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1 | 1 | # Copyright (c) 2012-2021 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """Classes to plot Spectra data |
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6 | 6 | |
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7 | 7 | """ |
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8 | 8 | |
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9 | 9 | import os |
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10 | 10 | import numpy |
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11 | 11 | import datetime |
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12 | 12 | |
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13 | 13 | from schainpy.model.graphics.jroplot_base import Plot, plt, log |
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14 | 14 | from itertools import combinations |
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15 | 15 | from matplotlib.ticker import LinearLocator |
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16 | 16 | |
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17 | 17 | from schainpy.model.utils.BField import BField |
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18 | 18 | from scipy.interpolate import splrep |
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19 | 19 | from scipy.interpolate import splev |
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20 | 20 | |
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21 | 21 | from matplotlib import __version__ as plt_version |
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22 | 22 | |
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23 | 23 | if plt_version >='3.3.4': |
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24 | 24 | EXTRA_POINTS = 0 |
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25 | 25 | else: |
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26 | 26 | EXTRA_POINTS = 1 |
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27 | 27 | class SpectraPlot(Plot): |
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28 | 28 | ''' |
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29 | 29 | Plot for Spectra data |
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30 | 30 | ''' |
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31 | 31 | |
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32 | 32 | CODE = 'spc' |
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33 | 33 | colormap = 'jet' |
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34 | 34 | plot_type = 'pcolor' |
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35 | 35 | buffering = False |
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36 | 36 | channelList = [] |
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37 | 37 | elevationList = [] |
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38 | 38 | azimuthList = [] |
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39 | 39 | |
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40 | 40 | def setup(self): |
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41 | 41 | |
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42 | 42 | self.nplots = len(self.data.channels) |
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43 | 43 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
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44 | 44 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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45 | 45 | self.height = 3.4 * self.nrows |
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46 | 46 | self.cb_label = 'dB' |
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47 | 47 | if self.showprofile: |
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48 | 48 | self.width = 5.2 * self.ncols |
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49 | 49 | else: |
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50 | 50 | self.width = 4.2* self.ncols |
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51 | 51 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.12}) |
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52 | 52 | self.ylabel = 'Range [km]' |
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53 | 53 | |
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54 | 54 | def update_list(self,dataOut): |
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55 | 55 | |
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56 | 56 | if len(self.channelList) == 0: |
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57 | 57 | self.channelList = dataOut.channelList |
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58 | 58 | if len(self.elevationList) == 0: |
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59 | 59 | self.elevationList = dataOut.elevationList |
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60 | 60 | if len(self.azimuthList) == 0: |
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61 | 61 | self.azimuthList = dataOut.azimuthList |
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62 | 62 | |
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63 | 63 | def update(self, dataOut): |
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64 | 64 | |
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65 | 65 | self.update_list(dataOut) |
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66 | 66 | data = {} |
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67 | 67 | meta = {} |
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68 | 68 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter |
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69 | 69 | if dataOut.type == "Parameters": |
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70 | 70 | noise = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
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71 | 71 | spc = 10*numpy.log10(dataOut.data_spc/(dataOut.nProfiles)) |
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72 | 72 | else: |
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73 | 73 | noise = 10*numpy.log10(dataOut.getNoise()/norm) |
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74 | 74 | |
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75 | 75 | z = numpy.zeros((dataOut.nChannels, dataOut.nFFTPoints, dataOut.nHeights)) |
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76 | 76 | for ch in range(dataOut.nChannels): |
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77 | 77 | if hasattr(dataOut.normFactor,'ndim'): |
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78 | 78 | if dataOut.normFactor.ndim > 1: |
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79 | 79 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor[ch])) |
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80 | 80 | |
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81 | 81 | else: |
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82 | 82 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor)) |
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83 | 83 | else: |
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84 | 84 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor)) |
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85 | 85 | |
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86 | 86 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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87 | 87 | spc = 10*numpy.log10(z) |
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88 | 88 | |
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89 | 89 | data['spc'] = spc |
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90 | 90 | data['rti'] = spc.mean(axis=1) |
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91 | 91 | data['noise'] = noise |
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92 | 92 | meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS)) |
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93 | 93 | if self.CODE == 'spc_moments': |
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94 | 94 | data['moments'] = dataOut.moments |
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95 | 95 | |
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96 | 96 | return data, meta |
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97 | 97 | |
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98 | 98 | def plot(self): |
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99 | 99 | |
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100 | 100 | if self.xaxis == "frequency": |
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101 | 101 | x = self.data.xrange[0] |
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102 | 102 | self.xlabel = "Frequency (kHz)" |
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103 | 103 | elif self.xaxis == "time": |
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104 | 104 | x = self.data.xrange[1] |
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105 | 105 | self.xlabel = "Time (ms)" |
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106 | 106 | else: |
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107 | 107 | x = self.data.xrange[2] |
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108 | 108 | self.xlabel = "Velocity (m/s)" |
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109 | 109 | |
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110 | 110 | if (self.CODE == 'spc_moments') | (self.CODE == 'gaussian_fit'): |
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111 | 111 | x = self.data.xrange[2] |
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112 | 112 | self.xlabel = "Velocity (m/s)" |
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113 | 113 | |
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114 | 114 | self.titles = [] |
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115 | 115 | |
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116 | 116 | y = self.data.yrange |
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117 | 117 | self.y = y |
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118 | 118 | |
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119 | 119 | data = self.data[-1] |
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120 | 120 | z = data['spc'] |
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121 | 121 | |
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122 | 122 | for n, ax in enumerate(self.axes): |
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123 | 123 | noise = self.data['noise'][n][0] |
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124 | 124 | # noise = data['noise'][n] |
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125 | 125 | |
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126 | 126 | if self.CODE == 'spc_moments': |
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127 | 127 | mean = data['moments'][n, 1] |
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128 | 128 | if self.CODE == 'gaussian_fit': |
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129 | 129 | gau0 = data['gaussfit'][n][2,:,0] |
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130 | 130 | gau1 = data['gaussfit'][n][2,:,1] |
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131 | 131 | if ax.firsttime: |
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132 | 132 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
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133 | 133 | self.xmin = self.xmin if self.xmin else -self.xmax |
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134 | 134 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
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135 | 135 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
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136 | 136 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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137 | 137 | vmin=self.zmin, |
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138 | 138 | vmax=self.zmax, |
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139 | 139 | cmap=plt.get_cmap(self.colormap) |
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140 | 140 | ) |
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141 | 141 | |
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142 | 142 | if self.showprofile: |
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143 | 143 | ax.plt_profile = self.pf_axes[n].plot( |
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144 | 144 | data['rti'][n], y)[0] |
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145 | 145 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
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146 | 146 | color="k", linestyle="dashed", lw=1)[0] |
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147 | 147 | if self.CODE == 'spc_moments': |
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148 | 148 | ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] |
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149 | 149 | if self.CODE == 'gaussian_fit': |
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150 | 150 | ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0] |
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151 | 151 | ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0] |
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152 | 152 | else: |
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153 | 153 | ax.plt.set_array(z[n].T.ravel()) |
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154 | 154 | if self.showprofile: |
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155 | 155 | ax.plt_profile.set_data(data['rti'][n], y) |
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156 | 156 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
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157 | 157 | if self.CODE == 'spc_moments': |
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158 | 158 | ax.plt_mean.set_data(mean, y) |
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159 | 159 | if self.CODE == 'gaussian_fit': |
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160 | 160 | ax.plt_gau0.set_data(gau0, y) |
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161 | 161 | ax.plt_gau1.set_data(gau1, y) |
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162 | 162 | if len(self.azimuthList) > 0 and len(self.elevationList) > 0: |
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163 | 163 | self.titles.append('CH {}: {:2.1f}elv {:2.1f}az {:3.2f}dB'.format(self.channelList[n], noise, self.elevationList[n], self.azimuthList[n])) |
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164 | 164 | else: |
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165 | 165 | self.titles.append('CH {}: {:3.2f}dB'.format(self.channelList[n], noise)) |
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166 | 166 | |
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167 | 167 | class SpectraObliquePlot(Plot): |
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168 | 168 | ''' |
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169 | 169 | Plot for Spectra data |
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170 | 170 | ''' |
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171 | 171 | |
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172 | 172 | CODE = 'spc_oblique' |
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173 | 173 | colormap = 'jet' |
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174 | 174 | plot_type = 'pcolor' |
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175 | 175 | |
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176 | 176 | def setup(self): |
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177 | 177 | self.xaxis = "oblique" |
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178 | 178 | self.nplots = len(self.data.channels) |
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179 | 179 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
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180 | 180 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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181 | 181 | self.height = 2.6 * self.nrows |
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182 | 182 | self.cb_label = 'dB' |
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183 | 183 | if self.showprofile: |
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184 | 184 | self.width = 4 * self.ncols |
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185 | 185 | else: |
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186 | 186 | self.width = 3.5 * self.ncols |
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187 | 187 | self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18}) |
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188 | 188 | self.ylabel = 'Range [km]' |
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189 | 189 | |
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190 | 190 | def update(self, dataOut): |
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191 | 191 | |
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192 | 192 | data = {} |
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193 | 193 | meta = {} |
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194 | 194 | |
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195 | 195 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
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196 | 196 | data['spc'] = spc |
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197 | 197 | data['rti'] = dataOut.getPower() |
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198 | 198 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
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199 | 199 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
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200 | 200 | |
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201 | 201 | data['shift1'] = dataOut.Dop_EEJ_T1[0] |
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202 | 202 | data['shift2'] = dataOut.Dop_EEJ_T2[0] |
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203 | 203 | data['max_val_2'] = dataOut.Oblique_params[0,-1,:] |
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204 | 204 | data['shift1_error'] = dataOut.Err_Dop_EEJ_T1[0] |
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205 | 205 | data['shift2_error'] = dataOut.Err_Dop_EEJ_T2[0] |
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206 | 206 | |
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207 | 207 | return data, meta |
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208 | 208 | |
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209 | 209 | def plot(self): |
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210 | 210 | |
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211 | 211 | if self.xaxis == "frequency": |
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212 | 212 | x = self.data.xrange[0] |
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213 | 213 | self.xlabel = "Frequency (kHz)" |
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214 | 214 | elif self.xaxis == "time": |
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215 | 215 | x = self.data.xrange[1] |
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216 | 216 | self.xlabel = "Time (ms)" |
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217 | 217 | else: |
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218 | 218 | x = self.data.xrange[2] |
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219 | 219 | self.xlabel = "Velocity (m/s)" |
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220 | 220 | |
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221 | 221 | self.titles = [] |
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222 | 222 | |
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223 | 223 | y = self.data.yrange |
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224 | 224 | self.y = y |
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225 | 225 | |
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226 | 226 | data = self.data[-1] |
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227 | 227 | z = data['spc'] |
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228 | 228 | |
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229 | 229 | for n, ax in enumerate(self.axes): |
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230 | 230 | noise = self.data['noise'][n][-1] |
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231 | 231 | shift1 = data['shift1'] |
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232 | 232 | shift2 = data['shift2'] |
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233 | 233 | max_val_2 = data['max_val_2'] |
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234 | 234 | err1 = data['shift1_error'] |
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235 | 235 | err2 = data['shift2_error'] |
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236 | 236 | if ax.firsttime: |
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237 | 237 | |
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238 | 238 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
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239 | 239 | self.xmin = self.xmin if self.xmin else -self.xmax |
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240 | 240 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
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241 | 241 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
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242 | 242 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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243 | 243 | vmin=self.zmin, |
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244 | 244 | vmax=self.zmax, |
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245 | 245 | cmap=plt.get_cmap(self.colormap) |
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246 | 246 | ) |
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247 | 247 | |
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248 | 248 | if self.showprofile: |
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249 | 249 | ax.plt_profile = self.pf_axes[n].plot( |
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250 | 250 | self.data['rti'][n][-1], y)[0] |
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251 | 251 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
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252 | 252 | color="k", linestyle="dashed", lw=1)[0] |
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253 | 253 | |
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254 | 254 | self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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255 | 255 | self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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256 | 256 | self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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257 | 257 | |
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258 | 258 | else: |
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259 | 259 | self.ploterr1.remove() |
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260 | 260 | self.ploterr2.remove() |
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261 | 261 | self.ploterr3.remove() |
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262 | 262 | ax.plt.set_array(z[n].T.ravel()) |
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263 | 263 | if self.showprofile: |
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264 | 264 | ax.plt_profile.set_data(self.data['rti'][n][-1], y) |
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265 | 265 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
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266 | 266 | self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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267 | 267 | self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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268 | 268 | self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2) |
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269 | 269 | |
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270 | 270 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) |
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271 | 271 | |
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272 | 272 | |
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273 | 273 | class CrossSpectraPlot(Plot): |
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274 | 274 | |
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275 | 275 | CODE = 'cspc' |
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276 | 276 | colormap = 'jet' |
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277 | 277 | plot_type = 'pcolor' |
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278 | 278 | zmin_coh = None |
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279 | 279 | zmax_coh = None |
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280 | 280 | zmin_phase = None |
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281 | 281 | zmax_phase = None |
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282 | 282 | realChannels = None |
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283 | 283 | crossPairs = None |
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284 | 284 | |
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285 | 285 | def setup(self): |
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286 | 286 | |
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287 | 287 | self.ncols = 4 |
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288 | 288 | self.nplots = len(self.data.pairs) * 2 |
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289 | 289 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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290 | 290 | self.width = 3.1 * self.ncols |
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291 | 291 | self.height = 2.6 * self.nrows |
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292 | 292 | self.ylabel = 'Range [km]' |
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293 | 293 | self.showprofile = False |
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294 | 294 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
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295 | 295 | |
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296 | 296 | def update(self, dataOut): |
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297 | 297 | |
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298 | 298 | data = {} |
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299 | 299 | meta = {} |
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300 | 300 | |
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301 | 301 | spc = dataOut.data_spc |
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302 | 302 | cspc = dataOut.data_cspc |
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303 | 303 | meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS)) |
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304 | 304 | rawPairs = list(combinations(list(range(dataOut.nChannels)), 2)) |
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305 | 305 | meta['pairs'] = rawPairs |
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306 | 306 | if self.crossPairs == None: |
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307 | 307 | self.crossPairs = dataOut.pairsList |
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308 | 308 | tmp = [] |
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309 | 309 | |
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310 | 310 | for n, pair in enumerate(meta['pairs']): |
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311 | 311 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
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312 | 312 | coh = numpy.abs(out) |
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313 | 313 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
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314 | 314 | tmp.append(coh) |
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315 | 315 | tmp.append(phase) |
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316 | 316 | |
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317 | 317 | data['cspc'] = numpy.array(tmp) |
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318 | 318 | |
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319 | 319 | return data, meta |
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320 | 320 | |
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321 | 321 | def plot(self): |
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322 | 322 | |
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323 | 323 | if self.xaxis == "frequency": |
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324 | 324 | x = self.data.xrange[0] |
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325 | 325 | self.xlabel = "Frequency (kHz)" |
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326 | 326 | elif self.xaxis == "time": |
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327 | 327 | x = self.data.xrange[1] |
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328 | 328 | self.xlabel = "Time (ms)" |
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329 | 329 | else: |
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330 | 330 | x = self.data.xrange[2] |
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331 | 331 | self.xlabel = "Velocity (m/s)" |
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332 | 332 | |
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333 | 333 | self.titles = [] |
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334 | 334 | |
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335 | 335 | y = self.data.yrange |
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336 | 336 | self.y = y |
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337 | 337 | |
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338 | 338 | data = self.data[-1] |
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339 | 339 | cspc = data['cspc'] |
|
|
340 | 340 | |
|
|
341 | 341 | for n in range(len(self.data.pairs)): |
|
|
342 | 342 | pair = self.crossPairs[n] |
|
|
343 | 343 | coh = cspc[n*2] |
|
|
344 | 344 | phase = cspc[n*2+1] |
|
|
345 | 345 | ax = self.axes[2 * n] |
|
|
346 | 346 | if ax.firsttime: |
|
|
347 | 347 | ax.plt = ax.pcolormesh(x, y, coh.T, |
|
|
348 | 348 | vmin=self.zmin_coh, |
|
|
349 | 349 | vmax=self.zmax_coh, |
|
|
350 | 350 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
351 | 351 | ) |
|
|
352 | 352 | else: |
|
|
353 | 353 | ax.plt.set_array(coh.T.ravel()) |
|
|
354 | 354 | self.titles.append( |
|
|
355 | 355 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
356 | 356 | |
|
|
357 | 357 | ax = self.axes[2 * n + 1] |
|
|
358 | 358 | if ax.firsttime: |
|
|
359 | 359 | ax.plt = ax.pcolormesh(x, y, phase.T, |
|
|
360 | 360 | vmin=-180, |
|
|
361 | 361 | vmax=180, |
|
|
362 | 362 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
363 | 363 | ) |
|
|
364 | 364 | else: |
|
|
365 | 365 | ax.plt.set_array(phase.T.ravel()) |
|
|
366 | 366 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
367 | 367 | |
|
|
368 | 368 | |
|
|
369 | 369 | class CrossSpectra4Plot(Plot): |
|
|
370 | 370 | |
|
|
371 | 371 | CODE = 'cspc' |
|
|
372 | 372 | colormap = 'jet' |
|
|
373 | 373 | plot_type = 'pcolor' |
|
|
374 | 374 | zmin_coh = None |
|
|
375 | 375 | zmax_coh = None |
|
|
376 | 376 | zmin_phase = None |
|
|
377 | 377 | zmax_phase = None |
|
|
378 | 378 | |
|
|
379 | 379 | def setup(self): |
|
|
380 | 380 | |
|
|
381 | 381 | self.ncols = 4 |
|
|
382 | 382 | self.nrows = len(self.data.pairs) |
|
|
383 | 383 | self.nplots = self.nrows * 4 |
|
|
384 | 384 | self.width = 3.1 * self.ncols |
|
|
385 | 385 | self.height = 5 * self.nrows |
|
|
386 | 386 | self.ylabel = 'Range [km]' |
|
|
387 | 387 | self.showprofile = False |
|
|
388 | 388 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
|
|
389 | 389 | |
|
|
390 | 390 | def plot(self): |
|
|
391 | 391 | |
|
|
392 | 392 | if self.xaxis == "frequency": |
|
|
393 | 393 | x = self.data.xrange[0] |
|
|
394 | 394 | self.xlabel = "Frequency (kHz)" |
|
|
395 | 395 | elif self.xaxis == "time": |
|
|
396 | 396 | x = self.data.xrange[1] |
|
|
397 | 397 | self.xlabel = "Time (ms)" |
|
|
398 | 398 | else: |
|
|
399 | 399 | x = self.data.xrange[2] |
|
|
400 | 400 | self.xlabel = "Velocity (m/s)" |
|
|
401 | 401 | |
|
|
402 | 402 | self.titles = [] |
|
|
403 | 403 | |
|
|
404 | 404 | |
|
|
405 | 405 | y = self.data.heights |
|
|
406 | 406 | self.y = y |
|
|
407 | 407 | nspc = self.data['spc'] |
|
|
408 | 408 | spc = self.data['cspc'][0] |
|
|
409 | 409 | cspc = self.data['cspc'][1] |
|
|
410 | 410 | |
|
|
411 | 411 | for n in range(self.nrows): |
|
|
412 | 412 | noise = self.data['noise'][:,-1] |
|
|
413 | 413 | pair = self.data.pairs[n] |
|
|
414 | 414 | |
|
|
415 | 415 | ax = self.axes[4 * n] |
|
|
416 | 416 | if ax.firsttime: |
|
|
417 | 417 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
418 | 418 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
419 | 419 | self.zmin = self.zmin if self.zmin else numpy.nanmin(nspc) |
|
|
420 | 420 | self.zmax = self.zmax if self.zmax else numpy.nanmax(nspc) |
|
|
421 | 421 | ax.plt = ax.pcolormesh(x , y , nspc[pair[0]].T, |
|
|
422 | 422 | vmin=self.zmin, |
|
|
423 | 423 | vmax=self.zmax, |
|
|
424 | 424 | cmap=plt.get_cmap(self.colormap) |
|
|
425 | 425 | ) |
|
|
426 | 426 | else: |
|
|
427 | 427 | |
|
|
428 | 428 | ax.plt.set_array(nspc[pair[0]].T.ravel()) |
|
|
429 | 429 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise[pair[0]])) |
|
|
430 | 430 | |
|
|
431 | 431 | ax = self.axes[4 * n + 1] |
|
|
432 | 432 | |
|
|
433 | 433 | if ax.firsttime: |
|
|
434 | 434 | ax.plt = ax.pcolormesh(x , y, numpy.flip(nspc[pair[1]],axis=0).T, |
|
|
435 | 435 | vmin=self.zmin, |
|
|
436 | 436 | vmax=self.zmax, |
|
|
437 | 437 | cmap=plt.get_cmap(self.colormap) |
|
|
438 | 438 | ) |
|
|
439 | 439 | else: |
|
|
440 | 440 | |
|
|
441 | 441 | ax.plt.set_array(numpy.flip(nspc[pair[1]],axis=0).T.ravel()) |
|
|
442 | 442 | self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise[pair[1]])) |
|
|
443 | 443 | |
|
|
444 | 444 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
|
|
445 | 445 | coh = numpy.abs(out) |
|
|
446 | 446 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
|
|
447 | 447 | |
|
|
448 | 448 | ax = self.axes[4 * n + 2] |
|
|
449 | 449 | if ax.firsttime: |
|
|
450 | 450 | ax.plt = ax.pcolormesh(x, y, numpy.flip(coh,axis=0).T, |
|
|
451 | 451 | vmin=0, |
|
|
452 | 452 | vmax=1, |
|
|
453 | 453 | cmap=plt.get_cmap(self.colormap_coh) |
|
|
454 | 454 | ) |
|
|
455 | 455 | else: |
|
|
456 | 456 | ax.plt.set_array(numpy.flip(coh,axis=0).T.ravel()) |
|
|
457 | 457 | self.titles.append( |
|
|
458 | 458 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
459 | 459 | |
|
|
460 | 460 | ax = self.axes[4 * n + 3] |
|
|
461 | 461 | if ax.firsttime: |
|
|
462 | 462 | ax.plt = ax.pcolormesh(x, y, numpy.flip(phase,axis=0).T, |
|
|
463 | 463 | vmin=-180, |
|
|
464 | 464 | vmax=180, |
|
|
465 | 465 | cmap=plt.get_cmap(self.colormap_phase) |
|
|
466 | 466 | ) |
|
|
467 | 467 | else: |
|
|
468 | 468 | ax.plt.set_array(numpy.flip(phase,axis=0).T.ravel()) |
|
|
469 | 469 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
|
|
470 | 470 | |
|
|
471 | 471 | |
|
|
472 | 472 | class CrossSpectra2Plot(Plot): |
|
|
473 | 473 | |
|
|
474 | 474 | CODE = 'cspc' |
|
|
475 | 475 | colormap = 'jet' |
|
|
476 | 476 | plot_type = 'pcolor' |
|
|
477 | 477 | zmin_coh = None |
|
|
478 | 478 | zmax_coh = None |
|
|
479 | 479 | zmin_phase = None |
|
|
480 | 480 | zmax_phase = None |
|
|
481 | 481 | |
|
|
482 | 482 | def setup(self): |
|
|
483 | 483 | |
|
|
484 | 484 | self.ncols = 1 |
|
|
485 | 485 | self.nrows = len(self.data.pairs) |
|
|
486 | 486 | self.nplots = self.nrows * 1 |
|
|
487 | 487 | self.width = 3.1 * self.ncols |
|
|
488 | 488 | self.height = 5 * self.nrows |
|
|
489 | 489 | self.ylabel = 'Range [km]' |
|
|
490 | 490 | self.showprofile = False |
|
|
491 | 491 | self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
|
|
492 | 492 | |
|
|
493 | 493 | def plot(self): |
|
|
494 | 494 | |
|
|
495 | 495 | if self.xaxis == "frequency": |
|
|
496 | 496 | x = self.data.xrange[0] |
|
|
497 | 497 | self.xlabel = "Frequency (kHz)" |
|
|
498 | 498 | elif self.xaxis == "time": |
|
|
499 | 499 | x = self.data.xrange[1] |
|
|
500 | 500 | self.xlabel = "Time (ms)" |
|
|
501 | 501 | else: |
|
|
502 | 502 | x = self.data.xrange[2] |
|
|
503 | 503 | self.xlabel = "Velocity (m/s)" |
|
|
504 | 504 | |
|
|
505 | 505 | self.titles = [] |
|
|
506 | 506 | |
|
|
507 | 507 | |
|
|
508 | 508 | y = self.data.heights |
|
|
509 | 509 | self.y = y |
|
|
510 | 510 | cspc = self.data['cspc'][1] |
|
|
511 | 511 | |
|
|
512 | 512 | for n in range(self.nrows): |
|
|
513 | 513 | noise = self.data['noise'][:,-1] |
|
|
514 | 514 | pair = self.data.pairs[n] |
|
|
515 | 515 | out = cspc[n] |
|
|
516 | 516 | cross = numpy.abs(out) |
|
|
517 | 517 | z = cross/self.data.nFactor |
|
|
518 | 518 | cross = 10*numpy.log10(z) |
|
|
519 | 519 | |
|
|
520 | 520 | ax = self.axes[1 * n] |
|
|
521 | 521 | if ax.firsttime: |
|
|
522 | 522 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
523 | 523 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
524 | 524 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) |
|
|
525 | 525 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) |
|
|
526 | 526 | ax.plt = ax.pcolormesh(x, y, cross.T, |
|
|
527 | 527 | vmin=self.zmin, |
|
|
528 | 528 | vmax=self.zmax, |
|
|
529 | 529 | cmap=plt.get_cmap(self.colormap) |
|
|
530 | 530 | ) |
|
|
531 | 531 | else: |
|
|
532 | 532 | ax.plt.set_array(cross.T.ravel()) |
|
|
533 | 533 | self.titles.append( |
|
|
534 | 534 | 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
535 | 535 | |
|
|
536 | 536 | |
|
|
537 | 537 | class CrossSpectra3Plot(Plot): |
|
|
538 | 538 | |
|
|
539 | 539 | CODE = 'cspc' |
|
|
540 | 540 | colormap = 'jet' |
|
|
541 | 541 | plot_type = 'pcolor' |
|
|
542 | 542 | zmin_coh = None |
|
|
543 | 543 | zmax_coh = None |
|
|
544 | 544 | zmin_phase = None |
|
|
545 | 545 | zmax_phase = None |
|
|
546 | 546 | |
|
|
547 | 547 | def setup(self): |
|
|
548 | 548 | |
|
|
549 | 549 | self.ncols = 3 |
|
|
550 | 550 | self.nrows = len(self.data.pairs) |
|
|
551 | 551 | self.nplots = self.nrows * 3 |
|
|
552 | 552 | self.width = 3.1 * self.ncols |
|
|
553 | 553 | self.height = 5 * self.nrows |
|
|
554 | 554 | self.ylabel = 'Range [km]' |
|
|
555 | 555 | self.showprofile = False |
|
|
556 | 556 | self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) |
|
|
557 | 557 | |
|
|
558 | 558 | def plot(self): |
|
|
559 | 559 | |
|
|
560 | 560 | if self.xaxis == "frequency": |
|
|
561 | 561 | x = self.data.xrange[0] |
|
|
562 | 562 | self.xlabel = "Frequency (kHz)" |
|
|
563 | 563 | elif self.xaxis == "time": |
|
|
564 | 564 | x = self.data.xrange[1] |
|
|
565 | 565 | self.xlabel = "Time (ms)" |
|
|
566 | 566 | else: |
|
|
567 | 567 | x = self.data.xrange[2] |
|
|
568 | 568 | self.xlabel = "Velocity (m/s)" |
|
|
569 | 569 | |
|
|
570 | 570 | self.titles = [] |
|
|
571 | 571 | |
|
|
572 | 572 | |
|
|
573 | 573 | y = self.data.heights |
|
|
574 | 574 | self.y = y |
|
|
575 | 575 | |
|
|
576 | 576 | cspc = self.data['cspc'][1] |
|
|
577 | 577 | |
|
|
578 | 578 | for n in range(self.nrows): |
|
|
579 | 579 | noise = self.data['noise'][:,-1] |
|
|
580 | 580 | pair = self.data.pairs[n] |
|
|
581 | 581 | out = cspc[n] |
|
|
582 | 582 | |
|
|
583 | 583 | cross = numpy.abs(out) |
|
|
584 | 584 | z = cross/self.data.nFactor |
|
|
585 | 585 | cross = 10*numpy.log10(z) |
|
|
586 | 586 | |
|
|
587 | 587 | out_r= out.real/self.data.nFactor |
|
|
588 | 588 | |
|
|
589 | 589 | out_i= out.imag/self.data.nFactor |
|
|
590 | 590 | |
|
|
591 | 591 | ax = self.axes[3 * n] |
|
|
592 | 592 | if ax.firsttime: |
|
|
593 | 593 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
594 | 594 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
595 | 595 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) |
|
|
596 | 596 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) |
|
|
597 | 597 | ax.plt = ax.pcolormesh(x, y, cross.T, |
|
|
598 | 598 | vmin=self.zmin, |
|
|
599 | 599 | vmax=self.zmax, |
|
|
600 | 600 | cmap=plt.get_cmap(self.colormap) |
|
|
601 | 601 | ) |
|
|
602 | 602 | else: |
|
|
603 | 603 | ax.plt.set_array(cross.T.ravel()) |
|
|
604 | 604 | self.titles.append( |
|
|
605 | 605 | 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
606 | 606 | |
|
|
607 | 607 | ax = self.axes[3 * n + 1] |
|
|
608 | 608 | if ax.firsttime: |
|
|
609 | 609 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
610 | 610 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
611 | 611 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) |
|
|
612 | 612 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) |
|
|
613 | 613 | ax.plt = ax.pcolormesh(x, y, out_r.T, |
|
|
614 | 614 | vmin=-1.e6, |
|
|
615 | 615 | vmax=0, |
|
|
616 | 616 | cmap=plt.get_cmap(self.colormap) |
|
|
617 | 617 | ) |
|
|
618 | 618 | else: |
|
|
619 | 619 | ax.plt.set_array(out_r.T.ravel()) |
|
|
620 | 620 | self.titles.append( |
|
|
621 | 621 | 'Cross Spectra Real Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
622 | 622 | |
|
|
623 | 623 | ax = self.axes[3 * n + 2] |
|
|
624 | 624 | |
|
|
625 | 625 | |
|
|
626 | 626 | if ax.firsttime: |
|
|
627 | 627 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
628 | 628 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
629 | 629 | self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) |
|
|
630 | 630 | self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) |
|
|
631 | 631 | ax.plt = ax.pcolormesh(x, y, out_i.T, |
|
|
632 | 632 | vmin=-1.e6, |
|
|
633 | 633 | vmax=1.e6, |
|
|
634 | 634 | cmap=plt.get_cmap(self.colormap) |
|
|
635 | 635 | ) |
|
|
636 | 636 | else: |
|
|
637 | 637 | ax.plt.set_array(out_i.T.ravel()) |
|
|
638 | 638 | self.titles.append( |
|
|
639 | 639 | 'Cross Spectra Imag Ch{} * Ch{}'.format(pair[0], pair[1])) |
|
|
640 | 640 | |
|
|
641 | 641 | class RTIPlot(Plot): |
|
|
642 | 642 | ''' |
|
|
643 | 643 | Plot for RTI data |
|
|
644 | 644 | ''' |
|
|
645 | 645 | |
|
|
646 | 646 | CODE = 'rti' |
|
|
647 | 647 | colormap = 'jet' |
|
|
648 | 648 | plot_type = 'pcolorbuffer' |
|
|
649 | 649 | titles = None |
|
|
650 | 650 | channelList = [] |
|
|
651 | 651 | elevationList = [] |
|
|
652 | 652 | azimuthList = [] |
|
|
653 | 653 | |
|
|
654 | 654 | def setup(self): |
|
|
655 | 655 | self.xaxis = 'time' |
|
|
656 | 656 | self.ncols = 1 |
|
|
657 | 657 | self.nrows = len(self.data.channels) |
|
|
658 | 658 | self.nplots = len(self.data.channels) |
|
|
659 | 659 | self.ylabel = 'Range [km]' |
|
|
660 | 660 | #self.xlabel = 'Time' |
|
|
661 | 661 | self.cb_label = 'dB' |
|
|
662 | 662 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95}) |
|
|
663 | 663 | self.titles = ['{} Channel {}'.format( |
|
|
664 | 664 | self.CODE.upper(), x) for x in range(self.nplots)] |
|
|
665 | 665 | |
|
|
666 | 666 | def update_list(self,dataOut): |
|
|
667 | 667 | |
|
|
668 | 668 | if len(self.channelList) == 0: |
|
|
669 | 669 | self.channelList = dataOut.channelList |
|
|
670 | 670 | if len(self.elevationList) == 0: |
|
|
671 | 671 | self.elevationList = dataOut.elevationList |
|
|
672 | 672 | if len(self.azimuthList) == 0: |
|
|
673 | 673 | self.azimuthList = dataOut.azimuthList |
|
|
674 | 674 | |
|
|
675 | 675 | |
|
|
676 | 676 | def update(self, dataOut): |
|
|
677 | 677 | |
|
|
678 | 678 | if len(self.channelList) == 0: |
|
|
679 | 679 | self.update_list(dataOut) |
|
|
680 | 680 | data = {} |
|
|
681 | 681 | meta = {} |
|
|
682 | 682 | data['rti'] = dataOut.getPower() |
|
|
683 | 683 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter |
|
|
684 | 684 | noise = 10*numpy.log10(dataOut.getNoise()/norm) |
|
|
685 | 685 | data['noise'] = noise |
|
|
686 | 686 | |
|
|
687 | 687 | return data, meta |
|
|
688 | 688 | |
|
|
689 | 689 | def plot(self): |
|
|
690 | 690 | |
|
|
691 | 691 | self.x = self.data.times |
|
|
692 | 692 | self.y = self.data.yrange |
|
|
693 | 693 | self.z = self.data[self.CODE] |
|
|
694 | 694 | self.z = numpy.array(self.z, dtype=float) |
|
|
695 | 695 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
696 | 696 | |
|
|
697 | 697 | try: |
|
|
698 | 698 | if self.channelList != None: |
|
|
699 | 699 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
700 | 700 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
701 | 701 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
702 | 702 | else: |
|
|
703 | 703 | self.titles = ['{} Channel {}'.format( |
|
|
704 | 704 | self.CODE.upper(), x) for x in self.channelList] |
|
|
705 | 705 | except: |
|
|
706 | 706 | if self.channelList.any() != None: |
|
|
707 | 707 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
708 | 708 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
709 | 709 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
710 | 710 | else: |
|
|
711 | 711 | self.titles = ['{} Channel {}'.format( |
|
|
712 | 712 | self.CODE.upper(), x) for x in self.channelList] |
|
|
713 | 713 | |
|
|
714 | 714 | if self.decimation is None: |
|
|
715 | 715 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
716 | 716 | else: |
|
|
717 | 717 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
718 | 718 | |
|
|
719 | 719 | for n, ax in enumerate(self.axes): |
|
|
720 | 720 | |
|
|
721 | 721 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
722 | 722 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
723 | 723 | data = self.data[-1] |
|
|
724 | 724 | if ax.firsttime: |
|
|
725 | 725 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
726 | 726 | vmin=self.zmin, |
|
|
727 | 727 | vmax=self.zmax, |
|
|
728 | 728 | cmap=plt.get_cmap(self.colormap) |
|
|
729 | 729 | ) |
|
|
730 | 730 | if self.showprofile: |
|
|
731 | 731 | ax.plot_profile = self.pf_axes[n].plot( |
|
|
732 | 732 | data[self.CODE][n], self.y)[0] |
|
|
733 | 733 | if "noise" in self.data: |
|
|
734 | 734 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, |
|
|
735 | 735 | color="k", linestyle="dashed", lw=1)[0] |
|
|
736 | 736 | else: |
|
|
737 |
|
|
|
|
737 | ax.collections.remove(ax.collections[0]) # error while running in 3.12 | |
|
|
738 | 738 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
739 | 739 | vmin=self.zmin, |
|
|
740 | 740 | vmax=self.zmax, |
|
|
741 | 741 | cmap=plt.get_cmap(self.colormap) |
|
|
742 | 742 | ) |
|
|
743 | 743 | if self.showprofile: |
|
|
744 | 744 | ax.plot_profile.set_data(data[self.CODE][n], self.y) |
|
|
745 | 745 | if "noise" in self.data: |
|
|
746 | 746 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, |
|
|
747 | 747 | color="k", linestyle="dashed", lw=1)[0] |
|
|
748 | 748 | |
|
|
749 | 749 | class SpectrogramPlot(Plot): |
|
|
750 | 750 | ''' |
|
|
751 | 751 | Plot for Spectrogram data |
|
|
752 | 752 | ''' |
|
|
753 | 753 | |
|
|
754 | 754 | CODE = 'Spectrogram_Profile' |
|
|
755 | 755 | colormap = 'binary' |
|
|
756 | 756 | plot_type = 'pcolorbuffer' |
|
|
757 | 757 | |
|
|
758 | 758 | def setup(self): |
|
|
759 | 759 | self.xaxis = 'time' |
|
|
760 | 760 | self.ncols = 1 |
|
|
761 | 761 | self.nrows = len(self.data.channels) |
|
|
762 | 762 | self.nplots = len(self.data.channels) |
|
|
763 | 763 | self.xlabel = 'Time' |
|
|
764 | 764 | self.plots_adjust.update({'hspace':1.2, 'left': 0.1, 'bottom': 0.12, 'right':0.95}) |
|
|
765 | 765 | self.titles = [] |
|
|
766 | 766 | |
|
|
767 | 767 | self.titles = ['{} Channel {}'.format( |
|
|
768 | 768 | self.CODE.upper(), x) for x in range(self.nrows)] |
|
|
769 | 769 | |
|
|
770 | 770 | |
|
|
771 | 771 | def update(self, dataOut): |
|
|
772 | 772 | data = {} |
|
|
773 | 773 | meta = {} |
|
|
774 | 774 | |
|
|
775 | 775 | maxHei = 1620#+12000 |
|
|
776 | 776 | indb = numpy.where(dataOut.heightList <= maxHei) |
|
|
777 | 777 | hei = indb[0][-1] |
|
|
778 | 778 | |
|
|
779 | 779 | factor = dataOut.nIncohInt |
|
|
780 | 780 | z = dataOut.data_spc[:,:,hei] / factor |
|
|
781 | 781 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
782 | 782 | |
|
|
783 | 783 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
784 | 784 | data['Spectrogram_Profile'] = 10 * numpy.log10(z) |
|
|
785 | 785 | |
|
|
786 | 786 | data['hei'] = hei |
|
|
787 | 787 | data['DH'] = (dataOut.heightList[1] - dataOut.heightList[0])/dataOut.step |
|
|
788 | 788 | data['nProfiles'] = dataOut.nProfiles |
|
|
789 | 789 | |
|
|
790 | 790 | return data, meta |
|
|
791 | 791 | |
|
|
792 | 792 | def plot(self): |
|
|
793 | 793 | |
|
|
794 | 794 | self.x = self.data.times |
|
|
795 | 795 | self.z = self.data[self.CODE] |
|
|
796 | 796 | self.y = self.data.xrange[0] |
|
|
797 | 797 | |
|
|
798 | 798 | hei = self.data['hei'][-1] |
|
|
799 | 799 | DH = self.data['DH'][-1] |
|
|
800 | 800 | nProfiles = self.data['nProfiles'][-1] |
|
|
801 | 801 | |
|
|
802 | 802 | self.ylabel = "Frequency (kHz)" |
|
|
803 | 803 | |
|
|
804 | 804 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
805 | 805 | |
|
|
806 | 806 | if self.decimation is None: |
|
|
807 | 807 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
808 | 808 | else: |
|
|
809 | 809 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
810 | 810 | |
|
|
811 | 811 | for n, ax in enumerate(self.axes): |
|
|
812 | 812 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
813 | 813 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
814 | 814 | data = self.data[-1] |
|
|
815 | 815 | if ax.firsttime: |
|
|
816 | 816 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
817 | 817 | vmin=self.zmin, |
|
|
818 | 818 | vmax=self.zmax, |
|
|
819 | 819 | cmap=plt.get_cmap(self.colormap) |
|
|
820 | 820 | ) |
|
|
821 | 821 | else: |
|
|
822 | 822 | # ax.collections.remove(ax.collections[0]) # error while running |
|
|
823 | 823 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
824 | 824 | vmin=self.zmin, |
|
|
825 | 825 | vmax=self.zmax, |
|
|
826 | 826 | cmap=plt.get_cmap(self.colormap) |
|
|
827 | 827 | ) |
|
|
828 | 828 | |
|
|
829 | 829 | |
|
|
830 | 830 | |
|
|
831 | 831 | class CoherencePlot(RTIPlot): |
|
|
832 | 832 | ''' |
|
|
833 | 833 | Plot for Coherence data |
|
|
834 | 834 | ''' |
|
|
835 | 835 | |
|
|
836 | 836 | CODE = 'coh' |
|
|
837 | 837 | titles = None |
|
|
838 | 838 | |
|
|
839 | 839 | def setup(self): |
|
|
840 | 840 | self.xaxis = 'time' |
|
|
841 | 841 | self.ncols = 1 |
|
|
842 | 842 | self.nrows = len(self.data.pairs) |
|
|
843 | 843 | self.nplots = len(self.data.pairs) |
|
|
844 | 844 | self.ylabel = 'Range [km]' |
|
|
845 | 845 | self.xlabel = 'Time' |
|
|
846 | 846 | self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1,'right':0.95}) |
|
|
847 | 847 | if self.CODE == 'coh': |
|
|
848 | 848 | self.cb_label = '' |
|
|
849 | 849 | self.titles = [ |
|
|
850 | 850 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
851 | 851 | else: |
|
|
852 | 852 | self.cb_label = 'Degrees' |
|
|
853 | 853 | self.titles = [ |
|
|
854 | 854 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
|
|
855 | 855 | |
|
|
856 | 856 | def update(self, dataOut): |
|
|
857 | 857 | |
|
|
858 | 858 | data = {} |
|
|
859 | 859 | meta = {} |
|
|
860 | 860 | data['coh'] = dataOut.getCoherence() |
|
|
861 | 861 | meta['pairs'] = dataOut.pairsList |
|
|
862 | 862 | |
|
|
863 | 863 | return data, meta |
|
|
864 | 864 | |
|
|
865 | 865 | class PhasePlot(CoherencePlot): |
|
|
866 | 866 | ''' |
|
|
867 | 867 | Plot for Phase map data |
|
|
868 | 868 | ''' |
|
|
869 | 869 | |
|
|
870 | 870 | CODE = 'phase' |
|
|
871 | 871 | colormap = 'seismic' |
|
|
872 | 872 | |
|
|
873 | 873 | def update(self, dataOut): |
|
|
874 | 874 | |
|
|
875 | 875 | data = {} |
|
|
876 | 876 | meta = {} |
|
|
877 | 877 | data['phase'] = dataOut.getCoherence(phase=True) |
|
|
878 | 878 | meta['pairs'] = dataOut.pairsList |
|
|
879 | 879 | |
|
|
880 | 880 | return data, meta |
|
|
881 | 881 | |
|
|
882 | 882 | class NoisePlot(Plot): |
|
|
883 | 883 | ''' |
|
|
884 | 884 | Plot for noise |
|
|
885 | 885 | ''' |
|
|
886 | 886 | |
|
|
887 | 887 | CODE = 'noise' |
|
|
888 | 888 | plot_type = 'scatterbuffer' |
|
|
889 | 889 | |
|
|
890 | 890 | def setup(self): |
|
|
891 | 891 | self.xaxis = 'time' |
|
|
892 | 892 | self.ncols = 1 |
|
|
893 | 893 | self.nrows = 1 |
|
|
894 | 894 | self.nplots = 1 |
|
|
895 | 895 | self.ylabel = 'Intensity [dB]' |
|
|
896 | 896 | self.xlabel = 'Time' |
|
|
897 | 897 | self.titles = ['Noise'] |
|
|
898 | 898 | self.colorbar = False |
|
|
899 | 899 | self.plots_adjust.update({'right': 0.85 }) |
|
|
900 | 900 | self.titles = ['Noise Plot'] |
|
|
901 | 901 | |
|
|
902 | 902 | def update(self, dataOut): |
|
|
903 | 903 | |
|
|
904 | 904 | data = {} |
|
|
905 | 905 | meta = {} |
|
|
906 | 906 | noise = 10*numpy.log10(dataOut.getNoise()) |
|
|
907 | 907 | noise = noise.reshape(dataOut.nChannels, 1) |
|
|
908 | 908 | data['noise'] = noise |
|
|
909 | 909 | meta['yrange'] = numpy.array([]) |
|
|
910 | 910 | |
|
|
911 | 911 | return data, meta |
|
|
912 | 912 | |
|
|
913 | 913 | def plot(self): |
|
|
914 | 914 | |
|
|
915 | 915 | x = self.data.times |
|
|
916 | 916 | xmin = self.data.min_time |
|
|
917 | 917 | xmax = xmin + self.xrange * 60 * 60 |
|
|
918 | 918 | Y = self.data['noise'] |
|
|
919 | 919 | |
|
|
920 | 920 | if self.axes[0].firsttime: |
|
|
921 | 921 | self.ymin = numpy.nanmin(Y) - 5 |
|
|
922 | 922 | self.ymax = numpy.nanmax(Y) + 5 |
|
|
923 | 923 | for ch in self.data.channels: |
|
|
924 | 924 | y = Y[ch] |
|
|
925 | 925 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
|
|
926 | 926 | plt.legend(bbox_to_anchor=(1.18, 1.0)) |
|
|
927 | 927 | else: |
|
|
928 | 928 | for ch in self.data.channels: |
|
|
929 | 929 | y = Y[ch] |
|
|
930 | 930 | self.axes[0].lines[ch].set_data(x, y) |
|
|
931 | 931 | |
|
|
932 | 932 | class PowerProfilePlot(Plot): |
|
|
933 | 933 | |
|
|
934 | 934 | CODE = 'pow_profile' |
|
|
935 | 935 | plot_type = 'scatter' |
|
|
936 | 936 | |
|
|
937 | 937 | def setup(self): |
|
|
938 | 938 | |
|
|
939 | 939 | self.ncols = 1 |
|
|
940 | 940 | self.nrows = 1 |
|
|
941 | 941 | self.nplots = 1 |
|
|
942 | 942 | self.height = 4 |
|
|
943 | 943 | self.width = 3 |
|
|
944 | 944 | self.ylabel = 'Range [km]' |
|
|
945 | 945 | self.xlabel = 'Intensity [dB]' |
|
|
946 | 946 | self.titles = ['Power Profile'] |
|
|
947 | 947 | self.colorbar = False |
|
|
948 | 948 | |
|
|
949 | 949 | def update(self, dataOut): |
|
|
950 | 950 | |
|
|
951 | 951 | data = {} |
|
|
952 | 952 | meta = {} |
|
|
953 | 953 | data[self.CODE] = dataOut.getPower() |
|
|
954 | 954 | |
|
|
955 | 955 | return data, meta |
|
|
956 | 956 | |
|
|
957 | 957 | def plot(self): |
|
|
958 | 958 | |
|
|
959 | 959 | y = self.data.yrange |
|
|
960 | 960 | self.y = y |
|
|
961 | 961 | |
|
|
962 | 962 | x = self.data[-1][self.CODE] |
|
|
963 | 963 | |
|
|
964 | 964 | if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9 |
|
|
965 | 965 | if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1 |
|
|
966 | 966 | |
|
|
967 | 967 | if self.axes[0].firsttime: |
|
|
968 | 968 | for ch in self.data.channels: |
|
|
969 | 969 | self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) |
|
|
970 | 970 | plt.legend() |
|
|
971 | 971 | else: |
|
|
972 | 972 | for ch in self.data.channels: |
|
|
973 | 973 | self.axes[0].lines[ch].set_data(x[ch], y) |
|
|
974 | 974 | |
|
|
975 | 975 | |
|
|
976 | 976 | class SpectraCutPlot(Plot): |
|
|
977 | 977 | |
|
|
978 | 978 | CODE = 'spc_cut' |
|
|
979 | 979 | plot_type = 'scatter' |
|
|
980 | 980 | buffering = False |
|
|
981 | 981 | heights = [] |
|
|
982 | 982 | channelList = [] |
|
|
983 | 983 | maintitle = "Spectra Cuts" |
|
|
984 | 984 | flag_setIndex = False |
|
|
985 | 985 | |
|
|
986 | 986 | def setup(self): |
|
|
987 | 987 | |
|
|
988 | 988 | self.nplots = len(self.data.channels) |
|
|
989 | 989 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
990 | 990 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
991 | 991 | self.width = 4.5 * self.ncols + 2.5 |
|
|
992 | 992 | self.height = 4.8 * self.nrows |
|
|
993 | 993 | self.ylabel = 'Power [dB]' |
|
|
994 | 994 | self.colorbar = False |
|
|
995 | 995 | self.plots_adjust.update({'left':0.1, 'hspace':0.3, 'right': 0.9, 'bottom':0.08}) |
|
|
996 | 996 | |
|
|
997 | 997 | if len(self.selectedHeightsList) > 0: |
|
|
998 | 998 | self.maintitle = "Spectra Cut"# for %d km " %(int(self.selectedHeight)) |
|
|
999 | 999 | |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | |
|
|
1002 | 1002 | def update(self, dataOut): |
|
|
1003 | 1003 | if len(self.channelList) == 0: |
|
|
1004 | 1004 | self.channelList = dataOut.channelList |
|
|
1005 | 1005 | |
|
|
1006 | 1006 | self.heights = dataOut.heightList |
|
|
1007 | 1007 | #print("sels: ",self.selectedHeightsList) |
|
|
1008 | 1008 | if len(self.selectedHeightsList)>0 and not self.flag_setIndex: |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | for sel_height in self.selectedHeightsList: |
|
|
1011 | 1011 | index_list = numpy.where(self.heights >= sel_height) |
|
|
1012 | 1012 | index_list = index_list[0] |
|
|
1013 | 1013 | self.height_index.append(index_list[0]) |
|
|
1014 | 1014 | #print("sels i:"", self.height_index) |
|
|
1015 | 1015 | self.flag_setIndex = True |
|
|
1016 | 1016 | #print(self.height_index) |
|
|
1017 | 1017 | data = {} |
|
|
1018 | 1018 | meta = {} |
|
|
1019 | 1019 | |
|
|
1020 | 1020 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter#*dataOut.nFFTPoints |
|
|
1021 | 1021 | n0 = 10*numpy.log10(dataOut.getNoise()/norm) |
|
|
1022 | 1022 | noise = numpy.repeat(n0,(dataOut.nFFTPoints*dataOut.nHeights)).reshape(dataOut.nChannels,dataOut.nFFTPoints,dataOut.nHeights) |
|
|
1023 | 1023 | |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | z = [] |
|
|
1026 | 1026 | for ch in range(dataOut.nChannels): |
|
|
1027 | 1027 | if hasattr(dataOut.normFactor,'shape'): |
|
|
1028 | 1028 | z.append(numpy.divide(dataOut.data_spc[ch],dataOut.normFactor[ch])) |
|
|
1029 | 1029 | else: |
|
|
1030 | 1030 | z.append(numpy.divide(dataOut.data_spc[ch],dataOut.normFactor)) |
|
|
1031 | 1031 | |
|
|
1032 | 1032 | z = numpy.asarray(z) |
|
|
1033 | 1033 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1034 | 1034 | spc = 10*numpy.log10(z) |
|
|
1035 | 1035 | |
|
|
1036 | 1036 | |
|
|
1037 | 1037 | data['spc'] = spc - noise |
|
|
1038 | 1038 | meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS)) |
|
|
1039 | 1039 | |
|
|
1040 | 1040 | return data, meta |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | def plot(self): |
|
|
1043 | 1043 | if self.xaxis == "frequency": |
|
|
1044 | 1044 | x = self.data.xrange[0][0:] |
|
|
1045 | 1045 | self.xlabel = "Frequency (kHz)" |
|
|
1046 | 1046 | elif self.xaxis == "time": |
|
|
1047 | 1047 | x = self.data.xrange[1] |
|
|
1048 | 1048 | self.xlabel = "Time (ms)" |
|
|
1049 | 1049 | else: |
|
|
1050 | 1050 | x = self.data.xrange[2] |
|
|
1051 | 1051 | self.xlabel = "Velocity (m/s)" |
|
|
1052 | 1052 | |
|
|
1053 | 1053 | self.titles = [] |
|
|
1054 | 1054 | |
|
|
1055 | 1055 | y = self.data.yrange |
|
|
1056 | 1056 | z = self.data[-1]['spc'] |
|
|
1057 | 1057 | #print(z.shape) |
|
|
1058 | 1058 | if len(self.height_index) > 0: |
|
|
1059 | 1059 | index = self.height_index |
|
|
1060 | 1060 | else: |
|
|
1061 | 1061 | index = numpy.arange(0, len(y), int((len(y))/9)) |
|
|
1062 | 1062 | #print("inde x ", index, self.axes) |
|
|
1063 | 1063 | |
|
|
1064 | 1064 | for n, ax in enumerate(self.axes): |
|
|
1065 | 1065 | |
|
|
1066 | 1066 | if ax.firsttime: |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | |
|
|
1069 | 1069 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
1070 | 1070 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
1071 | 1071 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z) |
|
|
1072 | 1072 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z) |
|
|
1073 | 1073 | |
|
|
1074 | 1074 | |
|
|
1075 | 1075 | ax.plt = ax.plot(x, z[n, :, index].T) |
|
|
1076 | 1076 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] |
|
|
1077 | 1077 | self.figures[0].legend(ax.plt, labels, loc='center right', prop={'size': 8}) |
|
|
1078 | 1078 | ax.minorticks_on() |
|
|
1079 | 1079 | ax.grid(which='major', axis='both') |
|
|
1080 | 1080 | ax.grid(which='minor', axis='x') |
|
|
1081 | 1081 | else: |
|
|
1082 | 1082 | for i, line in enumerate(ax.plt): |
|
|
1083 | 1083 | line.set_data(x, z[n, :, index[i]]) |
|
|
1084 | 1084 | |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | self.titles.append('CH {}'.format(self.channelList[n])) |
|
|
1087 | 1087 | plt.suptitle(self.maintitle, fontsize=10) |
|
|
1088 | 1088 | |
|
|
1089 | 1089 | |
|
|
1090 | 1090 | class BeaconPhase(Plot): |
|
|
1091 | 1091 | |
|
|
1092 | 1092 | __isConfig = None |
|
|
1093 | 1093 | __nsubplots = None |
|
|
1094 | 1094 | |
|
|
1095 | 1095 | PREFIX = 'beacon_phase' |
|
|
1096 | 1096 | |
|
|
1097 | 1097 | def __init__(self): |
|
|
1098 | 1098 | Plot.__init__(self) |
|
|
1099 | 1099 | self.timerange = 24*60*60 |
|
|
1100 | 1100 | self.isConfig = False |
|
|
1101 | 1101 | self.__nsubplots = 1 |
|
|
1102 | 1102 | self.counter_imagwr = 0 |
|
|
1103 | 1103 | self.WIDTH = 800 |
|
|
1104 | 1104 | self.HEIGHT = 400 |
|
|
1105 | 1105 | self.WIDTHPROF = 120 |
|
|
1106 | 1106 | self.HEIGHTPROF = 0 |
|
|
1107 | 1107 | self.xdata = None |
|
|
1108 | 1108 | self.ydata = None |
|
|
1109 | 1109 | |
|
|
1110 | 1110 | self.PLOT_CODE = BEACON_CODE |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | self.FTP_WEI = None |
|
|
1113 | 1113 | self.EXP_CODE = None |
|
|
1114 | 1114 | self.SUB_EXP_CODE = None |
|
|
1115 | 1115 | self.PLOT_POS = None |
|
|
1116 | 1116 | |
|
|
1117 | 1117 | self.filename_phase = None |
|
|
1118 | 1118 | |
|
|
1119 | 1119 | self.figfile = None |
|
|
1120 | 1120 | |
|
|
1121 | 1121 | self.xmin = None |
|
|
1122 | 1122 | self.xmax = None |
|
|
1123 | 1123 | |
|
|
1124 | 1124 | def getSubplots(self): |
|
|
1125 | 1125 | |
|
|
1126 | 1126 | ncol = 1 |
|
|
1127 | 1127 | nrow = 1 |
|
|
1128 | 1128 | |
|
|
1129 | 1129 | return nrow, ncol |
|
|
1130 | 1130 | |
|
|
1131 | 1131 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
1132 | 1132 | |
|
|
1133 | 1133 | self.__showprofile = showprofile |
|
|
1134 | 1134 | self.nplots = nplots |
|
|
1135 | 1135 | |
|
|
1136 | 1136 | ncolspan = 7 |
|
|
1137 | 1137 | colspan = 6 |
|
|
1138 | 1138 | self.__nsubplots = 2 |
|
|
1139 | 1139 | |
|
|
1140 | 1140 | self.createFigure(id = id, |
|
|
1141 | 1141 | wintitle = wintitle, |
|
|
1142 | 1142 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
1143 | 1143 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
1144 | 1144 | show=show) |
|
|
1145 | 1145 | |
|
|
1146 | 1146 | nrow, ncol = self.getSubplots() |
|
|
1147 | 1147 | |
|
|
1148 | 1148 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
1149 | 1149 | |
|
|
1150 | 1150 | def save_phase(self, filename_phase): |
|
|
1151 | 1151 | f = open(filename_phase,'w+') |
|
|
1152 | 1152 | f.write('\n\n') |
|
|
1153 | 1153 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
1154 | 1154 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
1155 | 1155 | f.close() |
|
|
1156 | 1156 | |
|
|
1157 | 1157 | def save_data(self, filename_phase, data, data_datetime): |
|
|
1158 | 1158 | f=open(filename_phase,'a') |
|
|
1159 | 1159 | timetuple_data = data_datetime.timetuple() |
|
|
1160 | 1160 | day = str(timetuple_data.tm_mday) |
|
|
1161 | 1161 | month = str(timetuple_data.tm_mon) |
|
|
1162 | 1162 | year = str(timetuple_data.tm_year) |
|
|
1163 | 1163 | hour = str(timetuple_data.tm_hour) |
|
|
1164 | 1164 | minute = str(timetuple_data.tm_min) |
|
|
1165 | 1165 | second = str(timetuple_data.tm_sec) |
|
|
1166 | 1166 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
1167 | 1167 | f.close() |
|
|
1168 | 1168 | |
|
|
1169 | 1169 | def plot(self): |
|
|
1170 | 1170 | log.warning('TODO: Not yet implemented...') |
|
|
1171 | 1171 | |
|
|
1172 | 1172 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
1173 | 1173 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
1174 | 1174 | timerange=None, |
|
|
1175 | 1175 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
1176 | 1176 | server=None, folder=None, username=None, password=None, |
|
|
1177 | 1177 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1178 | 1178 | |
|
|
1179 | 1179 | if dataOut.flagNoData: |
|
|
1180 | 1180 | return dataOut |
|
|
1181 | 1181 | |
|
|
1182 | 1182 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
1183 | 1183 | return |
|
|
1184 | 1184 | |
|
|
1185 | 1185 | if pairsList == None: |
|
|
1186 | 1186 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
1187 | 1187 | else: |
|
|
1188 | 1188 | pairsIndexList = [] |
|
|
1189 | 1189 | for pair in pairsList: |
|
|
1190 | 1190 | if pair not in dataOut.pairsList: |
|
|
1191 | 1191 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
1192 | 1192 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
1193 | 1193 | |
|
|
1194 | 1194 | if pairsIndexList == []: |
|
|
1195 | 1195 | return |
|
|
1196 | 1196 | |
|
|
1197 | 1197 | # if len(pairsIndexList) > 4: |
|
|
1198 | 1198 | # pairsIndexList = pairsIndexList[0:4] |
|
|
1199 | 1199 | |
|
|
1200 | 1200 | hmin_index = None |
|
|
1201 | 1201 | hmax_index = None |
|
|
1202 | 1202 | |
|
|
1203 | 1203 | if hmin != None and hmax != None: |
|
|
1204 | 1204 | indexes = numpy.arange(dataOut.nHeights) |
|
|
1205 | 1205 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
1206 | 1206 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | if hmin_list.any(): |
|
|
1209 | 1209 | hmin_index = hmin_list[0] |
|
|
1210 | 1210 | |
|
|
1211 | 1211 | if hmax_list.any(): |
|
|
1212 | 1212 | hmax_index = hmax_list[-1]+1 |
|
|
1213 | 1213 | |
|
|
1214 | 1214 | x = dataOut.getTimeRange() |
|
|
1215 | 1215 | |
|
|
1216 | 1216 | thisDatetime = dataOut.datatime |
|
|
1217 | 1217 | |
|
|
1218 | 1218 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
1219 | 1219 | xlabel = "Local Time" |
|
|
1220 | 1220 | ylabel = "Phase (degrees)" |
|
|
1221 | 1221 | |
|
|
1222 | 1222 | update_figfile = False |
|
|
1223 | 1223 | |
|
|
1224 | 1224 | nplots = len(pairsIndexList) |
|
|
1225 | 1225 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
1226 | 1226 | for i in range(nplots): |
|
|
1227 | 1227 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
1228 | 1228 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
1229 | 1229 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
1230 | 1230 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
1231 | 1231 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
1232 | 1232 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | if dataOut.beacon_heiIndexList: |
|
|
1235 | 1235 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
1236 | 1236 | else: |
|
|
1237 | 1237 | phase_beacon[i] = numpy.average(phase) |
|
|
1238 | 1238 | |
|
|
1239 | 1239 | if not self.isConfig: |
|
|
1240 | 1240 | |
|
|
1241 | 1241 | nplots = len(pairsIndexList) |
|
|
1242 | 1242 | |
|
|
1243 | 1243 | self.setup(id=id, |
|
|
1244 | 1244 | nplots=nplots, |
|
|
1245 | 1245 | wintitle=wintitle, |
|
|
1246 | 1246 | showprofile=showprofile, |
|
|
1247 | 1247 | show=show) |
|
|
1248 | 1248 | |
|
|
1249 | 1249 | if timerange != None: |
|
|
1250 | 1250 | self.timerange = timerange |
|
|
1251 | 1251 | |
|
|
1252 | 1252 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1253 | 1253 | |
|
|
1254 | 1254 | if ymin == None: ymin = 0 |
|
|
1255 | 1255 | if ymax == None: ymax = 360 |
|
|
1256 | 1256 | |
|
|
1257 | 1257 | self.FTP_WEI = ftp_wei |
|
|
1258 | 1258 | self.EXP_CODE = exp_code |
|
|
1259 | 1259 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1260 | 1260 | self.PLOT_POS = plot_pos |
|
|
1261 | 1261 | |
|
|
1262 | 1262 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1263 | 1263 | self.isConfig = True |
|
|
1264 | 1264 | self.figfile = figfile |
|
|
1265 | 1265 | self.xdata = numpy.array([]) |
|
|
1266 | 1266 | self.ydata = numpy.array([]) |
|
|
1267 | 1267 | |
|
|
1268 | 1268 | update_figfile = True |
|
|
1269 | 1269 | |
|
|
1270 | 1270 | #open file beacon phase |
|
|
1271 | 1271 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
1272 | 1272 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
1273 | 1273 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
1274 | 1274 | |
|
|
1275 | 1275 | self.setWinTitle(title) |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | |
|
|
1278 | 1278 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1279 | 1279 | |
|
|
1280 | 1280 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
1281 | 1281 | |
|
|
1282 | 1282 | axes = self.axesList[0] |
|
|
1283 | 1283 | |
|
|
1284 | 1284 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
1285 | 1285 | |
|
|
1286 | 1286 | if len(self.ydata)==0: |
|
|
1287 | 1287 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
1288 | 1288 | else: |
|
|
1289 | 1289 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
1290 | 1290 | |
|
|
1291 | 1291 | |
|
|
1292 | 1292 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
1293 | 1293 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
1294 | 1294 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
1295 | 1295 | XAxisAsTime=True, grid='both' |
|
|
1296 | 1296 | ) |
|
|
1297 | 1297 | |
|
|
1298 | 1298 | self.draw() |
|
|
1299 | 1299 | |
|
|
1300 | 1300 | if dataOut.ltctime >= self.xmax: |
|
|
1301 | 1301 | self.counter_imagwr = wr_period |
|
|
1302 | 1302 | self.isConfig = False |
|
|
1303 | 1303 | update_figfile = True |
|
|
1304 | 1304 | |
|
|
1305 | 1305 | self.save(figpath=figpath, |
|
|
1306 | 1306 | figfile=figfile, |
|
|
1307 | 1307 | save=save, |
|
|
1308 | 1308 | ftp=ftp, |
|
|
1309 | 1309 | wr_period=wr_period, |
|
|
1310 | 1310 | thisDatetime=thisDatetime, |
|
|
1311 | 1311 | update_figfile=update_figfile) |
|
|
1312 | 1312 | |
|
|
1313 | 1313 | return dataOut |
|
|
1314 | 1314 | |
|
|
1315 | 1315 | ##################################### |
|
|
1316 | 1316 | class NoiselessSpectraPlot(Plot): |
|
|
1317 | 1317 | ''' |
|
|
1318 | 1318 | Plot for Spectra data, subtracting |
|
|
1319 | 1319 | the noise in all channels, using for |
|
|
1320 | 1320 | amisr-14 data |
|
|
1321 | 1321 | ''' |
|
|
1322 | 1322 | |
|
|
1323 | 1323 | CODE = 'noiseless_spc' |
|
|
1324 | 1324 | colormap = 'jet' |
|
|
1325 | 1325 | plot_type = 'pcolor' |
|
|
1326 | 1326 | buffering = False |
|
|
1327 | 1327 | channelList = [] |
|
|
1328 | 1328 | last_noise = None |
|
|
1329 | 1329 | |
|
|
1330 | 1330 | def setup(self): |
|
|
1331 | 1331 | |
|
|
1332 | 1332 | self.nplots = len(self.data.channels) |
|
|
1333 | 1333 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
1334 | 1334 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
1335 | 1335 | self.height = 3.5 * self.nrows |
|
|
1336 | 1336 | |
|
|
1337 | 1337 | self.cb_label = 'dB' |
|
|
1338 | 1338 | if self.showprofile: |
|
|
1339 | 1339 | self.width = 5.8 * self.ncols |
|
|
1340 | 1340 | else: |
|
|
1341 | 1341 | self.width = 4.8* self.ncols |
|
|
1342 | 1342 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.92, 'bottom': 0.12}) |
|
|
1343 | 1343 | |
|
|
1344 | 1344 | self.ylabel = 'Range [km]' |
|
|
1345 | 1345 | |
|
|
1346 | 1346 | |
|
|
1347 | 1347 | def update_list(self,dataOut): |
|
|
1348 | 1348 | if len(self.channelList) == 0: |
|
|
1349 | 1349 | self.channelList = dataOut.channelList |
|
|
1350 | 1350 | |
|
|
1351 | 1351 | def update(self, dataOut): |
|
|
1352 | 1352 | |
|
|
1353 | 1353 | self.update_list(dataOut) |
|
|
1354 | 1354 | data = {} |
|
|
1355 | 1355 | meta = {} |
|
|
1356 | 1356 | |
|
|
1357 | 1357 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter |
|
|
1358 | 1358 | n0 = (dataOut.getNoise()/norm) |
|
|
1359 | 1359 | noise = numpy.repeat(n0,(dataOut.nFFTPoints*dataOut.nHeights)).reshape(dataOut.nChannels,dataOut.nFFTPoints,dataOut.nHeights) |
|
|
1360 | 1360 | noise = 10*numpy.log10(noise) |
|
|
1361 | 1361 | |
|
|
1362 | 1362 | z = numpy.zeros((dataOut.nChannels, dataOut.nFFTPoints, dataOut.nHeights)) |
|
|
1363 | 1363 | for ch in range(dataOut.nChannels): |
|
|
1364 | 1364 | if hasattr(dataOut.normFactor,'ndim'): |
|
|
1365 | 1365 | if dataOut.normFactor.ndim > 1: |
|
|
1366 | 1366 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor[ch])) |
|
|
1367 | 1367 | else: |
|
|
1368 | 1368 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor)) |
|
|
1369 | 1369 | else: |
|
|
1370 | 1370 | z[ch] = (numpy.divide(dataOut.data_spc[ch],dataOut.normFactor)) |
|
|
1371 | 1371 | |
|
|
1372 | 1372 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
1373 | 1373 | spc = 10*numpy.log10(z) |
|
|
1374 | 1374 | |
|
|
1375 | 1375 | |
|
|
1376 | 1376 | data['spc'] = spc - noise |
|
|
1377 | 1377 | #print(spc.shape) |
|
|
1378 | 1378 | data['rti'] = spc.mean(axis=1) |
|
|
1379 | 1379 | data['noise'] = noise |
|
|
1380 | 1380 | |
|
|
1381 | 1381 | |
|
|
1382 | 1382 | |
|
|
1383 | 1383 | # data['noise'] = noise |
|
|
1384 | 1384 | meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS)) |
|
|
1385 | 1385 | |
|
|
1386 | 1386 | return data, meta |
|
|
1387 | 1387 | |
|
|
1388 | 1388 | def plot(self): |
|
|
1389 | 1389 | if self.xaxis == "frequency": |
|
|
1390 | 1390 | x = self.data.xrange[0] |
|
|
1391 | 1391 | self.xlabel = "Frequency (kHz)" |
|
|
1392 | 1392 | elif self.xaxis == "time": |
|
|
1393 | 1393 | x = self.data.xrange[1] |
|
|
1394 | 1394 | self.xlabel = "Time (ms)" |
|
|
1395 | 1395 | else: |
|
|
1396 | 1396 | x = self.data.xrange[2] |
|
|
1397 | 1397 | self.xlabel = "Velocity (m/s)" |
|
|
1398 | 1398 | |
|
|
1399 | 1399 | self.titles = [] |
|
|
1400 | 1400 | y = self.data.yrange |
|
|
1401 | 1401 | self.y = y |
|
|
1402 | 1402 | |
|
|
1403 | 1403 | data = self.data[-1] |
|
|
1404 | 1404 | z = data['spc'] |
|
|
1405 | 1405 | |
|
|
1406 | 1406 | for n, ax in enumerate(self.axes): |
|
|
1407 | 1407 | #noise = data['noise'][n] |
|
|
1408 | 1408 | |
|
|
1409 | 1409 | if ax.firsttime: |
|
|
1410 | 1410 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
1411 | 1411 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
1412 | 1412 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
|
|
1413 | 1413 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
|
|
1414 | 1414 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1415 | 1415 | vmin=self.zmin, |
|
|
1416 | 1416 | vmax=self.zmax, |
|
|
1417 | 1417 | cmap=plt.get_cmap(self.colormap) |
|
|
1418 | 1418 | ) |
|
|
1419 | 1419 | |
|
|
1420 | 1420 | if self.showprofile: |
|
|
1421 | 1421 | ax.plt_profile = self.pf_axes[n].plot( |
|
|
1422 | 1422 | data['rti'][n], y)[0] |
|
|
1423 | 1423 | |
|
|
1424 | 1424 | |
|
|
1425 | 1425 | else: |
|
|
1426 | 1426 | ax.plt.set_array(z[n].T.ravel()) |
|
|
1427 | 1427 | if self.showprofile: |
|
|
1428 | 1428 | ax.plt_profile.set_data(data['rti'][n], y) |
|
|
1429 | 1429 | |
|
|
1430 | 1430 | |
|
|
1431 | 1431 | self.titles.append('CH {}'.format(self.channelList[n])) |
|
|
1432 | 1432 | |
|
|
1433 | 1433 | |
|
|
1434 | 1434 | class NoiselessRTIPlot(RTIPlot): |
|
|
1435 | 1435 | ''' |
|
|
1436 | 1436 | Plot for RTI data |
|
|
1437 | 1437 | ''' |
|
|
1438 | 1438 | |
|
|
1439 | 1439 | CODE = 'noiseless_rti' |
|
|
1440 | 1440 | colormap = 'jet' |
|
|
1441 | 1441 | plot_type = 'pcolorbuffer' |
|
|
1442 | 1442 | titles = None |
|
|
1443 | 1443 | channelList = [] |
|
|
1444 | 1444 | elevationList = [] |
|
|
1445 | 1445 | azimuthList = [] |
|
|
1446 | 1446 | last_noise = None |
|
|
1447 | 1447 | |
|
|
1448 | 1448 | def setup(self): |
|
|
1449 | 1449 | self.xaxis = 'time' |
|
|
1450 | 1450 | self.ncols = 1 |
|
|
1451 | 1451 | #print("dataChannels ",self.data.channels) |
|
|
1452 | 1452 | self.nrows = len(self.data.channels) |
|
|
1453 | 1453 | self.nplots = len(self.data.channels) |
|
|
1454 | 1454 | self.ylabel = 'Range [km]' |
|
|
1455 | 1455 | #self.xlabel = 'Time' |
|
|
1456 | 1456 | self.cb_label = 'dB' |
|
|
1457 | 1457 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) |
|
|
1458 | 1458 | self.titles = ['{} Channel {}'.format( |
|
|
1459 | 1459 | self.CODE.upper(), x) for x in range(self.nplots)] |
|
|
1460 | 1460 | |
|
|
1461 | 1461 | def update_list(self,dataOut): |
|
|
1462 | 1462 | if len(self.channelList) == 0: |
|
|
1463 | 1463 | self.channelList = dataOut.channelList |
|
|
1464 | 1464 | if len(self.elevationList) == 0: |
|
|
1465 | 1465 | self.elevationList = dataOut.elevationList |
|
|
1466 | 1466 | if len(self.azimuthList) == 0: |
|
|
1467 | 1467 | self.azimuthList = dataOut.azimuthList |
|
|
1468 | 1468 | |
|
|
1469 | 1469 | def update(self, dataOut): |
|
|
1470 | 1470 | if len(self.channelList) == 0: |
|
|
1471 | 1471 | self.update_list(dataOut) |
|
|
1472 | 1472 | |
|
|
1473 | 1473 | data = {} |
|
|
1474 | 1474 | meta = {} |
|
|
1475 | 1475 | #print(dataOut.max_nIncohInt, dataOut.nIncohInt) |
|
|
1476 | 1476 | #print(dataOut.windowOfFilter,dataOut.nCohInt,dataOut.nProfiles,dataOut.max_nIncohInt,dataOut.nIncohInt |
|
|
1477 | 1477 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter |
|
|
1478 | 1478 | n0 = 10*numpy.log10(dataOut.getNoise()/norm) |
|
|
1479 | 1479 | data['noise'] = n0 |
|
|
1480 | 1480 | noise = numpy.repeat(n0,dataOut.nHeights).reshape(dataOut.nChannels,dataOut.nHeights) |
|
|
1481 | 1481 | noiseless_data = dataOut.getPower() - noise |
|
|
1482 | 1482 | |
|
|
1483 | 1483 | #print("power, noise:", dataOut.getPower(), n0) |
|
|
1484 | 1484 | #print(noise) |
|
|
1485 | 1485 | #print(noiseless_data) |
|
|
1486 | 1486 | |
|
|
1487 | 1487 | data['noiseless_rti'] = noiseless_data |
|
|
1488 | 1488 | |
|
|
1489 | 1489 | return data, meta |
|
|
1490 | 1490 | |
|
|
1491 | 1491 | def plot(self): |
|
|
1492 | 1492 | from matplotlib import pyplot as plt |
|
|
1493 | 1493 | self.x = self.data.times |
|
|
1494 | 1494 | self.y = self.data.yrange |
|
|
1495 | 1495 | self.z = self.data['noiseless_rti'] |
|
|
1496 | 1496 | self.z = numpy.array(self.z, dtype=float) |
|
|
1497 | 1497 | self.z = numpy.ma.masked_invalid(self.z) |
|
|
1498 | 1498 | |
|
|
1499 | 1499 | |
|
|
1500 | 1500 | try: |
|
|
1501 | 1501 | if self.channelList != None: |
|
|
1502 | 1502 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
1503 | 1503 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
1504 | 1504 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
1505 | 1505 | else: |
|
|
1506 | 1506 | self.titles = ['{} Channel {}'.format( |
|
|
1507 | 1507 | self.CODE.upper(), x) for x in self.channelList] |
|
|
1508 | 1508 | except: |
|
|
1509 | 1509 | if self.channelList.any() != None: |
|
|
1510 | 1510 | if len(self.elevationList) > 0 and len(self.azimuthList) > 0: |
|
|
1511 | 1511 | self.titles = ['{} Channel {} ({:2.1f} Elev, {:2.1f} Azth)'.format( |
|
|
1512 | 1512 | self.CODE.upper(), x, self.elevationList[x], self.azimuthList[x]) for x in self.channelList] |
|
|
1513 | 1513 | else: |
|
|
1514 | 1514 | self.titles = ['{} Channel {}'.format( |
|
|
1515 | 1515 | self.CODE.upper(), x) for x in self.channelList] |
|
|
1516 | 1516 | |
|
|
1517 | 1517 | |
|
|
1518 | 1518 | if self.decimation is None: |
|
|
1519 | 1519 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
1520 | 1520 | else: |
|
|
1521 | 1521 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
1522 | 1522 | |
|
|
1523 | 1523 | dummy_var = self.axes #Extrañamente esto actualiza el valor axes |
|
|
1524 | 1524 | #print("plot shapes ", z.shape, x.shape, y.shape) |
|
|
1525 | 1525 | #print(self.axes) |
|
|
1526 | 1526 | for n, ax in enumerate(self.axes): |
|
|
1527 | 1527 | |
|
|
1528 | 1528 | |
|
|
1529 | 1529 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
|
|
1530 | 1530 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
|
|
1531 | 1531 | data = self.data[-1] |
|
|
1532 | 1532 | if ax.firsttime: |
|
|
1533 | 1533 | if (n+1) == len(self.channelList): |
|
|
1534 | 1534 | ax.set_xlabel('Time') |
|
|
1535 | 1535 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1536 | 1536 | vmin=self.zmin, |
|
|
1537 | 1537 | vmax=self.zmax, |
|
|
1538 | 1538 | cmap=plt.get_cmap(self.colormap) |
|
|
1539 | 1539 | ) |
|
|
1540 | 1540 | if self.showprofile: |
|
|
1541 | 1541 | ax.plot_profile = self.pf_axes[n].plot(data['noiseless_rti'][n], self.y)[0] |
|
|
1542 | 1542 | |
|
|
1543 | 1543 | else: |
|
|
1544 | 1544 | # ax.collections.remove(ax.collections[0]) # error while running |
|
|
1545 | 1545 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1546 | 1546 | vmin=self.zmin, |
|
|
1547 | 1547 | vmax=self.zmax, |
|
|
1548 | 1548 | cmap=plt.get_cmap(self.colormap) |
|
|
1549 | 1549 | ) |
|
|
1550 | 1550 | if self.showprofile: |
|
|
1551 | 1551 | ax.plot_profile.set_data(data['noiseless_rti'][n], self.y) |
|
|
1552 | 1552 | # if "noise" in self.data: |
|
|
1553 | 1553 | # #ax.plot_noise.set_data(numpy.repeat(data['noise'][n], len(self.y)), self.y) |
|
|
1554 | 1554 | # ax.plot_noise.set_data(data['noise'][n], self.y) |
|
|
1555 | 1555 | |
|
|
1556 | 1556 | |
|
|
1557 | 1557 | class OutliersRTIPlot(Plot): |
|
|
1558 | 1558 | ''' |
|
|
1559 | 1559 | Plot for data_xxxx object |
|
|
1560 | 1560 | ''' |
|
|
1561 | 1561 | |
|
|
1562 | 1562 | CODE = 'outlier_rtc' # Range Time Counts |
|
|
1563 | 1563 | colormap = 'cool' |
|
|
1564 | 1564 | plot_type = 'pcolorbuffer' |
|
|
1565 | 1565 | |
|
|
1566 | 1566 | def setup(self): |
|
|
1567 | 1567 | self.xaxis = 'time' |
|
|
1568 | 1568 | self.ncols = 1 |
|
|
1569 | 1569 | self.nrows = self.data.shape('outlier_rtc')[0] |
|
|
1570 | 1570 | self.nplots = self.nrows |
|
|
1571 | 1571 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) |
|
|
1572 | 1572 | |
|
|
1573 | 1573 | |
|
|
1574 | 1574 | if not self.xlabel: |
|
|
1575 | 1575 | self.xlabel = 'Time' |
|
|
1576 | 1576 | |
|
|
1577 | 1577 | self.ylabel = 'Height [km]' |
|
|
1578 | 1578 | if not self.titles: |
|
|
1579 | 1579 | self.titles = ['Outliers Ch:{}'.format(x) for x in range(self.nrows)] |
|
|
1580 | 1580 | |
|
|
1581 | 1581 | def update(self, dataOut): |
|
|
1582 | 1582 | |
|
|
1583 | 1583 | data = {} |
|
|
1584 | 1584 | data['outlier_rtc'] = dataOut.data_outlier |
|
|
1585 | 1585 | |
|
|
1586 | 1586 | meta = {} |
|
|
1587 | 1587 | |
|
|
1588 | 1588 | return data, meta |
|
|
1589 | 1589 | |
|
|
1590 | 1590 | def plot(self): |
|
|
1591 | 1591 | # self.data.normalize_heights() |
|
|
1592 | 1592 | self.x = self.data.times |
|
|
1593 | 1593 | self.y = self.data.yrange |
|
|
1594 | 1594 | self.z = self.data['outlier_rtc'] |
|
|
1595 | 1595 | |
|
|
1596 | 1596 | #self.z = numpy.ma.masked_invalid(self.z) |
|
|
1597 | 1597 | |
|
|
1598 | 1598 | if self.decimation is None: |
|
|
1599 | 1599 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
1600 | 1600 | else: |
|
|
1601 | 1601 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
1602 | 1602 | |
|
|
1603 | 1603 | for n, ax in enumerate(self.axes): |
|
|
1604 | 1604 | |
|
|
1605 | 1605 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
1606 | 1606 | self.z[n]) |
|
|
1607 | 1607 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
1608 | 1608 | self.z[n]) |
|
|
1609 | 1609 | data = self.data[-1] |
|
|
1610 | 1610 | if ax.firsttime: |
|
|
1611 | 1611 | if self.zlimits is not None: |
|
|
1612 | 1612 | self.zmin, self.zmax = self.zlimits[n] |
|
|
1613 | 1613 | |
|
|
1614 | 1614 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1615 | 1615 | vmin=self.zmin, |
|
|
1616 | 1616 | vmax=self.zmax, |
|
|
1617 | 1617 | cmap=self.cmaps[n] |
|
|
1618 | 1618 | ) |
|
|
1619 | 1619 | if self.showprofile: |
|
|
1620 | 1620 | ax.plot_profile = self.pf_axes[n].plot(data['outlier_rtc'][n], self.y)[0] |
|
|
1621 | 1621 | self.pf_axes[n].set_xlabel('') |
|
|
1622 | 1622 | else: |
|
|
1623 | 1623 | if self.zlimits is not None: |
|
|
1624 | 1624 | self.zmin, self.zmax = self.zlimits[n] |
|
|
1625 | 1625 | # ax.collections.remove(ax.collections[0]) # error while running |
|
|
1626 | 1626 | ax.plt = ax.pcolormesh(x, y, z[n].T , |
|
|
1627 | 1627 | vmin=self.zmin, |
|
|
1628 | 1628 | vmax=self.zmax, |
|
|
1629 | 1629 | cmap=self.cmaps[n] |
|
|
1630 | 1630 | ) |
|
|
1631 | 1631 | if self.showprofile: |
|
|
1632 | 1632 | ax.plot_profile.set_data(data['outlier_rtc'][n], self.y) |
|
|
1633 | 1633 | self.pf_axes[n].set_xlabel('') |
|
|
1634 | 1634 | |
|
|
1635 | 1635 | class NIncohIntRTIPlot(Plot): |
|
|
1636 | 1636 | ''' |
|
|
1637 | 1637 | Plot for data_xxxx object |
|
|
1638 | 1638 | ''' |
|
|
1639 | 1639 | |
|
|
1640 | 1640 | CODE = 'integrations_rtc' # Range Time Counts |
|
|
1641 | 1641 | colormap = 'BuGn' |
|
|
1642 | 1642 | plot_type = 'pcolorbuffer' |
|
|
1643 | 1643 | |
|
|
1644 | 1644 | def setup(self): |
|
|
1645 | 1645 | self.xaxis = 'time' |
|
|
1646 | 1646 | self.ncols = 1 |
|
|
1647 | 1647 | self.nrows = self.data.shape('integrations_rtc')[0] |
|
|
1648 | 1648 | self.nplots = self.nrows |
|
|
1649 | 1649 | self.plots_adjust.update({'hspace':0.8, 'left': 0.08, 'bottom': 0.2, 'right':0.94}) |
|
|
1650 | 1650 | |
|
|
1651 | 1651 | |
|
|
1652 | 1652 | if not self.xlabel: |
|
|
1653 | 1653 | self.xlabel = 'Time' |
|
|
1654 | 1654 | |
|
|
1655 | 1655 | self.ylabel = 'Height [km]' |
|
|
1656 | 1656 | if not self.titles: |
|
|
1657 | 1657 | self.titles = ['Integration Ch:{}'.format(x) for x in range(self.nrows)] |
|
|
1658 | 1658 | |
|
|
1659 | 1659 | def update(self, dataOut): |
|
|
1660 | 1660 | |
|
|
1661 | 1661 | data = {} |
|
|
1662 | 1662 | data['integrations_rtc'] = dataOut.nIncohInt |
|
|
1663 | 1663 | |
|
|
1664 | 1664 | meta = {} |
|
|
1665 | 1665 | |
|
|
1666 | 1666 | return data, meta |
|
|
1667 | 1667 | |
|
|
1668 | 1668 | def plot(self): |
|
|
1669 | 1669 | # self.data.normalize_heights() |
|
|
1670 | 1670 | self.x = self.data.times |
|
|
1671 | 1671 | self.y = self.data.yrange |
|
|
1672 | 1672 | self.z = self.data['integrations_rtc'] |
|
|
1673 | 1673 | |
|
|
1674 | 1674 | #self.z = numpy.ma.masked_invalid(self.z) |
|
|
1675 | 1675 | |
|
|
1676 | 1676 | if self.decimation is None: |
|
|
1677 | 1677 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
|
|
1678 | 1678 | else: |
|
|
1679 | 1679 | x, y, z = self.fill_gaps(*self.decimate()) |
|
|
1680 | 1680 | |
|
|
1681 | 1681 | for n, ax in enumerate(self.axes): |
|
|
1682 | 1682 | |
|
|
1683 | 1683 | self.zmax = self.zmax if self.zmax is not None else numpy.max( |
|
|
1684 | 1684 | self.z[n]) |
|
|
1685 | 1685 | self.zmin = self.zmin if self.zmin is not None else numpy.min( |
|
|
1686 | 1686 | self.z[n]) |
|
|
1687 | 1687 | data = self.data[-1] |
|
|
1688 | 1688 | if ax.firsttime: |
|
|
1689 | 1689 | if self.zlimits is not None: |
|
|
1690 | 1690 | self.zmin, self.zmax = self.zlimits[n] |
|
|
1691 | 1691 | |
|
|
1692 | 1692 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
|
|
1693 | 1693 | vmin=self.zmin, |
|
|
1694 | 1694 | vmax=self.zmax, |
|
|
1695 | 1695 | cmap=self.cmaps[n] |
|
|
1696 | 1696 | ) |
|
|
1697 | 1697 | if self.showprofile: |
|
|
1698 | 1698 | ax.plot_profile = self.pf_axes[n].plot(data['integrations_rtc'][n], self.y)[0] |
|
|
1699 | 1699 | self.pf_axes[n].set_xlabel('') |
|
|
1700 | 1700 | else: |
|
|
1701 | 1701 | if self.zlimits is not None: |
|
|
1702 | 1702 | self.zmin, self.zmax = self.zlimits[n] |
|
|
1703 | 1703 | # ax.collections.remove(ax.collections[0]) # error while running |
|
|
1704 | 1704 | ax.plt = ax.pcolormesh(x, y, z[n].T , |
|
|
1705 | 1705 | vmin=self.zmin, |
|
|
1706 | 1706 | vmax=self.zmax, |
|
|
1707 | 1707 | cmap=self.cmaps[n] |
|
|
1708 | 1708 | ) |
|
|
1709 | 1709 | if self.showprofile: |
|
|
1710 | 1710 | ax.plot_profile.set_data(data['integrations_rtc'][n], self.y) |
|
|
1711 | 1711 | self.pf_axes[n].set_xlabel('') |
|
|
1712 | 1712 | |
|
|
1713 | 1713 | |
|
|
1714 | 1714 | |
|
|
1715 | 1715 | class RTIMapPlot(Plot): |
|
|
1716 | 1716 | ''' |
|
|
1717 | 1717 | Plot for RTI data |
|
|
1718 | 1718 | |
|
|
1719 | 1719 | Example: |
|
|
1720 | 1720 | |
|
|
1721 | 1721 | controllerObj = Project() |
|
|
1722 | 1722 | controllerObj.setup(id = '11', name='eej_proc', description=desc) |
|
|
1723 | 1723 | ##....................................................................................... |
|
|
1724 | 1724 | ##....................................................................................... |
|
|
1725 | 1725 | readUnitConfObj = controllerObj.addReadUnit(datatype='AMISRReader', path=inPath, startDate='2023/05/24',endDate='2023/05/24', |
|
|
1726 | 1726 | startTime='12:00:00',endTime='12:45:59',walk=1,timezone='lt',margin_days=1,code = code,nCode = nCode, |
|
|
1727 | 1727 | nBaud = nBaud,nOsamp = nosamp,nChannels=nChannels,nFFT=NFFT, |
|
|
1728 | 1728 | syncronization=False,shiftChannels=0) |
|
|
1729 | 1729 | |
|
|
1730 | 1730 | volts_proc = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) |
|
|
1731 | 1731 | |
|
|
1732 | 1732 | opObj01 = volts_proc.addOperation(name='Decoder', optype='other') |
|
|
1733 | 1733 | opObj01.addParameter(name='code', value=code, format='floatlist') |
|
|
1734 | 1734 | opObj01.addParameter(name='nCode', value=1, format='int') |
|
|
1735 | 1735 | opObj01.addParameter(name='nBaud', value=nBaud, format='int') |
|
|
1736 | 1736 | opObj01.addParameter(name='osamp', value=nosamp, format='int') |
|
|
1737 | 1737 | |
|
|
1738 | 1738 | opObj12 = volts_proc.addOperation(name='selectHeights', optype='self') |
|
|
1739 | 1739 | opObj12.addParameter(name='minHei', value='90', format='float') |
|
|
1740 | 1740 | opObj12.addParameter(name='maxHei', value='150', format='float') |
|
|
1741 | 1741 | |
|
|
1742 | 1742 | proc_spc = controllerObj.addProcUnit(datatype='SpectraProc', inputId=volts_proc.getId()) |
|
|
1743 | 1743 | proc_spc.addParameter(name='nFFTPoints', value='8', format='int') |
|
|
1744 | 1744 | |
|
|
1745 | 1745 | opObj11 = proc_spc.addOperation(name='IncohInt', optype='other') |
|
|
1746 | 1746 | opObj11.addParameter(name='n', value='1', format='int') |
|
|
1747 | 1747 | |
|
|
1748 | 1748 | beamMapFile = "/home/japaza/Documents/AMISR_sky_mapper/UMET_beamcodes.csv" |
|
|
1749 | 1749 | |
|
|
1750 | 1750 | opObj12 = proc_spc.addOperation(name='RTIMapPlot', optype='external') |
|
|
1751 | 1751 | opObj12.addParameter(name='selectedHeightsList', value='95, 100, 105, 110 ', format='int') |
|
|
1752 | 1752 | opObj12.addParameter(name='bField', value='100', format='int') |
|
|
1753 | 1753 | opObj12.addParameter(name='filename', value=beamMapFile, format='str') |
|
|
1754 | 1754 | |
|
|
1755 | 1755 | ''' |
|
|
1756 | 1756 | |
|
|
1757 | 1757 | CODE = 'rti_skymap' |
|
|
1758 | 1758 | |
|
|
1759 | 1759 | plot_type = 'scatter' |
|
|
1760 | 1760 | titles = None |
|
|
1761 | 1761 | colormap = 'jet' |
|
|
1762 | 1762 | channelList = [] |
|
|
1763 | 1763 | elevationList = [] |
|
|
1764 | 1764 | azimuthList = [] |
|
|
1765 | 1765 | last_noise = None |
|
|
1766 | 1766 | flag_setIndex = False |
|
|
1767 | 1767 | heights = [] |
|
|
1768 | 1768 | dcosx = [] |
|
|
1769 | 1769 | dcosy = [] |
|
|
1770 | 1770 | fullDcosy = None |
|
|
1771 | 1771 | fullDcosy = None |
|
|
1772 | 1772 | hindex = [] |
|
|
1773 | 1773 | mapFile = False |
|
|
1774 | 1774 | ##### BField #### |
|
|
1775 | 1775 | flagBField = False |
|
|
1776 | 1776 | dcosxB = [] |
|
|
1777 | 1777 | dcosyB = [] |
|
|
1778 | 1778 | Bmarker = ['+','*','D','x','s','>','o','^'] |
|
|
1779 | 1779 | |
|
|
1780 | 1780 | |
|
|
1781 | 1781 | def setup(self): |
|
|
1782 | 1782 | |
|
|
1783 | 1783 | self.xaxis = 'Range (Km)' |
|
|
1784 | 1784 | if len(self.selectedHeightsList) > 0: |
|
|
1785 | 1785 | self.nplots = len(self.selectedHeightsList) |
|
|
1786 | 1786 | else: |
|
|
1787 | 1787 | self.nplots = 4 |
|
|
1788 | 1788 | self.ncols = int(numpy.ceil(self.nplots/2)) |
|
|
1789 | 1789 | self.nrows = int(numpy.ceil(self.nplots/self.ncols)) |
|
|
1790 | 1790 | self.ylabel = 'dcosy' |
|
|
1791 | 1791 | self.xlabel = 'dcosx' |
|
|
1792 | 1792 | self.colorbar = True |
|
|
1793 | 1793 | self.width = 6 + 4.1*self.nrows |
|
|
1794 | 1794 | self.height = 3 + 3.5*self.ncols |
|
|
1795 | 1795 | |
|
|
1796 | 1796 | |
|
|
1797 | 1797 | if self.extFile!=None: |
|
|
1798 | 1798 | try: |
|
|
1799 | 1799 | pointings = numpy.genfromtxt(self.extFile, delimiter=',') |
|
|
1800 | 1800 | full_azi = pointings[:,1] |
|
|
1801 | 1801 | full_elev = pointings[:,2] |
|
|
1802 | 1802 | self.fullDcosx = numpy.cos(numpy.radians(full_elev))*numpy.sin(numpy.radians(full_azi)) |
|
|
1803 | 1803 | self.fullDcosy = numpy.cos(numpy.radians(full_elev))*numpy.cos(numpy.radians(full_azi)) |
|
|
1804 | 1804 | mapFile = True |
|
|
1805 | 1805 | except Exception as e: |
|
|
1806 | 1806 | self.extFile = None |
|
|
1807 | 1807 | print(e) |
|
|
1808 | 1808 | |
|
|
1809 | 1809 | |
|
|
1810 | 1810 | def update_list(self,dataOut): |
|
|
1811 | 1811 | if len(self.channelList) == 0: |
|
|
1812 | 1812 | self.channelList = dataOut.channelList |
|
|
1813 | 1813 | if len(self.elevationList) == 0: |
|
|
1814 | 1814 | self.elevationList = dataOut.elevationList |
|
|
1815 | 1815 | if len(self.azimuthList) == 0: |
|
|
1816 | 1816 | self.azimuthList = dataOut.azimuthList |
|
|
1817 | 1817 | a = numpy.radians(numpy.asarray(self.azimuthList)) |
|
|
1818 | 1818 | e = numpy.radians(numpy.asarray(self.elevationList)) |
|
|
1819 | 1819 | self.heights = dataOut.heightList |
|
|
1820 | 1820 | self.dcosx = numpy.cos(e)*numpy.sin(a) |
|
|
1821 | 1821 | self.dcosy = numpy.cos(e)*numpy.cos(a) |
|
|
1822 | 1822 | |
|
|
1823 | 1823 | if len(self.bFieldList)>0: |
|
|
1824 | 1824 | datetObj = datetime.datetime.fromtimestamp(dataOut.utctime) |
|
|
1825 | 1825 | doy = datetObj.timetuple().tm_yday |
|
|
1826 | 1826 | year = datetObj.year |
|
|
1827 | 1827 | # self.dcosxB, self.dcosyB |
|
|
1828 | 1828 | ObjB = BField(year=year,doy=doy,site=2,heights=self.bFieldList) |
|
|
1829 | 1829 | [dcos, alpha, nlon, nlat] = ObjB.getBField() |
|
|
1830 | 1830 | |
|
|
1831 | 1831 | alpha_location = numpy.zeros((nlon,2,len(self.bFieldList))) |
|
|
1832 | 1832 | for ih in range(len(self.bFieldList)): |
|
|
1833 | 1833 | alpha_location[:,0,ih] = dcos[:,0,ih,0] |
|
|
1834 | 1834 | for ilon in numpy.arange(nlon): |
|
|
1835 | 1835 | myx = (alpha[ilon,:,ih])[::-1] |
|
|
1836 | 1836 | myy = (dcos[ilon,:,ih,0])[::-1] |
|
|
1837 | 1837 | tck = splrep(myx,myy,s=0) |
|
|
1838 | 1838 | mydcosx = splev(ObjB.alpha_i,tck,der=0) |
|
|
1839 | 1839 | |
|
|
1840 | 1840 | myx = (alpha[ilon,:,ih])[::-1] |
|
|
1841 | 1841 | myy = (dcos[ilon,:,ih,1])[::-1] |
|
|
1842 | 1842 | tck = splrep(myx,myy,s=0) |
|
|
1843 | 1843 | mydcosy = splev(ObjB.alpha_i,tck,der=0) |
|
|
1844 | 1844 | alpha_location[ilon,:,ih] = numpy.array([mydcosx, mydcosy]) |
|
|
1845 | 1845 | self.dcosxB.append(alpha_location[:,0,ih]) |
|
|
1846 | 1846 | self.dcosyB.append(alpha_location[:,1,ih]) |
|
|
1847 | 1847 | self.flagBField = True |
|
|
1848 | 1848 | |
|
|
1849 | 1849 | if len(self.celestialList)>0: |
|
|
1850 | 1850 | #getBField(self.bFieldList, date) |
|
|
1851 | 1851 | #pass = kwargs.get('celestial', []) |
|
|
1852 | 1852 | pass |
|
|
1853 | 1853 | |
|
|
1854 | 1854 | |
|
|
1855 | 1855 | def update(self, dataOut): |
|
|
1856 | 1856 | |
|
|
1857 | 1857 | if len(self.channelList) == 0: |
|
|
1858 | 1858 | self.update_list(dataOut) |
|
|
1859 | 1859 | |
|
|
1860 | 1860 | if not self.flag_setIndex: |
|
|
1861 | 1861 | if len(self.selectedHeightsList)>0: |
|
|
1862 | 1862 | for sel_height in self.selectedHeightsList: |
|
|
1863 | 1863 | index_list = numpy.where(self.heights >= sel_height) |
|
|
1864 | 1864 | index_list = index_list[0] |
|
|
1865 | 1865 | self.hindex.append(index_list[0]) |
|
|
1866 | 1866 | self.flag_setIndex = True |
|
|
1867 | 1867 | |
|
|
1868 | 1868 | data = {} |
|
|
1869 | 1869 | meta = {} |
|
|
1870 | 1870 | |
|
|
1871 | 1871 | data['rti_skymap'] = dataOut.getPower() |
|
|
1872 | 1872 | norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter |
|
|
1873 | 1873 | noise = 10*numpy.log10(dataOut.getNoise()/norm) |
|
|
1874 | 1874 | data['noise'] = noise |
|
|
1875 | 1875 | |
|
|
1876 | 1876 | return data, meta |
|
|
1877 | 1877 | |
|
|
1878 | 1878 | def plot(self): |
|
|
1879 | 1879 | |
|
|
1880 | 1880 | self.x = self.dcosx |
|
|
1881 | 1881 | self.y = self.dcosy |
|
|
1882 | 1882 | self.z = self.data[-1]['rti_skymap'] |
|
|
1883 | 1883 | self.z = numpy.array(self.z, dtype=float) |
|
|
1884 | 1884 | |
|
|
1885 | 1885 | if len(self.hindex) > 0: |
|
|
1886 | 1886 | index = self.hindex |
|
|
1887 | 1887 | else: |
|
|
1888 | 1888 | index = numpy.arange(0, len(self.heights), int((len(self.heights))/4.2)) |
|
|
1889 | 1889 | |
|
|
1890 | 1890 | self.titles = ['Height {:.2f} km '.format(self.heights[i])+" " for i in index] |
|
|
1891 | 1891 | for n, ax in enumerate(self.axes): |
|
|
1892 | 1892 | |
|
|
1893 | 1893 | if ax.firsttime: |
|
|
1894 | 1894 | |
|
|
1895 | 1895 | self.xmax = self.xmax if self.xmax else numpy.nanmax(self.x) |
|
|
1896 | 1896 | self.xmin = self.xmin if self.xmin else numpy.nanmin(self.x) |
|
|
1897 | 1897 | self.ymax = self.ymax if self.ymax else numpy.nanmax(self.y) |
|
|
1898 | 1898 | self.ymin = self.ymin if self.ymin else numpy.nanmin(self.y) |
|
|
1899 | 1899 | self.zmax = self.zmax if self.zmax else numpy.nanmax(self.z) |
|
|
1900 | 1900 | self.zmin = self.zmin if self.zmin else numpy.nanmin(self.z) |
|
|
1901 | 1901 | |
|
|
1902 | 1902 | if self.extFile!=None: |
|
|
1903 | 1903 | ax.scatter(self.fullDcosx, self.fullDcosy, marker="+", s=20) |
|
|
1904 | 1904 | |
|
|
1905 | 1905 | ax.plt = ax.scatter(self.x, self.y, c=self.z[:,index[n]], cmap = 'jet',vmin = self.zmin, |
|
|
1906 | 1906 | s=60, marker="s", vmax = self.zmax) |
|
|
1907 | 1907 | |
|
|
1908 | 1908 | |
|
|
1909 | 1909 | ax.minorticks_on() |
|
|
1910 | 1910 | ax.grid(which='major', axis='both') |
|
|
1911 | 1911 | ax.grid(which='minor', axis='x') |
|
|
1912 | 1912 | |
|
|
1913 | 1913 | if self.flagBField : |
|
|
1914 | 1914 | |
|
|
1915 | 1915 | for ih in range(len(self.bFieldList)): |
|
|
1916 | 1916 | label = str(self.bFieldList[ih]) + ' km' |
|
|
1917 | 1917 | ax.plot(self.dcosxB[ih], self.dcosyB[ih], color='k', marker=self.Bmarker[ih % 8], |
|
|
1918 | 1918 | label=label, linestyle='--', ms=4.0,lw=0.5) |
|
|
1919 | 1919 | handles, labels = ax.get_legend_handles_labels() |
|
|
1920 | 1920 | a = -0.05 |
|
|
1921 | 1921 | b = 1.15 - 1.19*(self.nrows) |
|
|
1922 | 1922 | self.axes[0].legend(handles,labels, bbox_to_anchor=(a,b), prop={'size': (5.8+ 1.1*self.nplots)}, title='B Field ⊥') |
|
|
1923 | 1923 | |
|
|
1924 | 1924 | else: |
|
|
1925 | 1925 | |
|
|
1926 | 1926 | ax.plt = ax.scatter(self.x, self.y, c=self.z[:,index[n]], cmap = 'jet',vmin = self.zmin, |
|
|
1927 | 1927 | s=80, marker="s", vmax = self.zmax) |
|
|
1928 | 1928 | |
|
|
1929 | 1929 | if self.flagBField : |
|
|
1930 | 1930 | for ih in range(len(self.bFieldList)): |
|
|
1931 | 1931 | ax.plot (self.dcosxB[ih], self.dcosyB[ih], color='k', marker=self.Bmarker[ih % 8], |
|
|
1932 | 1932 | linestyle='--', ms=4.0,lw=0.5) |
|
|
1933 | 1933 | |
|
|
1934 | 1934 | |
|
|
1935 | 1935 | |
| @@ -1,779 +1,778 | |||
|
|
1 | 1 | '''' |
|
|
2 | 2 | Created on Set 9, 2015 |
|
|
3 | 3 | |
|
|
4 | 4 | @author: roj-idl71 Karim Kuyeng |
|
|
5 | 5 | |
|
|
6 | 6 | @upgrade: 2021, Joab Apaza |
|
|
7 | 7 | |
|
|
8 | 8 | ''' |
|
|
9 | 9 | |
|
|
10 | 10 | import os |
|
|
11 | 11 | import sys |
|
|
12 | 12 | import glob |
|
|
13 | 13 | import fnmatch |
|
|
14 | 14 | import datetime |
|
|
15 | 15 | import time |
|
|
16 | 16 | import re |
|
|
17 | 17 | import h5py |
|
|
18 | 18 | import numpy |
|
|
19 | 19 | |
|
|
20 | 20 | try: |
|
|
21 | 21 | from gevent import sleep |
|
|
22 | 22 | except: |
|
|
23 | 23 | from time import sleep |
|
|
24 | 24 | |
|
|
25 | 25 | from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader,ProcessingHeader |
|
|
26 | 26 | from schainpy.model.data.jrodata import Voltage |
|
|
27 | 27 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
28 | 28 | from numpy import imag |
|
|
29 | 29 | from schainpy.utils import log |
|
|
30 | 30 | |
|
|
31 | 31 | |
|
|
32 | 32 | class AMISRReader(ProcessingUnit): |
|
|
33 | 33 | ''' |
|
|
34 | 34 | classdocs |
|
|
35 | 35 | ''' |
|
|
36 | 36 | |
|
|
37 | 37 | def __init__(self): |
|
|
38 | 38 | ''' |
|
|
39 | 39 | Constructor |
|
|
40 | 40 | ''' |
|
|
41 | 41 | |
|
|
42 | 42 | ProcessingUnit.__init__(self) |
|
|
43 | 43 | |
|
|
44 | 44 | self.set = None |
|
|
45 | 45 | self.subset = None |
|
|
46 | 46 | self.extension_file = '.h5' |
|
|
47 | 47 | self.dtc_str = 'dtc' |
|
|
48 | 48 | self.dtc_id = 0 |
|
|
49 | 49 | self.status = True |
|
|
50 | 50 | self.isConfig = False |
|
|
51 | 51 | self.dirnameList = [] |
|
|
52 | 52 | self.filenameList = [] |
|
|
53 | 53 | self.fileIndex = None |
|
|
54 | 54 | self.flagNoMoreFiles = False |
|
|
55 | 55 | self.flagIsNewFile = 0 |
|
|
56 | 56 | self.filename = '' |
|
|
57 | 57 | self.amisrFilePointer = None |
|
|
58 | 58 | |
|
|
59 | 59 | self.beamCodeMap = None |
|
|
60 | 60 | self.azimuthList = [] |
|
|
61 | 61 | self.elevationList = [] |
|
|
62 | 62 | self.dataShape = None |
|
|
63 | 63 | self.flag_old_beams = False |
|
|
64 | 64 | |
|
|
65 | 65 | self.flagAsync = False #Use when the experiment has no syncronization |
|
|
66 | 66 | self.shiftChannels = 0 |
|
|
67 | 67 | self.profileIndex = 0 |
|
|
68 | 68 | |
|
|
69 | 69 | |
|
|
70 | 70 | self.beamCodeByFrame = None |
|
|
71 | 71 | self.radacTimeByFrame = None |
|
|
72 | 72 | |
|
|
73 | 73 | self.dataset = None |
|
|
74 | 74 | |
|
|
75 | 75 | self.__firstFile = True |
|
|
76 | 76 | |
|
|
77 | 77 | self.buffer = None |
|
|
78 | 78 | |
|
|
79 | 79 | self.timezone = 'ut' |
|
|
80 | 80 | |
|
|
81 | 81 | self.__waitForNewFile = 20 |
|
|
82 | 82 | self.__filename_online = None |
|
|
83 | 83 | #Is really necessary create the output object in the initializer |
|
|
84 | 84 | self.dataOut = Voltage() |
|
|
85 | 85 | self.dataOut.error=False |
|
|
86 | 86 | self.margin_days = 1 |
|
|
87 | 87 | self.flag_ignoreFiles = False #to activate the ignoring Files flag |
|
|
88 | 88 | self.flag_standby = False # just keep waiting, use when ignoring files |
|
|
89 | 89 | self.ignStartDateTime=None |
|
|
90 | 90 | self.ignEndDateTime=None |
|
|
91 | 91 | |
|
|
92 | 92 | def setup(self,path=None, |
|
|
93 | 93 | startDate=None, |
|
|
94 | 94 | endDate=None, |
|
|
95 | 95 | startTime=None, |
|
|
96 | 96 | endTime=None, |
|
|
97 | 97 | walk=True, |
|
|
98 | 98 | timezone='ut', |
|
|
99 | 99 | all=0, |
|
|
100 | 100 | code = 1, |
|
|
101 | 101 | nCode = 1, |
|
|
102 | 102 | nBaud = 0, |
|
|
103 | 103 | nOsamp = 0, |
|
|
104 | 104 | online=False, |
|
|
105 | 105 | old_beams=False, |
|
|
106 | 106 | margin_days=1, |
|
|
107 | 107 | nFFT = None, |
|
|
108 | 108 | nChannels = None, |
|
|
109 | 109 | ignStartDate=None, |
|
|
110 | 110 | ignEndDate=None, |
|
|
111 | 111 | ignStartTime=None, |
|
|
112 | 112 | ignEndTime=None, |
|
|
113 | 113 | syncronization=True, |
|
|
114 | 114 | shiftChannels=0 |
|
|
115 | 115 | ): |
|
|
116 | 116 | |
|
|
117 | 117 | |
|
|
118 | 118 | |
|
|
119 | 119 | self.timezone = timezone |
|
|
120 | 120 | self.all = all |
|
|
121 | 121 | self.online = online |
|
|
122 | 122 | self.flag_old_beams = old_beams |
|
|
123 | 123 | self.code = code |
|
|
124 | 124 | self.nCode = int(nCode) |
|
|
125 | 125 | self.nBaud = int(nBaud) |
|
|
126 | 126 | self.nOsamp = int(nOsamp) |
|
|
127 | 127 | self.margin_days = margin_days |
|
|
128 | 128 | self.__sampleRate = None |
|
|
129 | 129 | self.flagAsync = not syncronization |
|
|
130 | 130 | self.shiftChannels = shiftChannels |
|
|
131 | 131 | self.nFFT = nFFT |
|
|
132 | 132 | self.nChannels = nChannels |
|
|
133 | 133 | if ignStartTime!=None and ignEndTime!=None: |
|
|
134 | 134 | if ignStartDate!=None and ignEndDate!=None: |
|
|
135 | 135 | self.ignStartDateTime=datetime.datetime.combine(ignStartDate,ignStartTime) |
|
|
136 | 136 | self.ignEndDateTime=datetime.datetime.combine(ignEndDate,ignEndTime) |
|
|
137 | 137 | else: |
|
|
138 | 138 | self.ignStartDateTime=datetime.datetime.combine(startDate,ignStartTime) |
|
|
139 | 139 | self.ignEndDateTime=datetime.datetime.combine(endDate,ignEndTime) |
|
|
140 | 140 | self.flag_ignoreFiles = True |
|
|
141 | 141 | |
|
|
142 | 142 | #self.findFiles() |
|
|
143 | 143 | if not(online): |
|
|
144 | 144 | #Busqueda de archivos offline |
|
|
145 | 145 | self.searchFilesOffLine(path, startDate, endDate, startTime, endTime, walk,) |
|
|
146 | 146 | else: |
|
|
147 | 147 | self.searchFilesOnLine(path, startDate, endDate, startTime,endTime,walk) |
|
|
148 | 148 | |
|
|
149 | 149 | if not(self.filenameList): |
|
|
150 | 150 | raise schainpy.admin.SchainWarning("There is no files into the folder: %s"%(path)) |
|
|
151 | 151 | #sys.exit(0) |
|
|
152 | 152 | self.dataOut.error = True |
|
|
153 | 153 | |
|
|
154 | 154 | self.fileIndex = 0 |
|
|
155 | 155 | |
|
|
156 | 156 | self.readNextFile(online) |
|
|
157 | 157 | |
|
|
158 | 158 | ''' |
|
|
159 | 159 | Add code |
|
|
160 | 160 | ''' |
|
|
161 | 161 | self.isConfig = True |
|
|
162 | 162 | # print("Setup Done") |
|
|
163 | 163 | pass |
|
|
164 | 164 | |
|
|
165 | 165 | |
|
|
166 | 166 | def readAMISRHeader(self,fp): |
|
|
167 | 167 | |
|
|
168 | 168 | if self.isConfig and (not self.flagNoMoreFiles): |
|
|
169 | 169 | newShape = fp.get('Raw11/Data/Samples/Data').shape[1:] |
|
|
170 | 170 | if self.dataShape != newShape and newShape != None and not self.flag_standby: |
|
|
171 | 171 | raise schainpy.admin.SchainError("NEW FILE HAS A DIFFERENT SHAPE: ") |
|
|
172 | 172 | print(self.dataShape,newShape,"\n") |
|
|
173 | 173 | return 0 |
|
|
174 | 174 | else: |
|
|
175 | 175 | self.dataShape = fp.get('Raw11/Data/Samples/Data').shape[1:] |
|
|
176 | 176 | |
|
|
177 | 177 | |
|
|
178 | 178 | header = 'Raw11/Data/RadacHeader' |
|
|
179 | 179 | if self.nChannels == None: |
|
|
180 | 180 | expFile = fp['Setup/Experimentfile'][()].decode() |
|
|
181 | 181 | linesExp = expFile.split("\n") |
|
|
182 | 182 | a = [line for line in linesExp if "nbeamcodes" in line] |
|
|
183 | 183 | self.nChannels = int(a[0][11:]) |
|
|
184 | 184 | |
|
|
185 | 185 | if not self.flagAsync: #for experiments with no syncronization |
|
|
186 | 186 | self.shiftChannels = 0 |
|
|
187 | 187 | |
|
|
188 | 188 | |
|
|
189 | 189 | |
|
|
190 | 190 | self.beamCodeByPulse = fp.get(header+'/BeamCode') # LIST OF BEAMS PER PROFILE, TO BE USED ON REARRANGE |
|
|
191 | 191 | |
|
|
192 | 192 | |
|
|
193 | 193 | if (self.startDate > datetime.date(2021, 7, 15)) or self.flag_old_beams: #Se cambió la forma de extracción de Apuntes el 17 o forzar con flag de reorganización |
|
|
194 | 194 | self.beamcodeFile = fp['Setup/Beamcodefile'][()].decode() |
|
|
195 | 195 | self.trueBeams = self.beamcodeFile.split("\n") |
|
|
196 | 196 | self.trueBeams.pop()#remove last |
|
|
197 | 197 | if self.nFFT == None: |
|
|
198 | 198 | log.error("FFT or number of repetitions per channels is needed",self.name) |
|
|
199 | 199 | beams_idx = [k*self.nFFT for k in range(self.nChannels)] |
|
|
200 | 200 | beams = [self.trueBeams[b] for b in beams_idx] |
|
|
201 | 201 | self.beamCode = [int(x, 16) for x in beams] |
|
|
202 | 202 | |
|
|
203 | 203 | if(self.flagAsync and self.shiftChannels == 0): |
|
|
204 | 204 | initBeam = self.beamCodeByPulse[0, 0] |
|
|
205 | 205 | self.shiftChannels = numpy.argwhere(self.beamCode ==initBeam)[0,0] |
|
|
206 | 206 | |
|
|
207 | 207 | else: |
|
|
208 | 208 | _beamCode= fp.get('Raw11/Data/Beamcodes') #se usa la manera previa al cambio de apuntes |
|
|
209 | 209 | self.beamCode = _beamCode[0,:] |
|
|
210 | 210 | |
|
|
211 | 211 | |
|
|
212 | 212 | |
|
|
213 | 213 | |
|
|
214 | 214 | if self.beamCodeMap == None: |
|
|
215 | 215 | self.beamCodeMap = fp['Setup/BeamcodeMap'] |
|
|
216 | 216 | for beam in self.beamCode: |
|
|
217 | 217 | beamAziElev = numpy.where(self.beamCodeMap[:,0]==beam) |
|
|
218 | 218 | beamAziElev = beamAziElev[0].squeeze() |
|
|
219 | 219 | self.azimuthList.append(self.beamCodeMap[beamAziElev,1]) |
|
|
220 | 220 | self.elevationList.append(self.beamCodeMap[beamAziElev,2]) |
|
|
221 | 221 | #print("Beamssss: ",self.beamCodeMap[beamAziElev,1],self.beamCodeMap[beamAziElev,2]) |
|
|
222 | 222 | #print(self.beamCode) |
|
|
223 | 223 | #self.code = fp.get(header+'/Code') # NOT USE FOR THIS |
|
|
224 | 224 | self.frameCount = fp.get(header+'/FrameCount')# NOT USE FOR THIS |
|
|
225 | 225 | self.modeGroup = fp.get(header+'/ModeGroup')# NOT USE FOR THIS |
|
|
226 | 226 | self.nsamplesPulse = fp.get(header+'/NSamplesPulse')# TO GET NSA OR USING DATA FOR THAT |
|
|
227 | 227 | self.pulseCount = fp.get(header+'/PulseCount')# NOT USE FOR THIS |
|
|
228 | 228 | self.radacTime = fp.get(header+'/RadacTime')# 1st TIME ON FILE ANDE CALCULATE THE REST WITH IPP*nindexprofile |
|
|
229 | 229 | self.timeCount = fp.get(header+'/TimeCount')# NOT USE FOR THIS |
|
|
230 | 230 | self.timeStatus = fp.get(header+'/TimeStatus')# NOT USE FOR THIS |
|
|
231 | 231 | self.rangeFromFile = fp.get('Raw11/Data/Samples/Range') |
|
|
232 | 232 | self.frequency = fp.get('Rx/Frequency') |
|
|
233 | 233 | txAus = fp.get('Raw11/Data/Pulsewidth') #seconds |
|
|
234 | 234 | self.baud = fp.get('Raw11/Data/TxBaud') |
|
|
235 | 235 | sampleRate = fp.get('Rx/SampleRate') |
|
|
236 | 236 | self.__sampleRate = sampleRate[()] |
|
|
237 | 237 | self.nblocks = self.pulseCount.shape[0] #nblocks |
|
|
238 | 238 | self.profPerBlockRAW = self.pulseCount.shape[1] #profiles per block in raw data |
|
|
239 | 239 | self.nprofiles = self.pulseCount.shape[1] #nprofile |
|
|
240 | 240 | #self.nsa = self.nsamplesPulse[0,0] #ngates |
|
|
241 | 241 | self.nsa = len(self.rangeFromFile[0]) |
|
|
242 | 242 | self.nchannels = len(self.beamCode) |
|
|
243 | 243 | self.ippSeconds = (self.radacTime[0][1] -self.radacTime[0][0]) #Ipp in seconds |
|
|
244 | 244 | #print("IPPS secs: ",self.ippSeconds) |
|
|
245 | 245 | #self.__waitForNewFile = self.nblocks # wait depending on the number of blocks since each block is 1 sec |
|
|
246 | 246 | self.__waitForNewFile = self.nblocks * self.nprofiles * self.ippSeconds # wait until new file is created |
|
|
247 | 247 | |
|
|
248 | 248 | #filling radar controller header parameters |
|
|
249 | 249 | self.__ippKm = self.ippSeconds *.15*1e6 # in km |
|
|
250 | 250 | #self.__txA = txAus[()]*.15 #(ipp[us]*.15km/1us) in km |
|
|
251 | 251 | self.__txA = txAus[()] #seconds |
|
|
252 | 252 | self.__txAKm = self.__txA*1e6*.15 |
|
|
253 | 253 | self.__txB = 0 |
|
|
254 | 254 | nWindows=1 |
|
|
255 | 255 | self.__nSamples = self.nsa |
|
|
256 | 256 | self.__firstHeight = self.rangeFromFile[0][0]/1000 #in km |
|
|
257 | 257 | self.__deltaHeight = (self.rangeFromFile[0][1] - self.rangeFromFile[0][0])/1000 |
|
|
258 | 258 | #print("amisr-ipp:",self.ippSeconds, self.__ippKm) |
|
|
259 | 259 | #for now until understand why the code saved is different (code included even though code not in tuf file) |
|
|
260 | 260 | #self.__codeType = 0 |
|
|
261 | 261 | # self.__nCode = None |
|
|
262 | 262 | # self.__nBaud = None |
|
|
263 | 263 | self.__code = self.code |
|
|
264 | 264 | self.__codeType = 0 |
|
|
265 | 265 | if self.code != None: |
|
|
266 | 266 | self.__codeType = 1 |
|
|
267 | 267 | self.__nCode = self.nCode |
|
|
268 | 268 | self.__nBaud = self.nBaud |
|
|
269 | 269 | self.__baudTX = self.__txA/(self.nBaud) |
|
|
270 | 270 | #self.__code = 0 |
|
|
271 | 271 | |
|
|
272 | 272 | #filling system header parameters |
|
|
273 | 273 | self.__nSamples = self.nsa |
|
|
274 | 274 | self.newProfiles = self.nprofiles/self.nchannels |
|
|
275 | 275 | self.__channelList = [n for n in range(self.nchannels)] |
|
|
276 | 276 | |
|
|
277 | 277 | self.__frequency = self.frequency[0][0] |
|
|
278 | 278 | |
|
|
279 | 279 | |
|
|
280 | 280 | return 1 |
|
|
281 | 281 | |
|
|
282 | 282 | |
|
|
283 | 283 | def createBuffers(self): |
|
|
284 | 284 | |
|
|
285 | 285 | pass |
|
|
286 | 286 | |
|
|
287 | 287 | def __setParameters(self,path='', startDate='',endDate='',startTime='', endTime='', walk=''): |
|
|
288 | 288 | self.path = path |
|
|
289 | 289 | self.startDate = startDate |
|
|
290 | 290 | self.endDate = endDate |
|
|
291 | 291 | self.startTime = startTime |
|
|
292 | 292 | self.endTime = endTime |
|
|
293 | 293 | self.walk = walk |
|
|
294 | 294 | |
|
|
295 | 295 | |
|
|
296 | 296 | def __checkPath(self): |
|
|
297 | 297 | if os.path.exists(self.path): |
|
|
298 | 298 | self.status = 1 |
|
|
299 | 299 | else: |
|
|
300 | 300 | self.status = 0 |
|
|
301 | 301 | print('Path:%s does not exists'%self.path) |
|
|
302 | 302 | |
|
|
303 | 303 | return |
|
|
304 | 304 | |
|
|
305 | 305 | |
|
|
306 | 306 | def __selDates(self, amisr_dirname_format): |
|
|
307 | 307 | try: |
|
|
308 | 308 | year = int(amisr_dirname_format[0:4]) |
|
|
309 | 309 | month = int(amisr_dirname_format[4:6]) |
|
|
310 | 310 | dom = int(amisr_dirname_format[6:8]) |
|
|
311 | 311 | thisDate = datetime.date(year,month,dom) |
|
|
312 | 312 | #margen de un día extra, igual luego se filtra for fecha y hora |
|
|
313 | 313 | if (thisDate>=(self.startDate - datetime.timedelta(days=self.margin_days)) and thisDate <= (self.endDate)+ datetime.timedelta(days=1)): |
|
|
314 | 314 | return amisr_dirname_format |
|
|
315 | 315 | except: |
|
|
316 | 316 | return None |
|
|
317 | 317 | |
|
|
318 | 318 | |
|
|
319 | 319 | def __findDataForDates(self,online=False): |
|
|
320 | 320 | |
|
|
321 | 321 | if not(self.status): |
|
|
322 | 322 | return None |
|
|
323 | 323 | |
|
|
324 | 324 | pat = '\d+.\d+' |
|
|
325 | 325 | dirnameList = [re.search(pat,x) for x in os.listdir(self.path)] |
|
|
326 | 326 | dirnameList = [x for x in dirnameList if x!=None] |
|
|
327 | 327 | dirnameList = [x.string for x in dirnameList] |
|
|
328 | 328 | if not(online): |
|
|
329 | 329 | dirnameList = [self.__selDates(x) for x in dirnameList] |
|
|
330 | 330 | dirnameList = [x for x in dirnameList if x!=None] |
|
|
331 | 331 | if len(dirnameList)>0: |
|
|
332 | 332 | self.status = 1 |
|
|
333 | 333 | self.dirnameList = dirnameList |
|
|
334 | 334 | self.dirnameList.sort() |
|
|
335 | 335 | else: |
|
|
336 | 336 | self.status = 0 |
|
|
337 | 337 | return None |
|
|
338 | 338 | |
|
|
339 | 339 | def __getTimeFromData(self): |
|
|
340 | 340 | startDateTime_Reader = datetime.datetime.combine(self.startDate,self.startTime) |
|
|
341 | 341 | endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime) |
|
|
342 | 342 | |
|
|
343 | 343 | print('Filtering Files from %s to %s'%(startDateTime_Reader, endDateTime_Reader)) |
|
|
344 | 344 | print('........................................') |
|
|
345 | 345 | filter_filenameList = [] |
|
|
346 | 346 | self.filenameList.sort() |
|
|
347 | 347 | total_files = len(self.filenameList) |
|
|
348 | 348 | #for i in range(len(self.filenameList)-1): |
|
|
349 | 349 | for i in range(total_files): |
|
|
350 | 350 | filename = self.filenameList[i] |
|
|
351 | 351 | #print("file-> ",filename) |
|
|
352 | 352 | try: |
|
|
353 | 353 | fp = h5py.File(filename,'r') |
|
|
354 | 354 | time_str = fp.get('Time/RadacTimeString') |
|
|
355 | 355 | |
|
|
356 | 356 | startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0] |
|
|
357 | 357 | #startDateTimeStr_File = "2019-12-16 09:21:11" |
|
|
358 | 358 | junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
359 | 359 | startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
360 | 360 | |
|
|
361 | 361 | #endDateTimeStr_File = "2019-12-16 11:10:11" |
|
|
362 | 362 | endDateTimeStr_File = time_str[-1][-1].decode('UTF-8').split('.')[0] |
|
|
363 | 363 | junk = time.strptime(endDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
364 | 364 | endDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
365 | 365 | |
|
|
366 | 366 | fp.close() |
|
|
367 | 367 | |
|
|
368 | 368 | #print("check time", startDateTime_File) |
|
|
369 | 369 | if self.timezone == 'lt': |
|
|
370 | 370 | startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300) |
|
|
371 | 371 | endDateTime_File = endDateTime_File - datetime.timedelta(minutes = 300) |
|
|
372 | 372 | if (startDateTime_File >=startDateTime_Reader and endDateTime_File<=endDateTime_Reader): |
|
|
373 | 373 | filter_filenameList.append(filename) |
|
|
374 | 374 | |
|
|
375 | 375 | if (startDateTime_File>endDateTime_Reader): |
|
|
376 | 376 | break |
|
|
377 | 377 | except Exception as e: |
|
|
378 | 378 | log.warning("Error opening file {} -> {}".format(os.path.split(filename)[1],e)) |
|
|
379 | 379 | |
|
|
380 | 380 | filter_filenameList.sort() |
|
|
381 | 381 | self.filenameList = filter_filenameList |
|
|
382 | 382 | |
|
|
383 | 383 | return 1 |
|
|
384 | 384 | |
|
|
385 | 385 | def __filterByGlob1(self, dirName): |
|
|
386 | 386 | filter_files = glob.glob1(dirName, '*.*%s'%self.extension_file) |
|
|
387 | 387 | filter_files.sort() |
|
|
388 | 388 | filterDict = {} |
|
|
389 | 389 | filterDict.setdefault(dirName) |
|
|
390 | 390 | filterDict[dirName] = filter_files |
|
|
391 | 391 | return filterDict |
|
|
392 | 392 | |
|
|
393 | 393 | def __getFilenameList(self, fileListInKeys, dirList): |
|
|
394 | 394 | for value in fileListInKeys: |
|
|
395 | 395 | dirName = list(value.keys())[0] |
|
|
396 | 396 | for file in value[dirName]: |
|
|
397 | 397 | filename = os.path.join(dirName, file) |
|
|
398 | 398 | self.filenameList.append(filename) |
|
|
399 | 399 | |
|
|
400 | 400 | |
|
|
401 | 401 | def __selectDataForTimes(self, online=False): |
|
|
402 | 402 | #aun no esta implementado el filtro for tiempo-> implementado en readNextFile |
|
|
403 | 403 | if not(self.status): |
|
|
404 | 404 | return None |
|
|
405 | 405 | |
|
|
406 | 406 | dirList = [os.path.join(self.path,x) for x in self.dirnameList] |
|
|
407 | 407 | fileListInKeys = [self.__filterByGlob1(x) for x in dirList] |
|
|
408 | 408 | self.__getFilenameList(fileListInKeys, dirList) |
|
|
409 | 409 | if not(online): |
|
|
410 | 410 | #filtro por tiempo |
|
|
411 | 411 | if not(self.all): |
|
|
412 | 412 | self.__getTimeFromData() |
|
|
413 | 413 | |
|
|
414 | 414 | if len(self.filenameList)>0: |
|
|
415 | 415 | self.status = 1 |
|
|
416 | 416 | self.filenameList.sort() |
|
|
417 | 417 | else: |
|
|
418 | 418 | self.status = 0 |
|
|
419 | 419 | return None |
|
|
420 | 420 | |
|
|
421 | 421 | else: |
|
|
422 | 422 | #get the last file - 1 |
|
|
423 | 423 | self.filenameList = [self.filenameList[-2]] |
|
|
424 | 424 | new_dirnameList = [] |
|
|
425 | 425 | for dirname in self.dirnameList: |
|
|
426 | 426 | junk = numpy.array([dirname in x for x in self.filenameList]) |
|
|
427 | 427 | junk_sum = junk.sum() |
|
|
428 | 428 | if junk_sum > 0: |
|
|
429 | 429 | new_dirnameList.append(dirname) |
|
|
430 | 430 | self.dirnameList = new_dirnameList |
|
|
431 | 431 | return 1 |
|
|
432 | 432 | |
|
|
433 | 433 | def searchFilesOnLine(self, path, startDate, endDate, startTime=datetime.time(0,0,0), |
|
|
434 | 434 | endTime=datetime.time(23,59,59),walk=True): |
|
|
435 | 435 | |
|
|
436 | 436 | if endDate ==None: |
|
|
437 | 437 | startDate = datetime.datetime.utcnow().date() |
|
|
438 | 438 | endDate = datetime.datetime.utcnow().date() |
|
|
439 | 439 | |
|
|
440 | 440 | self.__setParameters(path=path, startDate=startDate, endDate=endDate,startTime = startTime,endTime=endTime, walk=walk) |
|
|
441 | 441 | |
|
|
442 | 442 | self.__checkPath() |
|
|
443 | 443 | |
|
|
444 | 444 | self.__findDataForDates(online=True) |
|
|
445 | 445 | |
|
|
446 | 446 | self.dirnameList = [self.dirnameList[-1]] |
|
|
447 | 447 | |
|
|
448 | 448 | self.__selectDataForTimes(online=True) |
|
|
449 | 449 | |
|
|
450 | 450 | return |
|
|
451 | 451 | |
|
|
452 | 452 | |
|
|
453 | 453 | def searchFilesOffLine(self, |
|
|
454 | 454 | path, |
|
|
455 | 455 | startDate, |
|
|
456 | 456 | endDate, |
|
|
457 | 457 | startTime=datetime.time(0,0,0), |
|
|
458 | 458 | endTime=datetime.time(23,59,59), |
|
|
459 | 459 | walk=True): |
|
|
460 | 460 | |
|
|
461 | 461 | self.__setParameters(path, startDate, endDate, startTime, endTime, walk) |
|
|
462 | 462 | |
|
|
463 | 463 | self.__checkPath() |
|
|
464 | 464 | |
|
|
465 | 465 | self.__findDataForDates() |
|
|
466 | 466 | |
|
|
467 | 467 | self.__selectDataForTimes() |
|
|
468 | 468 | |
|
|
469 | 469 | for i in range(len(self.filenameList)): |
|
|
470 | 470 | print("%s" %(self.filenameList[i])) |
|
|
471 | 471 | |
|
|
472 | 472 | return |
|
|
473 | 473 | |
|
|
474 | 474 | def __setNextFileOffline(self): |
|
|
475 | 475 | |
|
|
476 | 476 | try: |
|
|
477 | 477 | self.filename = self.filenameList[self.fileIndex] |
|
|
478 | 478 | self.amisrFilePointer = h5py.File(self.filename,'r') |
|
|
479 | 479 | self.fileIndex += 1 |
|
|
480 | 480 | except: |
|
|
481 | 481 | self.flagNoMoreFiles = 1 |
|
|
482 | 482 | raise schainpy.admin.SchainError('No more files to read') |
|
|
483 | 483 | return 0 |
|
|
484 | 484 | |
|
|
485 | 485 | self.flagIsNewFile = 1 |
|
|
486 | 486 | print("Setting the file: %s"%self.filename) |
|
|
487 | 487 | |
|
|
488 | 488 | return 1 |
|
|
489 | 489 | |
|
|
490 | 490 | |
|
|
491 | 491 | def __setNextFileOnline(self): |
|
|
492 | 492 | filename = self.filenameList[0] |
|
|
493 | 493 | if self.__filename_online != None: |
|
|
494 | 494 | self.__selectDataForTimes(online=True) |
|
|
495 | 495 | filename = self.filenameList[0] |
|
|
496 | 496 | wait = 0 |
|
|
497 | 497 | self.__waitForNewFile=300 ## DEBUG: |
|
|
498 | 498 | while self.__filename_online == filename: |
|
|
499 | 499 | print('waiting %d seconds to get a new file...'%(self.__waitForNewFile)) |
|
|
500 | 500 | if wait == 5: |
|
|
501 | 501 | self.flagNoMoreFiles = 1 |
|
|
502 | 502 | return 0 |
|
|
503 | 503 | sleep(self.__waitForNewFile) |
|
|
504 | 504 | self.__selectDataForTimes(online=True) |
|
|
505 | 505 | filename = self.filenameList[0] |
|
|
506 | 506 | wait += 1 |
|
|
507 | 507 | |
|
|
508 | 508 | self.__filename_online = filename |
|
|
509 | 509 | |
|
|
510 | 510 | self.amisrFilePointer = h5py.File(filename,'r') |
|
|
511 | 511 | self.flagIsNewFile = 1 |
|
|
512 | 512 | self.filename = filename |
|
|
513 | 513 | print("Setting the file: %s"%self.filename) |
|
|
514 | 514 | return 1 |
|
|
515 | 515 | |
|
|
516 | 516 | |
|
|
517 | 517 | def readData(self): |
|
|
518 | 518 | buffer = self.amisrFilePointer.get('Raw11/Data/Samples/Data') |
|
|
519 | 519 | re = buffer[:,:,:,0] |
|
|
520 | 520 | im = buffer[:,:,:,1] |
|
|
521 | 521 | dataset = re + im*1j |
|
|
522 | 522 | |
|
|
523 | 523 | self.radacTime = self.amisrFilePointer.get('Raw11/Data/RadacHeader/RadacTime') |
|
|
524 | 524 | timeset = self.radacTime[:,0] |
|
|
525 | 525 | |
|
|
526 | 526 | return dataset,timeset |
|
|
527 | 527 | |
|
|
528 | 528 | def reshapeData(self): |
|
|
529 | 529 | #print(self.beamCodeByPulse, self.beamCode, self.nblocks, self.nprofiles, self.nsa) |
|
|
530 | 530 | channels = self.beamCodeByPulse[0,:] |
|
|
531 | 531 | nchan = self.nchannels |
|
|
532 | 532 | #self.newProfiles = self.nprofiles/nchan #must be defined on filljroheader |
|
|
533 | 533 | nblocks = self.nblocks |
|
|
534 | 534 | nsamples = self.nsa |
|
|
535 | 535 | #print("Channels: ",self.nChannels) |
|
|
536 | 536 | #Dimensions : nChannels, nProfiles, nSamples |
|
|
537 | 537 | new_block = numpy.empty((nblocks, nchan, numpy.int_(self.newProfiles), nsamples), dtype="complex64") |
|
|
538 | 538 | ############################################ |
|
|
539 | 539 | profPerCH = int(self.profPerBlockRAW / (self.nFFT* self.nChannels)) |
|
|
540 | 540 | #profPerCH = int(self.profPerBlockRAW / self.nChannels) |
|
|
541 | 541 | for thisChannel in range(nchan): |
|
|
542 | 542 | |
|
|
543 | 543 | ich = thisChannel |
|
|
544 | 544 | |
|
|
545 | 545 | idx_ch = [self.nFFT*(ich + nchan*k) for k in range(profPerCH)] |
|
|
546 | 546 | #print(idx_ch) |
|
|
547 | 547 | if self.nFFT > 1: |
|
|
548 | 548 | aux = [numpy.arange(i, i+self.nFFT) for i in idx_ch] |
|
|
549 | 549 | idx_ch = None |
|
|
550 | 550 | idx_ch =aux |
|
|
551 | 551 | idx_ch = numpy.array(idx_ch, dtype=int).flatten() |
|
|
552 | 552 | else: |
|
|
553 | 553 | idx_ch = numpy.array(idx_ch, dtype=int) |
|
|
554 | 554 | |
|
|
555 | 555 | #print(ich,profPerCH,idx_ch) |
|
|
556 | 556 | #print(numpy.where(channels==self.beamCode[ich])[0]) |
|
|
557 | 557 | #new_block[:,ich,:,:] = self.dataset[:,numpy.where(channels==self.beamCode[ich])[0],:] |
|
|
558 | 558 | new_block[:,ich,:,:] = self.dataset[:,idx_ch,:] |
|
|
559 | 559 | |
|
|
560 | 560 | new_block = numpy.transpose(new_block, (1,0,2,3)) |
|
|
561 | 561 | new_block = numpy.reshape(new_block, (nchan,-1, nsamples)) |
|
|
562 | 562 | if self.flagAsync: |
|
|
563 | 563 | new_block = numpy.roll(new_block, self.shiftChannels, axis=0) |
|
|
564 | 564 | return new_block |
|
|
565 | 565 | |
|
|
566 | 566 | def updateIndexes(self): |
|
|
567 | 567 | |
|
|
568 | 568 | pass |
|
|
569 | 569 | |
|
|
570 | 570 | def fillJROHeader(self): |
|
|
571 | 571 | |
|
|
572 | 572 | #fill radar controller header |
|
|
573 | 573 | |
|
|
574 | 574 | #fill system header |
|
|
575 | 575 | self.dataOut.systemHeaderObj = SystemHeader(nSamples=self.__nSamples, |
|
|
576 | 576 | nProfiles=self.newProfiles, |
|
|
577 | 577 | nChannels=len(self.__channelList), |
|
|
578 | 578 | adcResolution=14, |
|
|
579 | 579 | pciDioBusWidth=32) |
|
|
580 | 580 | |
|
|
581 | 581 | self.dataOut.type = "Voltage" |
|
|
582 | 582 | self.dataOut.data = None |
|
|
583 | 583 | self.dataOut.dtype = numpy.dtype([('real','<i8'),('imag','<i8')]) |
|
|
584 | 584 | # self.dataOut.nChannels = 0 |
|
|
585 | 585 | |
|
|
586 | 586 | # self.dataOut.nHeights = 0 |
|
|
587 | 587 | |
|
|
588 | 588 | self.dataOut.nProfiles = self.newProfiles*self.nblocks |
|
|
589 | 589 | #self.dataOut.heightList = self.__firstHeigth + numpy.arange(self.__nSamples, dtype = numpy.float)*self.__deltaHeigth |
|
|
590 | 590 | ranges = numpy.reshape(self.rangeFromFile[()],(-1)) |
|
|
591 | 591 | self.dataOut.heightList = ranges/1000.0 #km |
|
|
592 | 592 | self.dataOut.channelList = self.__channelList |
|
|
593 | 593 | |
|
|
594 | 594 | self.dataOut.blocksize = self.dataOut.nChannels * self.dataOut.nHeights |
|
|
595 | 595 | |
|
|
596 | 596 | # self.dataOut.channelIndexList = None |
|
|
597 | 597 | |
|
|
598 | 598 | |
|
|
599 | 599 | # #self.dataOut.azimuthList = numpy.roll( numpy.array(self.azimuthList) ,self.shiftChannels) |
|
|
600 | 600 | # #self.dataOut.elevationList = numpy.roll(numpy.array(self.elevationList) ,self.shiftChannels) |
|
|
601 | 601 | # #self.dataOut.codeList = numpy.roll(numpy.array(self.beamCode), self.shiftChannels) |
|
|
602 | 602 | |
|
|
603 | 603 | self.dataOut.azimuthList = self.azimuthList |
|
|
604 | 604 | self.dataOut.elevationList = self.elevationList |
|
|
605 | 605 | self.dataOut.codeList = self.beamCode |
|
|
606 | 606 | |
|
|
607 | 607 | |
|
|
608 | 608 | |
|
|
609 | 609 | #print(self.dataOut.elevationList) |
|
|
610 | 610 | self.dataOut.flagNoData = True |
|
|
611 | 611 | |
|
|
612 | 612 | #Set to TRUE if the data is discontinuous |
|
|
613 | 613 | self.dataOut.flagDiscontinuousBlock = False |
|
|
614 | 614 | |
|
|
615 | 615 | self.dataOut.utctime = None |
|
|
616 | 616 | |
|
|
617 | 617 | #self.dataOut.timeZone = -5 #self.__timezone/60 #timezone like jroheader, difference in minutes between UTC and localtime |
|
|
618 | 618 | if self.timezone == 'lt': |
|
|
619 | 619 | self.dataOut.timeZone = time.timezone / 60. #get the timezone in minutes |
|
|
620 | 620 | else: |
|
|
621 | 621 | self.dataOut.timeZone = 0 #by default time is UTC |
|
|
622 | 622 | |
|
|
623 | 623 | self.dataOut.dstFlag = 0 |
|
|
624 | 624 | self.dataOut.errorCount = 0 |
|
|
625 | 625 | self.dataOut.nCohInt = 1 |
|
|
626 | 626 | self.dataOut.flagDecodeData = False #asumo que la data esta decodificada |
|
|
627 | 627 | self.dataOut.flagDeflipData = False #asumo que la data esta sin flip |
|
|
628 | 628 | self.dataOut.flagShiftFFT = False |
|
|
629 | 629 | self.dataOut.ippSeconds = self.ippSeconds |
|
|
630 | 630 | self.dataOut.ipp = self.__ippKm |
|
|
631 | 631 | self.dataOut.nCode = self.__nCode |
|
|
632 | 632 | self.dataOut.code = self.__code |
|
|
633 | 633 | self.dataOut.nBaud = self.__nBaud |
|
|
634 | 634 | |
|
|
635 | 635 | |
|
|
636 | 636 | self.dataOut.frequency = self.__frequency |
|
|
637 | 637 | self.dataOut.realtime = self.online |
|
|
638 | 638 | |
|
|
639 | 639 | self.dataOut.radarControllerHeaderObj = RadarControllerHeader(ipp=self.__ippKm, |
|
|
640 | 640 | txA=self.__txAKm, |
|
|
641 | 641 | txB=0, |
|
|
642 | 642 | nWindows=1, |
|
|
643 | 643 | nHeights=self.__nSamples, |
|
|
644 | 644 | firstHeight=self.__firstHeight, |
|
|
645 | 645 | codeType=self.__codeType, |
|
|
646 | 646 | nCode=self.__nCode, nBaud=self.__nBaud, |
|
|
647 | 647 | code = self.__code, |
|
|
648 | 648 | nOsamp=self.nOsamp, |
|
|
649 | 649 | frequency = self.__frequency, |
|
|
650 | 650 | sampleRate= self.__sampleRate, |
|
|
651 | 651 | fClock=self.__sampleRate) |
|
|
652 | 652 | |
|
|
653 | 653 | |
|
|
654 | 654 | self.dataOut.radarControllerHeaderObj.heightList = ranges/1000.0 #km |
|
|
655 | 655 | self.dataOut.radarControllerHeaderObj.heightResolution = self.__deltaHeight |
|
|
656 | 656 | self.dataOut.radarControllerHeaderObj.rangeIpp = self.__ippKm #km |
|
|
657 | 657 | self.dataOut.radarControllerHeaderObj.rangeTxA = self.__txA*1e6*.15 #km |
|
|
658 | 658 | self.dataOut.radarControllerHeaderObj.nChannels = self.nchannels |
|
|
659 | 659 | self.dataOut.radarControllerHeaderObj.channelList = self.__channelList |
|
|
660 | 660 | self.dataOut.radarControllerHeaderObj.azimuthList = self.azimuthList |
|
|
661 | 661 | self.dataOut.radarControllerHeaderObj.elevationList = self.elevationList |
|
|
662 | 662 | self.dataOut.radarControllerHeaderObj.dtype = "Voltage" |
|
|
663 | 663 | self.dataOut.ippSeconds = self.ippSeconds |
|
|
664 | 664 | self.dataOut.ippFactor = self.nFFT |
|
|
665 | 665 | pass |
|
|
666 | 666 | |
|
|
667 | 667 | def readNextFile(self,online=False): |
|
|
668 | 668 | |
|
|
669 | 669 | if not(online): |
|
|
670 | 670 | newFile = self.__setNextFileOffline() |
|
|
671 | 671 | else: |
|
|
672 | 672 | newFile = self.__setNextFileOnline() |
|
|
673 | 673 | |
|
|
674 | 674 | if not(newFile): |
|
|
675 | 675 | self.dataOut.error = True |
|
|
676 | 676 | return 0 |
|
|
677 | 677 | |
|
|
678 | 678 | if not self.readAMISRHeader(self.amisrFilePointer): |
|
|
679 | 679 | self.dataOut.error = True |
|
|
680 | 680 | return 0 |
|
|
681 | 681 | |
|
|
682 | 682 | #self.createBuffers() |
|
|
683 | 683 | self.fillJROHeader() |
|
|
684 | 684 | |
|
|
685 | 685 | #self.__firstFile = False |
|
|
686 | 686 | |
|
|
687 | 687 | self.dataset,self.timeset = self.readData() |
|
|
688 | ||
|
|
689 | 688 | if self.endDate!=None: |
|
|
690 | 689 | endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime) |
|
|
691 | 690 | time_str = self.amisrFilePointer.get('Time/RadacTimeString') |
|
|
692 | 691 | startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0] |
|
|
693 | 692 | junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S') |
|
|
694 | 693 | startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec) |
|
|
695 | 694 | if self.timezone == 'lt': |
|
|
696 | 695 | startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300) |
|
|
697 | 696 | if (startDateTime_File>endDateTime_Reader): |
|
|
698 | 697 | self.flag_standby = False |
|
|
699 | 698 | return 0 |
|
|
700 | 699 | if self.flag_ignoreFiles and (startDateTime_File >= self.ignStartDateTime and startDateTime_File <= self.ignEndDateTime): |
|
|
701 | 700 | print("Ignoring...") |
|
|
702 | 701 | self.flag_standby = True |
|
|
702 | self.profileIndex = 99999999999999999 | |
|
|
703 | 703 | return 1 |
|
|
704 | 704 | self.flag_standby = False |
|
|
705 | 705 | |
|
|
706 | 706 | self.jrodataset = self.reshapeData() |
|
|
707 | 707 | #----self.updateIndexes() |
|
|
708 | 708 | self.profileIndex = 0 |
|
|
709 | 709 | |
|
|
710 | 710 | return 1 |
|
|
711 | 711 | |
|
|
712 | 712 | |
|
|
713 | 713 | def __hasNotDataInBuffer(self): |
|
|
714 | 714 | if self.profileIndex >= (self.newProfiles*self.nblocks): |
|
|
715 | 715 | return 1 |
|
|
716 | 716 | return 0 |
|
|
717 | 717 | |
|
|
718 | 718 | |
|
|
719 | 719 | def getData(self): |
|
|
720 | 720 | |
|
|
721 | 721 | if self.flagNoMoreFiles: |
|
|
722 | 722 | self.dataOut.flagNoData = True |
|
|
723 | 723 | return 0 |
|
|
724 | ||
|
|
725 | 724 | if self.profileIndex >= (self.newProfiles*self.nblocks): # |
|
|
726 | 725 | #if self.__hasNotDataInBuffer(): |
|
|
727 | 726 | if not (self.readNextFile(self.online)): |
|
|
728 | 727 | print("Profile Index break...") |
|
|
729 | 728 | return 0 |
|
|
730 | 729 | |
|
|
731 | 730 | if self.flag_standby: #Standby mode, if files are being ignoring, just return with no error flag |
|
|
732 | 731 | return 0 |
|
|
733 | 732 | |
|
|
734 | 733 | if self.dataset is None: # setear esta condicion cuando no hayan datos por leer |
|
|
735 | 734 | self.dataOut.flagNoData = True |
|
|
736 | 735 | print("No more data break...") |
|
|
737 | 736 | return 0 |
|
|
738 | 737 | |
|
|
739 | 738 | #self.dataOut.data = numpy.reshape(self.jrodataset[self.profileIndex,:],(1,-1)) |
|
|
740 | 739 | |
|
|
741 | 740 | self.dataOut.data = self.jrodataset[:,self.profileIndex,:] |
|
|
742 | 741 | |
|
|
743 | 742 | #print("R_t",self.timeset) |
|
|
744 | 743 | |
|
|
745 | 744 | #self.dataOut.utctime = self.jrotimeset[self.profileIndex] |
|
|
746 | 745 | #verificar basic header de jro data y ver si es compatible con este valor |
|
|
747 | 746 | #self.dataOut.utctime = self.timeset + (self.profileIndex * self.ippSeconds * self.nchannels) |
|
|
748 | 747 | indexprof = numpy.mod(self.profileIndex, self.newProfiles) |
|
|
749 | 748 | indexblock = self.profileIndex/self.newProfiles |
|
|
750 | 749 | #print (indexblock, indexprof) |
|
|
751 | 750 | diffUTC = 0 |
|
|
752 | 751 | t_comp = (indexprof * self.ippSeconds * self.nchannels) + diffUTC # |
|
|
753 | 752 | |
|
|
754 | 753 | #print("utc :",indexblock," __ ",t_comp) |
|
|
755 | 754 | #print(numpy.shape(self.timeset)) |
|
|
756 | 755 | self.dataOut.utctime = self.timeset[numpy.int_(indexblock)] + t_comp |
|
|
757 | 756 | #self.dataOut.utctime = self.timeset[self.profileIndex] + t_comp |
|
|
758 | 757 | |
|
|
759 | 758 | self.dataOut.profileIndex = self.profileIndex |
|
|
760 | 759 | #print("N profile:",self.profileIndex,self.newProfiles,self.nblocks,self.dataOut.utctime) |
|
|
761 | 760 | self.dataOut.flagNoData = False |
|
|
762 | 761 | # if indexprof == 0: |
|
|
763 | 762 | # print("kamisr: ",self.dataOut.utctime) |
|
|
764 | 763 | |
|
|
765 | 764 | self.profileIndex += 1 |
|
|
766 | ||
|
|
765 | ||
|
|
767 | 766 | return self.dataOut.data #retorno necesario?? |
|
|
768 | 767 | |
|
|
769 | 768 | |
|
|
770 | 769 | def run(self, **kwargs): |
|
|
771 | 770 | ''' |
|
|
772 | 771 | This method will be called many times so here you should put all your code |
|
|
773 | 772 | ''' |
|
|
774 | 773 | #print("running kamisr") |
|
|
775 | 774 | if not self.isConfig: |
|
|
776 | 775 | self.setup(**kwargs) |
|
|
777 | 776 | self.isConfig = True |
|
|
778 | 777 | |
|
|
779 | 778 | self.getData() |
| @@ -1,1735 +1,1737 | |||
|
|
1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
|
2 | 2 | # All rights reserved. |
|
|
3 | 3 | # |
|
|
4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
|
|
5 | 5 | """Spectra processing Unit and operations |
|
|
6 | 6 | |
|
|
7 | 7 | Here you will find the processing unit `SpectraProc` and several operations |
|
|
8 | 8 | to work with Spectra data type |
|
|
9 | 9 | """ |
|
|
10 | 10 | |
|
|
11 | 11 | import time |
|
|
12 | 12 | import itertools |
|
|
13 | 13 | |
|
|
14 | 14 | import numpy |
|
|
15 | 15 | |
|
|
16 | 16 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation |
|
|
17 | 17 | from schainpy.model.data.jrodata import Spectra |
|
|
18 | 18 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
|
19 | 19 | from schainpy.model.data import _noise |
|
|
20 | 20 | from schainpy.utils import log |
|
|
21 | 21 | import matplotlib.pyplot as plt |
|
|
22 | 22 | from schainpy.model.io.utilsIO import getHei_index |
|
|
23 | 23 | import datetime |
|
|
24 | 24 | |
|
|
25 | 25 | class SpectraProc(ProcessingUnit): |
|
|
26 | 26 | |
|
|
27 | 27 | def __init__(self): |
|
|
28 | 28 | |
|
|
29 | 29 | ProcessingUnit.__init__(self) |
|
|
30 | 30 | |
|
|
31 | 31 | self.buffer = None |
|
|
32 | 32 | self.firstdatatime = None |
|
|
33 | 33 | self.profIndex = 0 |
|
|
34 | 34 | self.dataOut = Spectra() |
|
|
35 | self.dataOut.error=False | |
|
|
35 | 36 | self.id_min = None |
|
|
36 | 37 | self.id_max = None |
|
|
37 | 38 | self.setupReq = False #Agregar a todas las unidades de proc |
|
|
38 | 39 | self.nsamplesFFT = 0 |
|
|
39 | 40 | |
|
|
40 | 41 | def __updateSpecFromVoltage(self): |
|
|
41 | 42 | |
|
|
42 | 43 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
43 | 44 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
44 | 45 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
45 | 46 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
46 | 47 | try: |
|
|
47 | 48 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
|
|
48 | 49 | except: |
|
|
49 | 50 | pass |
|
|
50 | 51 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
51 | 52 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
52 | 53 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
53 | 54 | self.dataOut.ipp = self.dataIn.ipp |
|
|
54 | 55 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
55 | 56 | self.dataOut.channelList = self.dataIn.channelList |
|
|
56 | 57 | self.dataOut.heightList = self.dataIn.heightList |
|
|
57 | 58 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
|
58 | 59 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
59 | 60 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
60 | 61 | self.dataOut.utctime = self.firstdatatime |
|
|
61 | 62 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData |
|
|
62 | 63 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData |
|
|
63 | 64 | self.dataOut.flagShiftFFT = False |
|
|
64 | 65 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
65 | 66 | self.dataOut.nIncohInt = 1 |
|
|
66 | 67 | self.dataOut.deltaHeight = self.dataIn.deltaHeight |
|
|
67 | 68 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
68 | 69 | self.dataOut.frequency = self.dataIn.frequency |
|
|
69 | 70 | self.dataOut.realtime = self.dataIn.realtime |
|
|
70 | 71 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
71 | 72 | self.dataOut.zenith = self.dataIn.zenith |
|
|
72 | 73 | self.dataOut.codeList = self.dataIn.codeList |
|
|
73 | 74 | self.dataOut.azimuthList = self.dataIn.azimuthList |
|
|
74 | 75 | self.dataOut.elevationList = self.dataIn.elevationList |
|
|
75 | 76 | self.dataOut.code = self.dataIn.code |
|
|
76 | 77 | self.dataOut.nCode = self.dataIn.nCode |
|
|
77 | 78 | self.dataOut.flagProfilesByRange = self.dataIn.flagProfilesByRange |
|
|
78 | 79 | self.dataOut.nProfilesByRange = self.dataIn.nProfilesByRange |
|
|
79 | 80 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
|
|
80 | 81 | try: |
|
|
81 | 82 | self.dataOut.step = self.dataIn.step |
|
|
82 | 83 | except: |
|
|
83 | 84 | pass |
|
|
84 | 85 | |
|
|
85 | 86 | def __getFft(self): |
|
|
86 | 87 | """ |
|
|
87 | 88 | Convierte valores de Voltaje a Spectra |
|
|
88 | 89 | |
|
|
89 | 90 | Affected: |
|
|
90 | 91 | self.dataOut.data_spc |
|
|
91 | 92 | self.dataOut.data_cspc |
|
|
92 | 93 | self.dataOut.data_dc |
|
|
93 | 94 | self.dataOut.heightList |
|
|
94 | 95 | self.profIndex |
|
|
95 | 96 | self.buffer |
|
|
96 | 97 | self.dataOut.flagNoData |
|
|
97 | 98 | """ |
|
|
98 | 99 | fft_volt = numpy.fft.fft( |
|
|
99 | 100 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) |
|
|
100 | 101 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
|
101 | 102 | dc = fft_volt[:, 0, :] |
|
|
102 | 103 | |
|
|
103 | 104 | # calculo de self-spectra |
|
|
104 | 105 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
|
|
105 | 106 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
|
106 | 107 | spc = spc.real |
|
|
107 | 108 | |
|
|
108 | 109 | blocksize = 0 |
|
|
109 | 110 | blocksize += dc.size |
|
|
110 | 111 | blocksize += spc.size |
|
|
111 | 112 | |
|
|
112 | 113 | cspc = None |
|
|
113 | 114 | pairIndex = 0 |
|
|
114 | 115 | if self.dataOut.pairsList != None: |
|
|
115 | 116 | # calculo de cross-spectra |
|
|
116 | 117 | cspc = numpy.zeros( |
|
|
117 | 118 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
|
118 | 119 | for pair in self.dataOut.pairsList: |
|
|
119 | 120 | if pair[0] not in self.dataOut.channelList: |
|
|
120 | 121 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( |
|
|
121 | 122 | str(pair), str(self.dataOut.channelList))) |
|
|
122 | 123 | if pair[1] not in self.dataOut.channelList: |
|
|
123 | 124 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( |
|
|
124 | 125 | str(pair), str(self.dataOut.channelList))) |
|
|
125 | 126 | |
|
|
126 | 127 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ |
|
|
127 | 128 | numpy.conjugate(fft_volt[pair[1], :, :]) |
|
|
128 | 129 | pairIndex += 1 |
|
|
129 | 130 | blocksize += cspc.size |
|
|
130 | 131 | |
|
|
131 | 132 | self.dataOut.data_spc = spc |
|
|
132 | 133 | self.dataOut.data_cspc = cspc |
|
|
133 | 134 | self.dataOut.data_dc = dc |
|
|
134 | 135 | self.dataOut.blockSize = blocksize |
|
|
135 | 136 | self.dataOut.flagShiftFFT = False |
|
|
136 | 137 | |
|
|
137 | 138 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False, |
|
|
138 |
zeroPad=False, zeroPoints=0, runNextUnit |
|
|
|
139 | ||
|
|
139 | zeroPad=False, zeroPoints=0, runNextUnit=0): | |
|
|
140 | 140 | self.dataIn.runNextUnit = runNextUnit |
|
|
141 | 141 | try: |
|
|
142 | 142 | type = self.dataIn.type.decode("utf-8") |
|
|
143 | 143 | self.dataIn.type = type |
|
|
144 | except: | |
|
|
144 | except Exception as e: | |
|
|
145 | # print("spc -> ",e) | |
|
|
145 | 146 | pass |
|
|
146 | 147 | |
|
|
147 | 148 | if self.dataIn.type == "Spectra": |
|
|
149 | #print("AQUI") | |
|
|
148 | 150 | try: |
|
|
149 | 151 | self.dataOut.copy(self.dataIn) |
|
|
150 | 152 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
151 | 153 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
|
|
152 | 154 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
153 | 155 | #self.dataOut.nHeights = len(self.dataOut.heightList) |
|
|
154 | 156 | except Exception as e: |
|
|
155 | 157 | print("Error dataIn ",e) |
|
|
156 | 158 | |
|
|
157 | 159 | if shift_fft: |
|
|
158 | 160 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
159 | 161 | shift = int(self.dataOut.nFFTPoints/2) |
|
|
160 | 162 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) |
|
|
161 | 163 | |
|
|
162 | 164 | if self.dataOut.data_cspc is not None: |
|
|
163 | 165 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
|
164 | 166 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) |
|
|
165 | 167 | if pairsList: |
|
|
166 | 168 | self.__selectPairs(pairsList) |
|
|
167 | 169 | |
|
|
168 | 170 | elif self.dataIn.type == "Voltage": |
|
|
169 | 171 | |
|
|
170 | 172 | self.dataOut.flagNoData = True |
|
|
171 | 173 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
172 | 174 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
|
|
173 | 175 | |
|
|
174 | 176 | if nFFTPoints == None: |
|
|
175 | 177 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") |
|
|
176 | 178 | |
|
|
177 | 179 | if nProfiles == None: |
|
|
178 | 180 | nProfiles = nFFTPoints |
|
|
179 | 181 | |
|
|
180 | 182 | if ippFactor == None: |
|
|
181 | 183 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
182 | 184 | else: |
|
|
183 | 185 | self.dataOut.ippFactor = ippFactor |
|
|
184 | 186 | |
|
|
185 | 187 | if self.buffer is None: |
|
|
186 | 188 | if not zeroPad: |
|
|
187 | 189 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
188 | 190 | nProfiles, |
|
|
189 | 191 | self.dataIn.nHeights), |
|
|
190 | 192 | dtype='complex') |
|
|
191 | 193 | zeroPoints = 0 |
|
|
192 | 194 | else: |
|
|
193 | 195 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
194 | 196 | nFFTPoints+int(zeroPoints), |
|
|
195 | 197 | self.dataIn.nHeights), |
|
|
196 | 198 | dtype='complex') |
|
|
197 | 199 | |
|
|
198 | 200 | self.dataOut.nFFTPoints = nFFTPoints |
|
|
199 | 201 | |
|
|
200 | 202 | if self.buffer is None: |
|
|
201 | 203 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
202 | 204 | nProfiles, |
|
|
203 | 205 | self.dataIn.nHeights), |
|
|
204 | 206 | dtype='complex') |
|
|
205 | 207 | |
|
|
206 | 208 | if self.dataIn.flagDataAsBlock: |
|
|
207 | 209 | nVoltProfiles = self.dataIn.data.shape[1] |
|
|
208 | 210 | zeroPoints = 0 |
|
|
209 | 211 | if nVoltProfiles == nProfiles or zeroPad: |
|
|
210 | 212 | self.buffer = self.dataIn.data.copy() |
|
|
211 | 213 | self.profIndex = nVoltProfiles |
|
|
212 | 214 | |
|
|
213 | 215 | elif nVoltProfiles < nProfiles: |
|
|
214 | 216 | |
|
|
215 | 217 | if self.profIndex == 0: |
|
|
216 | 218 | self.id_min = 0 |
|
|
217 | 219 | self.id_max = nVoltProfiles |
|
|
218 | 220 | |
|
|
219 | 221 | self.buffer[:, self.id_min:self.id_max, |
|
|
220 | 222 | :] = self.dataIn.data |
|
|
221 | 223 | self.profIndex += nVoltProfiles |
|
|
222 | 224 | self.id_min += nVoltProfiles |
|
|
223 | 225 | self.id_max += nVoltProfiles |
|
|
224 | 226 | elif nVoltProfiles > nProfiles: |
|
|
225 | 227 | self.reader.bypass = True |
|
|
226 | 228 | if self.profIndex == 0: |
|
|
227 | 229 | self.id_min = 0 |
|
|
228 | 230 | self.id_max = nProfiles |
|
|
229 | 231 | |
|
|
230 | 232 | self.buffer = self.dataIn.data[:, self.id_min:self.id_max,:] |
|
|
231 | 233 | self.profIndex += nProfiles |
|
|
232 | 234 | self.id_min += nProfiles |
|
|
233 | 235 | self.id_max += nProfiles |
|
|
234 | 236 | if self.id_max == nVoltProfiles: |
|
|
235 | 237 | self.reader.bypass = False |
|
|
236 | 238 | |
|
|
237 | 239 | else: |
|
|
238 | 240 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( |
|
|
239 | 241 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) |
|
|
240 | 242 | self.dataOut.flagNoData = True |
|
|
241 | 243 | else: |
|
|
242 | 244 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() |
|
|
243 | 245 | self.profIndex += 1 |
|
|
244 | 246 | |
|
|
245 | 247 | if self.firstdatatime == None: |
|
|
246 | 248 | self.firstdatatime = self.dataIn.utctime |
|
|
247 | 249 | |
|
|
248 | 250 | if self.profIndex == nProfiles or (zeroPad and zeroPoints==0): |
|
|
249 | 251 | |
|
|
250 | 252 | self.__updateSpecFromVoltage() |
|
|
251 | 253 | if pairsList == None: |
|
|
252 | 254 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] |
|
|
253 | 255 | else: |
|
|
254 | 256 | self.dataOut.pairsList = pairsList |
|
|
255 | 257 | self.__getFft() |
|
|
256 | 258 | self.dataOut.flagNoData = False |
|
|
257 | 259 | self.firstdatatime = None |
|
|
258 | 260 | self.nsamplesFFT = self.profIndex |
|
|
259 | 261 | #if not self.reader.bypass: |
|
|
260 | 262 | self.profIndex = 0 |
|
|
261 | 263 | #update Processing Header: |
|
|
262 | 264 | self.dataOut.processingHeaderObj.dtype = "Spectra" |
|
|
263 | 265 | self.dataOut.processingHeaderObj.nFFTPoints = self.dataOut.nFFTPoints |
|
|
264 | 266 | self.dataOut.processingHeaderObj.nSamplesFFT = self.nsamplesFFT |
|
|
265 | 267 | self.dataOut.processingHeaderObj.nIncohInt = 1 |
|
|
266 | 268 | |
|
|
267 | 269 | elif self.dataIn.type == "Parameters": #when get data from h5 spc file |
|
|
268 | 270 | |
|
|
269 | 271 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
270 | 272 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
271 | 273 | self.dataOut.data_outlier = self.dataIn.data_outlier |
|
|
272 | 274 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
273 | 275 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
274 | 276 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
275 | 277 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
276 | 278 | self.dataOut.max_nIncohInt = self.dataIn.max_nIncohInt |
|
|
277 | 279 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
278 | 280 | self.dataOut.ProcessingHeader = self.dataIn.ProcessingHeader.copy() |
|
|
279 | 281 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
280 | 282 | self.dataOut.ipp = self.dataIn.ipp |
|
|
281 | 283 | #self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
282 | 284 | #self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
283 | 285 | #self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
284 | 286 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
|
285 | 287 | if hasattr(self.dataIn, 'channelList'): |
|
|
286 | 288 | self.dataOut.channelList = self.dataIn.channelList |
|
|
287 | 289 | if hasattr(self.dataIn, 'pairsList'): |
|
|
288 | 290 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
289 | 291 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
290 | 292 | |
|
|
291 | 293 | self.dataOut.flagNoData = False |
|
|
292 | 294 | |
|
|
293 | 295 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
294 | 296 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
295 | 297 | else: self.dataOut.ChanDist = None |
|
|
296 | 298 | |
|
|
297 | 299 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
298 | 300 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
|
299 | 301 | #else: self.dataOut.VelRange = None |
|
|
300 | 302 | |
|
|
301 | 303 | else: |
|
|
302 | 304 | raise ValueError("The type of input object '%s' is not valid".format( |
|
|
303 | 305 | self.dataIn.type)) |
|
|
304 | ||
|
|
306 | # print("SPC done") | |
|
|
305 | 307 | |
|
|
306 | 308 | def __selectPairs(self, pairsList): |
|
|
307 | 309 | |
|
|
308 | 310 | if not pairsList: |
|
|
309 | 311 | return |
|
|
310 | 312 | |
|
|
311 | 313 | pairs = [] |
|
|
312 | 314 | pairsIndex = [] |
|
|
313 | 315 | |
|
|
314 | 316 | for pair in pairsList: |
|
|
315 | 317 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
|
|
316 | 318 | continue |
|
|
317 | 319 | pairs.append(pair) |
|
|
318 | 320 | pairsIndex.append(pairs.index(pair)) |
|
|
319 | 321 | |
|
|
320 | 322 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
|
|
321 | 323 | self.dataOut.pairsList = pairs |
|
|
322 | 324 | |
|
|
323 | 325 | return |
|
|
324 | 326 | |
|
|
325 | 327 | def selectFFTs(self, minFFT, maxFFT ): |
|
|
326 | 328 | """ |
|
|
327 | 329 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
|
|
328 | 330 | minFFT<= FFT <= maxFFT |
|
|
329 | 331 | """ |
|
|
330 | 332 | |
|
|
331 | 333 | if (minFFT > maxFFT): |
|
|
332 | 334 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) |
|
|
333 | 335 | |
|
|
334 | 336 | if (minFFT < self.dataOut.getFreqRange()[0]): |
|
|
335 | 337 | minFFT = self.dataOut.getFreqRange()[0] |
|
|
336 | 338 | |
|
|
337 | 339 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
|
|
338 | 340 | maxFFT = self.dataOut.getFreqRange()[-1] |
|
|
339 | 341 | |
|
|
340 | 342 | minIndex = 0 |
|
|
341 | 343 | maxIndex = 0 |
|
|
342 | 344 | FFTs = self.dataOut.getFreqRange() |
|
|
343 | 345 | |
|
|
344 | 346 | inda = numpy.where(FFTs >= minFFT) |
|
|
345 | 347 | indb = numpy.where(FFTs <= maxFFT) |
|
|
346 | 348 | |
|
|
347 | 349 | try: |
|
|
348 | 350 | minIndex = inda[0][0] |
|
|
349 | 351 | except: |
|
|
350 | 352 | minIndex = 0 |
|
|
351 | 353 | |
|
|
352 | 354 | try: |
|
|
353 | 355 | maxIndex = indb[0][-1] |
|
|
354 | 356 | except: |
|
|
355 | 357 | maxIndex = len(FFTs) |
|
|
356 | 358 | |
|
|
357 | 359 | self.selectFFTsByIndex(minIndex, maxIndex) |
|
|
358 | 360 | |
|
|
359 | 361 | return 1 |
|
|
360 | 362 | |
|
|
361 | 363 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): |
|
|
362 | 364 | newheis = numpy.where( |
|
|
363 | 365 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
364 | 366 | |
|
|
365 | 367 | if hei_ref != None: |
|
|
366 | 368 | newheis = numpy.where(self.dataOut.heightList > hei_ref) |
|
|
367 | 369 | |
|
|
368 | 370 | minIndex = min(newheis[0]) |
|
|
369 | 371 | maxIndex = max(newheis[0]) |
|
|
370 | 372 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
371 | 373 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
372 | 374 | |
|
|
373 | 375 | # determina indices |
|
|
374 | 376 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / |
|
|
375 | 377 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) |
|
|
376 | 378 | avg_dB = 10 * \ |
|
|
377 | 379 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) |
|
|
378 | 380 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
379 | 381 | beacon_heiIndexList = [] |
|
|
380 | 382 | for val in avg_dB.tolist(): |
|
|
381 | 383 | if val >= beacon_dB[0]: |
|
|
382 | 384 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
|
383 | 385 | |
|
|
384 | 386 | data_cspc = None |
|
|
385 | 387 | if self.dataOut.data_cspc is not None: |
|
|
386 | 388 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
387 | 389 | |
|
|
388 | 390 | data_dc = None |
|
|
389 | 391 | if self.dataOut.data_dc is not None: |
|
|
390 | 392 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
391 | 393 | |
|
|
392 | 394 | self.dataOut.data_spc = data_spc |
|
|
393 | 395 | self.dataOut.data_cspc = data_cspc |
|
|
394 | 396 | self.dataOut.data_dc = data_dc |
|
|
395 | 397 | self.dataOut.heightList = heightList |
|
|
396 | 398 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
397 | 399 | |
|
|
398 | 400 | return 1 |
|
|
399 | 401 | |
|
|
400 | 402 | def selectFFTsByIndex(self, minIndex, maxIndex): |
|
|
401 | 403 | """ |
|
|
402 | 404 | |
|
|
403 | 405 | """ |
|
|
404 | 406 | |
|
|
405 | 407 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
406 | 408 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
407 | 409 | |
|
|
408 | 410 | if (maxIndex >= self.dataOut.nProfiles): |
|
|
409 | 411 | maxIndex = self.dataOut.nProfiles-1 |
|
|
410 | 412 | |
|
|
411 | 413 | #Spectra |
|
|
412 | 414 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
|
|
413 | 415 | |
|
|
414 | 416 | data_cspc = None |
|
|
415 | 417 | if self.dataOut.data_cspc is not None: |
|
|
416 | 418 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
|
|
417 | 419 | |
|
|
418 | 420 | data_dc = None |
|
|
419 | 421 | if self.dataOut.data_dc is not None: |
|
|
420 | 422 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
|
|
421 | 423 | |
|
|
422 | 424 | self.dataOut.data_spc = data_spc |
|
|
423 | 425 | self.dataOut.data_cspc = data_cspc |
|
|
424 | 426 | self.dataOut.data_dc = data_dc |
|
|
425 | 427 | |
|
|
426 | 428 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
|
|
427 | 429 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
|
|
428 | 430 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
|
|
429 | 431 | |
|
|
430 | 432 | return 1 |
|
|
431 | 433 | |
|
|
432 | 434 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
433 | 435 | # validacion de rango |
|
|
434 | 436 | if minHei == None: |
|
|
435 | 437 | minHei = self.dataOut.heightList[0] |
|
|
436 | 438 | |
|
|
437 | 439 | if maxHei == None: |
|
|
438 | 440 | maxHei = self.dataOut.heightList[-1] |
|
|
439 | 441 | |
|
|
440 | 442 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
441 | 443 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
442 | 444 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
443 | 445 | minHei = self.dataOut.heightList[0] |
|
|
444 | 446 | |
|
|
445 | 447 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
446 | 448 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
447 | 449 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
448 | 450 | maxHei = self.dataOut.heightList[-1] |
|
|
449 | 451 | |
|
|
450 | 452 | # validacion de velocidades |
|
|
451 | 453 | velrange = self.dataOut.getVelRange(1) |
|
|
452 | 454 | |
|
|
453 | 455 | if minVel == None: |
|
|
454 | 456 | minVel = velrange[0] |
|
|
455 | 457 | |
|
|
456 | 458 | if maxVel == None: |
|
|
457 | 459 | maxVel = velrange[-1] |
|
|
458 | 460 | |
|
|
459 | 461 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
460 | 462 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
|
461 | 463 | print('minVel is setting to %.2f' % (velrange[0])) |
|
|
462 | 464 | minVel = velrange[0] |
|
|
463 | 465 | |
|
|
464 | 466 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
465 | 467 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
|
466 | 468 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
|
467 | 469 | maxVel = velrange[-1] |
|
|
468 | 470 | |
|
|
469 | 471 | # seleccion de indices para rango |
|
|
470 | 472 | minIndex = 0 |
|
|
471 | 473 | maxIndex = 0 |
|
|
472 | 474 | heights = self.dataOut.heightList |
|
|
473 | 475 | |
|
|
474 | 476 | inda = numpy.where(heights >= minHei) |
|
|
475 | 477 | indb = numpy.where(heights <= maxHei) |
|
|
476 | 478 | |
|
|
477 | 479 | try: |
|
|
478 | 480 | minIndex = inda[0][0] |
|
|
479 | 481 | except: |
|
|
480 | 482 | minIndex = 0 |
|
|
481 | 483 | |
|
|
482 | 484 | try: |
|
|
483 | 485 | maxIndex = indb[0][-1] |
|
|
484 | 486 | except: |
|
|
485 | 487 | maxIndex = len(heights) |
|
|
486 | 488 | |
|
|
487 | 489 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
488 | 490 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
489 | 491 | minIndex, maxIndex)) |
|
|
490 | 492 | |
|
|
491 | 493 | if (maxIndex >= self.dataOut.nHeights): |
|
|
492 | 494 | maxIndex = self.dataOut.nHeights - 1 |
|
|
493 | 495 | |
|
|
494 | 496 | # seleccion de indices para velocidades |
|
|
495 | 497 | indminvel = numpy.where(velrange >= minVel) |
|
|
496 | 498 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
497 | 499 | try: |
|
|
498 | 500 | minIndexVel = indminvel[0][0] |
|
|
499 | 501 | except: |
|
|
500 | 502 | minIndexVel = 0 |
|
|
501 | 503 | |
|
|
502 | 504 | try: |
|
|
503 | 505 | maxIndexVel = indmaxvel[0][-1] |
|
|
504 | 506 | except: |
|
|
505 | 507 | maxIndexVel = len(velrange) |
|
|
506 | 508 | |
|
|
507 | 509 | # seleccion del espectro |
|
|
508 | 510 | data_spc = self.dataOut.data_spc[:, |
|
|
509 | 511 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] |
|
|
510 | 512 | # estimacion de ruido |
|
|
511 | 513 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
512 | 514 | |
|
|
513 | 515 | for channel in range(self.dataOut.nChannels): |
|
|
514 | 516 | daux = data_spc[channel, :, :] |
|
|
515 | 517 | sortdata = numpy.sort(daux, axis=None) |
|
|
516 | 518 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) |
|
|
517 | 519 | |
|
|
518 | 520 | self.dataOut.noise_estimation = noise.copy() |
|
|
519 | 521 | |
|
|
520 | 522 | return 1 |
|
|
521 | 523 | |
|
|
522 | 524 | class GetSNR(Operation): |
|
|
523 | 525 | ''' |
|
|
524 | 526 | Written by R. Flores |
|
|
525 | 527 | ''' |
|
|
526 | 528 | """Operation to get SNR. |
|
|
527 | 529 | |
|
|
528 | 530 | Parameters: |
|
|
529 | 531 | ----------- |
|
|
530 | 532 | |
|
|
531 | 533 | Example |
|
|
532 | 534 | -------- |
|
|
533 | 535 | |
|
|
534 | 536 | op = proc_unit.addOperation(name='GetSNR', optype='other') |
|
|
535 | 537 | |
|
|
536 | 538 | """ |
|
|
537 | 539 | |
|
|
538 | 540 | def __init__(self, **kwargs): |
|
|
539 | 541 | |
|
|
540 | 542 | Operation.__init__(self, **kwargs) |
|
|
541 | 543 | |
|
|
542 | 544 | def run(self,dataOut): |
|
|
543 | 545 | |
|
|
544 | 546 | noise = dataOut.getNoise(ymin_index=-10) #Región superior donde solo debería de haber ruido |
|
|
545 | 547 | dataOut.data_snr = (dataOut.data_spc.sum(axis=1)-noise[:,None]*dataOut.nFFTPoints)/(noise[:,None]*dataOut.nFFTPoints) #It works apparently |
|
|
546 | 548 | dataOut.snl = numpy.log10(dataOut.data_snr) |
|
|
547 | 549 | dataOut.snl = numpy.where(dataOut.data_snr<.01, numpy.nan, dataOut.snl) |
|
|
548 | 550 | |
|
|
549 | 551 | return dataOut |
|
|
550 | 552 | |
|
|
551 | 553 | class removeDC(Operation): |
|
|
552 | 554 | |
|
|
553 | 555 | def run(self, dataOut, mode=2): |
|
|
554 | 556 | self.dataOut = dataOut |
|
|
555 | 557 | jspectra = self.dataOut.data_spc |
|
|
556 | 558 | jcspectra = self.dataOut.data_cspc |
|
|
557 | 559 | |
|
|
558 | 560 | num_chan = jspectra.shape[0] |
|
|
559 | 561 | num_hei = jspectra.shape[2] |
|
|
560 | 562 | |
|
|
561 | 563 | if jcspectra is not None: |
|
|
562 | 564 | jcspectraExist = True |
|
|
563 | 565 | num_pairs = jcspectra.shape[0] |
|
|
564 | 566 | else: |
|
|
565 | 567 | jcspectraExist = False |
|
|
566 | 568 | |
|
|
567 | 569 | freq_dc = int(jspectra.shape[1] / 2) |
|
|
568 | 570 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
569 | 571 | ind_vel = ind_vel.astype(int) |
|
|
570 | 572 | |
|
|
571 | 573 | if ind_vel[0] < 0: |
|
|
572 | 574 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof |
|
|
573 | 575 | |
|
|
574 | 576 | if mode == 1: |
|
|
575 | 577 | jspectra[:, freq_dc, :] = ( |
|
|
576 | 578 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
577 | 579 | |
|
|
578 | 580 | if jcspectraExist: |
|
|
579 | 581 | jcspectra[:, freq_dc, :] = ( |
|
|
580 | 582 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 |
|
|
581 | 583 | |
|
|
582 | 584 | if mode == 2: |
|
|
583 | 585 | |
|
|
584 | 586 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
585 | 587 | xx = numpy.zeros([4, 4]) |
|
|
586 | 588 | |
|
|
587 | 589 | for fil in range(4): |
|
|
588 | 590 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
589 | 591 | |
|
|
590 | 592 | xx_inv = numpy.linalg.inv(xx) |
|
|
591 | 593 | xx_aux = xx_inv[0, :] |
|
|
592 | 594 | |
|
|
593 | 595 | for ich in range(num_chan): |
|
|
594 | 596 | yy = jspectra[ich, ind_vel, :] |
|
|
595 | 597 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
596 | 598 | |
|
|
597 | 599 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
598 | 600 | cjunkid = sum(junkid) |
|
|
599 | 601 | |
|
|
600 | 602 | if cjunkid.any(): |
|
|
601 | 603 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
602 | 604 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
603 | 605 | |
|
|
604 | 606 | if jcspectraExist: |
|
|
605 | 607 | for ip in range(num_pairs): |
|
|
606 | 608 | yy = jcspectra[ip, ind_vel, :] |
|
|
607 | 609 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
608 | 610 | |
|
|
609 | 611 | self.dataOut.data_spc = jspectra |
|
|
610 | 612 | self.dataOut.data_cspc = jcspectra |
|
|
611 | 613 | |
|
|
612 | 614 | return self.dataOut |
|
|
613 | 615 | class getNoiseB(Operation): |
|
|
614 | 616 | """ |
|
|
615 | 617 | Get noise from custom heights and frequency ranges, |
|
|
616 | 618 | offset for additional manual correction |
|
|
617 | 619 | J. Apaza -> developed to amisr isr spectra |
|
|
618 | 620 | |
|
|
619 | 621 | """ |
|
|
620 | 622 | __slots__ =('offset','warnings', 'isConfig', 'minIndex','maxIndex','minIndexFFT','maxIndexFFT') |
|
|
621 | 623 | def __init__(self): |
|
|
622 | 624 | |
|
|
623 | 625 | Operation.__init__(self) |
|
|
624 | 626 | self.isConfig = False |
|
|
625 | 627 | |
|
|
626 | 628 | def setup(self, offset=None, minHei=None, maxHei=None,minVel=None, maxVel=None, minFreq= None, maxFreq=None, warnings=False): |
|
|
627 | 629 | |
|
|
628 | 630 | self.warnings = warnings |
|
|
629 | 631 | if minHei == None: |
|
|
630 | 632 | minHei = self.dataOut.heightList[0] |
|
|
631 | 633 | |
|
|
632 | 634 | if maxHei == None: |
|
|
633 | 635 | maxHei = self.dataOut.heightList[-1] |
|
|
634 | 636 | |
|
|
635 | 637 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
636 | 638 | if self.warnings: |
|
|
637 | 639 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
638 | 640 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
639 | 641 | minHei = self.dataOut.heightList[0] |
|
|
640 | 642 | |
|
|
641 | 643 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
642 | 644 | if self.warnings: |
|
|
643 | 645 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
644 | 646 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
645 | 647 | maxHei = self.dataOut.heightList[-1] |
|
|
646 | 648 | |
|
|
647 | 649 | |
|
|
648 | 650 | #indices relativos a los puntos de fft, puede ser de acuerdo a velocidad o frecuencia |
|
|
649 | 651 | minIndexFFT = 0 |
|
|
650 | 652 | maxIndexFFT = 0 |
|
|
651 | 653 | # validacion de velocidades |
|
|
652 | 654 | indminPoint = None |
|
|
653 | 655 | indmaxPoint = None |
|
|
654 | 656 | if self.dataOut.type == 'Spectra': |
|
|
655 | 657 | if minVel == None and maxVel == None : |
|
|
656 | 658 | |
|
|
657 | 659 | freqrange = self.dataOut.getFreqRange(1) |
|
|
658 | 660 | |
|
|
659 | 661 | if minFreq == None: |
|
|
660 | 662 | minFreq = freqrange[0] |
|
|
661 | 663 | |
|
|
662 | 664 | if maxFreq == None: |
|
|
663 | 665 | maxFreq = freqrange[-1] |
|
|
664 | 666 | |
|
|
665 | 667 | if (minFreq < freqrange[0]) or (minFreq > maxFreq): |
|
|
666 | 668 | if self.warnings: |
|
|
667 | 669 | print('minFreq: %.2f is out of the frequency range' % (minFreq)) |
|
|
668 | 670 | print('minFreq is setting to %.2f' % (freqrange[0])) |
|
|
669 | 671 | minFreq = freqrange[0] |
|
|
670 | 672 | |
|
|
671 | 673 | if (maxFreq > freqrange[-1]) or (maxFreq < minFreq): |
|
|
672 | 674 | if self.warnings: |
|
|
673 | 675 | print('maxFreq: %.2f is out of the frequency range' % (maxFreq)) |
|
|
674 | 676 | print('maxFreq is setting to %.2f' % (freqrange[-1])) |
|
|
675 | 677 | maxFreq = freqrange[-1] |
|
|
676 | 678 | |
|
|
677 | 679 | indminPoint = numpy.where(freqrange >= minFreq) |
|
|
678 | 680 | indmaxPoint = numpy.where(freqrange <= maxFreq) |
|
|
679 | 681 | |
|
|
680 | 682 | else: |
|
|
681 | 683 | |
|
|
682 | 684 | velrange = self.dataOut.getVelRange(1) |
|
|
683 | 685 | |
|
|
684 | 686 | if minVel == None: |
|
|
685 | 687 | minVel = velrange[0] |
|
|
686 | 688 | |
|
|
687 | 689 | if maxVel == None: |
|
|
688 | 690 | maxVel = velrange[-1] |
|
|
689 | 691 | |
|
|
690 | 692 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
691 | 693 | if self.warnings: |
|
|
692 | 694 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
|
693 | 695 | print('minVel is setting to %.2f' % (velrange[0])) |
|
|
694 | 696 | minVel = velrange[0] |
|
|
695 | 697 | |
|
|
696 | 698 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
697 | 699 | if self.warnings: |
|
|
698 | 700 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
|
699 | 701 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
|
700 | 702 | maxVel = velrange[-1] |
|
|
701 | 703 | |
|
|
702 | 704 | indminPoint = numpy.where(velrange >= minVel) |
|
|
703 | 705 | indmaxPoint = numpy.where(velrange <= maxVel) |
|
|
704 | 706 | |
|
|
705 | 707 | |
|
|
706 | 708 | # seleccion de indices para rango REEMPLAZAR FOR FUNCION EXTERNA LUEGO |
|
|
707 | 709 | # minIndex = 0 |
|
|
708 | 710 | # maxIndex = 0 |
|
|
709 | 711 | # heights = self.dataOut.heightList |
|
|
710 | 712 | # inda = numpy.where(heights >= minHei) |
|
|
711 | 713 | # indb = numpy.where(heights <= maxHei) |
|
|
712 | 714 | # try: |
|
|
713 | 715 | # minIndex = inda[0][0] |
|
|
714 | 716 | # except: |
|
|
715 | 717 | # minIndex = 0 |
|
|
716 | 718 | # try: |
|
|
717 | 719 | # maxIndex = indb[0][-1] |
|
|
718 | 720 | # except: |
|
|
719 | 721 | # maxIndex = len(heights) |
|
|
720 | 722 | # if (minIndex < 0) or (minIndex > maxIndex): |
|
|
721 | 723 | # raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
722 | 724 | # minIndex, maxIndex)) |
|
|
723 | 725 | # if (maxIndex >= self.dataOut.nHeights): |
|
|
724 | 726 | # maxIndex = self.dataOut.nHeights - 1 |
|
|
725 | 727 | |
|
|
726 | 728 | minIndex, maxIndex = getHei_index(minHei,maxHei,self.dataOut.heightList) |
|
|
727 | 729 | |
|
|
728 | 730 | |
|
|
729 | 731 | #############################################################3 |
|
|
730 | 732 | # seleccion de indices para velocidades |
|
|
731 | 733 | if self.dataOut.type == 'Spectra': |
|
|
732 | 734 | try: |
|
|
733 | 735 | minIndexFFT = indminPoint[0][0] |
|
|
734 | 736 | except: |
|
|
735 | 737 | minIndexFFT = 0 |
|
|
736 | 738 | |
|
|
737 | 739 | try: |
|
|
738 | 740 | maxIndexFFT = indmaxPoint[0][-1] |
|
|
739 | 741 | except: |
|
|
740 | 742 | maxIndexFFT = len( self.dataOut.getFreqRange(1)) |
|
|
741 | 743 | |
|
|
742 | 744 | self.minIndex, self.maxIndex, self.minIndexFFT, self.maxIndexFFT = minIndex, maxIndex, minIndexFFT, maxIndexFFT |
|
|
743 | 745 | self.isConfig = True |
|
|
744 | 746 | self.offset = 1 |
|
|
745 | 747 | if offset!=None: |
|
|
746 | 748 | self.offset = 10**(offset/10) |
|
|
747 | 749 | |
|
|
748 | 750 | |
|
|
749 | 751 | def run(self, dataOut, offset=None, minHei=None, maxHei=None,minVel=None, maxVel=None, minFreq= None, maxFreq=None, warnings=False): |
|
|
750 | 752 | self.dataOut = dataOut |
|
|
751 | 753 | |
|
|
752 | 754 | if not self.isConfig: |
|
|
753 | 755 | self.setup(offset, minHei, maxHei,minVel, maxVel, minFreq, maxFreq, warnings) |
|
|
754 | 756 | |
|
|
755 | 757 | self.dataOut.noise_estimation = None |
|
|
756 | 758 | noise = None |
|
|
757 | 759 | if self.dataOut.type == 'Voltage': |
|
|
758 | 760 | noise = self.dataOut.getNoise(ymin_index=self.minIndex, ymax_index=self.maxIndex) |
|
|
759 | 761 | elif self.dataOut.type == 'Spectra': |
|
|
760 | 762 | noise = numpy.zeros( self.dataOut.nChannels) |
|
|
761 | 763 | norm = 1 |
|
|
762 | 764 | |
|
|
763 | 765 | for channel in range( self.dataOut.nChannels): |
|
|
764 | 766 | if not hasattr(self.dataOut.nIncohInt,'__len__'): |
|
|
765 | 767 | norm = 1 |
|
|
766 | 768 | else: |
|
|
767 | 769 | norm = self.dataOut.max_nIncohInt[channel]/self.dataOut.nIncohInt[channel, self.minIndex:self.maxIndex] |
|
|
768 | 770 | |
|
|
769 | 771 | daux = self.dataOut.data_spc[channel,self.minIndexFFT:self.maxIndexFFT, self.minIndex:self.maxIndex] |
|
|
770 | 772 | daux = numpy.multiply(daux, norm) |
|
|
771 | 773 | sortdata = numpy.sort(daux, axis=None) |
|
|
772 | 774 | noise[channel] = _noise.hildebrand_sekhon(sortdata, self.dataOut.max_nIncohInt[channel])/self.offset |
|
|
773 | 775 | |
|
|
774 | 776 | else: |
|
|
775 | 777 | noise = self.dataOut.getNoise(xmin_index=self.minIndexFFT, xmax_index=self.maxIndexFFT, ymin_index=self.minIndex, ymax_index=self.maxIndex) |
|
|
776 | 778 | |
|
|
777 | 779 | self.dataOut.noise_estimation = noise.copy() # dataOut.noise |
|
|
778 | 780 | |
|
|
779 | 781 | return self.dataOut |
|
|
780 | 782 | |
|
|
781 | 783 | def getNoiseByMean(self,data): |
|
|
782 | 784 | #data debe estar ordenado |
|
|
783 | 785 | data = numpy.mean(data,axis=1) |
|
|
784 | 786 | sortdata = numpy.sort(data, axis=None) |
|
|
785 | 787 | pnoise = None |
|
|
786 | 788 | j = 0 |
|
|
787 | 789 | |
|
|
788 | 790 | mean = numpy.mean(sortdata) |
|
|
789 | 791 | min = numpy.min(sortdata) |
|
|
790 | 792 | delta = mean - min |
|
|
791 | 793 | indexes = numpy.where(sortdata > (mean+delta))[0] #only array of indexes |
|
|
792 | 794 | #print(len(indexes)) |
|
|
793 | 795 | if len(indexes)==0: |
|
|
794 | 796 | pnoise = numpy.mean(sortdata) |
|
|
795 | 797 | else: |
|
|
796 | 798 | j = indexes[0] |
|
|
797 | 799 | pnoise = numpy.mean(sortdata[0:j]) |
|
|
798 | 800 | |
|
|
799 | 801 | return pnoise |
|
|
800 | 802 | |
|
|
801 | 803 | def getNoiseByHS(self,data, navg): |
|
|
802 | 804 | #data debe estar ordenado |
|
|
803 | 805 | #data = numpy.mean(data,axis=1) |
|
|
804 | 806 | sortdata = numpy.sort(data, axis=None) |
|
|
805 | 807 | |
|
|
806 | 808 | lenOfData = len(sortdata) |
|
|
807 | 809 | nums_min = lenOfData*0.2 |
|
|
808 | 810 | |
|
|
809 | 811 | if nums_min <= 5: |
|
|
810 | 812 | |
|
|
811 | 813 | nums_min = 5 |
|
|
812 | 814 | |
|
|
813 | 815 | sump = 0. |
|
|
814 | 816 | sumq = 0. |
|
|
815 | 817 | |
|
|
816 | 818 | j = 0 |
|
|
817 | 819 | cont = 1 |
|
|
818 | 820 | |
|
|
819 | 821 | while((cont == 1)and(j < lenOfData)): |
|
|
820 | 822 | |
|
|
821 | 823 | sump += sortdata[j] |
|
|
822 | 824 | sumq += sortdata[j]**2 |
|
|
823 | 825 | #sumq -= sump**2 |
|
|
824 | 826 | if j > nums_min: |
|
|
825 | 827 | rtest = float(j)/(j-1) + 1.0/navg |
|
|
826 | 828 | #if ((sumq*j) > (sump**2)): |
|
|
827 | 829 | if ((sumq*j) > (rtest*sump**2)): |
|
|
828 | 830 | j = j - 1 |
|
|
829 | 831 | sump = sump - sortdata[j] |
|
|
830 | 832 | sumq = sumq - sortdata[j]**2 |
|
|
831 | 833 | cont = 0 |
|
|
832 | 834 | |
|
|
833 | 835 | j += 1 |
|
|
834 | 836 | |
|
|
835 | 837 | lnoise = sump / j |
|
|
836 | 838 | |
|
|
837 | 839 | return lnoise |
|
|
838 | 840 | |
|
|
839 | 841 | class removeInterference(Operation): |
|
|
840 | 842 | |
|
|
841 | 843 | def removeInterference2(self): |
|
|
842 | 844 | |
|
|
843 | 845 | cspc = self.dataOut.data_cspc |
|
|
844 | 846 | spc = self.dataOut.data_spc |
|
|
845 | 847 | Heights = numpy.arange(cspc.shape[2]) |
|
|
846 | 848 | realCspc = numpy.abs(cspc) |
|
|
847 | 849 | |
|
|
848 | 850 | for i in range(cspc.shape[0]): |
|
|
849 | 851 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
|
850 | 852 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
851 | 853 | SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] |
|
|
852 | 854 | InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) |
|
|
853 | 855 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
854 | 856 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
|
855 | 857 | |
|
|
856 | 858 | |
|
|
857 | 859 | InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
|
858 | 860 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
|
859 | 861 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
|
860 | 862 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
|
861 | 863 | |
|
|
862 | 864 | self.dataOut.data_cspc = cspc |
|
|
863 | 865 | |
|
|
864 | 866 | def removeInterference(self, interf=2, hei_interf=None, nhei_interf=None, offhei_interf=None): |
|
|
865 | 867 | |
|
|
866 | 868 | jspectra = self.dataOut.data_spc |
|
|
867 | 869 | jcspectra = self.dataOut.data_cspc |
|
|
868 | 870 | jnoise = self.dataOut.getNoise() |
|
|
869 | 871 | num_incoh = self.dataOut.nIncohInt |
|
|
870 | 872 | |
|
|
871 | 873 | num_channel = jspectra.shape[0] |
|
|
872 | 874 | num_prof = jspectra.shape[1] |
|
|
873 | 875 | num_hei = jspectra.shape[2] |
|
|
874 | 876 | |
|
|
875 | 877 | # hei_interf |
|
|
876 | 878 | if hei_interf is None: |
|
|
877 | 879 | count_hei = int(num_hei / 2) |
|
|
878 | 880 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei |
|
|
879 | 881 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
880 | 882 | # nhei_interf |
|
|
881 | 883 | if (nhei_interf == None): |
|
|
882 | 884 | nhei_interf = 5 |
|
|
883 | 885 | if (nhei_interf < 1): |
|
|
884 | 886 | nhei_interf = 1 |
|
|
885 | 887 | if (nhei_interf > count_hei): |
|
|
886 | 888 | nhei_interf = count_hei |
|
|
887 | 889 | if (offhei_interf == None): |
|
|
888 | 890 | offhei_interf = 0 |
|
|
889 | 891 | |
|
|
890 | 892 | ind_hei = list(range(num_hei)) |
|
|
891 | 893 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
892 | 894 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
893 | 895 | mask_prof = numpy.asarray(list(range(num_prof))) |
|
|
894 | 896 | num_mask_prof = mask_prof.size |
|
|
895 | 897 | comp_mask_prof = [0, num_prof / 2] |
|
|
896 | 898 | |
|
|
897 | 899 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
898 | 900 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
899 | 901 | jnoise = numpy.nan |
|
|
900 | 902 | noise_exist = jnoise[0] < numpy.Inf |
|
|
901 | 903 | |
|
|
902 | 904 | # Subrutina de Remocion de la Interferencia |
|
|
903 | 905 | for ich in range(num_channel): |
|
|
904 | 906 | # Se ordena los espectros segun su potencia (menor a mayor) |
|
|
905 | 907 | power = jspectra[ich, mask_prof, :] |
|
|
906 | 908 | power = power[:, hei_interf] |
|
|
907 | 909 | power = power.sum(axis=0) |
|
|
908 | 910 | psort = power.ravel().argsort() |
|
|
909 | 911 | |
|
|
910 | 912 | # Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
911 | 913 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( |
|
|
912 | 914 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
913 | 915 | |
|
|
914 | 916 | if noise_exist: |
|
|
915 | 917 | # tmp_noise = jnoise[ich] / num_prof |
|
|
916 | 918 | tmp_noise = jnoise[ich] |
|
|
917 | 919 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
918 | 920 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
919 | 921 | |
|
|
920 | 922 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf |
|
|
921 | 923 | jspc_interf = jspc_interf.transpose() |
|
|
922 | 924 | # Calculando el espectro de interferencia promedio |
|
|
923 | 925 | noiseid = numpy.where( |
|
|
924 | 926 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) |
|
|
925 | 927 | noiseid = noiseid[0] |
|
|
926 | 928 | cnoiseid = noiseid.size |
|
|
927 | 929 | interfid = numpy.where( |
|
|
928 | 930 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) |
|
|
929 | 931 | interfid = interfid[0] |
|
|
930 | 932 | cinterfid = interfid.size |
|
|
931 | 933 | |
|
|
932 | 934 | if (cnoiseid > 0): |
|
|
933 | 935 | jspc_interf[noiseid] = 0 |
|
|
934 | 936 | |
|
|
935 | 937 | # Expandiendo los perfiles a limpiar |
|
|
936 | 938 | if (cinterfid > 0): |
|
|
937 | 939 | new_interfid = ( |
|
|
938 | 940 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof |
|
|
939 | 941 | new_interfid = numpy.asarray(new_interfid) |
|
|
940 | 942 | new_interfid = {x for x in new_interfid} |
|
|
941 | 943 | new_interfid = numpy.array(list(new_interfid)) |
|
|
942 | 944 | new_cinterfid = new_interfid.size |
|
|
943 | 945 | else: |
|
|
944 | 946 | new_cinterfid = 0 |
|
|
945 | 947 | |
|
|
946 | 948 | for ip in range(new_cinterfid): |
|
|
947 | 949 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() |
|
|
948 | 950 | jspc_interf[new_interfid[ip] |
|
|
949 | 951 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] |
|
|
950 | 952 | |
|
|
951 | 953 | jspectra[ich, :, ind_hei] = jspectra[ich, :, |
|
|
952 | 954 | ind_hei] - jspc_interf # Corregir indices |
|
|
953 | 955 | |
|
|
954 | 956 | # Removiendo la interferencia del punto de mayor interferencia |
|
|
955 | 957 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
956 | 958 | maxid = ListAux.index(max(ListAux)) |
|
|
957 | 959 | |
|
|
958 | 960 | if cinterfid > 0: |
|
|
959 | 961 | for ip in range(cinterfid * (interf == 2) - 1): |
|
|
960 | 962 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * |
|
|
961 | 963 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
962 | 964 | cind = len(ind) |
|
|
963 | 965 | |
|
|
964 | 966 | if (cind > 0): |
|
|
965 | 967 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ |
|
|
966 | 968 | (1 + (numpy.random.uniform(cind) - 0.5) / |
|
|
967 | 969 | numpy.sqrt(num_incoh)) |
|
|
968 | 970 | |
|
|
969 | 971 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
970 | 972 | xx = numpy.zeros([4, 4]) |
|
|
971 | 973 | |
|
|
972 | 974 | for id1 in range(4): |
|
|
973 | 975 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
974 | 976 | |
|
|
975 | 977 | xx_inv = numpy.linalg.inv(xx) |
|
|
976 | 978 | xx = xx_inv[:, 0] |
|
|
977 | 979 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
978 | 980 | yy = jspectra[ich, mask_prof[ind], :] |
|
|
979 | 981 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( |
|
|
980 | 982 | yy.transpose(), xx) |
|
|
981 | 983 | |
|
|
982 | 984 | indAux = (jspectra[ich, :, :] < tmp_noise * |
|
|
983 | 985 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
984 | 986 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ |
|
|
985 | 987 | (1 - 1 / numpy.sqrt(num_incoh)) |
|
|
986 | 988 | |
|
|
987 | 989 | # Remocion de Interferencia en el Cross Spectra |
|
|
988 | 990 | if jcspectra is None: |
|
|
989 | 991 | return jspectra, jcspectra |
|
|
990 | 992 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) |
|
|
991 | 993 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
992 | 994 | |
|
|
993 | 995 | for ip in range(num_pairs): |
|
|
994 | 996 | |
|
|
995 | 997 | #------------------------------------------- |
|
|
996 | 998 | |
|
|
997 | 999 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) |
|
|
998 | 1000 | cspower = cspower[:, hei_interf] |
|
|
999 | 1001 | cspower = cspower.sum(axis=0) |
|
|
1000 | 1002 | |
|
|
1001 | 1003 | cspsort = cspower.ravel().argsort() |
|
|
1002 | 1004 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( |
|
|
1003 | 1005 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
1004 | 1006 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
1005 | 1007 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf |
|
|
1006 | 1008 | |
|
|
1007 | 1009 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
1008 | 1010 | |
|
|
1009 | 1011 | median_real = int(numpy.median(numpy.real( |
|
|
1010 | 1012 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1011 | 1013 | median_imag = int(numpy.median(numpy.imag( |
|
|
1012 | 1014 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1013 | 1015 | comp_mask_prof = [int(e) for e in comp_mask_prof] |
|
|
1014 | 1016 | junkcspc_interf[comp_mask_prof, :] = numpy.complex_( |
|
|
1015 | 1017 | median_real, median_imag) |
|
|
1016 | 1018 | |
|
|
1017 | 1019 | for iprof in range(num_prof): |
|
|
1018 | 1020 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() |
|
|
1019 | 1021 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] |
|
|
1020 | 1022 | |
|
|
1021 | 1023 | # Removiendo la Interferencia |
|
|
1022 | 1024 | jcspectra[ip, :, ind_hei] = jcspectra[ip, |
|
|
1023 | 1025 | :, ind_hei] - jcspc_interf |
|
|
1024 | 1026 | |
|
|
1025 | 1027 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
1026 | 1028 | maxid = ListAux.index(max(ListAux)) |
|
|
1027 | 1029 | |
|
|
1028 | 1030 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
1029 | 1031 | xx = numpy.zeros([4, 4]) |
|
|
1030 | 1032 | |
|
|
1031 | 1033 | for id1 in range(4): |
|
|
1032 | 1034 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
1033 | 1035 | |
|
|
1034 | 1036 | xx_inv = numpy.linalg.inv(xx) |
|
|
1035 | 1037 | xx = xx_inv[:, 0] |
|
|
1036 | 1038 | |
|
|
1037 | 1039 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
1038 | 1040 | yy = jcspectra[ip, mask_prof[ind], :] |
|
|
1039 | 1041 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) |
|
|
1040 | 1042 | |
|
|
1041 | 1043 | # Guardar Resultados |
|
|
1042 | 1044 | self.dataOut.data_spc = jspectra |
|
|
1043 | 1045 | self.dataOut.data_cspc = jcspectra |
|
|
1044 | 1046 | |
|
|
1045 | 1047 | return 1 |
|
|
1046 | 1048 | |
|
|
1047 | 1049 | |
|
|
1048 | 1050 | def run(self, dataOut, interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None, mode=1): |
|
|
1049 | 1051 | |
|
|
1050 | 1052 | self.dataOut = dataOut |
|
|
1051 | 1053 | |
|
|
1052 | 1054 | if mode == 1: |
|
|
1053 | 1055 | self.removeInterference(interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None) |
|
|
1054 | 1056 | elif mode == 2: |
|
|
1055 | 1057 | self.removeInterference2() |
|
|
1056 | 1058 | |
|
|
1057 | 1059 | return self.dataOut |
|
|
1058 | 1060 | |
|
|
1059 | 1061 | |
|
|
1060 | 1062 | class deflip(Operation): |
|
|
1061 | 1063 | |
|
|
1062 | 1064 | def run(self, dataOut): |
|
|
1063 | 1065 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
1064 | 1066 | self.dataOut = dataOut |
|
|
1065 | 1067 | |
|
|
1066 | 1068 | # JULIA-oblicua, indice 2 |
|
|
1067 | 1069 | # arreglo 2: (num_profiles, num_heights) |
|
|
1068 | 1070 | jspectra = self.dataOut.data_spc[2] |
|
|
1069 | 1071 | jspectra_tmp=numpy.zeros(jspectra.shape) |
|
|
1070 | 1072 | num_profiles=jspectra.shape[0] |
|
|
1071 | 1073 | freq_dc = int(num_profiles / 2) |
|
|
1072 | 1074 | # Flip con for |
|
|
1073 | 1075 | for j in range(num_profiles): |
|
|
1074 | 1076 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
1075 | 1077 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
1076 | 1078 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
1077 | 1079 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
1078 | 1080 | # canal modificado es re-escrito en el arreglo de canales |
|
|
1079 | 1081 | self.dataOut.data_spc[2] = jspectra_tmp |
|
|
1080 | 1082 | |
|
|
1081 | 1083 | return self.dataOut |
|
|
1082 | 1084 | |
|
|
1083 | 1085 | |
|
|
1084 | 1086 | class IncohInt(Operation): |
|
|
1085 | 1087 | |
|
|
1086 | 1088 | __profIndex = 0 |
|
|
1087 | 1089 | __withOverapping = False |
|
|
1088 | 1090 | |
|
|
1089 | 1091 | __byTime = False |
|
|
1090 | 1092 | __initime = None |
|
|
1091 | 1093 | __lastdatatime = None |
|
|
1092 | 1094 | __integrationtime = None |
|
|
1093 | 1095 | |
|
|
1094 | 1096 | __buffer_spc = None |
|
|
1095 | 1097 | __buffer_cspc = None |
|
|
1096 | 1098 | __buffer_dc = None |
|
|
1097 | 1099 | |
|
|
1098 | 1100 | __dataReady = False |
|
|
1099 | 1101 | |
|
|
1100 | 1102 | __timeInterval = None |
|
|
1101 | 1103 | incohInt = 0 |
|
|
1102 | 1104 | nOutliers = 0 |
|
|
1103 | 1105 | n = None |
|
|
1104 | 1106 | |
|
|
1105 | 1107 | _flagProfilesByRange = False |
|
|
1106 | 1108 | _nProfilesByRange = 0 |
|
|
1107 | 1109 | def __init__(self): |
|
|
1108 | 1110 | |
|
|
1109 | 1111 | Operation.__init__(self) |
|
|
1110 | 1112 | |
|
|
1111 | 1113 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
1112 | 1114 | """ |
|
|
1113 | 1115 | Set the parameters of the integration class. |
|
|
1114 | 1116 | |
|
|
1115 | 1117 | Inputs: |
|
|
1116 | 1118 | |
|
|
1117 | 1119 | n : Number of coherent integrations |
|
|
1118 | 1120 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
1119 | 1121 | overlapping : |
|
|
1120 | 1122 | |
|
|
1121 | 1123 | """ |
|
|
1122 | 1124 | |
|
|
1123 | 1125 | self.__initime = None |
|
|
1124 | 1126 | self.__lastdatatime = 0 |
|
|
1125 | 1127 | |
|
|
1126 | 1128 | self.__buffer_spc = 0 |
|
|
1127 | 1129 | self.__buffer_cspc = 0 |
|
|
1128 | 1130 | self.__buffer_dc = 0 |
|
|
1129 | 1131 | |
|
|
1130 | 1132 | self.__profIndex = 0 |
|
|
1131 | 1133 | self.__dataReady = False |
|
|
1132 | 1134 | self.__byTime = False |
|
|
1133 | 1135 | self.incohInt = 0 |
|
|
1134 | 1136 | self.nOutliers = 0 |
|
|
1135 | 1137 | if n is None and timeInterval is None: |
|
|
1136 | 1138 | raise ValueError("n or timeInterval should be specified ...") |
|
|
1137 | 1139 | |
|
|
1138 | 1140 | if n is not None: |
|
|
1139 | 1141 | self.n = int(n) |
|
|
1140 | 1142 | else: |
|
|
1141 | 1143 | |
|
|
1142 | 1144 | self.__integrationtime = int(timeInterval) |
|
|
1143 | 1145 | self.n = None |
|
|
1144 | 1146 | self.__byTime = True |
|
|
1145 | 1147 | |
|
|
1146 | 1148 | |
|
|
1147 | 1149 | |
|
|
1148 | 1150 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
1149 | 1151 | """ |
|
|
1150 | 1152 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
1151 | 1153 | |
|
|
1152 | 1154 | """ |
|
|
1153 | 1155 | if data_spc.all() == numpy.nan : |
|
|
1154 | 1156 | print("nan ") |
|
|
1155 | 1157 | return |
|
|
1156 | 1158 | self.__buffer_spc += data_spc |
|
|
1157 | 1159 | |
|
|
1158 | 1160 | if data_cspc is None: |
|
|
1159 | 1161 | self.__buffer_cspc = None |
|
|
1160 | 1162 | else: |
|
|
1161 | 1163 | self.__buffer_cspc += data_cspc |
|
|
1162 | 1164 | |
|
|
1163 | 1165 | if data_dc is None: |
|
|
1164 | 1166 | self.__buffer_dc = None |
|
|
1165 | 1167 | else: |
|
|
1166 | 1168 | self.__buffer_dc += data_dc |
|
|
1167 | 1169 | |
|
|
1168 | 1170 | self.__profIndex += 1 |
|
|
1169 | 1171 | |
|
|
1170 | 1172 | return |
|
|
1171 | 1173 | |
|
|
1172 | 1174 | def pushData(self): |
|
|
1173 | 1175 | """ |
|
|
1174 | 1176 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
1175 | 1177 | |
|
|
1176 | 1178 | Affected: |
|
|
1177 | 1179 | |
|
|
1178 | 1180 | self.__profileIndex |
|
|
1179 | 1181 | |
|
|
1180 | 1182 | """ |
|
|
1181 | 1183 | |
|
|
1182 | 1184 | data_spc = self.__buffer_spc |
|
|
1183 | 1185 | data_cspc = self.__buffer_cspc |
|
|
1184 | 1186 | data_dc = self.__buffer_dc |
|
|
1185 | 1187 | n = self.__profIndex |
|
|
1186 | 1188 | |
|
|
1187 | 1189 | self.__buffer_spc = 0 |
|
|
1188 | 1190 | self.__buffer_cspc = 0 |
|
|
1189 | 1191 | self.__buffer_dc = 0 |
|
|
1190 | 1192 | |
|
|
1191 | 1193 | |
|
|
1192 | 1194 | return data_spc, data_cspc, data_dc, n |
|
|
1193 | 1195 | |
|
|
1194 | 1196 | def byProfiles(self, *args): |
|
|
1195 | 1197 | |
|
|
1196 | 1198 | self.__dataReady = False |
|
|
1197 | 1199 | avgdata_spc = None |
|
|
1198 | 1200 | avgdata_cspc = None |
|
|
1199 | 1201 | avgdata_dc = None |
|
|
1200 | 1202 | |
|
|
1201 | 1203 | self.putData(*args) |
|
|
1202 | 1204 | |
|
|
1203 | 1205 | if self.__profIndex == self.n: |
|
|
1204 | 1206 | |
|
|
1205 | 1207 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1206 | 1208 | self.n = n |
|
|
1207 | 1209 | self.__dataReady = True |
|
|
1208 | 1210 | |
|
|
1209 | 1211 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1210 | 1212 | |
|
|
1211 | 1213 | def byTime(self, datatime, *args): |
|
|
1212 | 1214 | |
|
|
1213 | 1215 | self.__dataReady = False |
|
|
1214 | 1216 | avgdata_spc = None |
|
|
1215 | 1217 | avgdata_cspc = None |
|
|
1216 | 1218 | avgdata_dc = None |
|
|
1217 | 1219 | |
|
|
1218 | 1220 | self.putData(*args) |
|
|
1219 | 1221 | |
|
|
1220 | 1222 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1221 | 1223 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1222 | 1224 | self.n = n |
|
|
1223 | 1225 | self.__dataReady = True |
|
|
1224 | 1226 | |
|
|
1225 | 1227 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1226 | 1228 | |
|
|
1227 | 1229 | def integrate(self, datatime, *args): |
|
|
1228 | 1230 | |
|
|
1229 | 1231 | if self.__profIndex == 0: |
|
|
1230 | 1232 | self.__initime = datatime |
|
|
1231 | 1233 | |
|
|
1232 | 1234 | if self.__byTime: |
|
|
1233 | 1235 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
1234 | 1236 | datatime, *args) |
|
|
1235 | 1237 | else: |
|
|
1236 | 1238 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1237 | 1239 | |
|
|
1238 | 1240 | if not self.__dataReady: |
|
|
1239 | 1241 | return None, None, None, None |
|
|
1240 | 1242 | |
|
|
1241 | 1243 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1242 | 1244 | |
|
|
1243 | 1245 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
1244 | 1246 | if n == 1: |
|
|
1245 | 1247 | return dataOut |
|
|
1246 | 1248 | |
|
|
1247 | 1249 | if dataOut.flagNoData == True: |
|
|
1248 | 1250 | return dataOut |
|
|
1249 | 1251 | |
|
|
1250 | 1252 | if dataOut.flagProfilesByRange == True: |
|
|
1251 | 1253 | self._flagProfilesByRange = True |
|
|
1252 | 1254 | |
|
|
1253 | 1255 | dataOut.flagNoData = True |
|
|
1254 | 1256 | dataOut.processingHeaderObj.timeIncohInt = timeInterval |
|
|
1255 | 1257 | if not self.isConfig: |
|
|
1256 | 1258 | self._nProfilesByRange = numpy.zeros((1,len(dataOut.heightList))) |
|
|
1257 | 1259 | self.setup(n, timeInterval, overlapping) |
|
|
1258 | 1260 | self.isConfig = True |
|
|
1259 | 1261 | |
|
|
1260 | 1262 | |
|
|
1261 | 1263 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
1262 | 1264 | dataOut.data_spc, |
|
|
1263 | 1265 | dataOut.data_cspc, |
|
|
1264 | 1266 | dataOut.data_dc) |
|
|
1265 | 1267 | |
|
|
1266 | 1268 | self.incohInt += dataOut.nIncohInt |
|
|
1267 | 1269 | |
|
|
1268 | 1270 | |
|
|
1269 | 1271 | if isinstance(dataOut.data_outlier,numpy.ndarray) or isinstance(dataOut.data_outlier,int) or isinstance(dataOut.data_outlier, float): |
|
|
1270 | 1272 | self.nOutliers += dataOut.data_outlier |
|
|
1271 | 1273 | |
|
|
1272 | 1274 | if self._flagProfilesByRange: |
|
|
1273 | 1275 | dataOut.flagProfilesByRange = True |
|
|
1274 | 1276 | self._nProfilesByRange += dataOut.nProfilesByRange |
|
|
1275 | 1277 | |
|
|
1276 | 1278 | if self.__dataReady: |
|
|
1277 | 1279 | #print("prof: ",dataOut.max_nIncohInt,self.__profIndex) |
|
|
1278 | 1280 | dataOut.data_spc = avgdata_spc |
|
|
1279 | 1281 | dataOut.data_cspc = avgdata_cspc |
|
|
1280 | 1282 | dataOut.data_dc = avgdata_dc |
|
|
1281 | 1283 | dataOut.nIncohInt = self.incohInt |
|
|
1282 | 1284 | dataOut.data_outlier = self.nOutliers |
|
|
1283 | 1285 | dataOut.utctime = avgdatatime |
|
|
1284 | 1286 | dataOut.flagNoData = False |
|
|
1285 | 1287 | self.incohInt = 0 |
|
|
1286 | 1288 | self.nOutliers = 0 |
|
|
1287 | 1289 | self.__profIndex = 0 |
|
|
1288 | 1290 | dataOut.nProfilesByRange = self._nProfilesByRange |
|
|
1289 | 1291 | self._nProfilesByRange = numpy.zeros((1,len(dataOut.heightList))) |
|
|
1290 | 1292 | self._flagProfilesByRange = False |
|
|
1291 | 1293 | # print("IncohInt Done") |
|
|
1292 | 1294 | return dataOut |
|
|
1293 | 1295 | |
|
|
1294 | 1296 | |
|
|
1295 | 1297 | class IntegrationFaradaySpectra(Operation): |
|
|
1296 | 1298 | |
|
|
1297 | 1299 | __profIndex = 0 |
|
|
1298 | 1300 | __withOverapping = False |
|
|
1299 | 1301 | |
|
|
1300 | 1302 | __byTime = False |
|
|
1301 | 1303 | __initime = None |
|
|
1302 | 1304 | __lastdatatime = None |
|
|
1303 | 1305 | __integrationtime = None |
|
|
1304 | 1306 | |
|
|
1305 | 1307 | __buffer_spc = None |
|
|
1306 | 1308 | __buffer_cspc = None |
|
|
1307 | 1309 | __buffer_dc = None |
|
|
1308 | 1310 | |
|
|
1309 | 1311 | __dataReady = False |
|
|
1310 | 1312 | |
|
|
1311 | 1313 | __timeInterval = None |
|
|
1312 | 1314 | n_ints = None #matriz de numero de integracions (CH,HEI) |
|
|
1313 | 1315 | n = None |
|
|
1314 | 1316 | minHei_ind = None |
|
|
1315 | 1317 | maxHei_ind = None |
|
|
1316 | 1318 | navg = 1.0 |
|
|
1317 | 1319 | factor = 0.0 |
|
|
1318 | 1320 | dataoutliers = None # (CHANNELS, HEIGHTS) |
|
|
1319 | 1321 | |
|
|
1320 | 1322 | _flagProfilesByRange = False |
|
|
1321 | 1323 | _nProfilesByRange = 0 |
|
|
1322 | 1324 | |
|
|
1323 | 1325 | def __init__(self): |
|
|
1324 | 1326 | |
|
|
1325 | 1327 | Operation.__init__(self) |
|
|
1326 | 1328 | |
|
|
1327 | 1329 | def setup(self, dataOut,n=None, timeInterval=None, overlapping=False, DPL=None, minHei=None, maxHei=None, avg=1,factor=0.75): |
|
|
1328 | 1330 | """ |
|
|
1329 | 1331 | Set the parameters of the integration class. |
|
|
1330 | 1332 | |
|
|
1331 | 1333 | Inputs: |
|
|
1332 | 1334 | |
|
|
1333 | 1335 | n : Number of coherent integrations |
|
|
1334 | 1336 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
1335 | 1337 | overlapping : |
|
|
1336 | 1338 | |
|
|
1337 | 1339 | """ |
|
|
1338 | 1340 | |
|
|
1339 | 1341 | self.__initime = None |
|
|
1340 | 1342 | self.__lastdatatime = 0 |
|
|
1341 | 1343 | |
|
|
1342 | 1344 | self.__buffer_spc = [] |
|
|
1343 | 1345 | self.__buffer_cspc = [] |
|
|
1344 | 1346 | self.__buffer_dc = 0 |
|
|
1345 | 1347 | |
|
|
1346 | 1348 | self.__profIndex = 0 |
|
|
1347 | 1349 | self.__dataReady = False |
|
|
1348 | 1350 | self.__byTime = False |
|
|
1349 | 1351 | |
|
|
1350 | 1352 | self.factor = factor |
|
|
1351 | 1353 | self.navg = avg |
|
|
1352 | 1354 | #self.ByLags = dataOut.ByLags ###REDEFINIR |
|
|
1353 | 1355 | self.ByLags = False |
|
|
1354 | 1356 | self.maxProfilesInt = 0 |
|
|
1355 | 1357 | self.__nChannels = dataOut.nChannels |
|
|
1356 | 1358 | if DPL != None: |
|
|
1357 | 1359 | self.DPL=DPL |
|
|
1358 | 1360 | else: |
|
|
1359 | 1361 | #self.DPL=dataOut.DPL ###REDEFINIR |
|
|
1360 | 1362 | self.DPL=0 |
|
|
1361 | 1363 | |
|
|
1362 | 1364 | if n is None and timeInterval is None: |
|
|
1363 | 1365 | raise ValueError("n or timeInterval should be specified ...") |
|
|
1364 | 1366 | |
|
|
1365 | 1367 | if n is not None: |
|
|
1366 | 1368 | self.n = int(n) |
|
|
1367 | 1369 | else: |
|
|
1368 | 1370 | self.__integrationtime = int(timeInterval) |
|
|
1369 | 1371 | self.n = None |
|
|
1370 | 1372 | self.__byTime = True |
|
|
1371 | 1373 | |
|
|
1372 | 1374 | |
|
|
1373 | 1375 | if minHei == None: |
|
|
1374 | 1376 | minHei = self.dataOut.heightList[0] |
|
|
1375 | 1377 | |
|
|
1376 | 1378 | if maxHei == None: |
|
|
1377 | 1379 | maxHei = self.dataOut.heightList[-1] |
|
|
1378 | 1380 | |
|
|
1379 | 1381 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
1380 | 1382 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
1381 | 1383 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
1382 | 1384 | minHei = self.dataOut.heightList[0] |
|
|
1383 | 1385 | |
|
|
1384 | 1386 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
1385 | 1387 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
1386 | 1388 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
1387 | 1389 | maxHei = self.dataOut.heightList[-1] |
|
|
1388 | 1390 | |
|
|
1389 | 1391 | ind_list1 = numpy.where(self.dataOut.heightList >= minHei) |
|
|
1390 | 1392 | ind_list2 = numpy.where(self.dataOut.heightList <= maxHei) |
|
|
1391 | 1393 | self.minHei_ind = ind_list1[0][0] |
|
|
1392 | 1394 | self.maxHei_ind = ind_list2[0][-1] |
|
|
1393 | 1395 | |
|
|
1394 | 1396 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
1395 | 1397 | """ |
|
|
1396 | 1398 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
1397 | 1399 | |
|
|
1398 | 1400 | """ |
|
|
1399 | 1401 | |
|
|
1400 | 1402 | self.__buffer_spc.append(data_spc) |
|
|
1401 | 1403 | |
|
|
1402 | 1404 | if self.__nChannels < 2: |
|
|
1403 | 1405 | self.__buffer_cspc = None |
|
|
1404 | 1406 | else: |
|
|
1405 | 1407 | self.__buffer_cspc.append(data_cspc) |
|
|
1406 | 1408 | |
|
|
1407 | 1409 | if data_dc is None: |
|
|
1408 | 1410 | self.__buffer_dc = None |
|
|
1409 | 1411 | else: |
|
|
1410 | 1412 | self.__buffer_dc += data_dc |
|
|
1411 | 1413 | |
|
|
1412 | 1414 | self.__profIndex += 1 |
|
|
1413 | 1415 | |
|
|
1414 | 1416 | return |
|
|
1415 | 1417 | |
|
|
1416 | 1418 | def hildebrand_sekhon_Integration(self,sortdata,navg, factor): |
|
|
1417 | 1419 | #data debe estar ordenado |
|
|
1418 | 1420 | #sortdata = numpy.sort(data, axis=None) |
|
|
1419 | 1421 | #sortID=data.argsort() |
|
|
1420 | 1422 | lenOfData = len(sortdata) |
|
|
1421 | 1423 | nums_min = lenOfData*factor |
|
|
1422 | 1424 | if nums_min <= 5: |
|
|
1423 | 1425 | nums_min = 5 |
|
|
1424 | 1426 | sump = 0. |
|
|
1425 | 1427 | sumq = 0. |
|
|
1426 | 1428 | j = 0 |
|
|
1427 | 1429 | cont = 1 |
|
|
1428 | 1430 | while((cont == 1)and(j < lenOfData)): |
|
|
1429 | 1431 | sump += sortdata[j] |
|
|
1430 | 1432 | sumq += sortdata[j]**2 |
|
|
1431 | 1433 | if j > nums_min: |
|
|
1432 | 1434 | rtest = float(j)/(j-1) + 1.0/navg |
|
|
1433 | 1435 | if ((sumq*j) > (rtest*sump**2)): |
|
|
1434 | 1436 | j = j - 1 |
|
|
1435 | 1437 | sump = sump - sortdata[j] |
|
|
1436 | 1438 | sumq = sumq - sortdata[j]**2 |
|
|
1437 | 1439 | cont = 0 |
|
|
1438 | 1440 | j += 1 |
|
|
1439 | 1441 | #lnoise = sump / j |
|
|
1440 | 1442 | #print("H S done") |
|
|
1441 | 1443 | #return j,sortID |
|
|
1442 | 1444 | return j |
|
|
1443 | 1445 | |
|
|
1444 | 1446 | |
|
|
1445 | 1447 | def pushData(self): |
|
|
1446 | 1448 | """ |
|
|
1447 | 1449 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
1448 | 1450 | |
|
|
1449 | 1451 | Affected: |
|
|
1450 | 1452 | |
|
|
1451 | 1453 | self.__profileIndex |
|
|
1452 | 1454 | |
|
|
1453 | 1455 | """ |
|
|
1454 | 1456 | bufferH=None |
|
|
1455 | 1457 | buffer=None |
|
|
1456 | 1458 | buffer1=None |
|
|
1457 | 1459 | buffer_cspc=None |
|
|
1458 | 1460 | #print("aes: ", self.__buffer_cspc) |
|
|
1459 | 1461 | self.__buffer_spc=numpy.array(self.__buffer_spc) |
|
|
1460 | 1462 | if self.__nChannels > 1 : |
|
|
1461 | 1463 | self.__buffer_cspc=numpy.array(self.__buffer_cspc) |
|
|
1462 | 1464 | |
|
|
1463 | 1465 | #print("FREQ_DC",self.__buffer_spc.shape,self.__buffer_cspc.shape) |
|
|
1464 | 1466 | |
|
|
1465 | 1467 | freq_dc = int(self.__buffer_spc.shape[2] / 2) |
|
|
1466 | 1468 | #print("FREQ_DC",freq_dc,self.__buffer_spc.shape,self.nHeights) |
|
|
1467 | 1469 | |
|
|
1468 | 1470 | self.dataOutliers = numpy.zeros((self.nChannels,self.nHeights)) # --> almacen de outliers |
|
|
1469 | 1471 | |
|
|
1470 | 1472 | for k in range(self.minHei_ind,self.maxHei_ind): |
|
|
1471 | 1473 | if self.__nChannels > 1: |
|
|
1472 | 1474 | buffer_cspc=numpy.copy(self.__buffer_cspc[:,:,:,k]) |
|
|
1473 | 1475 | |
|
|
1474 | 1476 | outliers_IDs_cspc=[] |
|
|
1475 | 1477 | cspc_outliers_exist=False |
|
|
1476 | 1478 | for i in range(self.nChannels):#dataOut.nChannels): |
|
|
1477 | 1479 | |
|
|
1478 | 1480 | buffer1=numpy.copy(self.__buffer_spc[:,i,:,k]) |
|
|
1479 | 1481 | indexes=[] |
|
|
1480 | 1482 | #sortIDs=[] |
|
|
1481 | 1483 | outliers_IDs=[] |
|
|
1482 | 1484 | |
|
|
1483 | 1485 | for j in range(self.nProfiles): #frecuencias en el tiempo |
|
|
1484 | 1486 | # if i==0 and j==freq_dc: #NOT CONSIDERING DC PROFILE AT CHANNEL 0 |
|
|
1485 | 1487 | # continue |
|
|
1486 | 1488 | # if i==1 and j==0: #NOT CONSIDERING DC PROFILE AT CHANNEL 1 |
|
|
1487 | 1489 | # continue |
|
|
1488 | 1490 | buffer=buffer1[:,j] |
|
|
1489 | 1491 | sortdata = numpy.sort(buffer, axis=None) |
|
|
1490 | 1492 | |
|
|
1491 | 1493 | sortID=buffer.argsort() |
|
|
1492 | 1494 | index = _noise.hildebrand_sekhon2(sortdata,self.navg) |
|
|
1493 | 1495 | |
|
|
1494 | 1496 | #index,sortID=self.hildebrand_sekhon_Integration(buffer,1,self.factor) |
|
|
1495 | 1497 | |
|
|
1496 | 1498 | # fig,ax = plt.subplots() |
|
|
1497 | 1499 | # ax.set_title(str(k)+" "+str(j)) |
|
|
1498 | 1500 | # x=range(len(sortdata)) |
|
|
1499 | 1501 | # ax.scatter(x,sortdata) |
|
|
1500 | 1502 | # ax.axvline(index) |
|
|
1501 | 1503 | # plt.show() |
|
|
1502 | 1504 | |
|
|
1503 | 1505 | indexes.append(index) |
|
|
1504 | 1506 | #sortIDs.append(sortID) |
|
|
1505 | 1507 | outliers_IDs=numpy.append(outliers_IDs,sortID[index:]) |
|
|
1506 | 1508 | |
|
|
1507 | 1509 | #print("Outliers: ",outliers_IDs) |
|
|
1508 | 1510 | outliers_IDs=numpy.array(outliers_IDs) |
|
|
1509 | 1511 | outliers_IDs=outliers_IDs.ravel() |
|
|
1510 | 1512 | outliers_IDs=numpy.unique(outliers_IDs) |
|
|
1511 | 1513 | outliers_IDs=outliers_IDs.astype(numpy.dtype('int64')) |
|
|
1512 | 1514 | indexes=numpy.array(indexes) |
|
|
1513 | 1515 | indexmin=numpy.min(indexes) |
|
|
1514 | 1516 | |
|
|
1515 | 1517 | |
|
|
1516 | 1518 | #print(indexmin,buffer1.shape[0], k) |
|
|
1517 | 1519 | |
|
|
1518 | 1520 | # fig,ax = plt.subplots() |
|
|
1519 | 1521 | # ax.plot(sortdata) |
|
|
1520 | 1522 | # ax2 = ax.twinx() |
|
|
1521 | 1523 | # x=range(len(indexes)) |
|
|
1522 | 1524 | # #plt.scatter(x,indexes) |
|
|
1523 | 1525 | # ax2.scatter(x,indexes) |
|
|
1524 | 1526 | # plt.show() |
|
|
1525 | 1527 | |
|
|
1526 | 1528 | if indexmin != buffer1.shape[0]: |
|
|
1527 | 1529 | if self.__nChannels > 1: |
|
|
1528 | 1530 | cspc_outliers_exist= True |
|
|
1529 | 1531 | |
|
|
1530 | 1532 | lt=outliers_IDs |
|
|
1531 | 1533 | #avg=numpy.mean(buffer1[[t for t in range(buffer1.shape[0]) if t not in lt],:],axis=0) |
|
|
1532 | 1534 | |
|
|
1533 | 1535 | for p in list(outliers_IDs): |
|
|
1534 | 1536 | #buffer1[p,:]=avg |
|
|
1535 | 1537 | buffer1[p,:] = numpy.NaN |
|
|
1536 | 1538 | |
|
|
1537 | 1539 | self.dataOutliers[i,k] = len(outliers_IDs) |
|
|
1538 | 1540 | |
|
|
1539 | 1541 | |
|
|
1540 | 1542 | self.__buffer_spc[:,i,:,k]=numpy.copy(buffer1) |
|
|
1541 | 1543 | |
|
|
1542 | 1544 | |
|
|
1543 | 1545 | if self.__nChannels > 1: |
|
|
1544 | 1546 | outliers_IDs_cspc=numpy.append(outliers_IDs_cspc,outliers_IDs) |
|
|
1545 | 1547 | |
|
|
1546 | 1548 | |
|
|
1547 | 1549 | if self.__nChannels > 1: |
|
|
1548 | 1550 | outliers_IDs_cspc=outliers_IDs_cspc.astype(numpy.dtype('int64')) |
|
|
1549 | 1551 | if cspc_outliers_exist: |
|
|
1550 | 1552 | |
|
|
1551 | 1553 | lt=outliers_IDs_cspc |
|
|
1552 | 1554 | |
|
|
1553 | 1555 | #avg=numpy.mean(buffer_cspc[[t for t in range(buffer_cspc.shape[0]) if t not in lt],:],axis=0) |
|
|
1554 | 1556 | for p in list(outliers_IDs_cspc): |
|
|
1555 | 1557 | #buffer_cspc[p,:]=avg |
|
|
1556 | 1558 | buffer_cspc[p,:] = numpy.NaN |
|
|
1557 | 1559 | |
|
|
1558 | 1560 | if self.__nChannels > 1: |
|
|
1559 | 1561 | self.__buffer_cspc[:,:,:,k]=numpy.copy(buffer_cspc) |
|
|
1560 | 1562 | |
|
|
1561 | 1563 | |
|
|
1562 | 1564 | |
|
|
1563 | 1565 | |
|
|
1564 | 1566 | nOutliers = len(outliers_IDs) |
|
|
1565 | 1567 | #print("Outliers n: ",self.dataOutliers,nOutliers) |
|
|
1566 | 1568 | buffer=None |
|
|
1567 | 1569 | bufferH=None |
|
|
1568 | 1570 | buffer1=None |
|
|
1569 | 1571 | buffer_cspc=None |
|
|
1570 | 1572 | |
|
|
1571 | 1573 | |
|
|
1572 | 1574 | buffer=None |
|
|
1573 | 1575 | |
|
|
1574 | 1576 | #data_spc = numpy.sum(self.__buffer_spc,axis=0) |
|
|
1575 | 1577 | data_spc = numpy.nansum(self.__buffer_spc,axis=0) |
|
|
1576 | 1578 | if self.__nChannels > 1: |
|
|
1577 | 1579 | #data_cspc = numpy.sum(self.__buffer_cspc,axis=0) |
|
|
1578 | 1580 | data_cspc = numpy.nansum(self.__buffer_cspc,axis=0) |
|
|
1579 | 1581 | else: |
|
|
1580 | 1582 | data_cspc = None |
|
|
1581 | 1583 | data_dc = self.__buffer_dc |
|
|
1582 | 1584 | #(CH, HEIGH) |
|
|
1583 | 1585 | self.maxProfilesInt = self.__profIndex - 1 |
|
|
1584 | 1586 | n = self.__profIndex - self.dataOutliers # n becomes a matrix |
|
|
1585 | 1587 | |
|
|
1586 | 1588 | self.__buffer_spc = [] |
|
|
1587 | 1589 | self.__buffer_cspc = [] |
|
|
1588 | 1590 | self.__buffer_dc = 0 |
|
|
1589 | 1591 | self.__profIndex = 0 |
|
|
1590 | 1592 | #print("cleaned ",data_cspc) |
|
|
1591 | 1593 | return data_spc, data_cspc, data_dc, n |
|
|
1592 | 1594 | |
|
|
1593 | 1595 | def byProfiles(self, *args): |
|
|
1594 | 1596 | |
|
|
1595 | 1597 | self.__dataReady = False |
|
|
1596 | 1598 | avgdata_spc = None |
|
|
1597 | 1599 | avgdata_cspc = None |
|
|
1598 | 1600 | avgdata_dc = None |
|
|
1599 | 1601 | |
|
|
1600 | 1602 | self.putData(*args) |
|
|
1601 | 1603 | |
|
|
1602 | 1604 | if self.__profIndex >= self.n: |
|
|
1603 | 1605 | |
|
|
1604 | 1606 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1605 | 1607 | self.n_ints = n |
|
|
1606 | 1608 | self.__dataReady = True |
|
|
1607 | 1609 | |
|
|
1608 | 1610 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1609 | 1611 | |
|
|
1610 | 1612 | def byTime(self, datatime, *args): |
|
|
1611 | 1613 | |
|
|
1612 | 1614 | self.__dataReady = False |
|
|
1613 | 1615 | avgdata_spc = None |
|
|
1614 | 1616 | avgdata_cspc = None |
|
|
1615 | 1617 | avgdata_dc = None |
|
|
1616 | 1618 | |
|
|
1617 | 1619 | self.putData(*args) |
|
|
1618 | 1620 | |
|
|
1619 | 1621 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1620 | 1622 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1621 | 1623 | self.n_ints = n |
|
|
1622 | 1624 | self.__dataReady = True |
|
|
1623 | 1625 | |
|
|
1624 | 1626 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1625 | 1627 | |
|
|
1626 | 1628 | def integrate(self, datatime, *args): |
|
|
1627 | 1629 | |
|
|
1628 | 1630 | if self.__profIndex == 0: |
|
|
1629 | 1631 | self.__initime = datatime |
|
|
1630 | 1632 | |
|
|
1631 | 1633 | if self.__byTime: |
|
|
1632 | 1634 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
1633 | 1635 | datatime, *args) |
|
|
1634 | 1636 | else: |
|
|
1635 | 1637 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1636 | 1638 | |
|
|
1637 | 1639 | if not self.__dataReady: |
|
|
1638 | 1640 | return None, None, None, None |
|
|
1639 | 1641 | |
|
|
1640 | 1642 | #print("integrate", avgdata_cspc) |
|
|
1641 | 1643 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1642 | 1644 | |
|
|
1643 | 1645 | def run(self, dataOut, n=None, DPL = None,timeInterval=None, overlapping=False, minHei=None, maxHei=None, avg=1, factor=0.75): |
|
|
1644 | 1646 | self.dataOut = dataOut |
|
|
1645 | 1647 | if n == 1: |
|
|
1646 | 1648 | return self.dataOut |
|
|
1647 | 1649 | self.dataOut.processingHeaderObj.timeIncohInt = timeInterval |
|
|
1648 | 1650 | |
|
|
1649 | 1651 | if dataOut.flagProfilesByRange: |
|
|
1650 | 1652 | self._flagProfilesByRange = True |
|
|
1651 | 1653 | |
|
|
1652 | 1654 | if self.dataOut.nChannels == 1: |
|
|
1653 | 1655 | self.dataOut.data_cspc = None #si es un solo canal no vale la pena acumular DATOS |
|
|
1654 | 1656 | #print("IN spc:", self.dataOut.data_spc.shape, self.dataOut.data_cspc) |
|
|
1655 | 1657 | if not self.isConfig: |
|
|
1656 | 1658 | self.setup(self.dataOut, n, timeInterval, overlapping,DPL ,minHei, maxHei, avg, factor) |
|
|
1657 | 1659 | self.isConfig = True |
|
|
1658 | 1660 | |
|
|
1659 | 1661 | if not self.ByLags: |
|
|
1660 | 1662 | self.nProfiles=self.dataOut.nProfiles |
|
|
1661 | 1663 | self.nChannels=self.dataOut.nChannels |
|
|
1662 | 1664 | self.nHeights=self.dataOut.nHeights |
|
|
1663 | 1665 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(self.dataOut.utctime, |
|
|
1664 | 1666 | self.dataOut.data_spc, |
|
|
1665 | 1667 | self.dataOut.data_cspc, |
|
|
1666 | 1668 | self.dataOut.data_dc) |
|
|
1667 | 1669 | else: |
|
|
1668 | 1670 | self.nProfiles=self.dataOut.nProfiles |
|
|
1669 | 1671 | self.nChannels=self.dataOut.nChannels |
|
|
1670 | 1672 | self.nHeights=self.dataOut.nHeights |
|
|
1671 | 1673 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(self.dataOut.utctime, |
|
|
1672 | 1674 | self.dataOut.dataLag_spc, |
|
|
1673 | 1675 | self.dataOut.dataLag_cspc, |
|
|
1674 | 1676 | self.dataOut.dataLag_dc) |
|
|
1675 | 1677 | self.dataOut.flagNoData = True |
|
|
1676 | 1678 | |
|
|
1677 | 1679 | if self._flagProfilesByRange: |
|
|
1678 | 1680 | dataOut.flagProfilesByRange = True |
|
|
1679 | 1681 | self._nProfilesByRange += dataOut.nProfilesByRange |
|
|
1680 | 1682 | |
|
|
1681 | 1683 | if self.__dataReady: |
|
|
1682 | 1684 | |
|
|
1683 | 1685 | if not self.ByLags: |
|
|
1684 | 1686 | if self.nChannels == 1: |
|
|
1685 | 1687 | #print("f int", avgdata_spc.shape) |
|
|
1686 | 1688 | self.dataOut.data_spc = avgdata_spc |
|
|
1687 | 1689 | self.dataOut.data_cspc = None |
|
|
1688 | 1690 | else: |
|
|
1689 | 1691 | self.dataOut.data_spc = numpy.squeeze(avgdata_spc) |
|
|
1690 | 1692 | self.dataOut.data_cspc = numpy.squeeze(avgdata_cspc) |
|
|
1691 | 1693 | self.dataOut.data_dc = avgdata_dc |
|
|
1692 | 1694 | self.dataOut.data_outlier = self.dataOutliers |
|
|
1693 | 1695 | |
|
|
1694 | 1696 | |
|
|
1695 | 1697 | else: |
|
|
1696 | 1698 | self.dataOut.dataLag_spc = avgdata_spc |
|
|
1697 | 1699 | self.dataOut.dataLag_cspc = avgdata_cspc |
|
|
1698 | 1700 | self.dataOut.dataLag_dc = avgdata_dc |
|
|
1699 | 1701 | |
|
|
1700 | 1702 | self.dataOut.data_spc=self.dataOut.dataLag_spc[:,:,:,self.dataOut.LagPlot] |
|
|
1701 | 1703 | self.dataOut.data_cspc=self.dataOut.dataLag_cspc[:,:,:,self.dataOut.LagPlot] |
|
|
1702 | 1704 | self.dataOut.data_dc=self.dataOut.dataLag_dc[:,:,self.dataOut.LagPlot] |
|
|
1703 | 1705 | |
|
|
1704 | 1706 | self.dataOut.nIncohInt *= self.n_ints |
|
|
1705 | 1707 | |
|
|
1706 | 1708 | self.dataOut.utctime = avgdatatime |
|
|
1707 | 1709 | self.dataOut.flagNoData = False |
|
|
1708 | 1710 | |
|
|
1709 | 1711 | dataOut.nProfilesByRange = self._nProfilesByRange |
|
|
1710 | 1712 | self._nProfilesByRange = numpy.zeros((1,len(dataOut.heightList))) |
|
|
1711 | 1713 | self._flagProfilesByRange = False |
|
|
1712 | 1714 | |
|
|
1713 | 1715 | return self.dataOut |
|
|
1714 | 1716 | |
|
|
1715 | 1717 | class dopplerFlip(Operation): |
|
|
1716 | 1718 | |
|
|
1717 | 1719 | def run(self, dataOut, chann = None): |
|
|
1718 | 1720 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
1719 | 1721 | self.dataOut = dataOut |
|
|
1720 | 1722 | # JULIA-oblicua, indice 2 |
|
|
1721 | 1723 | # arreglo 2: (num_profiles, num_heights) |
|
|
1722 | 1724 | jspectra = self.dataOut.data_spc[chann] |
|
|
1723 | 1725 | jspectra_tmp = numpy.zeros(jspectra.shape) |
|
|
1724 | 1726 | num_profiles = jspectra.shape[0] |
|
|
1725 | 1727 | freq_dc = int(num_profiles / 2) |
|
|
1726 | 1728 | # Flip con for |
|
|
1727 | 1729 | for j in range(num_profiles): |
|
|
1728 | 1730 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
1729 | 1731 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
1730 | 1732 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
1731 | 1733 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
1732 | 1734 | # canal modificado es re-escrito en el arreglo de canales |
|
|
1733 | 1735 | self.dataOut.data_spc[chann] = jspectra_tmp |
|
|
1734 | 1736 | |
|
|
1735 | 1737 | return self.dataOut No newline at end of file |
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