getTimeRange1(self, interval): |
|
|
1061 | 1061 | |
|
|
1062 | 1062 | datatime = [] |
|
|
1063 | 1063 | |
|
|
1064 | 1064 | if self.useLocalTime: |
|
|
1065 | 1065 | time1 = self.utctimeInit - self.timeZone * 60 |
|
|
1066 | 1066 | else: |
|
|
1067 | 1067 | time1 = self.utctimeInit |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | datatime.append(time1) |
|
|
1070 | 1070 | datatime.append(time1 + interval) |
|
|
1071 | 1071 | datatime = numpy.array(datatime) |
|
|
1072 | 1072 | |
|
|
1073 | 1073 | return datatime |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | def getTimeInterval(self): |
|
|
1076 | 1076 | |
|
|
1077 | 1077 | if hasattr(self, 'timeInterval1'): |
|
|
1078 | 1078 | return self.timeInterval1 |
|
|
1079 | 1079 | else: |
|
|
1080 | 1080 | return self.paramInterval |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | def setValue(self, value): |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | print("This property should not be initialized") |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | return |
|
|
1087 | 1087 | |
|
|
1088 | 1088 | def getNoise(self): |
|
|
1089 | 1089 | |
|
|
1090 | 1090 | return self.spc_noise |
|
|
1091 | 1091 | |
|
|
1092 | 1092 | timeInterval = property(getTimeInterval) |
|
|
1093 | 1093 | noise = property(getNoise, setValue, "I'm the 'Noise' property.") |
|
|
1094 | 1094 | |
|
|
1095 | 1095 | |
|
|
1096 | 1096 | class PlotterData(object): |
|
|
1097 | 1097 | ''' |
|
|
1098 | 1098 | Object to hold data to be plotted |
|
|
1099 | 1099 | ''' |
|
|
1100 | 1100 | |
|
|
1101 | 1101 | MAXNUMX = 100 |
|
|
1102 | 1102 | MAXNUMY = 100 |
|
|
1103 | 1103 | |
|
|
1104 | 1104 | def __init__(self, code, throttle_value, exp_code, buffering=True): |
|
|
1105 | 1105 | |
|
|
1106 | 1106 | self.throttle = throttle_value |
|
|
1107 | 1107 | self.exp_code = exp_code |
|
|
1108 | 1108 | self.buffering = buffering |
|
|
1109 | 1109 | self.ready = False |
|
|
1110 | 1110 | self.localtime = False |
|
|
1111 | 1111 | self.data = {} |
|
|
1112 | 1112 | self.meta = {} |
|
|
1113 | 1113 | self.__times = [] |
|
|
1114 | 1114 | self.__heights = [] |
|
|
1115 | 1115 | |
|
|
1116 | 1116 | if 'snr' in code: |
|
|
1117 | 1117 | self.plottypes = ['snr'] |
|
|
1118 | 1118 | elif code == 'spc': |
|
|
1119 | 1119 | self.plottypes = ['spc', 'noise', 'rti'] |
|
|
1120 | 1120 | elif code == 'rti': |
|
|
1121 | 1121 | self.plottypes = ['noise', 'rti'] |
|
|
1122 | 1122 | else: |
|
|
1123 | 1123 | self.plottypes = [code] |
|
|
1124 | 1124 | |
|
|
1125 | 1125 | for plot in self.plottypes: |
|
|
1126 | 1126 | self.data[plot] = {} |
|
|
1127 | 1127 | |
|
|
1128 | 1128 | def __str__(self): |
|
|
1129 | 1129 | dum = ['{}{}'.format(key, self.shape(key)) for key in self.data] |
|
|
1130 | 1130 | return 'Data[{}][{}]'.format(';'.join(dum), len(self.__times)) |
|
|
1131 | 1131 | |
|
|
1132 | 1132 | def __len__(self): |
|
|
1133 | 1133 | return len(self.__times) |
|
|
1134 | 1134 | |
|
|
1135 | 1135 | def __getitem__(self, key): |
|
|
1136 | 1136 | |
|
|
1137 | 1137 | if key not in self.data: |
|
|
1138 | 1138 | raise KeyError(log.error('Missing key: {}'.format(key))) |
|
|
1139 | 1139 | if 'spc' in key or not self.buffering: |
|
|
1140 | 1140 | ret = self.data[key] |
|
|
1141 | 1141 | elif 'scope' in key: |
|
|
1142 | 1142 | ret = numpy.array(self.data[key][float(self.tm)]) |
|
|
1143 | 1143 | else: |
|
|
1144 | 1144 | ret = numpy.array([self.data[key][x] for x in self.times]) |
|
|
1145 | 1145 | if ret.ndim > 1: |
|
|
1146 | 1146 | ret = numpy.swapaxes(ret, 0, 1) |
|
|
1147 | 1147 | return ret |
|
|
1148 | 1148 | |
|
|
1149 | 1149 | def __contains__(self, key): |
|
|
1150 | 1150 | return key in self.data |
|
|
1151 | 1151 | |
|
|
1152 | 1152 | def setup(self): |
|
|
1153 | 1153 | ''' |
|
|
1154 | 1154 | Configure object |
|
|
1155 | 1155 | ''' |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | self.type = '' |
|
|
1158 | 1158 | self.ready = False |
|
|
1159 | 1159 | self.data = {} |
|
|
1160 | 1160 | self.__times = [] |
|
|
1161 | 1161 | self.__heights = [] |
|
|
1162 | 1162 | self.__all_heights = set() |
|
|
1163 | 1163 | for plot in self.plottypes: |
|
|
1164 | 1164 | if 'snr' in plot: |
|
|
1165 | 1165 | plot = 'snr' |
|
|
1166 | elif 'spc_moments' == plot: | |
|
|
1167 | plot = 'moments' | |
|
|
1166 | 1168 | self.data[plot] = {} |
|
|
1167 | 1169 | |
|
|
1168 | if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data: | |
|
|
1170 | if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data: | |
|
|
1169 | 1171 | self.data['noise'] = {} |
|
|
1170 | 1172 | if 'noise' not in self.plottypes: |
|
|
1171 | 1173 | self.plottypes.append('noise') |
|
|
1172 | 1174 | |
|
|
1173 | 1175 | def shape(self, key): |
|
|
1174 | 1176 | ''' |
|
|
1175 | 1177 | Get the shape of the one-element data for the given key |
|
|
1176 | 1178 | ''' |
|
|
1177 | 1179 | |
|
|
1178 | 1180 | if len(self.data[key]): |
|
|
1179 | 1181 | if 'spc' in key or not self.buffering: |
|
|
1180 | 1182 | return self.data[key].shape |
|
|
1181 | 1183 | return self.data[key][self.__times[0]].shape |
|
|
1182 | 1184 | return (0,) |
|
|
1183 | 1185 | |
|
|
1184 | 1186 | def update(self, dataOut, tm): |
|
|
1185 | 1187 | ''' |
|
|
1186 | 1188 | Update data object with new dataOut |
|
|
1187 | 1189 | ''' |
|
|
1188 | 1190 | |
|
|
1189 | 1191 | if tm in self.__times: |
|
|
1190 | 1192 | return |
|
|
1191 | 1193 | self.profileIndex = dataOut.profileIndex |
|
|
1192 | 1194 | self.tm = tm |
|
|
1193 | 1195 | self.type = dataOut.type |
|
|
1194 | 1196 | self.parameters = getattr(dataOut, 'parameters', []) |
|
|
1195 | 1197 | if hasattr(dataOut, 'pairsList'): |
|
|
1196 | 1198 | self.pairs = dataOut.pairsList |
|
|
1197 | 1199 | if hasattr(dataOut, 'meta'): |
|
|
1198 | 1200 | self.meta = dataOut.meta |
|
|
1199 | 1201 | self.channels = dataOut.channelList |
|
|
1200 | 1202 | self.interval = dataOut.getTimeInterval() |
|
|
1201 | 1203 | self.localtime = dataOut.useLocalTime |
|
|
1202 | if 'spc' in self.plottypes or 'cspc' in self.plottypes: | |
|
|
1204 | if 'spc' in self.plottypes or 'cspc' in self.plottypes or 'spc_moments' in self.plottypes: | |
|
|
1203 | 1205 | self.xrange = (dataOut.getFreqRange(1)/1000., |
|
|
1204 | 1206 | dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
1205 | 1207 | self.factor = dataOut.normFactor |
|
|
1206 | 1208 | self.__heights.append(dataOut.heightList) |
|
|
1207 | 1209 | self.__all_heights.update(dataOut.heightList) |
|
|
1208 | 1210 | self.__times.append(tm) |
|
|
1209 | 1211 | |
|
|
1210 | 1212 | for plot in self.plottypes: |
|
|
1211 |
if plot |
|
|
|
1213 | if plot in ('spc', 'spc_moments'): | |
|
|
1212 | 1214 | z = dataOut.data_spc/dataOut.normFactor |
|
|
1213 | 1215 | buffer = 10*numpy.log10(z) |
|
|
1214 | 1216 | if plot == 'cspc': |
|
|
1215 | 1217 | z = dataOut.data_spc/dataOut.normFactor |
|
|
1216 | 1218 | buffer = (dataOut.data_spc, dataOut.data_cspc) |
|
|
1217 | 1219 | if plot == 'noise': |
|
|
1218 | 1220 | buffer = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
|
|
1219 | 1221 | if plot == 'rti': |
|
|
1220 | 1222 | buffer = dataOut.getPower() |
|
|
1221 | 1223 | if plot == 'snr_db': |
|
|
1222 | 1224 | buffer = dataOut.data_SNR |
|
|
1223 | 1225 | if plot == 'snr': |
|
|
1224 | 1226 | buffer = 10*numpy.log10(dataOut.data_SNR) |
|
|
1225 | 1227 | if plot == 'dop': |
|
|
1226 | 1228 | buffer = 10*numpy.log10(dataOut.data_DOP) |
|
|
1227 | 1229 | if plot == 'mean': |
|
|
1228 | 1230 | buffer = dataOut.data_MEAN |
|
|
1229 | 1231 | if plot == 'std': |
|
|
1230 | 1232 | buffer = dataOut.data_STD |
|
|
1231 | 1233 | if plot == 'coh': |
|
|
1232 | 1234 | buffer = dataOut.getCoherence() |
|
|
1233 | 1235 | if plot == 'phase': |
|
|
1234 | 1236 | buffer = dataOut.getCoherence(phase=True) |
|
|
1235 | 1237 | if plot == 'output': |
|
|
1236 | 1238 | buffer = dataOut.data_output |
|
|
1237 | 1239 | if plot == 'param': |
|
|
1238 | 1240 | buffer = dataOut.data_param |
|
|
1239 | 1241 | if plot == 'scope': |
|
|
1240 | 1242 | buffer = dataOut.data |
|
|
1241 | 1243 | self.flagDataAsBlock = dataOut.flagDataAsBlock |
|
|
1242 | 1244 | self.nProfiles = dataOut.nProfiles |
|
|
1243 | 1245 | |
|
|
1244 | 1246 | if plot == 'spc': |
|
|
1245 | 1247 | self.data[plot] = buffer |
|
|
1246 | 1248 | elif plot == 'cspc': |
|
|
1247 | 1249 | self.data['spc'] = buffer[0] |
|
|
1248 | 1250 | self.data['cspc'] = buffer[1] |
|
|
1251 | elif plot == 'spc_moments': | |
|
|
1252 | self.data['spc'] = buffer | |
|
|
1253 | self.data['moments'][tm] = dataOut.moments | |
|
|
1249 | 1254 | else: |
|
|
1250 | 1255 | if self.buffering: |
|
|
1251 | 1256 | self.data[plot][tm] = buffer |
|
|
1252 | 1257 | else: |
|
|
1253 | 1258 | self.data[plot] = buffer |
|
|
1254 | 1259 | |
|
|
1255 | 1260 | def normalize_heights(self): |
|
|
1256 | 1261 | ''' |
|
|
1257 | 1262 | Ensure same-dimension of the data for different heighList |
|
|
1258 | 1263 | ''' |
|
|
1259 | 1264 | |
|
|
1260 | 1265 | H = numpy.array(list(self.__all_heights)) |
|
|
1261 | 1266 | H.sort() |
|
|
1262 | 1267 | for key in self.data: |
|
|
1263 | 1268 | shape = self.shape(key)[:-1] + H.shape |
|
|
1264 | 1269 | for tm, obj in list(self.data[key].items()): |
|
|
1265 | 1270 | h = self.__heights[self.__times.index(tm)] |
|
|
1266 | 1271 | if H.size == h.size: |
|
|
1267 | 1272 | continue |
|
|
1268 | 1273 | index = numpy.where(numpy.in1d(H, h))[0] |
|
|
1269 | 1274 | dummy = numpy.zeros(shape) + numpy.nan |
|
|
1270 | 1275 | if len(shape) == 2: |
|
|
1271 | 1276 | dummy[:, index] = obj |
|
|
1272 | 1277 | else: |
|
|
1273 | 1278 | dummy[index] = obj |
|
|
1274 | 1279 | self.data[key][tm] = dummy |
|
|
1275 | 1280 | |
|
|
1276 | 1281 | self.__heights = [H for tm in self.__times] |
|
|
1277 | 1282 | |
|
|
1278 | 1283 | def jsonify(self, decimate=False): |
|
|
1279 | 1284 | ''' |
|
|
1280 | 1285 | Convert data to json |
|
|
1281 | 1286 | ''' |
|
|
1282 | 1287 | |
|
|
1283 | 1288 | data = {} |
|
|
1284 | 1289 | tm = self.times[-1] |
|
|
1285 | 1290 | dy = int(self.heights.size/self.MAXNUMY) + 1 |
|
|
1286 | 1291 | for key in self.data: |
|
|
1287 | 1292 | if key in ('spc', 'cspc') or not self.buffering: |
|
|
1288 | 1293 | dx = int(self.data[key].shape[1]/self.MAXNUMX) + 1 |
|
|
1289 | 1294 | data[key] = self.roundFloats( |
|
|
1290 | 1295 | self.data[key][::, ::dx, ::dy].tolist()) |
|
|
1291 | 1296 | else: |
|
|
1292 | 1297 | data[key] = self.roundFloats(self.data[key][tm].tolist()) |
|
|
1293 | 1298 | |
|
|
1294 | 1299 | ret = {'data': data} |
|
|
1295 | 1300 | ret['exp_code'] = self.exp_code |
|
|
1296 | 1301 | ret['time'] = float(tm) |
|
|
1297 | 1302 | ret['interval'] = float(self.interval) |
|
|
1298 | 1303 | ret['localtime'] = self.localtime |
|
|
1299 | 1304 | ret['yrange'] = self.roundFloats(self.heights[::dy].tolist()) |
|
|
1300 | 1305 | if 'spc' in self.data or 'cspc' in self.data: |
|
|
1301 | 1306 | ret['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist()) |
|
|
1302 | 1307 | else: |
|
|
1303 | 1308 | ret['xrange'] = [] |
|
|
1304 | 1309 | if hasattr(self, 'pairs'): |
|
|
1305 | 1310 | ret['pairs'] = [(int(p[0]), int(p[1])) for p in self.pairs] |
|
|
1306 | 1311 | else: |
|
|
1307 | 1312 | ret['pairs'] = [] |
|
|
1308 | 1313 | |
|
|
1309 | 1314 | for key, value in list(self.meta.items()): |
|
|
1310 | 1315 | ret[key] = value |
|
|
1311 | 1316 | |
|
|
1312 | 1317 | return json.dumps(ret) |
|
|
1313 | 1318 | |
|
|
1314 | 1319 | @property |
|
|
1315 | 1320 | def times(self): |
|
|
1316 | 1321 | ''' |
|
|
1317 | 1322 | Return the list of times of the current data |
|
|
1318 | 1323 | ''' |
|
|
1319 | 1324 | |
|
|
1320 | 1325 | ret = numpy.array(self.__times) |
|
|
1321 | 1326 | ret.sort() |
|
|
1322 | 1327 | return ret |
|
|
1323 | 1328 | |
|
|
1324 | 1329 | @property |
|
|
1325 | 1330 | def min_time(self): |
|
|
1326 | 1331 | ''' |
|
|
1327 | 1332 | Return the minimun time value |
|
|
1328 | 1333 | ''' |
|
|
1329 | 1334 | |
|
|
1330 | 1335 | return self.times[0] |
|
|
1331 | 1336 | |
|
|
1332 | 1337 | @property |
|
|
1333 | 1338 | def max_time(self): |
|
|
1334 | 1339 | ''' |
|
|
1335 | 1340 | Return the maximun time value |
|
|
1336 | 1341 | ''' |
|
|
1337 | 1342 | |
|
|
1338 | 1343 | return self.times[-1] |
|
|
1339 | 1344 | |
|
|
1340 | 1345 | @property |
|
|
1341 | 1346 | def heights(self): |
|
|
1342 | 1347 | ''' |
|
|
1343 | 1348 | Return the list of heights of the current data |
|
|
1344 | 1349 | ''' |
|
|
1345 | 1350 | |
|
|
1346 | 1351 | return numpy.array(self.__heights[-1]) |
|
|
1347 | 1352 | |
|
|
1348 | 1353 | @staticmethod |
|
|
1349 | 1354 | def roundFloats(obj): |
|
|
1350 | 1355 | if isinstance(obj, list): |
|
|
1351 | 1356 | return list(map(PlotterData.roundFloats, obj)) |
|
|
1352 | 1357 | elif isinstance(obj, float): |
|
|
1353 | 1358 | return round(obj, 2) |
| @@ -1,748 +1,747 | |||
|
|
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 |
if self.CODE == 'spc_me |
|
|
|
69 | if self.CODE == 'spc_moments': | |
|
|
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 |
if self.CODE == 'spc_me |
|
|
|
82 |
mean = self.data['me |
|
|
|
81 | if self.CODE == 'spc_moments': | |
|
|
82 | mean = self.data['moments'][n, :, 1, :][-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 |
if self.CODE == 'spc_me |
|
|
|
99 | if self.CODE == 'spc_moments': | |
|
|
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 |
if self.CODE == 'spc_me |
|
|
|
106 | if self.CODE == 'spc_moments': | |
|
|
107 | 107 | ax.plt_mean.set_data(mean, y) |
|
|
108 | ||
|
|
109 | 108 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
|
|
110 | 109 | |
|
|
111 | 110 | |
|
|
112 | 111 | class CrossSpectraPlot(Plot): |
|
|
113 | 112 | |
|
|
114 | 113 | CODE = 'cspc' |
|
|
115 | 114 | colormap = 'jet' |
|
|
116 | 115 | zmin_coh = None |
|
|
117 | 116 | zmax_coh = None |
|
|
118 | 117 | zmin_phase = None |
|
|
119 | 118 | zmax_phase = None |
|
|
120 | 119 | |
|
|
121 | 120 | def setup(self): |
|
|
122 | 121 | |
|
|
123 | 122 | self.ncols = 4 |
|
|
124 | 123 | self.nrows = len(self.data.pairs) |
|
|
125 | 124 | self.nplots = self.nrows * 4 |
|
|
126 | 125 | self.width = 3.4 * self.ncols |
|
|
127 | 126 | self.height = 3 * self.nrows |
|
|
128 | 127 | self.ylabel = 'Range [km]' |
|
|
129 | 128 | self.showprofile = False |
|
|
130 | 129 | |
|
|
131 | 130 | def plot(self): |
|
|
132 | 131 | |
|
|
133 | 132 | if self.xaxis == "frequency": |
|
|
134 | 133 | x = self.data.xrange[0] |
|
|
135 | 134 | self.xlabel = "Frequency (kHz)" |
|
|
136 | 135 | elif self.xaxis == "time": |
|
|
137 | 136 | x = self.data.xrange[1] |
|
|
138 | 137 | self.xlabel = "Time (ms)" |
|
|
139 | 138 | else: |
|
|
140 | 139 | x = self.data.xrange[2] |
|
|
141 | 140 | self.xlabel = "Velocity (m/s)" |
|
|
142 | 141 | |
|
|
143 | 142 | self.titles = [] |
|
|
144 | 143 | |
|
|
145 | 144 | y = self.data.heights |
|
|
146 | 145 | self.y = y |
|
|
147 | 146 | spc = self.data['spc'] |
|
|
148 | 147 | cspc = self.data['cspc'] |
|
|
149 | 148 | |
|
|
150 | 149 | for n in range(self.nrows): |
|
|
151 | 150 | noise = self.data['noise'][n][-1] |
|
|
152 | 151 | pair = self.data.pairs[n] |
|
|
153 | 152 | ax = self.axes[4 * n] |
|
|
154 | 153 | spc0 = 10.*numpy.log10(spc[pair[0]]/self.data.factor) |
|
|
155 | 154 | if ax.firsttime: |
|
|
156 | 155 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
157 | 156 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
158 | 157 | self.zmin = self.zmin if self.zmin else numpy.nanmin(spc) |
|
|
159 | 158 | self.zmax = self.zmax if self.zmax else numpy.nanmax(spc) |
|
|
160 | 159 | ax.plt = ax.pcolormesh(x , y , spc0.T, |
|
|
161 | 160 | vmin=self.zmin, |
|
|
162 | 161 | vmax=self.zmax, |
|
|
163 | 162 | cmap=plt.get_cmap(self.colormap) |
|
|
164 | 163 | ) |
|
|
165 | 164 | else: |
|
|
166 | 165 | ax.plt.set_array(spc0.T.ravel()) |
|
|
167 | 166 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise)) |
|
|
168 | 167 | |
|
|
169 | 168 | ax = self.axes[4 * n + 1] |
|
|
170 | 169 | spc1 = 10.*numpy.log10(spc[pair[1]]/self.data.factor) |
|
|
171 | 170 | if ax.firsttime: |
|
|
172 | 171 | ax.plt = ax.pcolormesh(x , y, spc1.T, |
|
|
173 | 172 | vmin=self.zmin, |
|
|
174 | 173 | vmax=self.zmax, |
|
|
175 | 174 | cmap=plt.get_cmap(self.colormap) |
|
|
176 | 175 | ) |
|
|
177 | 176 | else: |
|
|
178 | 177 | ax.plt.set_array(spc1.T.ravel()) |
|
|
179 | 178 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise)) |
|
|
180 | 179 | |
|
|
181 | 180 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
|
|
182 | 181 | coh = numpy.abs(out) |
|
|
183 | 182 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
|
|
184 | 183 | |
|
|
185 | 184 | ax = self.axes[4 * n + 2] |
|
|
186 | 185 | if ax.firsttime: |
|
|
187 | 186 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
188 | 187 | vmin=0, |
|
|
189 | 188 | vmax=1, |
|
|
190 | 189 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
191 | 190 | ) |
|
|
192 | 191 | else: |
|
|
193 | 192 | ax.plt.set_array(coh.T.ravel()) |
|
|
194 | 193 | self.titles.append( |
|
|
195 | 194 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
196 | 195 | |
|
|
197 | 196 | ax = self.axes[4 * n + 3] |
|
|
198 | 197 | if ax.firsttime: |
|
|
199 | 198 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
200 | 199 | vmin=-180, |
|
|
201 | 200 | vmax=180, |
|
|
202 | 201 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
203 | 202 | ) |
|
|
204 | 203 | else: |
|
|
205 | 204 | ax.plt.set_array(phase.T.ravel()) |
|
|
206 | 205 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
207 | 206 | |
|
|
208 | 207 | |
|
|
209 |
class SpectraMe |
|
|
|
208 | class SpectralMomentsPlot(SpectraPlot): | |
|
|
210 | 209 | ''' |
|
|
211 |
Plot for Spectra |
|
|
|
210 | Plot for Spectral Moments | |
|
|
212 | 211 | ''' |
|
|
213 |
CODE = 'spc_me |
|
|
|
212 | CODE = 'spc_moments' | |
|
|
214 | 213 | colormap = 'jro' |
|
|
215 | 214 | |
|
|
216 | 215 | |
|
|
217 | 216 | class RTIPlot(Plot): |
|
|
218 | 217 | ''' |
|
|
219 | 218 | Plot for RTI data |
|
|
220 | 219 | ''' |
|
|
221 | 220 | |
|
|
222 | 221 | CODE = 'rti' |
|
|
223 | 222 | colormap = 'jro' |
|
|
224 | 223 | |
|
|
225 | 224 | def setup(self): |
|
|
226 | 225 | self.xaxis = 'time' |
|
|
227 | 226 | self.ncols = 1 |
|
|
228 | 227 | self.nrows = len(self.data.channels) |
|
|
229 | 228 | self.nplots = len(self.data.channels) |
|
|
230 | 229 | self.ylabel = 'Range [km]' |
|
|
231 | 230 | self.cb_label = 'dB' |
|
|
232 | 231 | self.titles = ['{} Channel {}'.format( |
|
|
233 | 232 | self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
234 | 233 | |
|
|
235 | 234 | def plot(self): |
|
|
236 | 235 | self.x = self.data.times |
|
|
237 | 236 | self.y = self.data.heights |
|
|
238 | 237 | self.z = self.data[self.CODE] |
|
|
239 | 238 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
240 | 239 | |
|
|
241 | 240 | if self.decimation is None: |
|
|
242 | 241 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
243 | 242 | else: |
|
|
244 | 243 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
245 | 244 | |
|
|
246 | 245 | for n, ax in enumerate(self.axes): |
|
|
247 | 246 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
248 | 247 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
249 | 248 | if ax.firsttime: |
|
|
250 | 249 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
251 | 250 | vmin=self.zmin, |
|
|
252 | 251 | vmax=self.zmax, |
|
|
253 | 252 | cmap=plt.get_cmap(self.colormap) |
|
|
254 | 253 | ) |
|
|
255 | 254 | if self.showprofile: |
|
|
256 | 255 | ax.plot_profile = self.pf_axes[n].plot( |
|
|
257 | 256 | self.data['rti'][n][-1], self.y)[0] |
|
|
258 | 257 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y, |
|
|
259 | 258 | color="k", linestyle="dashed", lw=1)[0] |
|
|
260 | 259 | else: |
|
|
261 | 260 | ax.collections.remove(ax.collections[0]) |
|
|
262 | 261 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
263 | 262 | vmin=self.zmin, |
|
|
264 | 263 | vmax=self.zmax, |
|
|
265 | 264 | cmap=plt.get_cmap(self.colormap) |
|
|
266 | 265 | ) |
|
|
267 | 266 | if self.showprofile: |
|
|
268 | 267 | ax.plot_profile.set_data(self.data['rti'][n][-1], self.y) |
|
|
269 | 268 | ax.plot_noise.set_data(numpy.repeat( |
|
|
270 | 269 | self.data['noise'][n][-1], len(self.y)), self.y) |
|
|
271 | 270 | |
|
|
272 | 271 | |
|
|
273 | 272 | class CoherencePlot(RTIPlot): |
|
|
274 | 273 | ''' |
|
|
275 | 274 | Plot for Coherence data |
|
|
276 | 275 | ''' |
|
|
277 | 276 | |
|
|
278 | 277 | CODE = 'coh' |
|
|
279 | 278 | |
|
|
280 | 279 | def setup(self): |
|
|
281 | 280 | self.xaxis = 'time' |
|
|
282 | 281 | self.ncols = 1 |
|
|
283 | 282 | self.nrows = len(self.data.pairs) |
|
|
284 | 283 | self.nplots = len(self.data.pairs) |
|
|
285 | 284 | self.ylabel = 'Range [km]' |
|
|
286 | 285 | if self.CODE == 'coh': |
|
|
287 | 286 | self.cb_label = '' |
|
|
288 | 287 | self.titles = [ |
|
|
289 | 288 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
290 | 289 | else: |
|
|
291 | 290 | self.cb_label = 'Degrees' |
|
|
292 | 291 | self.titles = [ |
|
|
293 | 292 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
294 | 293 | |
|
|
295 | 294 | |
|
|
296 | 295 | class PhasePlot(CoherencePlot): |
|
|
297 | 296 | ''' |
|
|
298 | 297 | Plot for Phase map data |
|
|
299 | 298 | ''' |
|
|
300 | 299 | |
|
|
301 | 300 | CODE = 'phase' |
|
|
302 | 301 | colormap = 'seismic' |
|
|
303 | 302 | |
|
|
304 | 303 | |
|
|
305 | 304 | class NoisePlot(Plot): |
|
|
306 | 305 | ''' |
|
|
307 | 306 | Plot for noise |
|
|
308 | 307 | ''' |
|
|
309 | 308 | |
|
|
310 | 309 | CODE = 'noise' |
|
|
311 | 310 | |
|
|
312 | 311 | def setup(self): |
|
|
313 | 312 | self.xaxis = 'time' |
|
|
314 | 313 | self.ncols = 1 |
|
|
315 | 314 | self.nrows = 1 |
|
|
316 | 315 | self.nplots = 1 |
|
|
317 | 316 | self.ylabel = 'Intensity [dB]' |
|
|
318 | 317 | self.titles = ['Noise'] |
|
|
319 | 318 | self.colorbar = False |
|
|
320 | 319 | |
|
|
321 | 320 | def plot(self): |
|
|
322 | 321 | |
|
|
323 | 322 | x = self.data.times |
|
|
324 | 323 | xmin = self.data.min_time |
|
|
325 | 324 | xmax = xmin + self.xrange * 60 * 60 |
|
|
326 | 325 | Y = self.data[self.CODE] |
|
|
327 | 326 | |
|
|
328 | 327 | if self.axes[0].firsttime: |
|
|
329 | 328 | for ch in self.data.channels: |
|
|
330 | 329 | y = Y[ch] |
|
|
331 | 330 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
332 | 331 | plt.legend() |
|
|
333 | 332 | else: |
|
|
334 | 333 | for ch in self.data.channels: |
|
|
335 | 334 | y = Y[ch] |
|
|
336 | 335 | self.axes[0].lines[ch].set_data(x, y) |
|
|
337 | 336 | |
|
|
338 | 337 | self.ymin = numpy.nanmin(Y) - 5 |
|
|
339 | 338 | self.ymax = numpy.nanmax(Y) + 5 |
|
|
340 | 339 | |
|
|
341 | 340 | |
|
|
342 | 341 | class SnrPlot(RTIPlot): |
|
|
343 | 342 | ''' |
|
|
344 | 343 | Plot for SNR Data |
|
|
345 | 344 | ''' |
|
|
346 | 345 | |
|
|
347 | 346 | CODE = 'snr' |
|
|
348 | 347 | colormap = 'jet' |
|
|
349 | 348 | |
|
|
350 | 349 | |
|
|
351 | 350 | class DopplerPlot(RTIPlot): |
|
|
352 | 351 | ''' |
|
|
353 | 352 | Plot for DOPPLER Data |
|
|
354 | 353 | ''' |
|
|
355 | 354 | |
|
|
356 | 355 | CODE = 'dop' |
|
|
357 | 356 | colormap = 'jet' |
|
|
358 | 357 | |
|
|
359 | 358 | |
|
|
360 | 359 | class SkyMapPlot(Plot): |
|
|
361 | 360 | ''' |
|
|
362 | 361 | Plot for meteors detection data |
|
|
363 | 362 | ''' |
|
|
364 | 363 | |
|
|
365 | 364 | CODE = 'param' |
|
|
366 | 365 | |
|
|
367 | 366 | def setup(self): |
|
|
368 | 367 | |
|
|
369 | 368 | self.ncols = 1 |
|
|
370 | 369 | self.nrows = 1 |
|
|
371 | 370 | self.width = 7.2 |
|
|
372 | 371 | self.height = 7.2 |
|
|
373 | 372 | self.nplots = 1 |
|
|
374 | 373 | self.xlabel = 'Zonal Zenith Angle (deg)' |
|
|
375 | 374 | self.ylabel = 'Meridional Zenith Angle (deg)' |
|
|
376 | 375 | self.polar = True |
|
|
377 | 376 | self.ymin = -180 |
|
|
378 | 377 | self.ymax = 180 |
|
|
379 | 378 | self.colorbar = False |
|
|
380 | 379 | |
|
|
381 | 380 | def plot(self): |
|
|
382 | 381 | |
|
|
383 | 382 | arrayParameters = numpy.concatenate(self.data['param']) |
|
|
384 | 383 | error = arrayParameters[:, -1] |
|
|
385 | 384 | indValid = numpy.where(error == 0)[0] |
|
|
386 | 385 | finalMeteor = arrayParameters[indValid, :] |
|
|
387 | 386 | finalAzimuth = finalMeteor[:, 3] |
|
|
388 | 387 | finalZenith = finalMeteor[:, 4] |
|
|
389 | 388 | |
|
|
390 | 389 | x = finalAzimuth * numpy.pi / 180 |
|
|
391 | 390 | y = finalZenith |
|
|
392 | 391 | |
|
|
393 | 392 | ax = self.axes[0] |
|
|
394 | 393 | |
|
|
395 | 394 | if ax.firsttime: |
|
|
396 | 395 | ax.plot = ax.plot(x, y, 'bo', markersize=5)[0] |
|
|
397 | 396 | else: |
|
|
398 | 397 | ax.plot.set_data(x, y) |
|
|
399 | 398 | |
|
|
400 | 399 | dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
401 | 400 | dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S') |
|
|
402 | 401 | title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1, |
|
|
403 | 402 | dt2, |
|
|
404 | 403 | len(x)) |
|
|
405 | 404 | self.titles[0] = title |
|
|
406 | 405 | |
|
|
407 | 406 | |
|
|
408 | 407 | class ParametersPlot(RTIPlot): |
|
|
409 | 408 | ''' |
|
|
410 | 409 | Plot for data_param object |
|
|
411 | 410 | ''' |
|
|
412 | 411 | |
|
|
413 | 412 | CODE = 'param' |
|
|
414 | 413 | colormap = 'seismic' |
|
|
415 | 414 | |
|
|
416 | 415 | def setup(self): |
|
|
417 | 416 | self.xaxis = 'time' |
|
|
418 | 417 | self.ncols = 1 |
|
|
419 | 418 | self.nrows = self.data.shape(self.CODE)[0] |
|
|
420 | 419 | self.nplots = self.nrows |
|
|
421 | 420 | if self.showSNR: |
|
|
422 | 421 | self.nrows += 1 |
|
|
423 | 422 | self.nplots += 1 |
|
|
424 | 423 | |
|
|
425 | 424 | self.ylabel = 'Height [km]' |
|
|
426 | 425 | if not self.titles: |
|
|
427 | 426 | self.titles = self.data.parameters \ |
|
|
428 | 427 | if self.data.parameters else ['Param {}'.format(x) for x in range(self.nrows)] |
|
|
429 | 428 | if self.showSNR: |
|
|
430 | 429 | self.titles.append('SNR') |
|
|
431 | 430 | |
|
|
432 | 431 | def plot(self): |
|
|
433 | 432 | self.data.normalize_heights() |
|
|
434 | 433 | self.x = self.data.times |
|
|
435 | 434 | self.y = self.data.heights |
|
|
436 | 435 | if self.showSNR: |
|
|
437 | 436 | self.z = numpy.concatenate( |
|
|
438 | 437 | (self.data[self.CODE], self.data['snr']) |
|
|
439 | 438 | ) |
|
|
440 | 439 | else: |
|
|
441 | 440 | self.z = self.data[self.CODE] |
|
|
442 | 441 | |
|
|
443 | 442 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
444 | 443 | |
|
|
445 | 444 | if self.decimation is None: |
|
|
446 | 445 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
447 | 446 | else: |
|
|
448 | 447 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
449 | 448 | |
|
|
450 | 449 | for n, ax in enumerate(self.axes): |
|
|
451 | 450 | |
|
|
452 | 451 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
453 | 452 | self.z[n]) |
|
|
454 | 453 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
455 | 454 | self.z[n]) |
|
|
456 | 455 | |
|
|
457 | 456 | if ax.firsttime: |
|
|
458 | 457 | if self.zlimits is not None: |
|
|
459 | 458 | self.zmin, self.zmax = self.zlimits[n] |
|
|
460 | 459 | |
|
|
461 | 460 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
462 | 461 | vmin=self.zmin, |
|
|
463 | 462 | vmax=self.zmax, |
|
|
464 | 463 | cmap=self.cmaps[n] |
|
|
465 | 464 | ) |
|
|
466 | 465 | else: |
|
|
467 | 466 | if self.zlimits is not None: |
|
|
468 | 467 | self.zmin, self.zmax = self.zlimits[n] |
|
|
469 | 468 | ax.collections.remove(ax.collections[0]) |
|
|
470 | 469 | ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n], |
|
|
471 | 470 | vmin=self.zmin, |
|
|
472 | 471 | vmax=self.zmax, |
|
|
473 | 472 | cmap=self.cmaps[n] |
|
|
474 | 473 | ) |
|
|
475 | 474 | |
|
|
476 | 475 | |
|
|
477 | 476 | class OutputPlot(ParametersPlot): |
|
|
478 | 477 | ''' |
|
|
479 | 478 | Plot data_output object |
|
|
480 | 479 | ''' |
|
|
481 | 480 | |
|
|
482 | 481 | CODE = 'output' |
|
|
483 | 482 | colormap = 'seismic' |
|
|
484 | 483 | |
|
|
485 | 484 | |
|
|
486 | 485 | class PolarMapPlot(Plot): |
|
|
487 | 486 | ''' |
|
|
488 | 487 | Plot for weather radar |
|
|
489 | 488 | ''' |
|
|
490 | 489 | |
|
|
491 | 490 | CODE = 'param' |
|
|
492 | 491 | colormap = 'seismic' |
|
|
493 | 492 | |
|
|
494 | 493 | def setup(self): |
|
|
495 | 494 | self.ncols = 1 |
|
|
496 | 495 | self.nrows = 1 |
|
|
497 | 496 | self.width = 9 |
|
|
498 | 497 | self.height = 8 |
|
|
499 | 498 | self.mode = self.data.meta['mode'] |
|
|
500 | 499 | if self.channels is not None: |
|
|
501 | 500 | self.nplots = len(self.channels) |
|
|
502 | 501 | self.nrows = len(self.channels) |
|
|
503 | 502 | else: |
|
|
504 | 503 | self.nplots = self.data.shape(self.CODE)[0] |
|
|
505 | 504 | self.nrows = self.nplots |
|
|
506 | 505 | self.channels = list(range(self.nplots)) |
|
|
507 | 506 | if self.mode == 'E': |
|
|
508 | 507 | self.xlabel = 'Longitude' |
|
|
509 | 508 | self.ylabel = 'Latitude' |
|
|
510 | 509 | else: |
|
|
511 | 510 | self.xlabel = 'Range (km)' |
|
|
512 | 511 | self.ylabel = 'Height (km)' |
|
|
513 | 512 | self.bgcolor = 'white' |
|
|
514 | 513 | self.cb_labels = self.data.meta['units'] |
|
|
515 | 514 | self.lat = self.data.meta['latitude'] |
|
|
516 | 515 | self.lon = self.data.meta['longitude'] |
|
|
517 | 516 | self.xmin, self.xmax = float( |
|
|
518 | 517 | km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon) |
|
|
519 | 518 | self.ymin, self.ymax = float( |
|
|
520 | 519 | km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat) |
|
|
521 | 520 | # self.polar = True |
|
|
522 | 521 | |
|
|
523 | 522 | def plot(self): |
|
|
524 | 523 | |
|
|
525 | 524 | for n, ax in enumerate(self.axes): |
|
|
526 | 525 | data = self.data['param'][self.channels[n]] |
|
|
527 | 526 | |
|
|
528 | 527 | zeniths = numpy.linspace( |
|
|
529 | 528 | 0, self.data.meta['max_range'], data.shape[1]) |
|
|
530 | 529 | if self.mode == 'E': |
|
|
531 | 530 | azimuths = -numpy.radians(self.data.heights)+numpy.pi/2 |
|
|
532 | 531 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
533 | 532 | x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin( |
|
|
534 | 533 | theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])) |
|
|
535 | 534 | x = km2deg(x) + self.lon |
|
|
536 | 535 | y = km2deg(y) + self.lat |
|
|
537 | 536 | else: |
|
|
538 | 537 | azimuths = numpy.radians(self.data.heights) |
|
|
539 | 538 | r, theta = numpy.meshgrid(zeniths, azimuths) |
|
|
540 | 539 | x, y = r*numpy.cos(theta), r*numpy.sin(theta) |
|
|
541 | 540 | self.y = zeniths |
|
|
542 | 541 | |
|
|
543 | 542 | if ax.firsttime: |
|
|
544 | 543 | if self.zlimits is not None: |
|
|
545 | 544 | self.zmin, self.zmax = self.zlimits[n] |
|
|
546 | 545 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
547 | 546 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
548 | 547 | vmin=self.zmin, |
|
|
549 | 548 | vmax=self.zmax, |
|
|
550 | 549 | cmap=self.cmaps[n]) |
|
|
551 | 550 | else: |
|
|
552 | 551 | if self.zlimits is not None: |
|
|
553 | 552 | self.zmin, self.zmax = self.zlimits[n] |
|
|
554 | 553 | ax.collections.remove(ax.collections[0]) |
|
|
555 | 554 | ax.plt = ax.pcolormesh( # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
556 | 555 | x, y, numpy.ma.array(data, mask=numpy.isnan(data)), |
|
|
557 | 556 | vmin=self.zmin, |
|
|
558 | 557 | vmax=self.zmax, |
|
|
559 | 558 | cmap=self.cmaps[n]) |
|
|
560 | 559 | |
|
|
561 | 560 | if self.mode == 'A': |
|
|
562 | 561 | continue |
|
|
563 | 562 | |
|
|
564 | 563 | # plot district names |
|
|
565 | 564 | f = open('/data/workspace/schain_scripts/distrito.csv') |
|
|
566 | 565 | for line in f: |
|
|
567 | 566 | label, lon, lat = [s.strip() for s in line.split(',') if s] |
|
|
568 | 567 | lat = float(lat) |
|
|
569 | 568 | lon = float(lon) |
|
|
570 | 569 | # ax.plot(lon, lat, '.b', ms=2) |
|
|
571 | 570 | ax.text(lon, lat, label.decode('utf8'), ha='center', |
|
|
572 | 571 | va='bottom', size='8', color='black') |
|
|
573 | 572 | |
|
|
574 | 573 | # plot limites |
|
|
575 | 574 | limites = [] |
|
|
576 | 575 | tmp = [] |
|
|
577 | 576 | for line in open('/data/workspace/schain_scripts/lima.csv'): |
|
|
578 | 577 | if '#' in line: |
|
|
579 | 578 | if tmp: |
|
|
580 | 579 | limites.append(tmp) |
|
|
581 | 580 | tmp = [] |
|
|
582 | 581 | continue |
|
|
583 | 582 | values = line.strip().split(',') |
|
|
584 | 583 | tmp.append((float(values[0]), float(values[1]))) |
|
|
585 | 584 | for points in limites: |
|
|
586 | 585 | ax.add_patch( |
|
|
587 | 586 | Polygon(points, ec='k', fc='none', ls='--', lw=0.5)) |
|
|
588 | 587 | |
|
|
589 | 588 | # plot Cuencas |
|
|
590 | 589 | for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'): |
|
|
591 | 590 | f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca)) |
|
|
592 | 591 | values = [line.strip().split(',') for line in f] |
|
|
593 | 592 | points = [(float(s[0]), float(s[1])) for s in values] |
|
|
594 | 593 | ax.add_patch(Polygon(points, ec='b', fc='none')) |
|
|
595 | 594 | |
|
|
596 | 595 | # plot grid |
|
|
597 | 596 | for r in (15, 30, 45, 60): |
|
|
598 | 597 | ax.add_artist(plt.Circle((self.lon, self.lat), |
|
|
599 | 598 | km2deg(r), color='0.6', fill=False, lw=0.2)) |
|
|
600 | 599 | ax.text( |
|
|
601 | 600 | self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180), |
|
|
602 | 601 | self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180), |
|
|
603 | 602 | '{}km'.format(r), |
|
|
604 | 603 | ha='center', va='bottom', size='8', color='0.6', weight='heavy') |
|
|
605 | 604 | |
|
|
606 | 605 | if self.mode == 'E': |
|
|
607 | 606 | title = 'El={}$^\circ$'.format(self.data.meta['elevation']) |
|
|
608 | 607 | label = 'E{:02d}'.format(int(self.data.meta['elevation'])) |
|
|
609 | 608 | else: |
|
|
610 | 609 | title = 'Az={}$^\circ$'.format(self.data.meta['azimuth']) |
|
|
611 | 610 | label = 'A{:02d}'.format(int(self.data.meta['azimuth'])) |
|
|
612 | 611 | |
|
|
613 | 612 | self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels] |
|
|
614 | 613 | self.titles = ['{} {}'.format( |
|
|
615 | 614 | self.data.parameters[x], title) for x in self.channels] |
|
|
616 | 615 | |
|
|
617 | 616 | class ScopePlot(Plot): |
|
|
618 | 617 | |
|
|
619 | 618 | ''' |
|
|
620 | 619 | Plot for Scope |
|
|
621 | 620 | ''' |
|
|
622 | 621 | |
|
|
623 | 622 | CODE = 'scope' |
|
|
624 | 623 | |
|
|
625 | 624 | def setup(self): |
|
|
626 | 625 | |
|
|
627 | 626 | self.xaxis = 'Range (Km)' |
|
|
628 | 627 | self.ncols = 1 |
|
|
629 | 628 | self.nrows = 1 |
|
|
630 | 629 | self.nplots = 1 |
|
|
631 | 630 | self.ylabel = 'Intensity [dB]' |
|
|
632 | 631 | self.titles = ['Scope'] |
|
|
633 | 632 | self.colorbar = False |
|
|
634 | 633 | colspan = 3 |
|
|
635 | 634 | rowspan = 1 |
|
|
636 | 635 | |
|
|
637 | 636 | def plot_iq(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
638 | 637 | |
|
|
639 | 638 | yreal = y[channelIndexList,:].real |
|
|
640 | 639 | yimag = y[channelIndexList,:].imag |
|
|
641 | 640 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
642 | 641 | self.xlabel = "Range (Km)" |
|
|
643 | 642 | self.ylabel = "Intensity - IQ" |
|
|
644 | 643 | |
|
|
645 | 644 | self.y = yreal |
|
|
646 | 645 | self.x = x |
|
|
647 | 646 | self.xmin = min(x) |
|
|
648 | 647 | self.xmax = max(x) |
|
|
649 | 648 | |
|
|
650 | 649 | |
|
|
651 | 650 | self.titles[0] = title |
|
|
652 | 651 | |
|
|
653 | 652 | for i,ax in enumerate(self.axes): |
|
|
654 | 653 | title = "Channel %d" %(i) |
|
|
655 | 654 | if ax.firsttime: |
|
|
656 | 655 | ax.plt_r = ax.plot(x, yreal[i,:], color='b')[0] |
|
|
657 | 656 | ax.plt_i = ax.plot(x, yimag[i,:], color='r')[0] |
|
|
658 | 657 | else: |
|
|
659 | 658 | #pass |
|
|
660 | 659 | ax.plt_r.set_data(x, yreal[i,:]) |
|
|
661 | 660 | ax.plt_i.set_data(x, yimag[i,:]) |
|
|
662 | 661 | |
|
|
663 | 662 | def plot_power(self, x, y, channelIndexList, thisDatetime, wintitle): |
|
|
664 | 663 | y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:]) |
|
|
665 | 664 | yreal = y.real |
|
|
666 | 665 | self.y = yreal |
|
|
667 | 666 | title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
668 | 667 | self.xlabel = "Range (Km)" |
|
|
669 | 668 | self.ylabel = "Intensity" |
|
|
670 | 669 | self.xmin = min(x) |
|
|
671 | 670 | self.xmax = max(x) |
|
|
672 | 671 | |
|
|
673 | 672 | |
|
|
674 | 673 | self.titles[0] = title |
|
|
675 | 674 | |
|
|
676 | 675 | for i,ax in enumerate(self.axes): |
|
|
677 | 676 | title = "Channel %d" %(i) |
|
|
678 | 677 | |
|
|
679 | 678 | ychannel = yreal[i,:] |
|
|
680 | 679 | |
|
|
681 | 680 | if ax.firsttime: |
|
|
682 | 681 | ax.plt_r = ax.plot(x, ychannel)[0] |
|
|
683 | 682 | else: |
|
|
684 | 683 | #pass |
|
|
685 | 684 | ax.plt_r.set_data(x, ychannel) |
|
|
686 | 685 | |
|
|
687 | 686 | |
|
|
688 | 687 | def plot(self): |
|
|
689 | 688 | |
|
|
690 | 689 | if self.channels: |
|
|
691 | 690 | channels = self.channels |
|
|
692 | 691 | else: |
|
|
693 | 692 | channels = self.data.channels |
|
|
694 | 693 | |
|
|
695 | 694 | |
|
|
696 | 695 | |
|
|
697 | 696 | thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]) |
|
|
698 | 697 | |
|
|
699 | 698 | scope = self.data['scope'] |
|
|
700 | 699 | |
|
|
701 | 700 | |
|
|
702 | 701 | if self.data.flagDataAsBlock: |
|
|
703 | 702 | |
|
|
704 | 703 | for i in range(self.data.nProfiles): |
|
|
705 | 704 | |
|
|
706 | 705 | wintitle1 = " [Profile = %d] " %i |
|
|
707 | 706 | |
|
|
708 | 707 | if self.type == "power": |
|
|
709 | 708 | self.plot_power(self.data.heights, |
|
|
710 | 709 | scope[:,i,:], |
|
|
711 | 710 | channels, |
|
|
712 | 711 | thisDatetime, |
|
|
713 | 712 | wintitle1 |
|
|
714 | 713 | ) |
|
|
715 | 714 | |
|
|
716 | 715 | if self.type == "iq": |
|
|
717 | 716 | self.plot_iq(self.data.heights, |
|
|
718 | 717 | scope[:,i,:], |
|
|
719 | 718 | channels, |
|
|
720 | 719 | thisDatetime, |
|
|
721 | 720 | wintitle1 |
|
|
722 | 721 | ) |
|
|
723 | 722 | |
|
|
724 | 723 | |
|
|
725 | 724 | |
|
|
726 | 725 | |
|
|
727 | 726 | |
|
|
728 | 727 | else: |
|
|
729 | 728 | wintitle = " [Profile = %d] " %self.data.profileIndex |
|
|
730 | 729 | |
|
|
731 | 730 | if self.type == "power": |
|
|
732 | 731 | self.plot_power(self.data.heights, |
|
|
733 | 732 | scope, |
|
|
734 | 733 | channels, |
|
|
735 | 734 | thisDatetime, |
|
|
736 | 735 | wintitle |
|
|
737 | 736 | ) |
|
|
738 | 737 | |
|
|
739 | 738 | if self.type == "iq": |
|
|
740 | 739 | self.plot_iq(self.data.heights, |
|
|
741 | 740 | scope, |
|
|
742 | 741 | channels, |
|
|
743 | 742 | thisDatetime, |
|
|
744 | 743 | wintitle |
|
|
745 | 744 | ) |
|
|
746 | 745 | |
|
|
747 | 746 | |
|
|
748 | 747 | No newline at end of file |
|
|
1 | NO CONTENT: modified file |
| @@ -1,1588 +1,1589 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | Created on Jul 9, 2014 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 |
|
|
5 | 5 | ''' |
|
|
6 | 6 | import os |
|
|
7 | 7 | import datetime |
|
|
8 | 8 | import numpy |
|
|
9 | 9 | |
|
|
10 | 10 | from .figure import Figure, isRealtime, isTimeInHourRange |
|
|
11 | 11 | from .plotting_codes import * |
|
|
12 | 12 | from schainpy.model.proc.jroproc_base import MPDecorator |
|
|
13 | 13 | |
|
|
14 | 14 | from schainpy.utils import log |
|
|
15 | 15 | |
|
|
16 | 16 | @MPDecorator |
|
|
17 | 17 | class SpectraPlot_(Figure): |
|
|
18 | 18 | |
|
|
19 | 19 | isConfig = None |
|
|
20 | 20 | __nsubplots = None |
|
|
21 | 21 | |
|
|
22 | 22 | WIDTHPROF = None |
|
|
23 | 23 | HEIGHTPROF = None |
|
|
24 | 24 | PREFIX = 'spc' |
|
|
25 | 25 | |
|
|
26 | 26 | def __init__(self): |
|
|
27 | 27 | Figure.__init__(self) |
|
|
28 | 28 | self.isConfig = False |
|
|
29 | 29 | self.__nsubplots = 1 |
|
|
30 | 30 | self.WIDTH = 250 |
|
|
31 | 31 | self.HEIGHT = 250 |
|
|
32 | 32 | self.WIDTHPROF = 120 |
|
|
33 | 33 | self.HEIGHTPROF = 0 |
|
|
34 | 34 | self.counter_imagwr = 0 |
|
|
35 | 35 | |
|
|
36 | 36 | self.PLOT_CODE = SPEC_CODE |
|
|
37 | 37 | |
|
|
38 | 38 | self.FTP_WEI = None |
|
|
39 | 39 | self.EXP_CODE = None |
|
|
40 | 40 | self.SUB_EXP_CODE = None |
|
|
41 | 41 | self.PLOT_POS = None |
|
|
42 | 42 | |
|
|
43 | 43 | self.__xfilter_ena = False |
|
|
44 | 44 | self.__yfilter_ena = False |
|
|
45 | 45 | |
|
|
46 | 46 | self.indice=1 |
|
|
47 | 47 | |
|
|
48 | 48 | def getSubplots(self): |
|
|
49 | 49 | |
|
|
50 | 50 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
51 | 51 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
52 | 52 | |
|
|
53 | 53 | return nrow, ncol |
|
|
54 | 54 | |
|
|
55 | 55 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
56 | 56 | |
|
|
57 | 57 | self.__showprofile = showprofile |
|
|
58 | 58 | self.nplots = nplots |
|
|
59 | 59 | |
|
|
60 | 60 | ncolspan = 1 |
|
|
61 | 61 | colspan = 1 |
|
|
62 | 62 | if showprofile: |
|
|
63 | 63 | ncolspan = 3 |
|
|
64 | 64 | colspan = 2 |
|
|
65 | 65 | self.__nsubplots = 2 |
|
|
66 | 66 | |
|
|
67 | 67 | self.createFigure(id = id, |
|
|
68 | 68 | wintitle = wintitle, |
|
|
69 | 69 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
70 | 70 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
71 | 71 | show=show) |
|
|
72 | 72 | |
|
|
73 | 73 | nrow, ncol = self.getSubplots() |
|
|
74 | 74 | |
|
|
75 | 75 | counter = 0 |
|
|
76 | 76 | for y in range(nrow): |
|
|
77 | 77 | for x in range(ncol): |
|
|
78 | 78 | |
|
|
79 | 79 | if counter >= self.nplots: |
|
|
80 | 80 | break |
|
|
81 | 81 | |
|
|
82 | 82 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
83 | 83 | |
|
|
84 | 84 | if showprofile: |
|
|
85 | 85 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
86 | 86 | |
|
|
87 | 87 | counter += 1 |
|
|
88 | 88 | |
|
|
89 | 89 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
90 | 90 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
91 | 91 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
92 | 92 | server=None, folder=None, username=None, password=None, |
|
|
93 | 93 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False, |
|
|
94 | 94 | xaxis="frequency", colormap='jet', normFactor=None): |
|
|
95 | 95 | |
|
|
96 | 96 | """ |
|
|
97 | 97 | |
|
|
98 | 98 | Input: |
|
|
99 | 99 | dataOut : |
|
|
100 | 100 | id : |
|
|
101 | 101 | wintitle : |
|
|
102 | 102 | channelList : |
|
|
103 | 103 | showProfile : |
|
|
104 | 104 | xmin : None, |
|
|
105 | 105 | xmax : None, |
|
|
106 | 106 | ymin : None, |
|
|
107 | 107 | ymax : None, |
|
|
108 | 108 | zmin : None, |
|
|
109 | 109 | zmax : None |
|
|
110 | 110 | """ |
|
|
111 | 111 | if dataOut.flagNoData: |
|
|
112 | 112 | return dataOut |
|
|
113 | 113 | |
|
|
114 | 114 | if realtime: |
|
|
115 | 115 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
116 | 116 | print('Skipping this plot function') |
|
|
117 | 117 | return |
|
|
118 | 118 | |
|
|
119 | 119 | if channelList == None: |
|
|
120 | 120 | channelIndexList = dataOut.channelIndexList |
|
|
121 | 121 | else: |
|
|
122 | 122 | channelIndexList = [] |
|
|
123 | 123 | for channel in channelList: |
|
|
124 | 124 | if channel not in dataOut.channelList: |
|
|
125 | 125 | raise ValueError("Channel %d is not in dataOut.channelList" %channel) |
|
|
126 | 126 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
127 | 127 | |
|
|
128 | 128 | if normFactor is None: |
|
|
129 | 129 | factor = dataOut.normFactor |
|
|
130 | 130 | else: |
|
|
131 | 131 | factor = normFactor |
|
|
132 | 132 | if xaxis == "frequency": |
|
|
133 | 133 | x = dataOut.getFreqRange(1)/1000. |
|
|
134 | 134 | xlabel = "Frequency (kHz)" |
|
|
135 | 135 | |
|
|
136 | 136 | elif xaxis == "time": |
|
|
137 | 137 | x = dataOut.getAcfRange(1) |
|
|
138 | 138 | xlabel = "Time (ms)" |
|
|
139 | 139 | |
|
|
140 | 140 | else: |
|
|
141 | 141 | x = dataOut.getVelRange(1) |
|
|
142 | 142 | xlabel = "Velocity (m/s)" |
|
|
143 | 143 | |
|
|
144 | 144 | ylabel = "Range (km)" |
|
|
145 | 145 | |
|
|
146 | 146 | y = dataOut.getHeiRange() |
|
|
147 | 147 | z = dataOut.data_spc/factor |
|
|
148 | 148 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
149 | 149 | zdB = 10*numpy.log10(z) |
|
|
150 | 150 | |
|
|
151 | 151 | avg = numpy.average(z, axis=1) |
|
|
152 | 152 | avgdB = 10*numpy.log10(avg) |
|
|
153 | 153 | |
|
|
154 | 154 | noise = dataOut.getNoise()/factor |
|
|
155 | 155 | noisedB = 10*numpy.log10(noise) |
|
|
156 | 156 | |
|
|
157 | 157 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
158 | 158 | title = wintitle + " Spectra" |
|
|
159 | 159 | |
|
|
160 | 160 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
161 | 161 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
162 | 162 | |
|
|
163 | 163 | if not self.isConfig: |
|
|
164 | 164 | |
|
|
165 | 165 | nplots = len(channelIndexList) |
|
|
166 | 166 | |
|
|
167 | 167 | self.setup(id=id, |
|
|
168 | 168 | nplots=nplots, |
|
|
169 | 169 | wintitle=wintitle, |
|
|
170 | 170 | showprofile=showprofile, |
|
|
171 | 171 | show=show) |
|
|
172 | 172 | |
|
|
173 | 173 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
174 | 174 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
175 | 175 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
176 | 176 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
177 | 177 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
178 | 178 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
179 | 179 | |
|
|
180 | 180 | self.FTP_WEI = ftp_wei |
|
|
181 | 181 | self.EXP_CODE = exp_code |
|
|
182 | 182 | self.SUB_EXP_CODE = sub_exp_code |
|
|
183 | 183 | self.PLOT_POS = plot_pos |
|
|
184 | 184 | |
|
|
185 | 185 | self.isConfig = True |
|
|
186 | 186 | |
|
|
187 | 187 | self.setWinTitle(title) |
|
|
188 | 188 | |
|
|
189 | 189 | for i in range(self.nplots): |
|
|
190 | 190 | index = channelIndexList[i] |
|
|
191 | 191 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
192 | 192 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[index], noisedB[index], str_datetime) |
|
|
193 | 193 | if len(dataOut.beam.codeList) != 0: |
|
|
194 | 194 | title = "Ch%d:%4.2fdB,%2.2f,%2.2f:%s" %(dataOut.channelList[index], noisedB[index], dataOut.beam.azimuthList[index], dataOut.beam.zenithList[index], str_datetime) |
|
|
195 | 195 | |
|
|
196 | 196 | axes = self.axesList[i*self.__nsubplots] |
|
|
197 | 197 | axes.pcolor(x, y, zdB[index,:,:], |
|
|
198 | 198 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
199 | 199 | xlabel=xlabel, ylabel=ylabel, title=title, colormap=colormap, |
|
|
200 | 200 | ticksize=9, cblabel='') |
|
|
201 | 201 | |
|
|
202 | 202 | if self.__showprofile: |
|
|
203 | 203 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
204 | 204 | axes.pline(avgdB[index,:], y, |
|
|
205 | 205 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
206 | 206 | xlabel='dB', ylabel='', title='', |
|
|
207 | 207 | ytick_visible=False, |
|
|
208 | 208 | grid='x') |
|
|
209 | 209 | |
|
|
210 | 210 | noiseline = numpy.repeat(noisedB[index], len(y)) |
|
|
211 | 211 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
212 | 212 | |
|
|
213 | 213 | self.draw() |
|
|
214 | 214 | |
|
|
215 | 215 | if figfile == None: |
|
|
216 | 216 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
217 | 217 | name = str_datetime |
|
|
218 | 218 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
219 | 219 | name = name + '_az' + '_%2.2f'%(dataOut.azimuth) + '_zn' + '_%2.2f'%(dataOut.zenith) |
|
|
220 | 220 | figfile = self.getFilename(name) |
|
|
221 | 221 | |
|
|
222 | 222 | self.save(figpath=figpath, |
|
|
223 | 223 | figfile=figfile, |
|
|
224 | 224 | save=save, |
|
|
225 | 225 | ftp=ftp, |
|
|
226 | 226 | wr_period=wr_period, |
|
|
227 | 227 | thisDatetime=thisDatetime) |
|
|
228 | 228 | |
|
|
229 | 229 | |
|
|
230 | 230 | return dataOut |
|
|
231 | ||
|
|
231 | 232 | @MPDecorator |
|
|
232 | 233 | class CrossSpectraPlot_(Figure): |
|
|
233 | 234 | |
|
|
234 | 235 | isConfig = None |
|
|
235 | 236 | __nsubplots = None |
|
|
236 | 237 | |
|
|
237 | 238 | WIDTH = None |
|
|
238 | 239 | HEIGHT = None |
|
|
239 | 240 | WIDTHPROF = None |
|
|
240 | 241 | HEIGHTPROF = None |
|
|
241 | 242 | PREFIX = 'cspc' |
|
|
242 | 243 | |
|
|
243 | 244 | def __init__(self): |
|
|
244 | 245 | Figure.__init__(self) |
|
|
245 | 246 | self.isConfig = False |
|
|
246 | 247 | self.__nsubplots = 4 |
|
|
247 | 248 | self.counter_imagwr = 0 |
|
|
248 | 249 | self.WIDTH = 250 |
|
|
249 | 250 | self.HEIGHT = 250 |
|
|
250 | 251 | self.WIDTHPROF = 0 |
|
|
251 | 252 | self.HEIGHTPROF = 0 |
|
|
252 | 253 | |
|
|
253 | 254 | self.PLOT_CODE = CROSS_CODE |
|
|
254 | 255 | self.FTP_WEI = None |
|
|
255 | 256 | self.EXP_CODE = None |
|
|
256 | 257 | self.SUB_EXP_CODE = None |
|
|
257 | 258 | self.PLOT_POS = None |
|
|
258 | 259 | |
|
|
259 | 260 | self.indice=0 |
|
|
260 | 261 | |
|
|
261 | 262 | def getSubplots(self): |
|
|
262 | 263 | |
|
|
263 | 264 | ncol = 4 |
|
|
264 | 265 | nrow = self.nplots |
|
|
265 | 266 | |
|
|
266 | 267 | return nrow, ncol |
|
|
267 | 268 | |
|
|
268 | 269 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
269 | 270 | |
|
|
270 | 271 | self.__showprofile = showprofile |
|
|
271 | 272 | self.nplots = nplots |
|
|
272 | 273 | |
|
|
273 | 274 | ncolspan = 1 |
|
|
274 | 275 | colspan = 1 |
|
|
275 | 276 | |
|
|
276 | 277 | self.createFigure(id = id, |
|
|
277 | 278 | wintitle = wintitle, |
|
|
278 | 279 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
279 | 280 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
280 | 281 | show=True) |
|
|
281 | 282 | |
|
|
282 | 283 | nrow, ncol = self.getSubplots() |
|
|
283 | 284 | |
|
|
284 | 285 | counter = 0 |
|
|
285 | 286 | for y in range(nrow): |
|
|
286 | 287 | for x in range(ncol): |
|
|
287 | 288 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
288 | 289 | |
|
|
289 | 290 | counter += 1 |
|
|
290 | 291 | |
|
|
291 | 292 | def run(self, dataOut, id, wintitle="", pairsList=None, |
|
|
292 | 293 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
293 | 294 | coh_min=None, coh_max=None, phase_min=None, phase_max=None, |
|
|
294 | 295 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
295 | 296 | power_cmap='jet', coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
296 | 297 | server=None, folder=None, username=None, password=None, |
|
|
297 | 298 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, normFactor=None, |
|
|
298 | 299 | xaxis='frequency'): |
|
|
299 | 300 | |
|
|
300 | 301 | """ |
|
|
301 | 302 | |
|
|
302 | 303 | Input: |
|
|
303 | 304 | dataOut : |
|
|
304 | 305 | id : |
|
|
305 | 306 | wintitle : |
|
|
306 | 307 | channelList : |
|
|
307 | 308 | showProfile : |
|
|
308 | 309 | xmin : None, |
|
|
309 | 310 | xmax : None, |
|
|
310 | 311 | ymin : None, |
|
|
311 | 312 | ymax : None, |
|
|
312 | 313 | zmin : None, |
|
|
313 | 314 | zmax : None |
|
|
314 | 315 | """ |
|
|
315 | 316 | |
|
|
316 | 317 | if dataOut.flagNoData: |
|
|
317 | 318 | return dataOut |
|
|
318 | 319 | |
|
|
319 | 320 | if pairsList == None: |
|
|
320 | 321 | pairsIndexList = dataOut.pairsIndexList |
|
|
321 | 322 | else: |
|
|
322 | 323 | pairsIndexList = [] |
|
|
323 | 324 | for pair in pairsList: |
|
|
324 | 325 | if pair not in dataOut.pairsList: |
|
|
325 | 326 | raise ValueError("Pair %s is not in dataOut.pairsList" %str(pair)) |
|
|
326 | 327 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
327 | 328 | |
|
|
328 | 329 | if not pairsIndexList: |
|
|
329 | 330 | return |
|
|
330 | 331 | |
|
|
331 | 332 | if len(pairsIndexList) > 4: |
|
|
332 | 333 | pairsIndexList = pairsIndexList[0:4] |
|
|
333 | 334 | |
|
|
334 | 335 | if normFactor is None: |
|
|
335 | 336 | factor = dataOut.normFactor |
|
|
336 | 337 | else: |
|
|
337 | 338 | factor = normFactor |
|
|
338 | 339 | x = dataOut.getVelRange(1) |
|
|
339 | 340 | y = dataOut.getHeiRange() |
|
|
340 | 341 | z = dataOut.data_spc[:,:,:]/factor |
|
|
341 | 342 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
342 | 343 | |
|
|
343 | 344 | noise = dataOut.noise/factor |
|
|
344 | 345 | |
|
|
345 | 346 | zdB = 10*numpy.log10(z) |
|
|
346 | 347 | noisedB = 10*numpy.log10(noise) |
|
|
347 | 348 | |
|
|
348 | 349 | if coh_min == None: |
|
|
349 | 350 | coh_min = 0.0 |
|
|
350 | 351 | if coh_max == None: |
|
|
351 | 352 | coh_max = 1.0 |
|
|
352 | 353 | |
|
|
353 | 354 | if phase_min == None: |
|
|
354 | 355 | phase_min = -180 |
|
|
355 | 356 | if phase_max == None: |
|
|
356 | 357 | phase_max = 180 |
|
|
357 | 358 | |
|
|
358 | 359 | #thisDatetime = dataOut.datatime |
|
|
359 | 360 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
360 | 361 | title = wintitle + " Cross-Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
361 | 362 | # xlabel = "Velocity (m/s)" |
|
|
362 | 363 | ylabel = "Range (Km)" |
|
|
363 | 364 | |
|
|
364 | 365 | if xaxis == "frequency": |
|
|
365 | 366 | x = dataOut.getFreqRange(1)/1000. |
|
|
366 | 367 | xlabel = "Frequency (kHz)" |
|
|
367 | 368 | |
|
|
368 | 369 | elif xaxis == "time": |
|
|
369 | 370 | x = dataOut.getAcfRange(1) |
|
|
370 | 371 | xlabel = "Time (ms)" |
|
|
371 | 372 | |
|
|
372 | 373 | else: |
|
|
373 | 374 | x = dataOut.getVelRange(1) |
|
|
374 | 375 | xlabel = "Velocity (m/s)" |
|
|
375 | 376 | |
|
|
376 | 377 | if not self.isConfig: |
|
|
377 | 378 | |
|
|
378 | 379 | nplots = len(pairsIndexList) |
|
|
379 | 380 | |
|
|
380 | 381 | self.setup(id=id, |
|
|
381 | 382 | nplots=nplots, |
|
|
382 | 383 | wintitle=wintitle, |
|
|
383 | 384 | showprofile=False, |
|
|
384 | 385 | show=show) |
|
|
385 | 386 | |
|
|
386 | 387 | avg = numpy.abs(numpy.average(z, axis=1)) |
|
|
387 | 388 | avgdB = 10*numpy.log10(avg) |
|
|
388 | 389 | |
|
|
389 | 390 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
390 | 391 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
391 | 392 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
392 | 393 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
393 | 394 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
394 | 395 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
395 | 396 | |
|
|
396 | 397 | self.FTP_WEI = ftp_wei |
|
|
397 | 398 | self.EXP_CODE = exp_code |
|
|
398 | 399 | self.SUB_EXP_CODE = sub_exp_code |
|
|
399 | 400 | self.PLOT_POS = plot_pos |
|
|
400 | 401 | |
|
|
401 | 402 | self.isConfig = True |
|
|
402 | 403 | |
|
|
403 | 404 | self.setWinTitle(title) |
|
|
404 | 405 | |
|
|
405 | 406 | |
|
|
406 | 407 | for i in range(self.nplots): |
|
|
407 | 408 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
408 | 409 | |
|
|
409 | 410 | chan_index0 = dataOut.channelList.index(pair[0]) |
|
|
410 | 411 | chan_index1 = dataOut.channelList.index(pair[1]) |
|
|
411 | 412 | |
|
|
412 | 413 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
413 | 414 | title = "Ch%d: %4.2fdB: %s" %(pair[0], noisedB[chan_index0], str_datetime) |
|
|
414 | 415 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index0,:,:]/factor) |
|
|
415 | 416 | axes0 = self.axesList[i*self.__nsubplots] |
|
|
416 | 417 | axes0.pcolor(x, y, zdB, |
|
|
417 | 418 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
418 | 419 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
419 | 420 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
420 | 421 | |
|
|
421 | 422 | title = "Ch%d: %4.2fdB: %s" %(pair[1], noisedB[chan_index1], str_datetime) |
|
|
422 | 423 | zdB = 10.*numpy.log10(dataOut.data_spc[chan_index1,:,:]/factor) |
|
|
423 | 424 | axes0 = self.axesList[i*self.__nsubplots+1] |
|
|
424 | 425 | axes0.pcolor(x, y, zdB, |
|
|
425 | 426 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
426 | 427 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
427 | 428 | ticksize=9, colormap=power_cmap, cblabel='') |
|
|
428 | 429 | |
|
|
429 | 430 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:] / numpy.sqrt( dataOut.data_spc[chan_index0,:,:]*dataOut.data_spc[chan_index1,:,:] ) |
|
|
430 | 431 | coherence = numpy.abs(coherenceComplex) |
|
|
431 | 432 | # phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi |
|
|
432 | 433 | phase = numpy.arctan2(coherenceComplex.imag, coherenceComplex.real)*180/numpy.pi |
|
|
433 | 434 | |
|
|
434 | 435 | title = "Coherence Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
435 | 436 | axes0 = self.axesList[i*self.__nsubplots+2] |
|
|
436 | 437 | axes0.pcolor(x, y, coherence, |
|
|
437 | 438 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=coh_min, zmax=coh_max, |
|
|
438 | 439 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
439 | 440 | ticksize=9, colormap=coherence_cmap, cblabel='') |
|
|
440 | 441 | |
|
|
441 | 442 | title = "Phase Ch%d * Ch%d" %(pair[0], pair[1]) |
|
|
442 | 443 | axes0 = self.axesList[i*self.__nsubplots+3] |
|
|
443 | 444 | axes0.pcolor(x, y, phase, |
|
|
444 | 445 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
445 | 446 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
446 | 447 | ticksize=9, colormap=phase_cmap, cblabel='') |
|
|
447 | 448 | |
|
|
448 | 449 | self.draw() |
|
|
449 | 450 | |
|
|
450 | 451 | self.save(figpath=figpath, |
|
|
451 | 452 | figfile=figfile, |
|
|
452 | 453 | save=save, |
|
|
453 | 454 | ftp=ftp, |
|
|
454 | 455 | wr_period=wr_period, |
|
|
455 | 456 | thisDatetime=thisDatetime) |
|
|
456 | 457 | |
|
|
457 | 458 | return dataOut |
|
|
458 | 459 | |
|
|
459 | 460 | @MPDecorator |
|
|
460 | 461 | class RTIPlot_(Figure): |
|
|
461 | 462 | |
|
|
462 | 463 | __isConfig = None |
|
|
463 | 464 | __nsubplots = None |
|
|
464 | 465 | |
|
|
465 | 466 | WIDTHPROF = None |
|
|
466 | 467 | HEIGHTPROF = None |
|
|
467 | 468 | PREFIX = 'rti' |
|
|
468 | 469 | |
|
|
469 | 470 | def __init__(self): |
|
|
470 | 471 | |
|
|
471 | 472 | Figure.__init__(self) |
|
|
472 | 473 | self.timerange = None |
|
|
473 | 474 | self.isConfig = False |
|
|
474 | 475 | self.__nsubplots = 1 |
|
|
475 | 476 | |
|
|
476 | 477 | self.WIDTH = 800 |
|
|
477 | 478 | self.HEIGHT = 250 |
|
|
478 | 479 | self.WIDTHPROF = 120 |
|
|
479 | 480 | self.HEIGHTPROF = 0 |
|
|
480 | 481 | self.counter_imagwr = 0 |
|
|
481 | 482 | |
|
|
482 | 483 | self.PLOT_CODE = RTI_CODE |
|
|
483 | 484 | |
|
|
484 | 485 | self.FTP_WEI = None |
|
|
485 | 486 | self.EXP_CODE = None |
|
|
486 | 487 | self.SUB_EXP_CODE = None |
|
|
487 | 488 | self.PLOT_POS = None |
|
|
488 | 489 | self.tmin = None |
|
|
489 | 490 | self.tmax = None |
|
|
490 | 491 | |
|
|
491 | 492 | self.xmin = None |
|
|
492 | 493 | self.xmax = None |
|
|
493 | 494 | |
|
|
494 | 495 | self.figfile = None |
|
|
495 | 496 | |
|
|
496 | 497 | def getSubplots(self): |
|
|
497 | 498 | |
|
|
498 | 499 | ncol = 1 |
|
|
499 | 500 | nrow = self.nplots |
|
|
500 | 501 | |
|
|
501 | 502 | return nrow, ncol |
|
|
502 | 503 | |
|
|
503 | 504 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
504 | 505 | |
|
|
505 | 506 | self.__showprofile = showprofile |
|
|
506 | 507 | self.nplots = nplots |
|
|
507 | 508 | |
|
|
508 | 509 | ncolspan = 1 |
|
|
509 | 510 | colspan = 1 |
|
|
510 | 511 | if showprofile: |
|
|
511 | 512 | ncolspan = 7 |
|
|
512 | 513 | colspan = 6 |
|
|
513 | 514 | self.__nsubplots = 2 |
|
|
514 | 515 | |
|
|
515 | 516 | self.createFigure(id = id, |
|
|
516 | 517 | wintitle = wintitle, |
|
|
517 | 518 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
518 | 519 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
519 | 520 | show=show) |
|
|
520 | 521 | |
|
|
521 | 522 | nrow, ncol = self.getSubplots() |
|
|
522 | 523 | |
|
|
523 | 524 | counter = 0 |
|
|
524 | 525 | for y in range(nrow): |
|
|
525 | 526 | for x in range(ncol): |
|
|
526 | 527 | |
|
|
527 | 528 | if counter >= self.nplots: |
|
|
528 | 529 | break |
|
|
529 | 530 | |
|
|
530 | 531 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
531 | 532 | |
|
|
532 | 533 | if showprofile: |
|
|
533 | 534 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
534 | 535 | |
|
|
535 | 536 | counter += 1 |
|
|
536 | 537 | |
|
|
537 | 538 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
538 | 539 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
539 | 540 | timerange=None, colormap='jet', |
|
|
540 | 541 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
541 | 542 | server=None, folder=None, username=None, password=None, |
|
|
542 | 543 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, normFactor=None, HEIGHT=None): |
|
|
543 | 544 | |
|
|
544 | 545 | """ |
|
|
545 | 546 | |
|
|
546 | 547 | Input: |
|
|
547 | 548 | dataOut : |
|
|
548 | 549 | id : |
|
|
549 | 550 | wintitle : |
|
|
550 | 551 | channelList : |
|
|
551 | 552 | showProfile : |
|
|
552 | 553 | xmin : None, |
|
|
553 | 554 | xmax : None, |
|
|
554 | 555 | ymin : None, |
|
|
555 | 556 | ymax : None, |
|
|
556 | 557 | zmin : None, |
|
|
557 | 558 | zmax : None |
|
|
558 | 559 | """ |
|
|
559 | 560 | if dataOut.flagNoData: |
|
|
560 | 561 | return dataOut |
|
|
561 | 562 | |
|
|
562 | 563 | #colormap = kwargs.get('colormap', 'jet') |
|
|
563 | 564 | if HEIGHT is not None: |
|
|
564 | 565 | self.HEIGHT = HEIGHT |
|
|
565 | 566 | |
|
|
566 | 567 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
567 | 568 | return |
|
|
568 | 569 | |
|
|
569 | 570 | if channelList == None: |
|
|
570 | 571 | channelIndexList = dataOut.channelIndexList |
|
|
571 | 572 | else: |
|
|
572 | 573 | channelIndexList = [] |
|
|
573 | 574 | for channel in channelList: |
|
|
574 | 575 | if channel not in dataOut.channelList: |
|
|
575 | 576 | raise ValueError("Channel %d is not in dataOut.channelList") |
|
|
576 | 577 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
577 | 578 | |
|
|
578 | 579 | if normFactor is None: |
|
|
579 | 580 | factor = dataOut.normFactor |
|
|
580 | 581 | else: |
|
|
581 | 582 | factor = normFactor |
|
|
582 | 583 | |
|
|
583 | 584 | #factor = dataOut.normFactor |
|
|
584 | 585 | x = dataOut.getTimeRange() |
|
|
585 | 586 | y = dataOut.getHeiRange() |
|
|
586 | 587 | |
|
|
587 | 588 | z = dataOut.data_spc/factor |
|
|
588 | 589 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
589 | 590 | avg = numpy.average(z, axis=1) |
|
|
590 | 591 | avgdB = 10.*numpy.log10(avg) |
|
|
591 | 592 | # avgdB = dataOut.getPower() |
|
|
592 | 593 | |
|
|
593 | 594 | |
|
|
594 | 595 | thisDatetime = dataOut.datatime |
|
|
595 | 596 | #thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
596 | 597 | title = wintitle + " RTI" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
597 | 598 | xlabel = "" |
|
|
598 | 599 | ylabel = "Range (Km)" |
|
|
599 | 600 | |
|
|
600 | 601 | update_figfile = False |
|
|
601 | 602 | |
|
|
602 | 603 | if self.xmax is not None and dataOut.ltctime >= self.xmax: #yong |
|
|
603 | 604 | self.counter_imagwr = wr_period |
|
|
604 | 605 | self.isConfig = False |
|
|
605 | 606 | update_figfile = True |
|
|
606 | 607 | |
|
|
607 | 608 | if not self.isConfig: |
|
|
608 | 609 | |
|
|
609 | 610 | nplots = len(channelIndexList) |
|
|
610 | 611 | |
|
|
611 | 612 | self.setup(id=id, |
|
|
612 | 613 | nplots=nplots, |
|
|
613 | 614 | wintitle=wintitle, |
|
|
614 | 615 | showprofile=showprofile, |
|
|
615 | 616 | show=show) |
|
|
616 | 617 | |
|
|
617 | 618 | if timerange != None: |
|
|
618 | 619 | self.timerange = timerange |
|
|
619 | 620 | |
|
|
620 | 621 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
621 | 622 | |
|
|
622 | 623 | noise = dataOut.noise/factor |
|
|
623 | 624 | noisedB = 10*numpy.log10(noise) |
|
|
624 | 625 | |
|
|
625 | 626 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
626 | 627 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
627 | 628 | if zmin == None: zmin = numpy.floor(numpy.nanmin(noisedB)) - 3 |
|
|
628 | 629 | if zmax == None: zmax = numpy.ceil(numpy.nanmax(avgdB)) + 3 |
|
|
629 | 630 | |
|
|
630 | 631 | self.FTP_WEI = ftp_wei |
|
|
631 | 632 | self.EXP_CODE = exp_code |
|
|
632 | 633 | self.SUB_EXP_CODE = sub_exp_code |
|
|
633 | 634 | self.PLOT_POS = plot_pos |
|
|
634 | 635 | |
|
|
635 | 636 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
636 | 637 | self.isConfig = True |
|
|
637 | 638 | self.figfile = figfile |
|
|
638 | 639 | update_figfile = True |
|
|
639 | 640 | |
|
|
640 | 641 | self.setWinTitle(title) |
|
|
641 | 642 | |
|
|
642 | 643 | for i in range(self.nplots): |
|
|
643 | 644 | index = channelIndexList[i] |
|
|
644 | 645 | title = "Channel %d: %s" %(dataOut.channelList[index], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
645 | 646 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
646 | 647 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
647 | 648 | axes = self.axesList[i*self.__nsubplots] |
|
|
648 | 649 | zdB = avgdB[index].reshape((1,-1)) |
|
|
649 | 650 | axes.pcolorbuffer(x, y, zdB, |
|
|
650 | 651 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
651 | 652 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
652 | 653 | ticksize=9, cblabel='', cbsize="1%", colormap=colormap) |
|
|
653 | 654 | |
|
|
654 | 655 | if self.__showprofile: |
|
|
655 | 656 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
656 | 657 | axes.pline(avgdB[index], y, |
|
|
657 | 658 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
658 | 659 | xlabel='dB', ylabel='', title='', |
|
|
659 | 660 | ytick_visible=False, |
|
|
660 | 661 | grid='x') |
|
|
661 | 662 | |
|
|
662 | 663 | self.draw() |
|
|
663 | 664 | |
|
|
664 | 665 | self.save(figpath=figpath, |
|
|
665 | 666 | figfile=figfile, |
|
|
666 | 667 | save=save, |
|
|
667 | 668 | ftp=ftp, |
|
|
668 | 669 | wr_period=wr_period, |
|
|
669 | 670 | thisDatetime=thisDatetime, |
|
|
670 | 671 | update_figfile=update_figfile) |
|
|
671 | 672 | return dataOut |
|
|
672 | 673 | |
|
|
673 | 674 | @MPDecorator |
|
|
674 | 675 | class CoherenceMap_(Figure): |
|
|
675 | 676 | isConfig = None |
|
|
676 | 677 | __nsubplots = None |
|
|
677 | 678 | |
|
|
678 | 679 | WIDTHPROF = None |
|
|
679 | 680 | HEIGHTPROF = None |
|
|
680 | 681 | PREFIX = 'cmap' |
|
|
681 | 682 | |
|
|
682 | 683 | def __init__(self): |
|
|
683 | 684 | Figure.__init__(self) |
|
|
684 | 685 | self.timerange = 2*60*60 |
|
|
685 | 686 | self.isConfig = False |
|
|
686 | 687 | self.__nsubplots = 1 |
|
|
687 | 688 | |
|
|
688 | 689 | self.WIDTH = 800 |
|
|
689 | 690 | self.HEIGHT = 180 |
|
|
690 | 691 | self.WIDTHPROF = 120 |
|
|
691 | 692 | self.HEIGHTPROF = 0 |
|
|
692 | 693 | self.counter_imagwr = 0 |
|
|
693 | 694 | |
|
|
694 | 695 | self.PLOT_CODE = COH_CODE |
|
|
695 | 696 | |
|
|
696 | 697 | self.FTP_WEI = None |
|
|
697 | 698 | self.EXP_CODE = None |
|
|
698 | 699 | self.SUB_EXP_CODE = None |
|
|
699 | 700 | self.PLOT_POS = None |
|
|
700 | 701 | self.counter_imagwr = 0 |
|
|
701 | 702 | |
|
|
702 | 703 | self.xmin = None |
|
|
703 | 704 | self.xmax = None |
|
|
704 | 705 | |
|
|
705 | 706 | def getSubplots(self): |
|
|
706 | 707 | ncol = 1 |
|
|
707 | 708 | nrow = self.nplots*2 |
|
|
708 | 709 | |
|
|
709 | 710 | return nrow, ncol |
|
|
710 | 711 | |
|
|
711 | 712 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
712 | 713 | self.__showprofile = showprofile |
|
|
713 | 714 | self.nplots = nplots |
|
|
714 | 715 | |
|
|
715 | 716 | ncolspan = 1 |
|
|
716 | 717 | colspan = 1 |
|
|
717 | 718 | if showprofile: |
|
|
718 | 719 | ncolspan = 7 |
|
|
719 | 720 | colspan = 6 |
|
|
720 | 721 | self.__nsubplots = 2 |
|
|
721 | 722 | |
|
|
722 | 723 | self.createFigure(id = id, |
|
|
723 | 724 | wintitle = wintitle, |
|
|
724 | 725 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
725 | 726 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
726 | 727 | show=True) |
|
|
727 | 728 | |
|
|
728 | 729 | nrow, ncol = self.getSubplots() |
|
|
729 | 730 | |
|
|
730 | 731 | for y in range(nrow): |
|
|
731 | 732 | for x in range(ncol): |
|
|
732 | 733 | |
|
|
733 | 734 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
734 | 735 | |
|
|
735 | 736 | if showprofile: |
|
|
736 | 737 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
737 | 738 | |
|
|
738 | 739 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
739 | 740 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
740 | 741 | timerange=None, phase_min=None, phase_max=None, |
|
|
741 | 742 | save=False, figpath='./', figfile=None, ftp=False, wr_period=1, |
|
|
742 | 743 | coherence_cmap='jet', phase_cmap='RdBu_r', show=True, |
|
|
743 | 744 | server=None, folder=None, username=None, password=None, |
|
|
744 | 745 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
745 | 746 | |
|
|
746 | 747 | |
|
|
747 | 748 | if dataOut.flagNoData: |
|
|
748 | 749 | return dataOut |
|
|
749 | 750 | |
|
|
750 | 751 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
751 | 752 | return |
|
|
752 | 753 | |
|
|
753 | 754 | if pairsList == None: |
|
|
754 | 755 | pairsIndexList = dataOut.pairsIndexList |
|
|
755 | 756 | else: |
|
|
756 | 757 | pairsIndexList = [] |
|
|
757 | 758 | for pair in pairsList: |
|
|
758 | 759 | if pair not in dataOut.pairsList: |
|
|
759 | 760 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
760 | 761 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
761 | 762 | |
|
|
762 | 763 | if pairsIndexList == []: |
|
|
763 | 764 | return |
|
|
764 | 765 | |
|
|
765 | 766 | if len(pairsIndexList) > 4: |
|
|
766 | 767 | pairsIndexList = pairsIndexList[0:4] |
|
|
767 | 768 | |
|
|
768 | 769 | if phase_min == None: |
|
|
769 | 770 | phase_min = -180 |
|
|
770 | 771 | if phase_max == None: |
|
|
771 | 772 | phase_max = 180 |
|
|
772 | 773 | |
|
|
773 | 774 | x = dataOut.getTimeRange() |
|
|
774 | 775 | y = dataOut.getHeiRange() |
|
|
775 | 776 | |
|
|
776 | 777 | thisDatetime = dataOut.datatime |
|
|
777 | 778 | |
|
|
778 | 779 | title = wintitle + " CoherenceMap" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
779 | 780 | xlabel = "" |
|
|
780 | 781 | ylabel = "Range (Km)" |
|
|
781 | 782 | update_figfile = False |
|
|
782 | 783 | |
|
|
783 | 784 | if not self.isConfig: |
|
|
784 | 785 | nplots = len(pairsIndexList) |
|
|
785 | 786 | self.setup(id=id, |
|
|
786 | 787 | nplots=nplots, |
|
|
787 | 788 | wintitle=wintitle, |
|
|
788 | 789 | showprofile=showprofile, |
|
|
789 | 790 | show=show) |
|
|
790 | 791 | |
|
|
791 | 792 | if timerange != None: |
|
|
792 | 793 | self.timerange = timerange |
|
|
793 | 794 | |
|
|
794 | 795 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
795 | 796 | |
|
|
796 | 797 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
797 | 798 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
798 | 799 | if zmin == None: zmin = 0. |
|
|
799 | 800 | if zmax == None: zmax = 1. |
|
|
800 | 801 | |
|
|
801 | 802 | self.FTP_WEI = ftp_wei |
|
|
802 | 803 | self.EXP_CODE = exp_code |
|
|
803 | 804 | self.SUB_EXP_CODE = sub_exp_code |
|
|
804 | 805 | self.PLOT_POS = plot_pos |
|
|
805 | 806 | |
|
|
806 | 807 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
807 | 808 | |
|
|
808 | 809 | self.isConfig = True |
|
|
809 | 810 | update_figfile = True |
|
|
810 | 811 | |
|
|
811 | 812 | self.setWinTitle(title) |
|
|
812 | 813 | |
|
|
813 | 814 | for i in range(self.nplots): |
|
|
814 | 815 | |
|
|
815 | 816 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
816 | 817 | |
|
|
817 | 818 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i],:,:],axis=0) |
|
|
818 | 819 | powa = numpy.average(dataOut.data_spc[pair[0],:,:],axis=0) |
|
|
819 | 820 | powb = numpy.average(dataOut.data_spc[pair[1],:,:],axis=0) |
|
|
820 | 821 | |
|
|
821 | 822 | |
|
|
822 | 823 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
823 | 824 | coherence = numpy.abs(avgcoherenceComplex) |
|
|
824 | 825 | |
|
|
825 | 826 | z = coherence.reshape((1,-1)) |
|
|
826 | 827 | |
|
|
827 | 828 | counter = 0 |
|
|
828 | 829 | |
|
|
829 | 830 | title = "Coherence Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
830 | 831 | axes = self.axesList[i*self.__nsubplots*2] |
|
|
831 | 832 | axes.pcolorbuffer(x, y, z, |
|
|
832 | 833 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
833 | 834 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
834 | 835 | ticksize=9, cblabel='', colormap=coherence_cmap, cbsize="1%") |
|
|
835 | 836 | |
|
|
836 | 837 | if self.__showprofile: |
|
|
837 | 838 | counter += 1 |
|
|
838 | 839 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
839 | 840 | axes.pline(coherence, y, |
|
|
840 | 841 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
841 | 842 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
842 | 843 | ytick_visible=False, nxticks=5, |
|
|
843 | 844 | grid='x') |
|
|
844 | 845 | |
|
|
845 | 846 | counter += 1 |
|
|
846 | 847 | |
|
|
847 | 848 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
848 | 849 | |
|
|
849 | 850 | z = phase.reshape((1,-1)) |
|
|
850 | 851 | |
|
|
851 | 852 | title = "Phase Ch%d * Ch%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
852 | 853 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
853 | 854 | axes.pcolorbuffer(x, y, z, |
|
|
854 | 855 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=phase_min, zmax=phase_max, |
|
|
855 | 856 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
856 | 857 | ticksize=9, cblabel='', colormap=phase_cmap, cbsize="1%") |
|
|
857 | 858 | |
|
|
858 | 859 | if self.__showprofile: |
|
|
859 | 860 | counter += 1 |
|
|
860 | 861 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
|
861 | 862 | axes.pline(phase, y, |
|
|
862 | 863 | xmin=phase_min, xmax=phase_max, ymin=ymin, ymax=ymax, |
|
|
863 | 864 | xlabel='', ylabel='', title='', ticksize=7, |
|
|
864 | 865 | ytick_visible=False, nxticks=4, |
|
|
865 | 866 | grid='x') |
|
|
866 | 867 | |
|
|
867 | 868 | self.draw() |
|
|
868 | 869 | |
|
|
869 | 870 | if dataOut.ltctime >= self.xmax: |
|
|
870 | 871 | self.counter_imagwr = wr_period |
|
|
871 | 872 | self.isConfig = False |
|
|
872 | 873 | update_figfile = True |
|
|
873 | 874 | |
|
|
874 | 875 | self.save(figpath=figpath, |
|
|
875 | 876 | figfile=figfile, |
|
|
876 | 877 | save=save, |
|
|
877 | 878 | ftp=ftp, |
|
|
878 | 879 | wr_period=wr_period, |
|
|
879 | 880 | thisDatetime=thisDatetime, |
|
|
880 | 881 | update_figfile=update_figfile) |
|
|
881 | 882 | |
|
|
882 | 883 | return dataOut |
|
|
883 | 884 | |
|
|
884 | 885 | @MPDecorator |
|
|
885 | 886 | class PowerProfilePlot_(Figure): |
|
|
886 | 887 | |
|
|
887 | 888 | isConfig = None |
|
|
888 | 889 | __nsubplots = None |
|
|
889 | 890 | |
|
|
890 | 891 | WIDTHPROF = None |
|
|
891 | 892 | HEIGHTPROF = None |
|
|
892 | 893 | PREFIX = 'spcprofile' |
|
|
893 | 894 | |
|
|
894 | 895 | def __init__(self): |
|
|
895 | 896 | Figure.__init__(self) |
|
|
896 | 897 | self.isConfig = False |
|
|
897 | 898 | self.__nsubplots = 1 |
|
|
898 | 899 | |
|
|
899 | 900 | self.PLOT_CODE = POWER_CODE |
|
|
900 | 901 | |
|
|
901 | 902 | self.WIDTH = 300 |
|
|
902 | 903 | self.HEIGHT = 500 |
|
|
903 | 904 | self.counter_imagwr = 0 |
|
|
904 | 905 | |
|
|
905 | 906 | def getSubplots(self): |
|
|
906 | 907 | ncol = 1 |
|
|
907 | 908 | nrow = 1 |
|
|
908 | 909 | |
|
|
909 | 910 | return nrow, ncol |
|
|
910 | 911 | |
|
|
911 | 912 | def setup(self, id, nplots, wintitle, show): |
|
|
912 | 913 | |
|
|
913 | 914 | self.nplots = nplots |
|
|
914 | 915 | |
|
|
915 | 916 | ncolspan = 1 |
|
|
916 | 917 | colspan = 1 |
|
|
917 | 918 | |
|
|
918 | 919 | self.createFigure(id = id, |
|
|
919 | 920 | wintitle = wintitle, |
|
|
920 | 921 | widthplot = self.WIDTH, |
|
|
921 | 922 | heightplot = self.HEIGHT, |
|
|
922 | 923 | show=show) |
|
|
923 | 924 | |
|
|
924 | 925 | nrow, ncol = self.getSubplots() |
|
|
925 | 926 | |
|
|
926 | 927 | counter = 0 |
|
|
927 | 928 | for y in range(nrow): |
|
|
928 | 929 | for x in range(ncol): |
|
|
929 | 930 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
930 | 931 | |
|
|
931 | 932 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
932 | 933 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
933 | 934 | save=False, figpath='./', figfile=None, show=True, |
|
|
934 | 935 | ftp=False, wr_period=1, server=None, |
|
|
935 | 936 | folder=None, username=None, password=None): |
|
|
936 | 937 | |
|
|
937 | 938 | if dataOut.flagNoData: |
|
|
938 | 939 | return dataOut |
|
|
939 | 940 | |
|
|
940 | 941 | |
|
|
941 | 942 | if channelList == None: |
|
|
942 | 943 | channelIndexList = dataOut.channelIndexList |
|
|
943 | 944 | channelList = dataOut.channelList |
|
|
944 | 945 | else: |
|
|
945 | 946 | channelIndexList = [] |
|
|
946 | 947 | for channel in channelList: |
|
|
947 | 948 | if channel not in dataOut.channelList: |
|
|
948 | 949 | raise ValueError("Channel %d is not in dataOut.channelList") |
|
|
949 | 950 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
950 | 951 | |
|
|
951 | 952 | factor = dataOut.normFactor |
|
|
952 | 953 | |
|
|
953 | 954 | y = dataOut.getHeiRange() |
|
|
954 | 955 | |
|
|
955 | 956 | #for voltage |
|
|
956 | 957 | if dataOut.type == 'Voltage': |
|
|
957 | 958 | x = dataOut.data[channelIndexList,:] * numpy.conjugate(dataOut.data[channelIndexList,:]) |
|
|
958 | 959 | x = x.real |
|
|
959 | 960 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
960 | 961 | |
|
|
961 | 962 | #for spectra |
|
|
962 | 963 | if dataOut.type == 'Spectra': |
|
|
963 | 964 | x = dataOut.data_spc[channelIndexList,:,:]/factor |
|
|
964 | 965 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
|
|
965 | 966 | x = numpy.average(x, axis=1) |
|
|
966 | 967 | |
|
|
967 | 968 | |
|
|
968 | 969 | xdB = 10*numpy.log10(x) |
|
|
969 | 970 | |
|
|
970 | 971 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
971 | 972 | title = wintitle + " Power Profile %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
972 | 973 | xlabel = "dB" |
|
|
973 | 974 | ylabel = "Range (Km)" |
|
|
974 | 975 | |
|
|
975 | 976 | if not self.isConfig: |
|
|
976 | 977 | |
|
|
977 | 978 | nplots = 1 |
|
|
978 | 979 | |
|
|
979 | 980 | self.setup(id=id, |
|
|
980 | 981 | nplots=nplots, |
|
|
981 | 982 | wintitle=wintitle, |
|
|
982 | 983 | show=show) |
|
|
983 | 984 | |
|
|
984 | 985 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
985 | 986 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
986 | 987 | if xmin == None: xmin = numpy.nanmin(xdB)*0.9 |
|
|
987 | 988 | if xmax == None: xmax = numpy.nanmax(xdB)*1.1 |
|
|
988 | 989 | |
|
|
989 | 990 | self.isConfig = True |
|
|
990 | 991 | |
|
|
991 | 992 | self.setWinTitle(title) |
|
|
992 | 993 | |
|
|
993 | 994 | title = "Power Profile: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
994 | 995 | axes = self.axesList[0] |
|
|
995 | 996 | |
|
|
996 | 997 | legendlabels = ["channel %d"%x for x in channelList] |
|
|
997 | 998 | axes.pmultiline(xdB, y, |
|
|
998 | 999 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
999 | 1000 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
1000 | 1001 | ytick_visible=True, nxticks=5, |
|
|
1001 | 1002 | grid='x') |
|
|
1002 | 1003 | |
|
|
1003 | 1004 | self.draw() |
|
|
1004 | 1005 | |
|
|
1005 | 1006 | self.save(figpath=figpath, |
|
|
1006 | 1007 | figfile=figfile, |
|
|
1007 | 1008 | save=save, |
|
|
1008 | 1009 | ftp=ftp, |
|
|
1009 | 1010 | wr_period=wr_period, |
|
|
1010 | 1011 | thisDatetime=thisDatetime) |
|
|
1011 | 1012 | |
|
|
1012 | 1013 | return dataOut |
|
|
1013 | 1014 | |
|
|
1014 | 1015 | @MPDecorator |
|
|
1015 | 1016 | class SpectraCutPlot_(Figure): |
|
|
1016 | 1017 | |
|
|
1017 | 1018 | isConfig = None |
|
|
1018 | 1019 | __nsubplots = None |
|
|
1019 | 1020 | |
|
|
1020 | 1021 | WIDTHPROF = None |
|
|
1021 | 1022 | HEIGHTPROF = None |
|
|
1022 | 1023 | PREFIX = 'spc_cut' |
|
|
1023 | 1024 | |
|
|
1024 | 1025 | def __init__(self): |
|
|
1025 | 1026 | Figure.__init__(self) |
|
|
1026 | 1027 | self.isConfig = False |
|
|
1027 | 1028 | self.__nsubplots = 1 |
|
|
1028 | 1029 | |
|
|
1029 | 1030 | self.PLOT_CODE = POWER_CODE |
|
|
1030 | 1031 | |
|
|
1031 | 1032 | self.WIDTH = 700 |
|
|
1032 | 1033 | self.HEIGHT = 500 |
|
|
1033 | 1034 | self.counter_imagwr = 0 |
|
|
1034 | 1035 | |
|
|
1035 | 1036 | def getSubplots(self): |
|
|
1036 | 1037 | ncol = 1 |
|
|
1037 | 1038 | nrow = 1 |
|
|
1038 | 1039 | |
|
|
1039 | 1040 | return nrow, ncol |
|
|
1040 | 1041 | |
|
|
1041 | 1042 | def setup(self, id, nplots, wintitle, show): |
|
|
1042 | 1043 | |
|
|
1043 | 1044 | self.nplots = nplots |
|
|
1044 | 1045 | |
|
|
1045 | 1046 | ncolspan = 1 |
|
|
1046 | 1047 | colspan = 1 |
|
|
1047 | 1048 | |
|
|
1048 | 1049 | self.createFigure(id = id, |
|
|
1049 | 1050 | wintitle = wintitle, |
|
|
1050 | 1051 | widthplot = self.WIDTH, |
|
|
1051 | 1052 | heightplot = self.HEIGHT, |
|
|
1052 | 1053 | show=show) |
|
|
1053 | 1054 | |
|
|
1054 | 1055 | nrow, ncol = self.getSubplots() |
|
|
1055 | 1056 | |
|
|
1056 | 1057 | counter = 0 |
|
|
1057 | 1058 | for y in range(nrow): |
|
|
1058 | 1059 | for x in range(ncol): |
|
|
1059 | 1060 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
1060 | 1061 | |
|
|
1061 | 1062 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1062 | 1063 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1063 | 1064 | save=False, figpath='./', figfile=None, show=True, |
|
|
1064 | 1065 | ftp=False, wr_period=1, server=None, |
|
|
1065 | 1066 | folder=None, username=None, password=None, |
|
|
1066 | 1067 | xaxis="frequency"): |
|
|
1067 | 1068 | |
|
|
1068 | 1069 | if dataOut.flagNoData: |
|
|
1069 | 1070 | return dataOut |
|
|
1070 | 1071 | |
|
|
1071 | 1072 | if channelList == None: |
|
|
1072 | 1073 | channelIndexList = dataOut.channelIndexList |
|
|
1073 | 1074 | channelList = dataOut.channelList |
|
|
1074 | 1075 | else: |
|
|
1075 | 1076 | channelIndexList = [] |
|
|
1076 | 1077 | for channel in channelList: |
|
|
1077 | 1078 | if channel not in dataOut.channelList: |
|
|
1078 | 1079 | raise ValueError("Channel %d is not in dataOut.channelList") |
|
|
1079 | 1080 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1080 | 1081 | |
|
|
1081 | 1082 | factor = dataOut.normFactor |
|
|
1082 | 1083 | |
|
|
1083 | 1084 | y = dataOut.getHeiRange() |
|
|
1084 | 1085 | |
|
|
1085 | 1086 | z = dataOut.data_spc/factor |
|
|
1086 | 1087 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1087 | 1088 | |
|
|
1088 | 1089 | hei_index = numpy.arange(25)*3 + 20 |
|
|
1089 | 1090 | |
|
|
1090 | 1091 | if xaxis == "frequency": |
|
|
1091 | 1092 | x = dataOut.getFreqRange()/1000. |
|
|
1092 | 1093 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1093 | 1094 | xlabel = "Frequency (kHz)" |
|
|
1094 | 1095 | ylabel = "Power (dB)" |
|
|
1095 | 1096 | |
|
|
1096 | 1097 | elif xaxis == "time": |
|
|
1097 | 1098 | x = dataOut.getAcfRange() |
|
|
1098 | 1099 | zdB = z[0,:,hei_index] |
|
|
1099 | 1100 | xlabel = "Time (ms)" |
|
|
1100 | 1101 | ylabel = "ACF" |
|
|
1101 | 1102 | |
|
|
1102 | 1103 | else: |
|
|
1103 | 1104 | x = dataOut.getVelRange() |
|
|
1104 | 1105 | zdB = 10*numpy.log10(z[0,:,hei_index]) |
|
|
1105 | 1106 | xlabel = "Velocity (m/s)" |
|
|
1106 | 1107 | ylabel = "Power (dB)" |
|
|
1107 | 1108 | |
|
|
1108 | 1109 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
1109 | 1110 | title = wintitle + " Range Cuts %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1110 | 1111 | |
|
|
1111 | 1112 | if not self.isConfig: |
|
|
1112 | 1113 | |
|
|
1113 | 1114 | nplots = 1 |
|
|
1114 | 1115 | |
|
|
1115 | 1116 | self.setup(id=id, |
|
|
1116 | 1117 | nplots=nplots, |
|
|
1117 | 1118 | wintitle=wintitle, |
|
|
1118 | 1119 | show=show) |
|
|
1119 | 1120 | |
|
|
1120 | 1121 | if xmin == None: xmin = numpy.nanmin(x)*0.9 |
|
|
1121 | 1122 | if xmax == None: xmax = numpy.nanmax(x)*1.1 |
|
|
1122 | 1123 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
1123 | 1124 | if ymax == None: ymax = numpy.nanmax(zdB) |
|
|
1124 | 1125 | |
|
|
1125 | 1126 | self.isConfig = True |
|
|
1126 | 1127 | |
|
|
1127 | 1128 | self.setWinTitle(title) |
|
|
1128 | 1129 | |
|
|
1129 | 1130 | title = "Spectra Cuts: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
1130 | 1131 | axes = self.axesList[0] |
|
|
1131 | 1132 | |
|
|
1132 | 1133 | legendlabels = ["Range = %dKm" %y[i] for i in hei_index] |
|
|
1133 | 1134 | |
|
|
1134 | 1135 | axes.pmultilineyaxis( x, zdB, |
|
|
1135 | 1136 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
1136 | 1137 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, |
|
|
1137 | 1138 | ytick_visible=True, nxticks=5, |
|
|
1138 | 1139 | grid='x') |
|
|
1139 | 1140 | |
|
|
1140 | 1141 | self.draw() |
|
|
1141 | 1142 | |
|
|
1142 | 1143 | self.save(figpath=figpath, |
|
|
1143 | 1144 | figfile=figfile, |
|
|
1144 | 1145 | save=save, |
|
|
1145 | 1146 | ftp=ftp, |
|
|
1146 | 1147 | wr_period=wr_period, |
|
|
1147 | 1148 | thisDatetime=thisDatetime) |
|
|
1148 | 1149 | |
|
|
1149 | 1150 | return dataOut |
|
|
1150 | 1151 | |
|
|
1151 | 1152 | @MPDecorator |
|
|
1152 | 1153 | class Noise_(Figure): |
|
|
1153 | 1154 | |
|
|
1154 | 1155 | isConfig = None |
|
|
1155 | 1156 | __nsubplots = None |
|
|
1156 | 1157 | |
|
|
1157 | 1158 | PREFIX = 'noise' |
|
|
1158 | 1159 | |
|
|
1159 | 1160 | |
|
|
1160 | 1161 | def __init__(self): |
|
|
1161 | 1162 | Figure.__init__(self) |
|
|
1162 | 1163 | self.timerange = 24*60*60 |
|
|
1163 | 1164 | self.isConfig = False |
|
|
1164 | 1165 | self.__nsubplots = 1 |
|
|
1165 | 1166 | self.counter_imagwr = 0 |
|
|
1166 | 1167 | self.WIDTH = 800 |
|
|
1167 | 1168 | self.HEIGHT = 400 |
|
|
1168 | 1169 | self.WIDTHPROF = 120 |
|
|
1169 | 1170 | self.HEIGHTPROF = 0 |
|
|
1170 | 1171 | self.xdata = None |
|
|
1171 | 1172 | self.ydata = None |
|
|
1172 | 1173 | |
|
|
1173 | 1174 | self.PLOT_CODE = NOISE_CODE |
|
|
1174 | 1175 | |
|
|
1175 | 1176 | self.FTP_WEI = None |
|
|
1176 | 1177 | self.EXP_CODE = None |
|
|
1177 | 1178 | self.SUB_EXP_CODE = None |
|
|
1178 | 1179 | self.PLOT_POS = None |
|
|
1179 | 1180 | self.figfile = None |
|
|
1180 | 1181 | |
|
|
1181 | 1182 | self.xmin = None |
|
|
1182 | 1183 | self.xmax = None |
|
|
1183 | 1184 | |
|
|
1184 | 1185 | def getSubplots(self): |
|
|
1185 | 1186 | |
|
|
1186 | 1187 | ncol = 1 |
|
|
1187 | 1188 | nrow = 1 |
|
|
1188 | 1189 | |
|
|
1189 | 1190 | return nrow, ncol |
|
|
1190 | 1191 | |
|
|
1191 | 1192 | def openfile(self, filename): |
|
|
1192 | 1193 | dirname = os.path.dirname(filename) |
|
|
1193 | 1194 | |
|
|
1194 | 1195 | if not os.path.exists(dirname): |
|
|
1195 | 1196 | os.mkdir(dirname) |
|
|
1196 | 1197 | |
|
|
1197 | 1198 | f = open(filename,'w+') |
|
|
1198 | 1199 | f.write('\n\n') |
|
|
1199 | 1200 | f.write('JICAMARCA RADIO OBSERVATORY - Noise \n') |
|
|
1200 | 1201 | f.write('DD MM YYYY HH MM SS Channel0 Channel1 Channel2 Channel3\n\n' ) |
|
|
1201 | 1202 | f.close() |
|
|
1202 | 1203 | |
|
|
1203 | 1204 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1204 | 1205 | |
|
|
1205 | 1206 | f=open(filename_phase,'a') |
|
|
1206 | 1207 | |
|
|
1207 | 1208 | timetuple_data = data_datetime.timetuple() |
|
|
1208 | 1209 | day = str(timetuple_data.tm_mday) |
|
|
1209 | 1210 | month = str(timetuple_data.tm_mon) |
|
|
1210 | 1211 | year = str(timetuple_data.tm_year) |
|
|
1211 | 1212 | hour = str(timetuple_data.tm_hour) |
|
|
1212 | 1213 | minute = str(timetuple_data.tm_min) |
|
|
1213 | 1214 | second = str(timetuple_data.tm_sec) |
|
|
1214 | 1215 | |
|
|
1215 | 1216 | data_msg = '' |
|
|
1216 | 1217 | for i in range(len(data)): |
|
|
1217 | 1218 | data_msg += str(data[i]) + ' ' |
|
|
1218 | 1219 | |
|
|
1219 | 1220 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' ' + data_msg + '\n') |
|
|
1220 | 1221 | f.close() |
|
|
1221 | 1222 | |
|
|
1222 | 1223 | |
|
|
1223 | 1224 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1224 | 1225 | |
|
|
1225 | 1226 | self.__showprofile = showprofile |
|
|
1226 | 1227 | self.nplots = nplots |
|
|
1227 | 1228 | |
|
|
1228 | 1229 | ncolspan = 7 |
|
|
1229 | 1230 | colspan = 6 |
|
|
1230 | 1231 | self.__nsubplots = 2 |
|
|
1231 | 1232 | |
|
|
1232 | 1233 | self.createFigure(id = id, |
|
|
1233 | 1234 | wintitle = wintitle, |
|
|
1234 | 1235 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1235 | 1236 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1236 | 1237 | show=show) |
|
|
1237 | 1238 | |
|
|
1238 | 1239 | nrow, ncol = self.getSubplots() |
|
|
1239 | 1240 | |
|
|
1240 | 1241 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1241 | 1242 | |
|
|
1242 | 1243 | |
|
|
1243 | 1244 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='True', |
|
|
1244 | 1245 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
1245 | 1246 | timerange=None, |
|
|
1246 | 1247 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1247 | 1248 | server=None, folder=None, username=None, password=None, |
|
|
1248 | 1249 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1249 | 1250 | |
|
|
1250 | 1251 | if dataOut.flagNoData: |
|
|
1251 | 1252 | return dataOut |
|
|
1252 | 1253 | |
|
|
1253 | 1254 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1254 | 1255 | return |
|
|
1255 | 1256 | |
|
|
1256 | 1257 | if channelList == None: |
|
|
1257 | 1258 | channelIndexList = dataOut.channelIndexList |
|
|
1258 | 1259 | channelList = dataOut.channelList |
|
|
1259 | 1260 | else: |
|
|
1260 | 1261 | channelIndexList = [] |
|
|
1261 | 1262 | for channel in channelList: |
|
|
1262 | 1263 | if channel not in dataOut.channelList: |
|
|
1263 | 1264 | raise ValueError("Channel %d is not in dataOut.channelList") |
|
|
1264 | 1265 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
1265 | 1266 | |
|
|
1266 | 1267 | x = dataOut.getTimeRange() |
|
|
1267 | 1268 | #y = dataOut.getHeiRange() |
|
|
1268 | 1269 | factor = dataOut.normFactor |
|
|
1269 | 1270 | noise = dataOut.noise[channelIndexList]/factor |
|
|
1270 | 1271 | noisedB = 10*numpy.log10(noise) |
|
|
1271 | 1272 | |
|
|
1272 | 1273 | thisDatetime = dataOut.datatime |
|
|
1273 | 1274 | |
|
|
1274 | 1275 | title = wintitle + " Noise" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1275 | 1276 | xlabel = "" |
|
|
1276 | 1277 | ylabel = "Intensity (dB)" |
|
|
1277 | 1278 | update_figfile = False |
|
|
1278 | 1279 | |
|
|
1279 | 1280 | if not self.isConfig: |
|
|
1280 | 1281 | |
|
|
1281 | 1282 | nplots = 1 |
|
|
1282 | 1283 | |
|
|
1283 | 1284 | self.setup(id=id, |
|
|
1284 | 1285 | nplots=nplots, |
|
|
1285 | 1286 | wintitle=wintitle, |
|
|
1286 | 1287 | showprofile=showprofile, |
|
|
1287 | 1288 | show=show) |
|
|
1288 | 1289 | |
|
|
1289 | 1290 | if timerange != None: |
|
|
1290 | 1291 | self.timerange = timerange |
|
|
1291 | 1292 | |
|
|
1292 | 1293 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1293 | 1294 | |
|
|
1294 | 1295 | if ymin == None: ymin = numpy.floor(numpy.nanmin(noisedB)) - 10.0 |
|
|
1295 | 1296 | if ymax == None: ymax = numpy.nanmax(noisedB) + 10.0 |
|
|
1296 | 1297 | |
|
|
1297 | 1298 | self.FTP_WEI = ftp_wei |
|
|
1298 | 1299 | self.EXP_CODE = exp_code |
|
|
1299 | 1300 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1300 | 1301 | self.PLOT_POS = plot_pos |
|
|
1301 | 1302 | |
|
|
1302 | 1303 | |
|
|
1303 | 1304 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1304 | 1305 | self.isConfig = True |
|
|
1305 | 1306 | self.figfile = figfile |
|
|
1306 | 1307 | self.xdata = numpy.array([]) |
|
|
1307 | 1308 | self.ydata = numpy.array([]) |
|
|
1308 | 1309 | |
|
|
1309 | 1310 | update_figfile = True |
|
|
1310 | 1311 | |
|
|
1311 | 1312 | #open file beacon phase |
|
|
1312 | 1313 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1313 | 1314 | noise_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1314 | 1315 | self.filename_noise = os.path.join(figpath,noise_file) |
|
|
1315 | 1316 | |
|
|
1316 | 1317 | self.setWinTitle(title) |
|
|
1317 | 1318 | |
|
|
1318 | 1319 | title = "Noise %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1319 | 1320 | |
|
|
1320 | 1321 | legendlabels = ["channel %d"%(idchannel) for idchannel in channelList] |
|
|
1321 | 1322 | axes = self.axesList[0] |
|
|
1322 | 1323 | |
|
|
1323 | 1324 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1324 | 1325 | |
|
|
1325 | 1326 | if len(self.ydata)==0: |
|
|
1326 | 1327 | self.ydata = noisedB.reshape(-1,1) |
|
|
1327 | 1328 | else: |
|
|
1328 | 1329 | self.ydata = numpy.hstack((self.ydata, noisedB.reshape(-1,1))) |
|
|
1329 | 1330 | |
|
|
1330 | 1331 | |
|
|
1331 | 1332 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1332 | 1333 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1333 | 1334 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1334 | 1335 | XAxisAsTime=True, grid='both' |
|
|
1335 | 1336 | ) |
|
|
1336 | 1337 | |
|
|
1337 | 1338 | self.draw() |
|
|
1338 | 1339 | |
|
|
1339 | 1340 | if dataOut.ltctime >= self.xmax: |
|
|
1340 | 1341 | self.counter_imagwr = wr_period |
|
|
1341 | 1342 | self.isConfig = False |
|
|
1342 | 1343 | update_figfile = True |
|
|
1343 | 1344 | |
|
|
1344 | 1345 | self.save(figpath=figpath, |
|
|
1345 | 1346 | figfile=figfile, |
|
|
1346 | 1347 | save=save, |
|
|
1347 | 1348 | ftp=ftp, |
|
|
1348 | 1349 | wr_period=wr_period, |
|
|
1349 | 1350 | thisDatetime=thisDatetime, |
|
|
1350 | 1351 | update_figfile=update_figfile) |
|
|
1351 | 1352 | |
|
|
1352 | 1353 | #store data beacon phase |
|
|
1353 | 1354 | if save: |
|
|
1354 | 1355 | self.save_data(self.filename_noise, noisedB, thisDatetime) |
|
|
1355 | 1356 | |
|
|
1356 | 1357 | return dataOut |
|
|
1357 | 1358 | |
|
|
1358 | 1359 | @MPDecorator |
|
|
1359 | 1360 | class BeaconPhase_(Figure): |
|
|
1360 | 1361 | |
|
|
1361 | 1362 | __isConfig = None |
|
|
1362 | 1363 | __nsubplots = None |
|
|
1363 | 1364 | |
|
|
1364 | 1365 | PREFIX = 'beacon_phase' |
|
|
1365 | 1366 | |
|
|
1366 | 1367 | def __init__(self): |
|
|
1367 | 1368 | Figure.__init__(self) |
|
|
1368 | 1369 | self.timerange = 24*60*60 |
|
|
1369 | 1370 | self.isConfig = False |
|
|
1370 | 1371 | self.__nsubplots = 1 |
|
|
1371 | 1372 | self.counter_imagwr = 0 |
|
|
1372 | 1373 | self.WIDTH = 800 |
|
|
1373 | 1374 | self.HEIGHT = 400 |
|
|
1374 | 1375 | self.WIDTHPROF = 120 |
|
|
1375 | 1376 | self.HEIGHTPROF = 0 |
|
|
1376 | 1377 | self.xdata = None |
|
|
1377 | 1378 | self.ydata = None |
|
|
1378 | 1379 | |
|
|
1379 | 1380 | self.PLOT_CODE = BEACON_CODE |
|
|
1380 | 1381 | |
|
|
1381 | 1382 | self.FTP_WEI = None |
|
|
1382 | 1383 | self.EXP_CODE = None |
|
|
1383 | 1384 | self.SUB_EXP_CODE = None |
|
|
1384 | 1385 | self.PLOT_POS = None |
|
|
1385 | 1386 | |
|
|
1386 | 1387 | self.filename_phase = None |
|
|
1387 | 1388 | |
|
|
1388 | 1389 | self.figfile = None |
|
|
1389 | 1390 | |
|
|
1390 | 1391 | self.xmin = None |
|
|
1391 | 1392 | self.xmax = None |
|
|
1392 | 1393 | |
|
|
1393 | 1394 | def getSubplots(self): |
|
|
1394 | 1395 | |
|
|
1395 | 1396 | ncol = 1 |
|
|
1396 | 1397 | nrow = 1 |
|
|
1397 | 1398 | |
|
|
1398 | 1399 | return nrow, ncol |
|
|
1399 | 1400 | |
|
|
1400 | 1401 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1401 | 1402 | |
|
|
1402 | 1403 | self.__showprofile = showprofile |
|
|
1403 | 1404 | self.nplots = nplots |
|
|
1404 | 1405 | |
|
|
1405 | 1406 | ncolspan = 7 |
|
|
1406 | 1407 | colspan = 6 |
|
|
1407 | 1408 | self.__nsubplots = 2 |
|
|
1408 | 1409 | |
|
|
1409 | 1410 | self.createFigure(id = id, |
|
|
1410 | 1411 | wintitle = wintitle, |
|
|
1411 | 1412 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1412 | 1413 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1413 | 1414 | show=show) |
|
|
1414 | 1415 | |
|
|
1415 | 1416 | nrow, ncol = self.getSubplots() |
|
|
1416 | 1417 | |
|
|
1417 | 1418 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1418 | 1419 | |
|
|
1419 | 1420 | def save_phase(self, filename_phase): |
|
|
1420 | 1421 | f = open(filename_phase,'w+') |
|
|
1421 | 1422 | f.write('\n\n') |
|
|
1422 | 1423 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
1423 | 1424 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
1424 | 1425 | f.close() |
|
|
1425 | 1426 | |
|
|
1426 | 1427 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1427 | 1428 | f=open(filename_phase,'a') |
|
|
1428 | 1429 | timetuple_data = data_datetime.timetuple() |
|
|
1429 | 1430 | day = str(timetuple_data.tm_mday) |
|
|
1430 | 1431 | month = str(timetuple_data.tm_mon) |
|
|
1431 | 1432 | year = str(timetuple_data.tm_year) |
|
|
1432 | 1433 | hour = str(timetuple_data.tm_hour) |
|
|
1433 | 1434 | minute = str(timetuple_data.tm_min) |
|
|
1434 | 1435 | second = str(timetuple_data.tm_sec) |
|
|
1435 | 1436 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
1436 | 1437 | f.close() |
|
|
1437 | 1438 | |
|
|
1438 | 1439 | |
|
|
1439 | 1440 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1440 | 1441 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
1441 | 1442 | timerange=None, |
|
|
1442 | 1443 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1443 | 1444 | server=None, folder=None, username=None, password=None, |
|
|
1444 | 1445 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1445 | 1446 | |
|
|
1446 | 1447 | if dataOut.flagNoData: |
|
|
1447 | 1448 | return dataOut |
|
|
1448 | 1449 | |
|
|
1449 | 1450 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1450 | 1451 | return |
|
|
1451 | 1452 | |
|
|
1452 | 1453 | if pairsList == None: |
|
|
1453 | 1454 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
1454 | 1455 | else: |
|
|
1455 | 1456 | pairsIndexList = [] |
|
|
1456 | 1457 | for pair in pairsList: |
|
|
1457 | 1458 | if pair not in dataOut.pairsList: |
|
|
1458 | 1459 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
1459 | 1460 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
1460 | 1461 | |
|
|
1461 | 1462 | if pairsIndexList == []: |
|
|
1462 | 1463 | return |
|
|
1463 | 1464 | |
|
|
1464 | 1465 | # if len(pairsIndexList) > 4: |
|
|
1465 | 1466 | # pairsIndexList = pairsIndexList[0:4] |
|
|
1466 | 1467 | |
|
|
1467 | 1468 | hmin_index = None |
|
|
1468 | 1469 | hmax_index = None |
|
|
1469 | 1470 | |
|
|
1470 | 1471 | if hmin != None and hmax != None: |
|
|
1471 | 1472 | indexes = numpy.arange(dataOut.nHeights) |
|
|
1472 | 1473 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
1473 | 1474 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
1474 | 1475 | |
|
|
1475 | 1476 | if hmin_list.any(): |
|
|
1476 | 1477 | hmin_index = hmin_list[0] |
|
|
1477 | 1478 | |
|
|
1478 | 1479 | if hmax_list.any(): |
|
|
1479 | 1480 | hmax_index = hmax_list[-1]+1 |
|
|
1480 | 1481 | |
|
|
1481 | 1482 | x = dataOut.getTimeRange() |
|
|
1482 | 1483 | #y = dataOut.getHeiRange() |
|
|
1483 | 1484 | |
|
|
1484 | 1485 | |
|
|
1485 | 1486 | thisDatetime = dataOut.datatime |
|
|
1486 | 1487 | |
|
|
1487 | 1488 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1488 | 1489 | xlabel = "Local Time" |
|
|
1489 | 1490 | ylabel = "Phase (degrees)" |
|
|
1490 | 1491 | |
|
|
1491 | 1492 | update_figfile = False |
|
|
1492 | 1493 | |
|
|
1493 | 1494 | nplots = len(pairsIndexList) |
|
|
1494 | 1495 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
1495 | 1496 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
1496 | 1497 | for i in range(nplots): |
|
|
1497 | 1498 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
1498 | 1499 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
1499 | 1500 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
1500 | 1501 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
1501 | 1502 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
1502 | 1503 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
1503 | 1504 | |
|
|
1504 | 1505 | if dataOut.beacon_heiIndexList: |
|
|
1505 | 1506 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
1506 | 1507 | else: |
|
|
1507 | 1508 | phase_beacon[i] = numpy.average(phase) |
|
|
1508 | 1509 | |
|
|
1509 | 1510 | if not self.isConfig: |
|
|
1510 | 1511 | |
|
|
1511 | 1512 | nplots = len(pairsIndexList) |
|
|
1512 | 1513 | |
|
|
1513 | 1514 | self.setup(id=id, |
|
|
1514 | 1515 | nplots=nplots, |
|
|
1515 | 1516 | wintitle=wintitle, |
|
|
1516 | 1517 | showprofile=showprofile, |
|
|
1517 | 1518 | show=show) |
|
|
1518 | 1519 | |
|
|
1519 | 1520 | if timerange != None: |
|
|
1520 | 1521 | self.timerange = timerange |
|
|
1521 | 1522 | |
|
|
1522 | 1523 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1523 | 1524 | |
|
|
1524 | 1525 | if ymin == None: ymin = 0 |
|
|
1525 | 1526 | if ymax == None: ymax = 360 |
|
|
1526 | 1527 | |
|
|
1527 | 1528 | self.FTP_WEI = ftp_wei |
|
|
1528 | 1529 | self.EXP_CODE = exp_code |
|
|
1529 | 1530 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1530 | 1531 | self.PLOT_POS = plot_pos |
|
|
1531 | 1532 | |
|
|
1532 | 1533 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1533 | 1534 | self.isConfig = True |
|
|
1534 | 1535 | self.figfile = figfile |
|
|
1535 | 1536 | self.xdata = numpy.array([]) |
|
|
1536 | 1537 | self.ydata = numpy.array([]) |
|
|
1537 | 1538 | |
|
|
1538 | 1539 | update_figfile = True |
|
|
1539 | 1540 | |
|
|
1540 | 1541 | #open file beacon phase |
|
|
1541 | 1542 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1542 | 1543 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1543 | 1544 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1544 | 1545 | #self.save_phase(self.filename_phase) |
|
|
1545 | 1546 | |
|
|
1546 | 1547 | |
|
|
1547 | 1548 | #store data beacon phase |
|
|
1548 | 1549 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
1549 | 1550 | |
|
|
1550 | 1551 | self.setWinTitle(title) |
|
|
1551 | 1552 | |
|
|
1552 | 1553 | |
|
|
1553 | 1554 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1554 | 1555 | |
|
|
1555 | 1556 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
1556 | 1557 | |
|
|
1557 | 1558 | axes = self.axesList[0] |
|
|
1558 | 1559 | |
|
|
1559 | 1560 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1560 | 1561 | |
|
|
1561 | 1562 | if len(self.ydata)==0: |
|
|
1562 | 1563 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1563 | 1564 | else: |
|
|
1564 | 1565 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1565 | 1566 | |
|
|
1566 | 1567 | |
|
|
1567 | 1568 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1568 | 1569 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1569 | 1570 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1570 | 1571 | XAxisAsTime=True, grid='both' |
|
|
1571 | 1572 | ) |
|
|
1572 | 1573 | |
|
|
1573 | 1574 | self.draw() |
|
|
1574 | 1575 | |
|
|
1575 | 1576 | if dataOut.ltctime >= self.xmax: |
|
|
1576 | 1577 | self.counter_imagwr = wr_period |
|
|
1577 | 1578 | self.isConfig = False |
|
|
1578 | 1579 | update_figfile = True |
|
|
1579 | 1580 | |
|
|
1580 | 1581 | self.save(figpath=figpath, |
|
|
1581 | 1582 | figfile=figfile, |
|
|
1582 | 1583 | save=save, |
|
|
1583 | 1584 | ftp=ftp, |
|
|
1584 | 1585 | wr_period=wr_period, |
|
|
1585 | 1586 | thisDatetime=thisDatetime, |
|
|
1586 | 1587 | update_figfile=update_figfile) |
|
|
1587 | 1588 | |
|
|
1588 | 1589 | return dataOut No newline at end of file |
| @@ -1,500 +1,470 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import sys |
|
|
3 | 3 | import datetime |
|
|
4 | 4 | import numpy |
|
|
5 | import matplotlib | |
|
|
6 | ||
|
|
7 | if 'BACKEND' in os.environ: | |
|
|
8 | matplotlib.use(os.environ['BACKEND']) | |
|
|
9 | elif 'linux' in sys.platform: | |
|
|
10 | matplotlib.use("TkAgg") | |
|
|
11 | elif 'darwin' in sys.platform: | |
|
|
12 | matplotlib.use('TkAgg') | |
|
|
13 | else: | |
|
|
14 | from schainpy.utils import log | |
|
|
15 | log.warning('Using default Backend="Agg"', 'INFO') | |
|
|
16 | matplotlib.use('Agg') | |
|
|
17 | # Qt4Agg', 'GTK', 'GTKAgg', 'ps', 'agg', 'cairo', 'MacOSX', 'GTKCairo', 'WXAgg', 'template', 'TkAgg', 'GTK3Cairo', 'GTK3Agg', 'svg', 'WebAgg', 'CocoaAgg', 'emf', 'gdk', 'WX' | |
|
|
18 | import matplotlib.pyplot | |
|
|
19 | ||
|
|
20 | from mpl_toolkits.axes_grid1 import make_axes_locatable | |
|
|
21 | from matplotlib.ticker import FuncFormatter, LinearLocator | |
|
|
22 | ||
|
|
23 | ########################################### | |
|
|
24 | # Actualizacion de las funciones del driver | |
|
|
25 | ########################################### | |
|
|
26 | ||
|
|
27 | # create jro colormap | |
|
|
28 | ||
|
|
29 | jet_values = matplotlib.pyplot.get_cmap("jet", 100)(numpy.arange(100))[10:90] | |
|
|
30 | blu_values = matplotlib.pyplot.get_cmap( | |
|
|
31 | "seismic_r", 20)(numpy.arange(20))[10:15] | |
|
|
32 | ncmap = matplotlib.colors.LinearSegmentedColormap.from_list( | |
|
|
33 | "jro", numpy.vstack((blu_values, jet_values))) | |
|
|
34 | matplotlib.pyplot.register_cmap(cmap=ncmap) | |
|
|
35 | ||
|
|
5 | from .jroplot_base import matplotlib, make_axes_locatable, FuncFormatter, LinearLocator | |
|
|
36 | 6 | |
|
|
37 | 7 | def createFigure(id, wintitle, width, height, facecolor="w", show=True, dpi=80): |
|
|
38 | 8 | |
|
|
39 | 9 | matplotlib.pyplot.ioff() |
|
|
40 | 10 | |
|
|
41 | 11 | fig = matplotlib.pyplot.figure(num=id, facecolor=facecolor, figsize=( |
|
|
42 | 12 | 1.0 * width / dpi, 1.0 * height / dpi)) |
|
|
43 | 13 | fig.canvas.manager.set_window_title(wintitle) |
|
|
44 | 14 | # fig.canvas.manager.resize(width, height) |
|
|
45 | 15 | matplotlib.pyplot.ion() |
|
|
46 | 16 | |
|
|
47 | 17 | if show: |
|
|
48 | 18 | matplotlib.pyplot.show() |
|
|
49 | 19 | |
|
|
50 | 20 | return fig |
|
|
51 | 21 | |
|
|
52 | 22 | |
|
|
53 | 23 | def closeFigure(show=False, fig=None): |
|
|
54 | 24 | |
|
|
55 | 25 | # matplotlib.pyplot.ioff() |
|
|
56 | 26 | # matplotlib.pyplot.pause(0) |
|
|
57 | 27 | |
|
|
58 | 28 | if show: |
|
|
59 | 29 | matplotlib.pyplot.show() |
|
|
60 | 30 | |
|
|
61 | 31 | if fig != None: |
|
|
62 | 32 | matplotlib.pyplot.close(fig) |
|
|
63 | 33 | # matplotlib.pyplot.pause(0) |
|
|
64 | 34 | # matplotlib.pyplot.ion() |
|
|
65 | 35 | |
|
|
66 | 36 | return |
|
|
67 | 37 | |
|
|
68 | 38 | matplotlib.pyplot.close("all") |
|
|
69 | 39 | # matplotlib.pyplot.pause(0) |
|
|
70 | 40 | # matplotlib.pyplot.ion() |
|
|
71 | 41 | |
|
|
72 | 42 | return |
|
|
73 | 43 | |
|
|
74 | 44 | |
|
|
75 | 45 | def saveFigure(fig, filename): |
|
|
76 | 46 | |
|
|
77 | 47 | # matplotlib.pyplot.ioff() |
|
|
78 | 48 | fig.savefig(filename, dpi=matplotlib.pyplot.gcf().dpi) |
|
|
79 | 49 | # matplotlib.pyplot.ion() |
|
|
80 | 50 | |
|
|
81 | 51 | |
|
|
82 | 52 | def clearFigure(fig): |
|
|
83 | 53 | |
|
|
84 | 54 | fig.clf() |
|
|
85 | 55 | |
|
|
86 | 56 | |
|
|
87 | 57 | def setWinTitle(fig, title): |
|
|
88 | 58 | |
|
|
89 | 59 | fig.canvas.manager.set_window_title(title) |
|
|
90 | 60 | |
|
|
91 | 61 | |
|
|
92 | 62 | def setTitle(fig, title): |
|
|
93 | 63 | |
|
|
94 | 64 | fig.suptitle(title) |
|
|
95 | 65 | |
|
|
96 | 66 | |
|
|
97 | 67 | def createAxes(fig, nrow, ncol, xpos, ypos, colspan, rowspan, polar=False): |
|
|
98 | 68 | |
|
|
99 | 69 | matplotlib.pyplot.ioff() |
|
|
100 | 70 | matplotlib.pyplot.figure(fig.number) |
|
|
101 | 71 | axes = matplotlib.pyplot.subplot2grid((nrow, ncol), |
|
|
102 | 72 | (xpos, ypos), |
|
|
103 | 73 | colspan=colspan, |
|
|
104 | 74 | rowspan=rowspan, |
|
|
105 | 75 | polar=polar) |
|
|
106 | 76 | |
|
|
107 | 77 | matplotlib.pyplot.ion() |
|
|
108 | 78 | return axes |
|
|
109 | 79 | |
|
|
110 | 80 | |
|
|
111 | 81 | def setAxesText(ax, text): |
|
|
112 | 82 | |
|
|
113 | 83 | ax.annotate(text, |
|
|
114 | 84 | xy=(.1, .99), |
|
|
115 | 85 | xycoords='figure fraction', |
|
|
116 | 86 | horizontalalignment='left', |
|
|
117 | 87 | verticalalignment='top', |
|
|
118 | 88 | fontsize=10) |
|
|
119 | 89 | |
|
|
120 | 90 | |
|
|
121 | 91 | def printLabels(ax, xlabel, ylabel, title): |
|
|
122 | 92 | |
|
|
123 | 93 | ax.set_xlabel(xlabel, size=11) |
|
|
124 | 94 | ax.set_ylabel(ylabel, size=11) |
|
|
125 | 95 | ax.set_title(title, size=8) |
|
|
126 | 96 | |
|
|
127 | 97 | |
|
|
128 | 98 | def createPline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', |
|
|
129 | 99 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
130 | 100 | nxticks=4, nyticks=10, |
|
|
131 | 101 | grid=None, color='blue'): |
|
|
132 | 102 | """ |
|
|
133 | 103 | |
|
|
134 | 104 | Input: |
|
|
135 | 105 | grid : None, 'both', 'x', 'y' |
|
|
136 | 106 | """ |
|
|
137 | 107 | |
|
|
138 | 108 | matplotlib.pyplot.ioff() |
|
|
139 | 109 | |
|
|
140 | 110 | ax.set_xlim([xmin, xmax]) |
|
|
141 | 111 | ax.set_ylim([ymin, ymax]) |
|
|
142 | 112 | |
|
|
143 | 113 | printLabels(ax, xlabel, ylabel, title) |
|
|
144 | 114 | |
|
|
145 | 115 | ###################################################### |
|
|
146 | 116 | if (xmax - xmin) <= 1: |
|
|
147 | 117 | xtickspos = numpy.linspace(xmin, xmax, nxticks) |
|
|
148 | 118 | xtickspos = numpy.array([float("%.1f" % i) for i in xtickspos]) |
|
|
149 | 119 | ax.set_xticks(xtickspos) |
|
|
150 | 120 | else: |
|
|
151 | 121 | xtickspos = numpy.arange(nxticks) * \ |
|
|
152 | 122 | int((xmax - xmin) / (nxticks)) + int(xmin) |
|
|
153 | 123 | # xtickspos = numpy.arange(nxticks)*float(xmax-xmin)/float(nxticks) + int(xmin) |
|
|
154 | 124 | ax.set_xticks(xtickspos) |
|
|
155 | 125 | |
|
|
156 | 126 | for tick in ax.get_xticklabels(): |
|
|
157 | 127 | tick.set_visible(xtick_visible) |
|
|
158 | 128 | |
|
|
159 | 129 | for tick in ax.xaxis.get_major_ticks(): |
|
|
160 | 130 | tick.label.set_fontsize(ticksize) |
|
|
161 | 131 | |
|
|
162 | 132 | ###################################################### |
|
|
163 | 133 | for tick in ax.get_yticklabels(): |
|
|
164 | 134 | tick.set_visible(ytick_visible) |
|
|
165 | 135 | |
|
|
166 | 136 | for tick in ax.yaxis.get_major_ticks(): |
|
|
167 | 137 | tick.label.set_fontsize(ticksize) |
|
|
168 | 138 | |
|
|
169 | 139 | ax.plot(x, y, color=color) |
|
|
170 | 140 | iplot = ax.lines[-1] |
|
|
171 | 141 | |
|
|
172 | 142 | ###################################################### |
|
|
173 | 143 | if '0.' in matplotlib.__version__[0:2]: |
|
|
174 | 144 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
175 | 145 | return iplot |
|
|
176 | 146 | |
|
|
177 | 147 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
178 | 148 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
179 | 149 | return iplot |
|
|
180 | 150 | |
|
|
181 | 151 | if grid != None: |
|
|
182 | 152 | ax.grid(b=True, which='major', axis=grid) |
|
|
183 | 153 | |
|
|
184 | 154 | matplotlib.pyplot.tight_layout() |
|
|
185 | 155 | |
|
|
186 | 156 | matplotlib.pyplot.ion() |
|
|
187 | 157 | |
|
|
188 | 158 | return iplot |
|
|
189 | 159 | |
|
|
190 | 160 | |
|
|
191 | 161 | def set_linedata(ax, x, y, idline): |
|
|
192 | 162 | |
|
|
193 | 163 | ax.lines[idline].set_data(x, y) |
|
|
194 | 164 | |
|
|
195 | 165 | |
|
|
196 | 166 | def pline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
197 | 167 | |
|
|
198 | 168 | ax = iplot.axes |
|
|
199 | 169 | |
|
|
200 | 170 | printLabels(ax, xlabel, ylabel, title) |
|
|
201 | 171 | |
|
|
202 | 172 | set_linedata(ax, x, y, idline=0) |
|
|
203 | 173 | |
|
|
204 | 174 | |
|
|
205 | 175 | def addpline(ax, x, y, color, linestyle, lw): |
|
|
206 | 176 | |
|
|
207 | 177 | ax.plot(x, y, color=color, linestyle=linestyle, lw=lw) |
|
|
208 | 178 | |
|
|
209 | 179 | |
|
|
210 | 180 | def createPcolor(ax, x, y, z, xmin, xmax, ymin, ymax, zmin, zmax, |
|
|
211 | 181 | xlabel='', ylabel='', title='', ticksize=9, |
|
|
212 | 182 | colormap='jet', cblabel='', cbsize="5%", |
|
|
213 | 183 | XAxisAsTime=False): |
|
|
214 | 184 | |
|
|
215 | 185 | matplotlib.pyplot.ioff() |
|
|
216 | 186 | |
|
|
217 | 187 | divider = make_axes_locatable(ax) |
|
|
218 | 188 | ax_cb = divider.new_horizontal(size=cbsize, pad=0.05) |
|
|
219 | 189 | fig = ax.get_figure() |
|
|
220 | 190 | fig.add_axes(ax_cb) |
|
|
221 | 191 | |
|
|
222 | 192 | ax.set_xlim([xmin, xmax]) |
|
|
223 | 193 | ax.set_ylim([ymin, ymax]) |
|
|
224 | 194 | |
|
|
225 | 195 | printLabels(ax, xlabel, ylabel, title) |
|
|
226 | 196 | |
|
|
227 | 197 | z = numpy.ma.masked_invalid(z) |
|
|
228 | 198 | cmap = matplotlib.pyplot.get_cmap(colormap) |
|
|
229 | 199 | cmap.set_bad('white', 1.) |
|
|
230 | 200 | imesh = ax.pcolormesh(x, y, z.T, vmin=zmin, vmax=zmax, cmap=cmap) |
|
|
231 | 201 | cb = matplotlib.pyplot.colorbar(imesh, cax=ax_cb) |
|
|
232 | 202 | cb.set_label(cblabel) |
|
|
233 | 203 | |
|
|
234 | 204 | # for tl in ax_cb.get_yticklabels(): |
|
|
235 | 205 | # tl.set_visible(True) |
|
|
236 | 206 | |
|
|
237 | 207 | for tick in ax.yaxis.get_major_ticks(): |
|
|
238 | 208 | tick.label.set_fontsize(ticksize) |
|
|
239 | 209 | |
|
|
240 | 210 | for tick in ax.xaxis.get_major_ticks(): |
|
|
241 | 211 | tick.label.set_fontsize(ticksize) |
|
|
242 | 212 | |
|
|
243 | 213 | for tick in cb.ax.get_yticklabels(): |
|
|
244 | 214 | tick.set_fontsize(ticksize) |
|
|
245 | 215 | |
|
|
246 | 216 | ax_cb.yaxis.tick_right() |
|
|
247 | 217 | |
|
|
248 | 218 | if '0.' in matplotlib.__version__[0:2]: |
|
|
249 | 219 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
250 | 220 | return imesh |
|
|
251 | 221 | |
|
|
252 | 222 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
253 | 223 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
254 | 224 | return imesh |
|
|
255 | 225 | |
|
|
256 | 226 | matplotlib.pyplot.tight_layout() |
|
|
257 | 227 | |
|
|
258 | 228 | if XAxisAsTime: |
|
|
259 | 229 | |
|
|
260 | 230 | def func(x, pos): return ('%s') % ( |
|
|
261 | 231 | datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
262 | 232 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
263 | 233 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
264 | 234 | |
|
|
265 | 235 | matplotlib.pyplot.ion() |
|
|
266 | 236 | return imesh |
|
|
267 | 237 | |
|
|
268 | 238 | |
|
|
269 | 239 | def pcolor(imesh, z, xlabel='', ylabel='', title=''): |
|
|
270 | 240 | |
|
|
271 | 241 | z = z.T |
|
|
272 | 242 | ax = imesh.axes |
|
|
273 | 243 | printLabels(ax, xlabel, ylabel, title) |
|
|
274 | 244 | imesh.set_array(z.ravel()) |
|
|
275 | 245 | |
|
|
276 | 246 | |
|
|
277 | 247 | def addpcolor(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
278 | 248 | |
|
|
279 | 249 | printLabels(ax, xlabel, ylabel, title) |
|
|
280 | 250 | |
|
|
281 | 251 | ax.pcolormesh(x, y, z.T, vmin=zmin, vmax=zmax, |
|
|
282 | 252 | cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
283 | 253 | |
|
|
284 | 254 | |
|
|
285 | 255 | def addpcolorbuffer(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
286 | 256 | |
|
|
287 | 257 | printLabels(ax, xlabel, ylabel, title) |
|
|
288 | 258 | |
|
|
289 | 259 | ax.collections.remove(ax.collections[0]) |
|
|
290 | 260 | |
|
|
291 | 261 | z = numpy.ma.masked_invalid(z) |
|
|
292 | 262 | |
|
|
293 | 263 | cmap = matplotlib.pyplot.get_cmap(colormap) |
|
|
294 | 264 | cmap.set_bad('white', 1.) |
|
|
295 | 265 | |
|
|
296 | 266 | ax.pcolormesh(x, y, z.T, vmin=zmin, vmax=zmax, cmap=cmap) |
|
|
297 | 267 | |
|
|
298 | 268 | |
|
|
299 | 269 | def createPmultiline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
300 | 270 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
301 | 271 | nxticks=4, nyticks=10, |
|
|
302 | 272 | grid=None): |
|
|
303 | 273 | """ |
|
|
304 | 274 | |
|
|
305 | 275 | Input: |
|
|
306 | 276 | grid : None, 'both', 'x', 'y' |
|
|
307 | 277 | """ |
|
|
308 | 278 | |
|
|
309 | 279 | matplotlib.pyplot.ioff() |
|
|
310 | 280 | |
|
|
311 | 281 | lines = ax.plot(x.T, y) |
|
|
312 | 282 | leg = ax.legend(lines, legendlabels, loc='upper right') |
|
|
313 | 283 | leg.get_frame().set_alpha(0.5) |
|
|
314 | 284 | ax.set_xlim([xmin, xmax]) |
|
|
315 | 285 | ax.set_ylim([ymin, ymax]) |
|
|
316 | 286 | printLabels(ax, xlabel, ylabel, title) |
|
|
317 | 287 | |
|
|
318 | 288 | xtickspos = numpy.arange(nxticks) * \ |
|
|
319 | 289 | int((xmax - xmin) / (nxticks)) + int(xmin) |
|
|
320 | 290 | ax.set_xticks(xtickspos) |
|
|
321 | 291 | |
|
|
322 | 292 | for tick in ax.get_xticklabels(): |
|
|
323 | 293 | tick.set_visible(xtick_visible) |
|
|
324 | 294 | |
|
|
325 | 295 | for tick in ax.xaxis.get_major_ticks(): |
|
|
326 | 296 | tick.label.set_fontsize(ticksize) |
|
|
327 | 297 | |
|
|
328 | 298 | for tick in ax.get_yticklabels(): |
|
|
329 | 299 | tick.set_visible(ytick_visible) |
|
|
330 | 300 | |
|
|
331 | 301 | for tick in ax.yaxis.get_major_ticks(): |
|
|
332 | 302 | tick.label.set_fontsize(ticksize) |
|
|
333 | 303 | |
|
|
334 | 304 | iplot = ax.lines[-1] |
|
|
335 | 305 | |
|
|
336 | 306 | if '0.' in matplotlib.__version__[0:2]: |
|
|
337 | 307 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
338 | 308 | return iplot |
|
|
339 | 309 | |
|
|
340 | 310 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
341 | 311 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
342 | 312 | return iplot |
|
|
343 | 313 | |
|
|
344 | 314 | if grid != None: |
|
|
345 | 315 | ax.grid(b=True, which='major', axis=grid) |
|
|
346 | 316 | |
|
|
347 | 317 | matplotlib.pyplot.tight_layout() |
|
|
348 | 318 | |
|
|
349 | 319 | matplotlib.pyplot.ion() |
|
|
350 | 320 | |
|
|
351 | 321 | return iplot |
|
|
352 | 322 | |
|
|
353 | 323 | |
|
|
354 | 324 | def pmultiline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
355 | 325 | |
|
|
356 | 326 | ax = iplot.axes |
|
|
357 | 327 | |
|
|
358 | 328 | printLabels(ax, xlabel, ylabel, title) |
|
|
359 | 329 | |
|
|
360 | 330 | for i in range(len(ax.lines)): |
|
|
361 | 331 | line = ax.lines[i] |
|
|
362 | 332 | line.set_data(x[i, :], y) |
|
|
363 | 333 | |
|
|
364 | 334 | |
|
|
365 | 335 | def createPmultilineYAxis(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
366 | 336 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
367 | 337 | nxticks=4, nyticks=10, marker='.', markersize=10, linestyle="None", |
|
|
368 | 338 | grid=None, XAxisAsTime=False): |
|
|
369 | 339 | """ |
|
|
370 | 340 | |
|
|
371 | 341 | Input: |
|
|
372 | 342 | grid : None, 'both', 'x', 'y' |
|
|
373 | 343 | """ |
|
|
374 | 344 | |
|
|
375 | 345 | matplotlib.pyplot.ioff() |
|
|
376 | 346 | |
|
|
377 | 347 | # lines = ax.plot(x, y.T, marker=marker,markersize=markersize,linestyle=linestyle) |
|
|
378 | 348 | lines = ax.plot(x, y.T) |
|
|
379 | 349 | # leg = ax.legend(lines, legendlabels, loc=2, bbox_to_anchor=(1.01, 1.00), numpoints=1, handlelength=1.5, \ |
|
|
380 | 350 | # handletextpad=0.5, borderpad=0.5, labelspacing=0.5, borderaxespad=0.) |
|
|
381 | 351 | |
|
|
382 | 352 | leg = ax.legend(lines, legendlabels, |
|
|
383 | 353 | loc='upper right', bbox_to_anchor=(1.16, 1), borderaxespad=0) |
|
|
384 | 354 | |
|
|
385 | 355 | for label in leg.get_texts(): |
|
|
386 | 356 | label.set_fontsize(9) |
|
|
387 | 357 | |
|
|
388 | 358 | ax.set_xlim([xmin, xmax]) |
|
|
389 | 359 | ax.set_ylim([ymin, ymax]) |
|
|
390 | 360 | printLabels(ax, xlabel, ylabel, title) |
|
|
391 | 361 | |
|
|
392 | 362 | # xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
393 | 363 | # ax.set_xticks(xtickspos) |
|
|
394 | 364 | |
|
|
395 | 365 | for tick in ax.get_xticklabels(): |
|
|
396 | 366 | tick.set_visible(xtick_visible) |
|
|
397 | 367 | |
|
|
398 | 368 | for tick in ax.xaxis.get_major_ticks(): |
|
|
399 | 369 | tick.label.set_fontsize(ticksize) |
|
|
400 | 370 | |
|
|
401 | 371 | for tick in ax.get_yticklabels(): |
|
|
402 | 372 | tick.set_visible(ytick_visible) |
|
|
403 | 373 | |
|
|
404 | 374 | for tick in ax.yaxis.get_major_ticks(): |
|
|
405 | 375 | tick.label.set_fontsize(ticksize) |
|
|
406 | 376 | |
|
|
407 | 377 | iplot = ax.lines[-1] |
|
|
408 | 378 | |
|
|
409 | 379 | if '0.' in matplotlib.__version__[0:2]: |
|
|
410 | 380 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
411 | 381 | return iplot |
|
|
412 | 382 | |
|
|
413 | 383 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
414 | 384 | print("The matplotlib version has to be updated to 1.1 or newer") |
|
|
415 | 385 | return iplot |
|
|
416 | 386 | |
|
|
417 | 387 | if grid != None: |
|
|
418 | 388 | ax.grid(b=True, which='major', axis=grid) |
|
|
419 | 389 | |
|
|
420 | 390 | matplotlib.pyplot.tight_layout() |
|
|
421 | 391 | |
|
|
422 | 392 | if XAxisAsTime: |
|
|
423 | 393 | |
|
|
424 | 394 | def func(x, pos): return ('%s') % ( |
|
|
425 | 395 | datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
426 | 396 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
427 | 397 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
428 | 398 | |
|
|
429 | 399 | matplotlib.pyplot.ion() |
|
|
430 | 400 | |
|
|
431 | 401 | return iplot |
|
|
432 | 402 | |
|
|
433 | 403 | |
|
|
434 | 404 | def pmultilineyaxis(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
435 | 405 | |
|
|
436 | 406 | ax = iplot.axes |
|
|
437 | 407 | printLabels(ax, xlabel, ylabel, title) |
|
|
438 | 408 | |
|
|
439 | 409 | for i in range(len(ax.lines)): |
|
|
440 | 410 | line = ax.lines[i] |
|
|
441 | 411 | line.set_data(x, y[i, :]) |
|
|
442 | 412 | |
|
|
443 | 413 | |
|
|
444 | 414 | def createPolar(ax, x, y, |
|
|
445 | 415 | xlabel='', ylabel='', title='', ticksize=9, |
|
|
446 | 416 | colormap='jet', cblabel='', cbsize="5%", |
|
|
447 | 417 | XAxisAsTime=False): |
|
|
448 | 418 | |
|
|
449 | 419 | matplotlib.pyplot.ioff() |
|
|
450 | 420 | |
|
|
451 | 421 | ax.plot(x, y, 'bo', markersize=5) |
|
|
452 | 422 | # ax.set_rmax(90) |
|
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453 | 423 | ax.set_ylim(0, 90) |
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454 | 424 | ax.set_yticks(numpy.arange(0, 90, 20)) |
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455 | 425 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center' ,size='11') |
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456 | 426 | # ax.text(0, 50, ylabel, rotation='vertical', va ='center', ha = 'left' ,size='11') |
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457 | 427 | # ax.text(100, 100, 'example', ha='left', va='center', rotation='vertical') |
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458 | 428 | ax.yaxis.labelpad = 40 |
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459 | 429 | printLabels(ax, xlabel, ylabel, title) |
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460 | 430 | iplot = ax.lines[-1] |
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461 | 431 | |
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462 | 432 | if '0.' in matplotlib.__version__[0:2]: |
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463 | 433 | print("The matplotlib version has to be updated to 1.1 or newer") |
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464 | 434 | return iplot |
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465 | 435 | |
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466 | 436 | if '1.0.' in matplotlib.__version__[0:4]: |
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467 | 437 | print("The matplotlib version has to be updated to 1.1 or newer") |
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468 | 438 | return iplot |
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469 | 439 | |
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470 | 440 | # if grid != None: |
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471 | 441 | # ax.grid(b=True, which='major', axis=grid) |
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472 | 442 | |
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473 | 443 | matplotlib.pyplot.tight_layout() |
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474 | 444 | |
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475 | 445 | matplotlib.pyplot.ion() |
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476 | 446 | |
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477 | 447 | return iplot |
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478 | 448 | |
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479 | 449 | |
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480 | 450 | def polar(iplot, x, y, xlabel='', ylabel='', title=''): |
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481 | 451 | |
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482 | 452 | ax = iplot.axes |
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483 | 453 | |
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484 | 454 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center',size='11') |
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485 | 455 | printLabels(ax, xlabel, ylabel, title) |
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486 | 456 | |
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487 | 457 | set_linedata(ax, x, y, idline=0) |
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488 | 458 | |
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489 | 459 | |
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490 | 460 | def draw(fig): |
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491 | 461 | |
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492 | 462 | if type(fig) == 'int': |
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493 | 463 | raise ValueError("Error drawing: Fig parameter should be a matplotlib figure object figure") |
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494 | 464 | |
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495 | 465 | fig.canvas.draw() |
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496 | 466 | |
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497 | 467 | |
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498 | 468 | def pause(interval=0.000001): |
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499 | 469 | |
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500 | 470 | matplotlib.pyplot.pause(interval) No newline at end of file |
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1 | NO CONTENT: modified file |
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