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1 | 1 | ''' |
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2 | 2 | Created on Oct 24, 2016 |
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3 | 3 | |
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4 | 4 | @author: roj- LouVD |
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5 | 5 | ''' |
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6 | 6 | |
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7 | 7 | import numpy |
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8 | 8 | import datetime |
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9 | 9 | import time |
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10 | 10 | |
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11 | 11 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation |
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12 | 12 | from schainpy.model.data.jrodata import Parameters |
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13 | 13 | |
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14 | 14 | |
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15 | 15 | class BLTRParametersProc(ProcessingUnit): |
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16 | 16 | ''' |
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17 | 17 | Processing unit for BLTR parameters data (winds) |
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18 | 18 | |
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19 | 19 | Inputs: |
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20 | 20 | self.dataOut.nmodes - Number of operation modes |
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21 | 21 | self.dataOut.nchannels - Number of channels |
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22 | 22 | self.dataOut.nranges - Number of ranges |
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23 | 23 | |
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24 | 24 | self.dataOut.data_snr - SNR array |
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25 | 25 | self.dataOut.data_output - Zonal, Vertical and Meridional velocity array |
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26 | 26 | self.dataOut.height - Height array (km) |
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27 | 27 | self.dataOut.time - Time array (seconds) |
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28 | 28 | |
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29 | 29 | self.dataOut.fileIndex -Index of the file currently read |
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30 | 30 | self.dataOut.lat - Latitude coordinate of BLTR location |
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31 | 31 | |
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32 | 32 | self.dataOut.doy - Experiment doy (number of the day in the current year) |
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33 | 33 | self.dataOut.month - Experiment month |
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34 | 34 | self.dataOut.day - Experiment day |
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35 | 35 | self.dataOut.year - Experiment year |
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36 | 36 | ''' |
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37 | 37 | |
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38 | 38 | def __init__(self): |
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39 | 39 | ''' |
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40 | 40 | Inputs: None |
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41 | 41 | ''' |
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42 | 42 | ProcessingUnit.__init__(self) |
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43 | 43 | self.dataOut = Parameters() |
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44 | 44 | |
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45 | 45 | def setup(self, mode): |
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46 | 46 | ''' |
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47 | 47 | ''' |
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48 | 48 | self.dataOut.mode = mode |
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49 | 49 | |
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50 | 50 | def run(self, mode, snr_threshold=None): |
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51 | 51 | ''' |
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52 | 52 | Inputs: |
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53 | 53 | mode = High resolution (0) or Low resolution (1) data |
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54 | 54 | snr_threshold = snr filter value |
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55 | 55 | ''' |
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56 | 56 | |
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57 | 57 | if not self.isConfig: |
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58 | 58 | self.setup(mode) |
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59 | 59 | self.isConfig = True |
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60 | 60 | |
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61 | 61 | if self.dataIn.type == 'Parameters': |
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62 | 62 | self.dataOut.copy(self.dataIn) |
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63 | 63 | |
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64 | 64 | self.dataOut.data_param = self.dataOut.data[mode] |
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65 | 65 | self.dataOut.heightList = self.dataOut.height[0] |
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66 | 66 | self.dataOut.data_snr = self.dataOut.data_snr[mode] |
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67 | 67 | SNRavg = numpy.average(self.dataOut.data_snr, axis=0) |
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68 | 68 | SNRavgdB = 10*numpy.log10(SNRavg) |
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69 | 69 | self.dataOut.data_snr_avg_db = SNRavgdB.reshape(1, *SNRavgdB.shape) |
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70 | 70 | |
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71 | # Censoring Data | |
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71 | 72 | if snr_threshold is not None: |
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72 | 73 | for i in range(3): |
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73 | 74 | self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan |
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74 | 75 | |
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75 | 76 | # TODO |
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76 | 77 | |
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77 | 78 | class OutliersFilter(Operation): |
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78 | 79 | |
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79 | 80 | def __init__(self): |
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80 | 81 | ''' |
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81 | 82 | ''' |
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82 | 83 | Operation.__init__(self) |
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83 | 84 | |
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84 | 85 | def run(self, svalue2, method, factor, filter, npoints=9): |
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85 | 86 | ''' |
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86 | 87 | Inputs: |
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87 | 88 | svalue - string to select array velocity |
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88 | 89 | svalue2 - string to choose axis filtering |
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89 | 90 | method - 0 for SMOOTH or 1 for MEDIAN |
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90 | 91 | factor - number used to set threshold |
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91 | 92 | filter - 1 for data filtering using the standard deviation criteria else 0 |
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92 | 93 | npoints - number of points for mask filter |
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93 | 94 | ''' |
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94 | 95 | |
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95 | 96 | print(' Outliers Filter {} {} / threshold = {}'.format(svalue, svalue, factor)) |
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96 | 97 | |
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97 | 98 | |
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98 | 99 | yaxis = self.dataOut.heightList |
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99 | 100 | xaxis = numpy.array([[self.dataOut.utctime]]) |
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100 | 101 | |
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101 | 102 | # Zonal |
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102 | 103 | value_temp = self.dataOut.data_output[0] |
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103 | 104 | |
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104 | 105 | # Zonal |
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105 | 106 | value_temp = self.dataOut.data_output[1] |
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106 | 107 | |
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107 | 108 | # Vertical |
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108 | 109 | value_temp = numpy.transpose(self.dataOut.data_output[2]) |
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109 | 110 | |
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110 | 111 | htemp = yaxis |
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111 | 112 | std = value_temp |
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112 | 113 | for h in range(len(htemp)): |
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113 | 114 | nvalues_valid = len(numpy.where(numpy.isfinite(value_temp[h]))[0]) |
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114 | 115 | minvalid = npoints |
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115 | 116 | |
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116 | 117 | #only if valid values greater than the minimum required (10%) |
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117 | 118 | if nvalues_valid > minvalid: |
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118 | 119 | |
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119 | 120 | if method == 0: |
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120 | 121 | #SMOOTH |
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121 | 122 | w = value_temp[h] - self.Smooth(input=value_temp[h], width=npoints, edge_truncate=1) |
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122 | 123 | |
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123 | 124 | |
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124 | 125 | if method == 1: |
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125 | 126 | #MEDIAN |
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126 | 127 | w = value_temp[h] - self.Median(input=value_temp[h], width = npoints) |
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127 | 128 | |
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128 | 129 | dw = numpy.std(w[numpy.where(numpy.isfinite(w))],ddof = 1) |
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129 | 130 | |
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130 | 131 | threshold = dw*factor |
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131 | 132 | value_temp[numpy.where(w > threshold),h] = numpy.nan |
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132 | 133 | value_temp[numpy.where(w < -1*threshold),h] = numpy.nan |
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133 | 134 | |
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134 | 135 | |
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135 | 136 | #At the end |
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136 | 137 | if svalue2 == 'inHeight': |
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137 | 138 | value_temp = numpy.transpose(value_temp) |
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138 | 139 | output_array[:,m] = value_temp |
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139 | 140 | |
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140 | 141 | if svalue == 'zonal': |
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141 | 142 | self.dataOut.data_output[0] = output_array |
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142 | 143 | |
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143 | 144 | elif svalue == 'meridional': |
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144 | 145 | self.dataOut.data_output[1] = output_array |
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145 | 146 | |
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146 | 147 | elif svalue == 'vertical': |
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147 | 148 | self.dataOut.data_output[2] = output_array |
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148 | 149 | |
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149 | 150 | return self.dataOut.data_output |
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150 | 151 | |
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151 | 152 | |
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152 | 153 | def Median(self,input,width): |
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153 | 154 | ''' |
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154 | 155 | Inputs: |
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155 | 156 | input - Velocity array |
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156 | 157 | width - Number of points for mask filter |
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157 | 158 | |
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158 | 159 | ''' |
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159 | 160 | |
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160 | 161 | if numpy.mod(width,2) == 1: |
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161 | 162 | pc = int((width - 1) / 2) |
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162 | 163 | cont = 0 |
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163 | 164 | output = [] |
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164 | 165 | |
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165 | 166 | for i in range(len(input)): |
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166 | 167 | if i >= pc and i < len(input) - pc: |
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167 | 168 | new2 = input[i-pc:i+pc+1] |
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168 | 169 | temp = numpy.where(numpy.isfinite(new2)) |
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169 | 170 | new = new2[temp] |
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170 | 171 | value = numpy.median(new) |
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171 | 172 | output.append(value) |
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172 | 173 | |
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173 | 174 | output = numpy.array(output) |
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174 | 175 | output = numpy.hstack((input[0:pc],output)) |
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175 | 176 | output = numpy.hstack((output,input[-pc:len(input)])) |
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176 | 177 | |
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177 | 178 | return output |
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178 | 179 | |
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179 | 180 | def Smooth(self,input,width,edge_truncate = None): |
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180 | 181 | ''' |
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181 | 182 | Inputs: |
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182 | 183 | input - Velocity array |
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183 | 184 | width - Number of points for mask filter |
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184 | 185 | edge_truncate - 1 for truncate the convolution product else |
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185 | 186 | |
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186 | 187 | ''' |
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187 | 188 | |
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188 | 189 | if numpy.mod(width,2) == 0: |
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189 | 190 | real_width = width + 1 |
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190 | 191 | nzeros = width / 2 |
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191 | 192 | else: |
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192 | 193 | real_width = width |
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193 | 194 | nzeros = (width - 1) / 2 |
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194 | 195 | |
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195 | 196 | half_width = int(real_width)/2 |
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196 | 197 | length = len(input) |
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197 | 198 | |
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198 | 199 | gate = numpy.ones(real_width,dtype='float') |
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199 | 200 | norm_of_gate = numpy.sum(gate) |
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200 | 201 | |
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201 | 202 | nan_process = 0 |
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202 | 203 | nan_id = numpy.where(numpy.isnan(input)) |
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203 | 204 | if len(nan_id[0]) > 0: |
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204 | 205 | nan_process = 1 |
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205 | 206 | pb = numpy.zeros(len(input)) |
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206 | 207 | pb[nan_id] = 1. |
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207 | 208 | input[nan_id] = 0. |
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208 | 209 | |
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209 | 210 | if edge_truncate == True: |
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210 | 211 | output = numpy.convolve(input/norm_of_gate,gate,mode='same') |
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211 | 212 | elif edge_truncate == False or edge_truncate == None: |
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212 | 213 | output = numpy.convolve(input/norm_of_gate,gate,mode='valid') |
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213 | 214 | output = numpy.hstack((input[0:half_width],output)) |
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214 | 215 | output = numpy.hstack((output,input[len(input)-half_width:len(input)])) |
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215 | 216 | |
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216 | 217 | if nan_process: |
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217 | 218 | pb = numpy.convolve(pb/norm_of_gate,gate,mode='valid') |
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218 | 219 | pb = numpy.hstack((numpy.zeros(half_width),pb)) |
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219 | 220 | pb = numpy.hstack((pb,numpy.zeros(half_width))) |
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220 | 221 | output[numpy.where(pb > 0.9999)] = numpy.nan |
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221 | 222 | input[nan_id] = numpy.nan |
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222 | 223 | return output |
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223 | 224 | |
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224 | 225 | def Average(self,aver=0,nhaver=1): |
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225 | 226 | ''' |
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226 | 227 | Inputs: |
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227 | 228 | aver - Indicates the time period over which is averaged or consensus data |
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228 | 229 | nhaver - Indicates the decimation factor in heights |
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229 | 230 | |
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230 | 231 | ''' |
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231 | 232 | nhpoints = 48 |
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232 | 233 | |
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233 | 234 | lat_piura = -5.17 |
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234 | 235 | lat_huancayo = -12.04 |
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235 | 236 | lat_porcuya = -5.8 |
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236 | 237 | |
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237 | 238 | if '%2.2f'%self.dataOut.lat == '%2.2f'%lat_piura: |
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238 | 239 | hcm = 3. |
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239 | 240 | if self.dataOut.year == 2003 : |
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240 | 241 | if self.dataOut.doy >= 25 and self.dataOut.doy < 64: |
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241 | 242 | nhpoints = 12 |
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242 | 243 | |
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243 | 244 | elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_huancayo: |
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244 | 245 | hcm = 3. |
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245 | 246 | if self.dataOut.year == 2003 : |
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246 | 247 | if self.dataOut.doy >= 25 and self.dataOut.doy < 64: |
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247 | 248 | nhpoints = 12 |
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248 | 249 | |
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249 | 250 | |
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250 | 251 | elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_porcuya: |
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251 | 252 | hcm = 5.#2 |
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252 | 253 | |
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253 | 254 | pdata = 0.2 |
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254 | 255 | taver = [1,2,3,4,6,8,12,24] |
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255 | 256 | t0 = 0 |
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256 | 257 | tf = 24 |
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257 | 258 | ntime =(tf-t0)/taver[aver] |
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258 | 259 | ti = numpy.arange(ntime) |
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259 | 260 | tf = numpy.arange(ntime) + taver[aver] |
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260 | 261 | |
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261 | 262 | |
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262 | 263 | old_height = self.dataOut.heightList |
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263 | 264 | |
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264 | 265 | if nhaver > 1: |
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265 | 266 | num_hei = len(self.dataOut.heightList)/nhaver/self.dataOut.nmodes |
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266 | 267 | deltha = 0.05*nhaver |
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267 | 268 | minhvalid = pdata*nhaver |
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268 | 269 | for im in range(self.dataOut.nmodes): |
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269 | 270 | new_height = numpy.arange(num_hei)*deltha + self.dataOut.height[im,0] + deltha/2. |
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270 | 271 | |
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271 | 272 | |
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272 | 273 | data_fHeigths_List = [] |
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273 | 274 | data_fZonal_List = [] |
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274 | 275 | data_fMeridional_List = [] |
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275 | 276 | data_fVertical_List = [] |
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276 | 277 | startDTList = [] |
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277 | 278 | |
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278 | 279 | |
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279 | 280 | for i in range(ntime): |
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280 | 281 | height = old_height |
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281 | 282 | |
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282 | 283 | start = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(ti[i])) - datetime.timedelta(hours = 5) |
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283 | 284 | stop = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(tf[i])) - datetime.timedelta(hours = 5) |
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284 | 285 | |
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285 | 286 | |
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286 | 287 | limit_sec1 = time.mktime(start.timetuple()) |
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287 | 288 | limit_sec2 = time.mktime(stop.timetuple()) |
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288 | 289 | |
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289 | 290 | t1 = numpy.where(self.f_timesec >= limit_sec1) |
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290 | 291 | t2 = numpy.where(self.f_timesec < limit_sec2) |
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291 | 292 | time_select = [] |
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292 | 293 | for val_sec in t1[0]: |
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293 | 294 | if val_sec in t2[0]: |
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294 | 295 | time_select.append(val_sec) |
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295 | 296 | |
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296 | 297 | |
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297 | 298 | time_select = numpy.array(time_select,dtype = 'int') |
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298 | 299 | minvalid = numpy.ceil(pdata*nhpoints) |
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299 | 300 | |
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300 | 301 | zon_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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301 | 302 | mer_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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302 | 303 | ver_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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303 | 304 | |
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304 | 305 | if nhaver > 1: |
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305 | 306 | new_zon_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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306 | 307 | new_mer_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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307 | 308 | new_ver_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan |
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308 | 309 | |
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309 | 310 | if len(time_select) > minvalid: |
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310 | 311 | time_average = self.f_timesec[time_select] |
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311 | 312 | |
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312 | 313 | for im in range(self.dataOut.nmodes): |
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313 | 314 | |
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314 | 315 | for ih in range(self.dataOut.nranges): |
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315 | 316 | if numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im])) >= minvalid: |
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316 | 317 | zon_aver[ih,im] = numpy.nansum(self.f_zon[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im])) |
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317 | 318 | |
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318 | 319 | if numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im])) >= minvalid: |
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319 | 320 | mer_aver[ih,im] = numpy.nansum(self.f_mer[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im])) |
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320 | 321 | |
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321 | 322 | if numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im])) >= minvalid: |
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322 | 323 | ver_aver[ih,im] = numpy.nansum(self.f_ver[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im])) |
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323 | 324 | |
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324 | 325 | if nhaver > 1: |
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325 | 326 | for ih in range(num_hei): |
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326 | 327 | hvalid = numpy.arange(nhaver) + nhaver*ih |
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327 | 328 | |
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328 | 329 | if numpy.sum(numpy.isfinite(zon_aver[hvalid,im])) >= minvalid: |
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329 | 330 | new_zon_aver[ih,im] = numpy.nansum(zon_aver[hvalid,im]) / numpy.sum(numpy.isfinite(zon_aver[hvalid,im])) |
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330 | 331 | |
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331 | 332 | if numpy.sum(numpy.isfinite(mer_aver[hvalid,im])) >= minvalid: |
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332 | 333 | new_mer_aver[ih,im] = numpy.nansum(mer_aver[hvalid,im]) / numpy.sum(numpy.isfinite(mer_aver[hvalid,im])) |
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333 | 334 | |
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334 | 335 | if numpy.sum(numpy.isfinite(ver_aver[hvalid,im])) >= minvalid: |
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335 | 336 | new_ver_aver[ih,im] = numpy.nansum(ver_aver[hvalid,im]) / numpy.sum(numpy.isfinite(ver_aver[hvalid,im])) |
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336 | 337 | if nhaver > 1: |
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337 | 338 | zon_aver = new_zon_aver |
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338 | 339 | mer_aver = new_mer_aver |
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339 | 340 | ver_aver = new_ver_aver |
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340 | 341 | height = new_height |
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341 | 342 | |
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342 | 343 | |
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343 | 344 | tstart = time_average[0] |
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344 | 345 | tend = time_average[-1] |
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345 | 346 | startTime = time.gmtime(tstart) |
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346 | 347 | |
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347 | 348 | year = startTime.tm_year |
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348 | 349 | month = startTime.tm_mon |
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349 | 350 | day = startTime.tm_mday |
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350 | 351 | hour = startTime.tm_hour |
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351 | 352 | minute = startTime.tm_min |
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352 | 353 | second = startTime.tm_sec |
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353 | 354 | |
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354 | 355 | startDTList.append(datetime.datetime(year,month,day,hour,minute,second)) |
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355 | 356 | |
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356 | 357 | |
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357 | 358 | o_height = numpy.array([]) |
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358 | 359 | o_zon_aver = numpy.array([]) |
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359 | 360 | o_mer_aver = numpy.array([]) |
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360 | 361 | o_ver_aver = numpy.array([]) |
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361 | 362 | if self.dataOut.nmodes > 1: |
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362 | 363 | for im in range(self.dataOut.nmodes): |
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363 | 364 | |
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364 | 365 | if im == 0: |
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365 | 366 | h_select = numpy.where(numpy.bitwise_and(height[0,:] >=0,height[0,:] <= hcm,numpy.isfinite(height[0,:]))) |
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366 | 367 | else: |
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367 | 368 | h_select = numpy.where(numpy.bitwise_and(height[1,:] > hcm,height[1,:] < 20,numpy.isfinite(height[1,:]))) |
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368 | 369 | |
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369 | 370 | |
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370 | 371 | ht = h_select[0] |
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371 | 372 | |
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372 | 373 | o_height = numpy.hstack((o_height,height[im,ht])) |
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373 | 374 | o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im])) |
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374 | 375 | o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im])) |
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375 | 376 | o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im])) |
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376 | 377 | |
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377 | 378 | data_fHeigths_List.append(o_height) |
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378 | 379 | data_fZonal_List.append(o_zon_aver) |
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379 | 380 | data_fMeridional_List.append(o_mer_aver) |
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380 | 381 | data_fVertical_List.append(o_ver_aver) |
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381 | 382 | |
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382 | 383 | |
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383 | 384 | else: |
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384 | 385 | h_select = numpy.where(numpy.bitwise_and(height[0,:] <= hcm,numpy.isfinite(height[0,:]))) |
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385 | 386 | ht = h_select[0] |
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386 | 387 | o_height = numpy.hstack((o_height,height[im,ht])) |
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387 | 388 | o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im])) |
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388 | 389 | o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im])) |
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389 | 390 | o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im])) |
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390 | 391 | |
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391 | 392 | data_fHeigths_List.append(o_height) |
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392 | 393 | data_fZonal_List.append(o_zon_aver) |
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393 | 394 | data_fMeridional_List.append(o_mer_aver) |
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394 | 395 | data_fVertical_List.append(o_ver_aver) |
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395 | 396 | |
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396 | 397 | |
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397 | 398 | return startDTList, data_fHeigths_List, data_fZonal_List, data_fMeridional_List, data_fVertical_List |
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398 | 399 | |
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399 | 400 | |
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