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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """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 | |
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12 | 12 | from schainpy.model.graphics.jroplot_base import Plot, plt, log |
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13 | 13 | |
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14 | 14 | |
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15 | 15 | class SpectraPlot(Plot): |
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16 | 16 | ''' |
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17 | 17 | Plot for Spectra data |
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18 | 18 | ''' |
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19 | 19 | |
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20 | 20 | CODE = 'spc' |
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21 | 21 | colormap = 'jet' |
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22 | 22 | plot_type = 'pcolor' |
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23 | 23 | buffering = False |
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24 | channelList = None | |
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24 | 25 | |
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25 | 26 | def setup(self): |
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26 | 27 | self.nplots = len(self.data.channels) |
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27 | 28 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
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28 | 29 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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29 | 30 | self.height = 2.6 * self.nrows |
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31 | ||
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30 | 32 | self.cb_label = 'dB' |
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31 | 33 | if self.showprofile: |
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32 | 34 | self.width = 4 * self.ncols |
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33 | 35 | else: |
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34 | 36 | self.width = 3.5 * self.ncols |
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35 | 37 | self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08}) |
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36 | 38 | self.ylabel = 'Range [km]' |
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37 | 39 | |
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38 | 40 | def update(self, dataOut): |
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39 | ||
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41 | if self.channelList == None: | |
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42 | self.channelList = dataOut.channelList | |
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40 | 43 | data = {} |
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41 | 44 | meta = {} |
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42 | 45 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
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43 | 46 | data['spc'] = spc |
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44 | 47 | data['rti'] = dataOut.getPower() |
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45 | 48 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
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46 | 49 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
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47 | 50 | if self.CODE == 'spc_moments': |
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48 | 51 | data['moments'] = dataOut.moments |
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49 | ||
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50 |
return data, meta |
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51 | ||
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52 | ||
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53 | return data, meta | |
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54 | ||
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52 | 55 | def plot(self): |
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53 | 56 | if self.xaxis == "frequency": |
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54 | 57 | x = self.data.xrange[0] |
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55 | 58 | self.xlabel = "Frequency (kHz)" |
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56 | 59 | elif self.xaxis == "time": |
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57 | 60 | x = self.data.xrange[1] |
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58 | 61 | self.xlabel = "Time (ms)" |
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59 | 62 | else: |
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60 | 63 | x = self.data.xrange[2] |
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61 | 64 | self.xlabel = "Velocity (m/s)" |
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62 | 65 | |
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63 | 66 | if self.CODE == 'spc_moments': |
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64 | 67 | x = self.data.xrange[2] |
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65 | 68 | self.xlabel = "Velocity (m/s)" |
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66 | 69 | |
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67 | 70 | self.titles = [] |
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68 | 71 | |
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69 | 72 | y = self.data.yrange |
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70 | 73 | self.y = y |
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71 | 74 | |
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72 | 75 | data = self.data[-1] |
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73 | 76 | z = data['spc'] |
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74 | 77 | |
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75 | 78 | for n, ax in enumerate(self.axes): |
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76 | 79 | noise = data['noise'][n] |
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77 | 80 | if self.CODE == 'spc_moments': |
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78 | 81 | mean = data['moments'][n, 1] |
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79 | 82 | if ax.firsttime: |
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80 | 83 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
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81 | 84 | self.xmin = self.xmin if self.xmin else -self.xmax |
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82 | 85 | self.zmin = self.zmin if self.zmin else numpy.nanmin(z) |
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83 | 86 | self.zmax = self.zmax if self.zmax else numpy.nanmax(z) |
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84 | 87 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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85 | 88 | vmin=self.zmin, |
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86 | 89 | vmax=self.zmax, |
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87 | 90 | cmap=plt.get_cmap(self.colormap) |
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88 | 91 | ) |
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89 | 92 | |
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90 | 93 | if self.showprofile: |
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91 | 94 | ax.plt_profile = self.pf_axes[n].plot( |
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92 | 95 | data['rti'][n], y)[0] |
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93 | 96 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, |
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94 | 97 | color="k", linestyle="dashed", lw=1)[0] |
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95 | 98 | if self.CODE == 'spc_moments': |
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96 | 99 | ax.plt_mean = ax.plot(mean, y, color='k')[0] |
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97 | 100 | else: |
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98 | 101 | ax.plt.set_array(z[n].T.ravel()) |
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99 | 102 | if self.showprofile: |
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100 | 103 | ax.plt_profile.set_data(data['rti'][n], y) |
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101 | 104 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) |
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102 | 105 | if self.CODE == 'spc_moments': |
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103 | 106 | ax.plt_mean.set_data(mean, y) |
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104 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) | |
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107 | self.titles.append('CH {}: {:3.2f}dB'.format(self.channelList[n], noise)) | |
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105 | 108 | |
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106 | 109 | |
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107 | 110 | class CrossSpectraPlot(Plot): |
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108 | 111 | |
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109 | 112 | CODE = 'cspc' |
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110 | 113 | colormap = 'jet' |
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111 | 114 | plot_type = 'pcolor' |
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112 | 115 | zmin_coh = None |
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113 | 116 | zmax_coh = None |
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114 | 117 | zmin_phase = None |
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115 | 118 | zmax_phase = None |
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116 | 119 | |
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117 | 120 | def setup(self): |
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118 | 121 | |
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119 | 122 | self.ncols = 4 |
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120 | 123 | self.nplots = len(self.data.pairs) * 2 |
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121 | 124 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
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122 | 125 | self.width = 3.1 * self.ncols |
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123 | 126 | self.height = 2.6 * self.nrows |
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124 | 127 | self.ylabel = 'Range [km]' |
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125 | 128 | self.showprofile = False |
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126 | 129 | 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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127 | 130 | |
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128 | 131 | def update(self, dataOut): |
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129 | 132 | |
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130 | 133 | data = {} |
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131 | 134 | meta = {} |
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132 | 135 | |
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133 | 136 | spc = dataOut.data_spc |
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134 | 137 | cspc = dataOut.data_cspc |
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135 | 138 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
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136 | 139 | meta['pairs'] = dataOut.pairsList |
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137 | 140 | |
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138 | 141 | tmp = [] |
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139 | 142 | |
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140 | 143 | for n, pair in enumerate(meta['pairs']): |
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141 | 144 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
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142 | 145 | coh = numpy.abs(out) |
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143 | 146 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
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144 | 147 | tmp.append(coh) |
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145 | 148 | tmp.append(phase) |
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146 | 149 | |
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147 | 150 | data['cspc'] = numpy.array(tmp) |
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148 | 151 | |
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149 |
return data, meta |
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150 | ||
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152 | return data, meta | |
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153 | ||
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151 | 154 | def plot(self): |
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152 | 155 | |
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153 | 156 | if self.xaxis == "frequency": |
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154 | 157 | x = self.data.xrange[0] |
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155 | 158 | self.xlabel = "Frequency (kHz)" |
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156 | 159 | elif self.xaxis == "time": |
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157 | 160 | x = self.data.xrange[1] |
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158 | 161 | self.xlabel = "Time (ms)" |
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159 | 162 | else: |
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160 | 163 | x = self.data.xrange[2] |
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161 | 164 | self.xlabel = "Velocity (m/s)" |
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162 | ||
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165 | ||
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163 | 166 | self.titles = [] |
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164 | 167 | |
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165 | 168 | y = self.data.yrange |
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166 | 169 | self.y = y |
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167 | 170 | |
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168 | 171 | data = self.data[-1] |
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169 | 172 | cspc = data['cspc'] |
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170 | 173 | |
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171 | 174 | for n in range(len(self.data.pairs)): |
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172 | 175 | pair = self.data.pairs[n] |
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173 | 176 | coh = cspc[n*2] |
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174 | 177 | phase = cspc[n*2+1] |
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175 | 178 | ax = self.axes[2 * n] |
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176 | 179 | if ax.firsttime: |
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177 | 180 | ax.plt = ax.pcolormesh(x, y, coh.T, |
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178 | 181 | vmin=0, |
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179 | 182 | vmax=1, |
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180 | 183 | cmap=plt.get_cmap(self.colormap_coh) |
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181 | 184 | ) |
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182 | 185 | else: |
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183 | 186 | ax.plt.set_array(coh.T.ravel()) |
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184 | 187 | self.titles.append( |
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185 | 188 | 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) |
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186 | ||
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189 | ||
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187 | 190 | ax = self.axes[2 * n + 1] |
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188 | 191 | if ax.firsttime: |
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189 | 192 | ax.plt = ax.pcolormesh(x, y, phase.T, |
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190 | 193 | vmin=-180, |
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191 | 194 | vmax=180, |
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192 |
cmap=plt.get_cmap(self.colormap_phase) |
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195 | cmap=plt.get_cmap(self.colormap_phase) | |
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193 | 196 | ) |
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194 | 197 | else: |
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195 | 198 | ax.plt.set_array(phase.T.ravel()) |
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196 | 199 | self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) |
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197 | 200 | |
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198 | 201 | |
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199 | 202 | class RTIPlot(Plot): |
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200 | 203 | ''' |
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201 | 204 | Plot for RTI data |
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202 | 205 | ''' |
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203 | 206 | |
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204 | 207 | CODE = 'rti' |
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205 | 208 | colormap = 'jet' |
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206 | 209 | plot_type = 'pcolorbuffer' |
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210 | titles = None | |
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211 | channelList = None | |
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207 | 212 | |
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208 | 213 | def setup(self): |
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209 | 214 | self.xaxis = 'time' |
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210 | 215 | self.ncols = 1 |
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211 | 216 | self.nrows = len(self.data.channels) |
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212 | 217 | self.nplots = len(self.data.channels) |
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213 | 218 | self.ylabel = 'Range [km]' |
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214 | 219 | self.xlabel = 'Time' |
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215 | 220 | self.cb_label = 'dB' |
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216 | 221 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95}) |
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217 | 222 | self.titles = ['{} Channel {}'.format( |
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218 |
self.CODE.upper(), x) for x in range(self.n |
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223 | self.CODE.upper(), x) for x in range(self.nplots)] | |
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219 | 224 | |
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220 | 225 | def update(self, dataOut): |
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221 | ||
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226 | if self.channelList == None: | |
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227 | self.channelList = dataOut.channelList | |
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222 | 228 | data = {} |
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223 | 229 | meta = {} |
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224 | 230 | data['rti'] = dataOut.getPower() |
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225 | 231 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor) |
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226 | 232 | |
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227 | 233 | return data, meta |
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228 | 234 | |
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229 | 235 | def plot(self): |
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230 | 236 | self.x = self.data.times |
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231 | 237 | self.y = self.data.yrange |
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232 | 238 | self.z = self.data[self.CODE] |
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233 | 239 | self.z = numpy.ma.masked_invalid(self.z) |
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240 | if self.channelList != None: | |
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241 | self.titles = ['{} Channel {}'.format( | |
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242 | self.CODE.upper(), x) for x in self.channelList] | |
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234 | 243 | |
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235 | 244 | if self.decimation is None: |
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236 | 245 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
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237 | 246 | else: |
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238 | 247 | x, y, z = self.fill_gaps(*self.decimate()) |
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239 | 248 | |
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240 | 249 | for n, ax in enumerate(self.axes): |
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241 | 250 | self.zmin = self.zmin if self.zmin else numpy.min(self.z) |
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242 | 251 | self.zmax = self.zmax if self.zmax else numpy.max(self.z) |
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243 | 252 | data = self.data[-1] |
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244 | 253 | if ax.firsttime: |
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245 | 254 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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246 | 255 | vmin=self.zmin, |
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247 | 256 | vmax=self.zmax, |
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248 | 257 | cmap=plt.get_cmap(self.colormap) |
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249 | 258 | ) |
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250 | 259 | if self.showprofile: |
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251 | 260 | ax.plot_profile = self.pf_axes[n].plot( |
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252 | 261 | data['rti'][n], self.y)[0] |
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253 | 262 | ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, |
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254 | 263 | color="k", linestyle="dashed", lw=1)[0] |
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255 | 264 | else: |
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256 | 265 | ax.collections.remove(ax.collections[0]) |
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257 | 266 | ax.plt = ax.pcolormesh(x, y, z[n].T, |
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258 | 267 | vmin=self.zmin, |
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259 | 268 | vmax=self.zmax, |
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260 | 269 | cmap=plt.get_cmap(self.colormap) |
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261 | 270 | ) |
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262 | 271 | if self.showprofile: |
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263 | 272 | ax.plot_profile.set_data(data['rti'][n], self.y) |
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264 | 273 | ax.plot_noise.set_data(numpy.repeat( |
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265 | 274 | data['noise'][n], len(self.y)), self.y) |
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266 | 275 | |
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267 | 276 | |
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268 | 277 | class CoherencePlot(RTIPlot): |
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269 | 278 | ''' |
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270 | 279 | Plot for Coherence data |
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271 | 280 | ''' |
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272 | 281 | |
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273 | 282 | CODE = 'coh' |
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274 | 283 | |
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275 | 284 | def setup(self): |
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276 | 285 | self.xaxis = 'time' |
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277 | 286 | self.ncols = 1 |
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278 | 287 | self.nrows = len(self.data.pairs) |
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279 | 288 | self.nplots = len(self.data.pairs) |
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280 | 289 | self.ylabel = 'Range [km]' |
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281 | 290 | self.xlabel = 'Time' |
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282 | 291 | self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1,'right':0.95}) |
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283 | 292 | if self.CODE == 'coh': |
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284 | 293 | self.cb_label = '' |
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285 | 294 | self.titles = [ |
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286 | 295 | 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
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287 | 296 | else: |
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288 | 297 | self.cb_label = 'Degrees' |
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289 | 298 | self.titles = [ |
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290 | 299 | 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] |
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291 | 300 | |
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292 | 301 | def update(self, dataOut): |
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293 | 302 | |
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294 | 303 | data = {} |
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295 | 304 | meta = {} |
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296 | 305 | data['coh'] = dataOut.getCoherence() |
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297 | 306 | meta['pairs'] = dataOut.pairsList |
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298 | 307 | |
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299 | 308 | return data, meta |
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300 | 309 | |
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301 | 310 | class PhasePlot(CoherencePlot): |
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302 | 311 | ''' |
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303 | 312 | Plot for Phase map data |
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304 | 313 | ''' |
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305 | 314 | |
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306 | 315 | CODE = 'phase' |
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307 | 316 | colormap = 'seismic' |
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308 | 317 | |
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309 | 318 | def update(self, dataOut): |
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310 | 319 | |
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311 | 320 | data = {} |
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312 | 321 | meta = {} |
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313 | 322 | data['phase'] = dataOut.getCoherence(phase=True) |
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314 | 323 | meta['pairs'] = dataOut.pairsList |
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315 | 324 | |
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316 | 325 | return data, meta |
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317 | 326 | |
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318 | 327 | class NoisePlot(Plot): |
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319 | 328 | ''' |
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320 |
Plot for noise |
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329 | Plot for noise | |
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321 | 330 | ''' |
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322 | 331 | |
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323 | 332 | CODE = 'noise' |
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324 | 333 | plot_type = 'scatterbuffer' |
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325 | 334 | |
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326 | 335 | def setup(self): |
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327 | 336 | self.xaxis = 'time' |
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328 | 337 | self.ncols = 1 |
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329 | 338 | self.nrows = 1 |
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330 | 339 | self.nplots = 1 |
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331 | 340 | self.ylabel = 'Intensity [dB]' |
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332 | 341 | self.xlabel = 'Time' |
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333 | 342 | self.titles = ['Noise'] |
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334 | 343 | self.colorbar = False |
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335 | 344 | self.plots_adjust.update({'right': 0.85 }) |
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336 | 345 | |
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337 | 346 | def update(self, dataOut): |
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338 | 347 | |
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339 | 348 | data = {} |
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340 | 349 | meta = {} |
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341 | 350 | data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1) |
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342 | 351 | meta['yrange'] = numpy.array([]) |
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343 | 352 | |
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344 | 353 | return data, meta |
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345 | 354 | |
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346 | 355 | def plot(self): |
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347 | 356 | |
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348 | 357 | x = self.data.times |
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349 | 358 | xmin = self.data.min_time |
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350 | 359 | xmax = xmin + self.xrange * 60 * 60 |
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351 | 360 | Y = self.data['noise'] |
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352 | 361 | |
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353 | 362 | if self.axes[0].firsttime: |
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354 | 363 | self.ymin = numpy.nanmin(Y) - 5 |
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355 | 364 | self.ymax = numpy.nanmax(Y) + 5 |
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356 | 365 | for ch in self.data.channels: |
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357 | 366 | y = Y[ch] |
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358 | 367 | self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) |
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|
359 | 368 | plt.legend(bbox_to_anchor=(1.18, 1.0)) |
|
|
360 | 369 | else: |
|
|
361 | 370 | for ch in self.data.channels: |
|
|
362 | 371 | y = Y[ch] |
|
|
363 | 372 | self.axes[0].lines[ch].set_data(x, y) |
|
|
364 | 373 | |
|
|
365 | ||
|
|
374 | ||
|
|
366 | 375 | class PowerProfilePlot(Plot): |
|
|
367 | 376 | |
|
|
368 | 377 | CODE = 'pow_profile' |
|
|
369 | 378 | plot_type = 'scatter' |
|
|
370 | 379 | |
|
|
371 | 380 | def setup(self): |
|
|
372 | 381 | |
|
|
373 | 382 | self.ncols = 1 |
|
|
374 | 383 | self.nrows = 1 |
|
|
375 | 384 | self.nplots = 1 |
|
|
376 | 385 | self.height = 4 |
|
|
377 | 386 | self.width = 3 |
|
|
378 | 387 | self.ylabel = 'Range [km]' |
|
|
379 | 388 | self.xlabel = 'Intensity [dB]' |
|
|
380 | 389 | self.titles = ['Power Profile'] |
|
|
381 | 390 | self.colorbar = False |
|
|
382 | 391 | |
|
|
383 | 392 | def update(self, dataOut): |
|
|
384 | 393 | |
|
|
385 | 394 | data = {} |
|
|
386 | 395 | meta = {} |
|
|
387 | 396 | data[self.CODE] = dataOut.getPower() |
|
|
388 | 397 | |
|
|
389 | 398 | return data, meta |
|
|
390 | 399 | |
|
|
391 | 400 | def plot(self): |
|
|
392 | 401 | |
|
|
393 | 402 | y = self.data.yrange |
|
|
394 | 403 | self.y = y |
|
|
395 | 404 | |
|
|
396 | 405 | x = self.data[-1][self.CODE] |
|
|
397 | ||
|
|
406 | ||
|
|
398 | 407 | if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9 |
|
|
399 | 408 | if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1 |
|
|
400 | ||
|
|
409 | ||
|
|
401 | 410 | if self.axes[0].firsttime: |
|
|
402 | 411 | for ch in self.data.channels: |
|
|
403 | 412 | self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) |
|
|
404 | 413 | plt.legend() |
|
|
405 | 414 | else: |
|
|
406 | 415 | for ch in self.data.channels: |
|
|
407 | 416 | self.axes[0].lines[ch].set_data(x[ch], y) |
|
|
408 | 417 | |
|
|
409 | 418 | |
|
|
410 | 419 | class SpectraCutPlot(Plot): |
|
|
411 | 420 | |
|
|
412 | 421 | CODE = 'spc_cut' |
|
|
413 | 422 | plot_type = 'scatter' |
|
|
414 | 423 | buffering = False |
|
|
415 | 424 | |
|
|
416 | 425 | def setup(self): |
|
|
417 | 426 | |
|
|
418 | 427 | self.nplots = len(self.data.channels) |
|
|
419 | 428 | self.ncols = int(numpy.sqrt(self.nplots) + 0.9) |
|
|
420 | 429 | self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) |
|
|
421 | 430 | self.width = 3.4 * self.ncols + 1.5 |
|
|
422 | 431 | self.height = 3 * self.nrows |
|
|
423 | 432 | self.ylabel = 'Power [dB]' |
|
|
424 | 433 | self.colorbar = False |
|
|
425 | 434 | self.plots_adjust.update({'left':0.1, 'hspace':0.3, 'right': 0.75, 'bottom':0.08}) |
|
|
426 | 435 | |
|
|
427 | 436 | def update(self, dataOut): |
|
|
428 | 437 | |
|
|
429 | 438 | data = {} |
|
|
430 | 439 | meta = {} |
|
|
431 | 440 | spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor) |
|
|
432 | 441 | data['spc'] = spc |
|
|
433 | 442 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
|
|
434 | 443 | |
|
|
435 | 444 | return data, meta |
|
|
436 | 445 | |
|
|
437 | 446 | def plot(self): |
|
|
438 | 447 | if self.xaxis == "frequency": |
|
|
439 | 448 | x = self.data.xrange[0][1:] |
|
|
440 | 449 | self.xlabel = "Frequency (kHz)" |
|
|
441 | 450 | elif self.xaxis == "time": |
|
|
442 | 451 | x = self.data.xrange[1] |
|
|
443 | 452 | self.xlabel = "Time (ms)" |
|
|
444 | 453 | else: |
|
|
445 | 454 | x = self.data.xrange[2] |
|
|
446 | 455 | self.xlabel = "Velocity (m/s)" |
|
|
447 | 456 | |
|
|
448 | 457 | self.titles = [] |
|
|
449 | 458 | |
|
|
450 | 459 | y = self.data.yrange |
|
|
451 | 460 | z = self.data[-1]['spc'] |
|
|
452 | 461 | |
|
|
453 | 462 | if self.height_index: |
|
|
454 | 463 | index = numpy.array(self.height_index) |
|
|
455 | 464 | else: |
|
|
456 | 465 | index = numpy.arange(0, len(y), int((len(y))/9)) |
|
|
457 | 466 | |
|
|
458 | 467 | for n, ax in enumerate(self.axes): |
|
|
459 | 468 | if ax.firsttime: |
|
|
460 | 469 | self.xmax = self.xmax if self.xmax else numpy.nanmax(x) |
|
|
461 | 470 | self.xmin = self.xmin if self.xmin else -self.xmax |
|
|
462 | 471 | self.ymin = self.ymin if self.ymin else numpy.nanmin(z) |
|
|
463 | 472 | self.ymax = self.ymax if self.ymax else numpy.nanmax(z) |
|
|
464 | 473 | ax.plt = ax.plot(x, z[n, :, index].T) |
|
|
465 | 474 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] |
|
|
466 | 475 | self.figures[0].legend(ax.plt, labels, loc='center right') |
|
|
467 | 476 | else: |
|
|
468 | 477 | for i, line in enumerate(ax.plt): |
|
|
469 | 478 | line.set_data(x, z[n, :, index[i]]) |
|
|
470 | 479 | self.titles.append('CH {}'.format(n)) |
|
|
471 | 480 | |
|
|
472 | 481 | |
|
|
473 | 482 | class BeaconPhase(Plot): |
|
|
474 | 483 | |
|
|
475 | 484 | __isConfig = None |
|
|
476 | 485 | __nsubplots = None |
|
|
477 | 486 | |
|
|
478 | 487 | PREFIX = 'beacon_phase' |
|
|
479 | 488 | |
|
|
480 | 489 | def __init__(self): |
|
|
481 | 490 | Plot.__init__(self) |
|
|
482 | 491 | self.timerange = 24*60*60 |
|
|
483 | 492 | self.isConfig = False |
|
|
484 | 493 | self.__nsubplots = 1 |
|
|
485 | 494 | self.counter_imagwr = 0 |
|
|
486 | 495 | self.WIDTH = 800 |
|
|
487 | 496 | self.HEIGHT = 400 |
|
|
488 | 497 | self.WIDTHPROF = 120 |
|
|
489 | 498 | self.HEIGHTPROF = 0 |
|
|
490 | 499 | self.xdata = None |
|
|
491 | 500 | self.ydata = None |
|
|
492 | 501 | |
|
|
493 | 502 | self.PLOT_CODE = BEACON_CODE |
|
|
494 | 503 | |
|
|
495 | 504 | self.FTP_WEI = None |
|
|
496 | 505 | self.EXP_CODE = None |
|
|
497 | 506 | self.SUB_EXP_CODE = None |
|
|
498 | 507 | self.PLOT_POS = None |
|
|
499 | 508 | |
|
|
500 | 509 | self.filename_phase = None |
|
|
501 | 510 | |
|
|
502 | 511 | self.figfile = None |
|
|
503 | 512 | |
|
|
504 | 513 | self.xmin = None |
|
|
505 | 514 | self.xmax = None |
|
|
506 | 515 | |
|
|
507 | 516 | def getSubplots(self): |
|
|
508 | 517 | |
|
|
509 | 518 | ncol = 1 |
|
|
510 | 519 | nrow = 1 |
|
|
511 | 520 | |
|
|
512 | 521 | return nrow, ncol |
|
|
513 | 522 | |
|
|
514 | 523 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
515 | 524 | |
|
|
516 | 525 | self.__showprofile = showprofile |
|
|
517 | 526 | self.nplots = nplots |
|
|
518 | 527 | |
|
|
519 | 528 | ncolspan = 7 |
|
|
520 | 529 | colspan = 6 |
|
|
521 | 530 | self.__nsubplots = 2 |
|
|
522 | 531 | |
|
|
523 | 532 | self.createFigure(id = id, |
|
|
524 | 533 | wintitle = wintitle, |
|
|
525 | 534 | widthplot = self.WIDTH+self.WIDTHPROF, |
|
|
526 | 535 | heightplot = self.HEIGHT+self.HEIGHTPROF, |
|
|
527 | 536 | show=show) |
|
|
528 | 537 | |
|
|
529 | 538 | nrow, ncol = self.getSubplots() |
|
|
530 | 539 | |
|
|
531 | 540 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) |
|
|
532 | 541 | |
|
|
533 | 542 | def save_phase(self, filename_phase): |
|
|
534 | 543 | f = open(filename_phase,'w+') |
|
|
535 | 544 | f.write('\n\n') |
|
|
536 | 545 | f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') |
|
|
537 | 546 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' ) |
|
|
538 | 547 | f.close() |
|
|
539 | 548 | |
|
|
540 | 549 | def save_data(self, filename_phase, data, data_datetime): |
|
|
541 | 550 | f=open(filename_phase,'a') |
|
|
542 | 551 | timetuple_data = data_datetime.timetuple() |
|
|
543 | 552 | day = str(timetuple_data.tm_mday) |
|
|
544 | 553 | month = str(timetuple_data.tm_mon) |
|
|
545 | 554 | year = str(timetuple_data.tm_year) |
|
|
546 | 555 | hour = str(timetuple_data.tm_hour) |
|
|
547 | 556 | minute = str(timetuple_data.tm_min) |
|
|
548 | 557 | second = str(timetuple_data.tm_sec) |
|
|
549 | 558 | f.write(day+' '+month+' '+year+' '+hour+' '+minute+' '+second+' '+str(data[0])+' '+str(data[1])+' '+str(data[2])+' '+str(data[3])+'\n') |
|
|
550 | 559 | f.close() |
|
|
551 | 560 | |
|
|
552 | 561 | def plot(self): |
|
|
553 | 562 | log.warning('TODO: Not yet implemented...') |
|
|
554 | 563 | |
|
|
555 | 564 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', |
|
|
556 | 565 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, |
|
|
557 | 566 | timerange=None, |
|
|
558 | 567 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
559 | 568 | server=None, folder=None, username=None, password=None, |
|
|
560 | 569 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
561 | 570 | |
|
|
562 |
if dataOut.flagNoData: |
|
|
|
571 | if dataOut.flagNoData: | |
|
|
563 | 572 | return dataOut |
|
|
564 | 573 | |
|
|
565 | 574 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): |
|
|
566 | 575 | return |
|
|
567 | 576 | |
|
|
568 | 577 | if pairsList == None: |
|
|
569 | 578 | pairsIndexList = dataOut.pairsIndexList[:10] |
|
|
570 | 579 | else: |
|
|
571 | 580 | pairsIndexList = [] |
|
|
572 | 581 | for pair in pairsList: |
|
|
573 | 582 | if pair not in dataOut.pairsList: |
|
|
574 | 583 | raise ValueError("Pair %s is not in dataOut.pairsList" %(pair)) |
|
|
575 | 584 | pairsIndexList.append(dataOut.pairsList.index(pair)) |
|
|
576 | 585 | |
|
|
577 | 586 | if pairsIndexList == []: |
|
|
578 | 587 | return |
|
|
579 | 588 | |
|
|
580 | 589 | # if len(pairsIndexList) > 4: |
|
|
581 | 590 | # pairsIndexList = pairsIndexList[0:4] |
|
|
582 | 591 | |
|
|
583 | 592 | hmin_index = None |
|
|
584 | 593 | hmax_index = None |
|
|
585 | 594 | |
|
|
586 | 595 | if hmin != None and hmax != None: |
|
|
587 | 596 | indexes = numpy.arange(dataOut.nHeights) |
|
|
588 | 597 | hmin_list = indexes[dataOut.heightList >= hmin] |
|
|
589 | 598 | hmax_list = indexes[dataOut.heightList <= hmax] |
|
|
590 | 599 | |
|
|
591 | 600 | if hmin_list.any(): |
|
|
592 | 601 | hmin_index = hmin_list[0] |
|
|
593 | 602 | |
|
|
594 | 603 | if hmax_list.any(): |
|
|
595 | 604 | hmax_index = hmax_list[-1]+1 |
|
|
596 | 605 | |
|
|
597 | 606 | x = dataOut.getTimeRange() |
|
|
598 | 607 | |
|
|
599 | 608 | thisDatetime = dataOut.datatime |
|
|
600 | 609 | |
|
|
601 | 610 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
602 | 611 | xlabel = "Local Time" |
|
|
603 | 612 | ylabel = "Phase (degrees)" |
|
|
604 | 613 | |
|
|
605 | 614 | update_figfile = False |
|
|
606 | 615 | |
|
|
607 | 616 | nplots = len(pairsIndexList) |
|
|
608 | 617 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) |
|
|
609 | 618 | phase_beacon = numpy.zeros(len(pairsIndexList)) |
|
|
610 | 619 | for i in range(nplots): |
|
|
611 | 620 | pair = dataOut.pairsList[pairsIndexList[i]] |
|
|
612 | 621 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) |
|
|
613 | 622 | powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) |
|
|
614 | 623 | powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) |
|
|
615 | 624 | avgcoherenceComplex = ccf/numpy.sqrt(powa*powb) |
|
|
616 | 625 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi |
|
|
617 | 626 | |
|
|
618 | 627 | if dataOut.beacon_heiIndexList: |
|
|
619 | 628 | phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) |
|
|
620 | 629 | else: |
|
|
621 | 630 | phase_beacon[i] = numpy.average(phase) |
|
|
622 | 631 | |
|
|
623 | 632 | if not self.isConfig: |
|
|
624 | 633 | |
|
|
625 | 634 | nplots = len(pairsIndexList) |
|
|
626 | 635 | |
|
|
627 | 636 | self.setup(id=id, |
|
|
628 | 637 | nplots=nplots, |
|
|
629 | 638 | wintitle=wintitle, |
|
|
630 | 639 | showprofile=showprofile, |
|
|
631 | 640 | show=show) |
|
|
632 | 641 | |
|
|
633 | 642 | if timerange != None: |
|
|
634 | 643 | self.timerange = timerange |
|
|
635 | 644 | |
|
|
636 | 645 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
637 | 646 | |
|
|
638 | 647 | if ymin == None: ymin = 0 |
|
|
639 | 648 | if ymax == None: ymax = 360 |
|
|
640 | 649 | |
|
|
641 | 650 | self.FTP_WEI = ftp_wei |
|
|
642 | 651 | self.EXP_CODE = exp_code |
|
|
643 | 652 | self.SUB_EXP_CODE = sub_exp_code |
|
|
644 | 653 | self.PLOT_POS = plot_pos |
|
|
645 | 654 | |
|
|
646 | 655 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
647 | 656 | self.isConfig = True |
|
|
648 | 657 | self.figfile = figfile |
|
|
649 | 658 | self.xdata = numpy.array([]) |
|
|
650 | 659 | self.ydata = numpy.array([]) |
|
|
651 | 660 | |
|
|
652 | 661 | update_figfile = True |
|
|
653 | 662 | |
|
|
654 | 663 | #open file beacon phase |
|
|
655 | 664 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
656 | 665 | beacon_file = os.path.join(path,'%s.txt'%self.name) |
|
|
657 | 666 | self.filename_phase = os.path.join(figpath,beacon_file) |
|
|
658 | 667 | #self.save_phase(self.filename_phase) |
|
|
659 | 668 | |
|
|
660 | 669 | |
|
|
661 | 670 | #store data beacon phase |
|
|
662 | 671 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) |
|
|
663 | 672 | |
|
|
664 | 673 | self.setWinTitle(title) |
|
|
665 | 674 | |
|
|
666 | 675 | |
|
|
667 | 676 | title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
668 | 677 | |
|
|
669 | 678 | legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList] |
|
|
670 | 679 | |
|
|
671 | 680 | axes = self.axesList[0] |
|
|
672 | 681 | |
|
|
673 | 682 | self.xdata = numpy.hstack((self.xdata, x[0:1])) |
|
|
674 | 683 | |
|
|
675 | 684 | if len(self.ydata)==0: |
|
|
676 | 685 | self.ydata = phase_beacon.reshape(-1,1) |
|
|
677 | 686 | else: |
|
|
678 | 687 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) |
|
|
679 | 688 | |
|
|
680 | 689 | |
|
|
681 | 690 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, |
|
|
682 | 691 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, |
|
|
683 | 692 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", |
|
|
684 | 693 | XAxisAsTime=True, grid='both' |
|
|
685 | 694 | ) |
|
|
686 | 695 | |
|
|
687 | 696 | self.draw() |
|
|
688 | 697 | |
|
|
689 | 698 | if dataOut.ltctime >= self.xmax: |
|
|
690 | 699 | self.counter_imagwr = wr_period |
|
|
691 | 700 | self.isConfig = False |
|
|
692 | 701 | update_figfile = True |
|
|
693 | 702 | |
|
|
694 | 703 | self.save(figpath=figpath, |
|
|
695 | 704 | figfile=figfile, |
|
|
696 | 705 | save=save, |
|
|
697 | 706 | ftp=ftp, |
|
|
698 | 707 | wr_period=wr_period, |
|
|
699 | 708 | thisDatetime=thisDatetime, |
|
|
700 | 709 | update_figfile=update_figfile) |
|
|
701 | 710 | |
|
|
702 | return dataOut No newline at end of file | |
|
|
711 | return dataOut | |
| @@ -1,1457 +1,1456 | |||
|
|
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 | import math |
|
|
16 | 16 | |
|
|
17 | 17 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation |
|
|
18 | 18 | from schainpy.model.data.jrodata import Spectra |
|
|
19 | 19 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
|
20 | 20 | from schainpy.utils import log |
|
|
21 | 21 | |
|
|
22 | 22 | from scipy.optimize import curve_fit |
|
|
23 | 23 | |
|
|
24 | 24 | |
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25 | 25 | class SpectraProc(ProcessingUnit): |
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26 | 26 | |
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27 | 27 | def __init__(self): |
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28 | 28 | |
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29 | 29 | ProcessingUnit.__init__(self) |
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30 | 30 | |
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31 | 31 | self.buffer = None |
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32 | 32 | self.firstdatatime = None |
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33 | 33 | self.profIndex = 0 |
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34 | 34 | self.dataOut = Spectra() |
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35 | 35 | self.id_min = None |
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36 | 36 | self.id_max = None |
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37 | 37 | self.setupReq = False #Agregar a todas las unidades de proc |
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38 | 38 | |
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39 | 39 | def __updateSpecFromVoltage(self): |
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40 | 40 | |
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41 | 41 | self.dataOut.timeZone = self.dataIn.timeZone |
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42 | 42 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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43 | 43 | self.dataOut.errorCount = self.dataIn.errorCount |
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44 | 44 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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45 | 45 | try: |
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46 | 46 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
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47 | 47 | except: |
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48 | 48 | pass |
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49 | 49 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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50 | 50 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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51 | 51 | self.dataOut.channelList = self.dataIn.channelList |
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52 | 52 | self.dataOut.heightList = self.dataIn.heightList |
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53 | 53 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
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54 | 54 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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55 | 55 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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56 | 56 | self.dataOut.utctime = self.firstdatatime |
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57 | 57 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData |
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58 | 58 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData |
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59 | 59 | self.dataOut.flagShiftFFT = False |
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60 | 60 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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61 | 61 | self.dataOut.nIncohInt = 1 |
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62 | 62 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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63 | 63 | self.dataOut.frequency = self.dataIn.frequency |
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64 | 64 | self.dataOut.realtime = self.dataIn.realtime |
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65 | 65 | self.dataOut.azimuth = self.dataIn.azimuth |
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66 | 66 | self.dataOut.zenith = self.dataIn.zenith |
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67 | 67 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
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68 | 68 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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69 | 69 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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70 | 70 | |
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71 | 71 | def __getFft(self): |
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72 | 72 | """ |
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73 | 73 | Convierte valores de Voltaje a Spectra |
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74 | 74 | |
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75 | 75 | Affected: |
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76 | 76 | self.dataOut.data_spc |
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77 | 77 | self.dataOut.data_cspc |
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78 | 78 | self.dataOut.data_dc |
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79 | 79 | self.dataOut.heightList |
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80 | 80 | self.profIndex |
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81 | 81 | self.buffer |
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82 | 82 | self.dataOut.flagNoData |
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83 | 83 | """ |
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84 | 84 | fft_volt = numpy.fft.fft( |
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85 | 85 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) |
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86 | 86 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
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87 | 87 | dc = fft_volt[:, 0, :] |
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88 | 88 | |
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89 | 89 | # calculo de self-spectra |
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90 | 90 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
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91 | 91 | spc = fft_volt * numpy.conjugate(fft_volt) |
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92 | 92 | spc = spc.real |
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93 | 93 | |
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94 | 94 | blocksize = 0 |
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95 | 95 | blocksize += dc.size |
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96 | 96 | blocksize += spc.size |
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97 | 97 | |
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98 | 98 | cspc = None |
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99 | 99 | pairIndex = 0 |
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100 | 100 | if self.dataOut.pairsList != None: |
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101 | 101 | # calculo de cross-spectra |
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102 | 102 | cspc = numpy.zeros( |
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103 | 103 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
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104 | 104 | for pair in self.dataOut.pairsList: |
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105 | 105 | if pair[0] not in self.dataOut.channelList: |
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106 | 106 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( |
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107 | 107 | str(pair), str(self.dataOut.channelList))) |
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108 | 108 | if pair[1] not in self.dataOut.channelList: |
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109 | 109 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( |
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110 | 110 | str(pair), str(self.dataOut.channelList))) |
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111 | 111 | |
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112 | 112 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ |
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113 | 113 | numpy.conjugate(fft_volt[pair[1], :, :]) |
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114 | 114 | pairIndex += 1 |
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115 | 115 | blocksize += cspc.size |
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116 | 116 | |
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117 | 117 | self.dataOut.data_spc = spc |
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118 | 118 | self.dataOut.data_cspc = cspc |
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119 | 119 | self.dataOut.data_dc = dc |
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120 | 120 | self.dataOut.blockSize = blocksize |
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121 | 121 | self.dataOut.flagShiftFFT = False |
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122 | 122 | |
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123 | 123 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False): |
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124 | 124 | |
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125 | 125 | if self.dataIn.type == "Spectra": |
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126 | 126 | self.dataOut.copy(self.dataIn) |
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127 | 127 | if shift_fft: |
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128 | 128 | #desplaza a la derecha en el eje 2 determinadas posiciones |
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129 | 129 | shift = int(self.dataOut.nFFTPoints/2) |
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130 | 130 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) |
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131 | 131 | |
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132 | 132 | if self.dataOut.data_cspc is not None: |
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133 | 133 | #desplaza a la derecha en el eje 2 determinadas posiciones |
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134 | 134 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) |
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135 | 135 | if pairsList: |
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136 | 136 | self.__selectPairs(pairsList) |
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137 | 137 | |
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138 | 138 | elif self.dataIn.type == "Voltage": |
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139 | 139 | |
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140 | 140 | self.dataOut.flagNoData = True |
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141 | 141 | |
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142 | 142 | if nFFTPoints == None: |
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143 | 143 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") |
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144 | 144 | |
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145 | 145 | if nProfiles == None: |
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146 | 146 | nProfiles = nFFTPoints |
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147 | 147 | |
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148 | 148 | if ippFactor == None: |
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149 | 149 | self.dataOut.ippFactor = 1 |
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150 | 150 | |
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151 | 151 | self.dataOut.nFFTPoints = nFFTPoints |
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152 | 152 | |
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153 | 153 | if self.buffer is None: |
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154 | 154 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
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155 | 155 | nProfiles, |
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156 | 156 | self.dataIn.nHeights), |
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157 | 157 | dtype='complex') |
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158 | 158 | |
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159 | 159 | if self.dataIn.flagDataAsBlock: |
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160 | 160 | nVoltProfiles = self.dataIn.data.shape[1] |
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161 | 161 | |
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162 | 162 | if nVoltProfiles == nProfiles: |
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163 | 163 | self.buffer = self.dataIn.data.copy() |
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164 | 164 | self.profIndex = nVoltProfiles |
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165 | 165 | |
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166 | 166 | elif nVoltProfiles < nProfiles: |
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167 | 167 | |
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168 | 168 | if self.profIndex == 0: |
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169 | 169 | self.id_min = 0 |
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170 | 170 | self.id_max = nVoltProfiles |
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171 | 171 | |
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172 | 172 | self.buffer[:, self.id_min:self.id_max, |
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173 | 173 | :] = self.dataIn.data |
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174 | 174 | self.profIndex += nVoltProfiles |
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175 | 175 | self.id_min += nVoltProfiles |
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176 | 176 | self.id_max += nVoltProfiles |
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177 | 177 | else: |
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178 | 178 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( |
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179 | 179 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) |
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180 | 180 | self.dataOut.flagNoData = True |
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181 | 181 | else: |
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182 | 182 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() |
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183 | 183 | self.profIndex += 1 |
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184 | 184 | |
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185 | 185 | if self.firstdatatime == None: |
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186 | 186 | self.firstdatatime = self.dataIn.utctime |
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187 | 187 | |
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188 | 188 | if self.profIndex == nProfiles: |
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189 | 189 | self.__updateSpecFromVoltage() |
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190 | 190 | if pairsList == None: |
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191 | 191 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] |
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192 | 192 | else: |
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193 | 193 | self.dataOut.pairsList = pairsList |
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194 | 194 | self.__getFft() |
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195 | 195 | self.dataOut.flagNoData = False |
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196 | 196 | self.firstdatatime = None |
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197 | 197 | self.profIndex = 0 |
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198 | 198 | else: |
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199 | 199 | raise ValueError("The type of input object '%s' is not valid".format( |
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200 | 200 | self.dataIn.type)) |
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201 | 201 | |
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202 | 202 | def __selectPairs(self, pairsList): |
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203 | 203 | |
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204 | 204 | if not pairsList: |
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205 | 205 | return |
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206 | 206 | |
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207 | 207 | pairs = [] |
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208 | 208 | pairsIndex = [] |
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209 | 209 | |
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210 | 210 | for pair in pairsList: |
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211 | 211 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
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212 | 212 | continue |
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213 | 213 | pairs.append(pair) |
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214 | 214 | pairsIndex.append(pairs.index(pair)) |
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215 | 215 | |
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216 | 216 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
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217 | 217 | self.dataOut.pairsList = pairs |
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218 | 218 | |
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219 | 219 | return |
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220 | 220 | |
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221 | 221 | def selectFFTs(self, minFFT, maxFFT ): |
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222 | 222 | """ |
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223 | 223 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
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224 | 224 | minFFT<= FFT <= maxFFT |
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225 | 225 | """ |
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226 | 226 | |
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227 | 227 | if (minFFT > maxFFT): |
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228 | 228 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) |
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229 | 229 | |
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230 | 230 | if (minFFT < self.dataOut.getFreqRange()[0]): |
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231 | 231 | minFFT = self.dataOut.getFreqRange()[0] |
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232 | 232 | |
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233 | 233 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
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234 | 234 | maxFFT = self.dataOut.getFreqRange()[-1] |
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235 | 235 | |
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236 | 236 | minIndex = 0 |
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237 | 237 | maxIndex = 0 |
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238 | 238 | FFTs = self.dataOut.getFreqRange() |
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239 | 239 | |
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240 | 240 | inda = numpy.where(FFTs >= minFFT) |
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241 | 241 | indb = numpy.where(FFTs <= maxFFT) |
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242 | 242 | |
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243 | 243 | try: |
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244 | 244 | minIndex = inda[0][0] |
|
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245 | 245 | except: |
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246 | 246 | minIndex = 0 |
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247 | 247 | |
|
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248 | 248 | try: |
|
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249 | 249 | maxIndex = indb[0][-1] |
|
|
250 | 250 | except: |
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251 | 251 | maxIndex = len(FFTs) |
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252 | 252 | |
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253 | 253 | self.selectFFTsByIndex(minIndex, maxIndex) |
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254 | 254 | |
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255 | 255 | return 1 |
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256 | 256 | |
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257 | 257 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): |
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258 | 258 | newheis = numpy.where( |
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259 | 259 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
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260 | 260 | |
|
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261 | 261 | if hei_ref != None: |
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262 | 262 | newheis = numpy.where(self.dataOut.heightList > hei_ref) |
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263 | 263 | |
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264 | 264 | minIndex = min(newheis[0]) |
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265 | 265 | maxIndex = max(newheis[0]) |
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266 | 266 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
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267 | 267 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
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268 | 268 | |
|
|
269 | 269 | # determina indices |
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|
270 | 270 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / |
|
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271 | 271 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) |
|
|
272 | 272 | avg_dB = 10 * \ |
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273 | 273 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) |
|
|
274 | 274 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
|
275 | 275 | beacon_heiIndexList = [] |
|
|
276 | 276 | for val in avg_dB.tolist(): |
|
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277 | 277 | if val >= beacon_dB[0]: |
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278 | 278 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
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279 | 279 | |
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|
280 | 280 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
|
|
281 | 281 | data_cspc = None |
|
|
282 | 282 | if self.dataOut.data_cspc is not None: |
|
|
283 | 283 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
284 | 284 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
285 | 285 | |
|
|
286 | 286 | data_dc = None |
|
|
287 | 287 | if self.dataOut.data_dc is not None: |
|
|
288 | 288 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
289 | 289 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
290 | 290 | |
|
|
291 | 291 | self.dataOut.data_spc = data_spc |
|
|
292 | 292 | self.dataOut.data_cspc = data_cspc |
|
|
293 | 293 | self.dataOut.data_dc = data_dc |
|
|
294 | 294 | self.dataOut.heightList = heightList |
|
|
295 | 295 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
296 | 296 | |
|
|
297 | 297 | return 1 |
|
|
298 | 298 | |
|
|
299 | 299 | def selectFFTsByIndex(self, minIndex, maxIndex): |
|
|
300 | 300 | """ |
|
|
301 | 301 | |
|
|
302 | 302 | """ |
|
|
303 | 303 | |
|
|
304 | 304 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
305 | 305 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
306 | 306 | |
|
|
307 | 307 | if (maxIndex >= self.dataOut.nProfiles): |
|
|
308 | 308 | maxIndex = self.dataOut.nProfiles-1 |
|
|
309 | 309 | |
|
|
310 | 310 | #Spectra |
|
|
311 | 311 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
|
|
312 | 312 | |
|
|
313 | 313 | data_cspc = None |
|
|
314 | 314 | if self.dataOut.data_cspc is not None: |
|
|
315 | 315 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
|
|
316 | 316 | |
|
|
317 | 317 | data_dc = None |
|
|
318 | 318 | if self.dataOut.data_dc is not None: |
|
|
319 | 319 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
|
|
320 | 320 | |
|
|
321 | 321 | self.dataOut.data_spc = data_spc |
|
|
322 | 322 | self.dataOut.data_cspc = data_cspc |
|
|
323 | 323 | self.dataOut.data_dc = data_dc |
|
|
324 | 324 | |
|
|
325 | 325 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
|
|
326 | 326 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
|
|
327 | 327 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
|
|
328 | 328 | |
|
|
329 | 329 | return 1 |
|
|
330 | 330 | |
|
|
331 | 331 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
332 | 332 | # validacion de rango |
|
|
333 | 333 | if minHei == None: |
|
|
334 | 334 | minHei = self.dataOut.heightList[0] |
|
|
335 | 335 | |
|
|
336 | 336 | if maxHei == None: |
|
|
337 | 337 | maxHei = self.dataOut.heightList[-1] |
|
|
338 | 338 | |
|
|
339 | 339 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
340 | 340 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
|
341 | 341 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
|
342 | 342 | minHei = self.dataOut.heightList[0] |
|
|
343 | 343 | |
|
|
344 | 344 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
345 | 345 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
|
346 | 346 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
|
347 | 347 | maxHei = self.dataOut.heightList[-1] |
|
|
348 | 348 | |
|
|
349 | 349 | # validacion de velocidades |
|
|
350 | 350 | velrange = self.dataOut.getVelRange(1) |
|
|
351 | 351 | |
|
|
352 | 352 | if minVel == None: |
|
|
353 | 353 | minVel = velrange[0] |
|
|
354 | 354 | |
|
|
355 | 355 | if maxVel == None: |
|
|
356 | 356 | maxVel = velrange[-1] |
|
|
357 | 357 | |
|
|
358 | 358 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
359 | 359 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
|
360 | 360 | print('minVel is setting to %.2f' % (velrange[0])) |
|
|
361 | 361 | minVel = velrange[0] |
|
|
362 | 362 | |
|
|
363 | 363 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
364 | 364 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
|
365 | 365 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
|
366 | 366 | maxVel = velrange[-1] |
|
|
367 | 367 | |
|
|
368 | 368 | # seleccion de indices para rango |
|
|
369 | 369 | minIndex = 0 |
|
|
370 | 370 | maxIndex = 0 |
|
|
371 | 371 | heights = self.dataOut.heightList |
|
|
372 | 372 | |
|
|
373 | 373 | inda = numpy.where(heights >= minHei) |
|
|
374 | 374 | indb = numpy.where(heights <= maxHei) |
|
|
375 | 375 | |
|
|
376 | 376 | try: |
|
|
377 | 377 | minIndex = inda[0][0] |
|
|
378 | 378 | except: |
|
|
379 | 379 | minIndex = 0 |
|
|
380 | 380 | |
|
|
381 | 381 | try: |
|
|
382 | 382 | maxIndex = indb[0][-1] |
|
|
383 | 383 | except: |
|
|
384 | 384 | maxIndex = len(heights) |
|
|
385 | 385 | |
|
|
386 | 386 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
387 | 387 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
388 | 388 | minIndex, maxIndex)) |
|
|
389 | 389 | |
|
|
390 | 390 | if (maxIndex >= self.dataOut.nHeights): |
|
|
391 | 391 | maxIndex = self.dataOut.nHeights - 1 |
|
|
392 | 392 | |
|
|
393 | 393 | # seleccion de indices para velocidades |
|
|
394 | 394 | indminvel = numpy.where(velrange >= minVel) |
|
|
395 | 395 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
396 | 396 | try: |
|
|
397 | 397 | minIndexVel = indminvel[0][0] |
|
|
398 | 398 | except: |
|
|
399 | 399 | minIndexVel = 0 |
|
|
400 | 400 | |
|
|
401 | 401 | try: |
|
|
402 | 402 | maxIndexVel = indmaxvel[0][-1] |
|
|
403 | 403 | except: |
|
|
404 | 404 | maxIndexVel = len(velrange) |
|
|
405 | 405 | |
|
|
406 | 406 | # seleccion del espectro |
|
|
407 | 407 | data_spc = self.dataOut.data_spc[:, |
|
|
408 | 408 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] |
|
|
409 | 409 | # estimacion de ruido |
|
|
410 | 410 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
411 | 411 | |
|
|
412 | 412 | for channel in range(self.dataOut.nChannels): |
|
|
413 | 413 | daux = data_spc[channel, :, :] |
|
|
414 | 414 | sortdata = numpy.sort(daux, axis=None) |
|
|
415 | 415 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) |
|
|
416 | 416 | |
|
|
417 | 417 | self.dataOut.noise_estimation = noise.copy() |
|
|
418 | 418 | |
|
|
419 | 419 | return 1 |
|
|
420 | 420 | |
|
|
421 | 421 | class removeDC(Operation): |
|
|
422 | 422 | |
|
|
423 | 423 | def run(self, dataOut, mode=2): |
|
|
424 | 424 | self.dataOut = dataOut |
|
|
425 | 425 | jspectra = self.dataOut.data_spc |
|
|
426 | 426 | jcspectra = self.dataOut.data_cspc |
|
|
427 | 427 | |
|
|
428 | 428 | num_chan = jspectra.shape[0] |
|
|
429 | 429 | num_hei = jspectra.shape[2] |
|
|
430 | 430 | |
|
|
431 | 431 | if jcspectra is not None: |
|
|
432 | 432 | jcspectraExist = True |
|
|
433 | 433 | num_pairs = jcspectra.shape[0] |
|
|
434 | 434 | else: |
|
|
435 | 435 | jcspectraExist = False |
|
|
436 | 436 | |
|
|
437 | 437 | freq_dc = int(jspectra.shape[1] / 2) |
|
|
438 | 438 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
439 | 439 | ind_vel = ind_vel.astype(int) |
|
|
440 | 440 | |
|
|
441 | 441 | if ind_vel[0] < 0: |
|
|
442 | 442 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof |
|
|
443 | 443 | |
|
|
444 | 444 | if mode == 1: |
|
|
445 | 445 | jspectra[:, freq_dc, :] = ( |
|
|
446 | 446 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
447 | 447 | |
|
|
448 | 448 | if jcspectraExist: |
|
|
449 | 449 | jcspectra[:, freq_dc, :] = ( |
|
|
450 | 450 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 |
|
|
451 | 451 | |
|
|
452 | 452 | if mode == 2: |
|
|
453 | 453 | |
|
|
454 | 454 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
455 | 455 | xx = numpy.zeros([4, 4]) |
|
|
456 | 456 | |
|
|
457 | 457 | for fil in range(4): |
|
|
458 | 458 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
459 | 459 | |
|
|
460 | 460 | xx_inv = numpy.linalg.inv(xx) |
|
|
461 | 461 | xx_aux = xx_inv[0, :] |
|
|
462 | 462 | |
|
|
463 | 463 | for ich in range(num_chan): |
|
|
464 | 464 | yy = jspectra[ich, ind_vel, :] |
|
|
465 | 465 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
466 | 466 | |
|
|
467 | 467 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
468 | 468 | cjunkid = sum(junkid) |
|
|
469 | 469 | |
|
|
470 | 470 | if cjunkid.any(): |
|
|
471 | 471 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
472 | 472 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
473 | 473 | |
|
|
474 | 474 | if jcspectraExist: |
|
|
475 | 475 | for ip in range(num_pairs): |
|
|
476 | 476 | yy = jcspectra[ip, ind_vel, :] |
|
|
477 | 477 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
478 | 478 | |
|
|
479 | 479 | self.dataOut.data_spc = jspectra |
|
|
480 | 480 | self.dataOut.data_cspc = jcspectra |
|
|
481 | 481 | |
|
|
482 | 482 | return self.dataOut |
|
|
483 | 483 | |
|
|
484 | 484 | # import matplotlib.pyplot as plt |
|
|
485 | 485 | |
|
|
486 | 486 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): |
|
|
487 | 487 | z = (x - a1) / a2 |
|
|
488 | 488 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 |
|
|
489 | 489 | return y |
|
|
490 | 490 | class CleanRayleigh(Operation): |
|
|
491 | 491 | |
|
|
492 | 492 | def __init__(self): |
|
|
493 | 493 | |
|
|
494 | 494 | Operation.__init__(self) |
|
|
495 | 495 | self.i=0 |
|
|
496 | 496 | self.isConfig = False |
|
|
497 | 497 | self.__dataReady = False |
|
|
498 | 498 | self.__profIndex = 0 |
|
|
499 | 499 | self.byTime = False |
|
|
500 | 500 | self.byProfiles = False |
|
|
501 | 501 | |
|
|
502 | 502 | self.bloques = None |
|
|
503 | 503 | self.bloque0 = None |
|
|
504 | 504 | |
|
|
505 | 505 | self.index = 0 |
|
|
506 | 506 | |
|
|
507 | 507 | self.buffer = 0 |
|
|
508 | 508 | self.buffer2 = 0 |
|
|
509 | 509 | self.buffer3 = 0 |
|
|
510 | #self.min_hei = None | |
|
|
511 | #self.max_hei = None | |
|
|
510 | ||
|
|
512 | 511 | |
|
|
513 | 512 | def setup(self,dataOut,min_hei,max_hei,n, timeInterval,factor_stdv): |
|
|
514 | 513 | |
|
|
515 | 514 | self.nChannels = dataOut.nChannels |
|
|
516 | 515 | self.nProf = dataOut.nProfiles |
|
|
517 | 516 | self.nPairs = dataOut.data_cspc.shape[0] |
|
|
518 | 517 | self.pairsArray = numpy.array(dataOut.pairsList) |
|
|
519 | 518 | self.spectra = dataOut.data_spc |
|
|
520 | 519 | self.cspectra = dataOut.data_cspc |
|
|
521 | 520 | self.heights = dataOut.heightList #alturas totales |
|
|
522 | 521 | self.nHeights = len(self.heights) |
|
|
523 | 522 | self.min_hei = min_hei |
|
|
524 | 523 | self.max_hei = max_hei |
|
|
525 | 524 | if (self.min_hei == None): |
|
|
526 | 525 | self.min_hei = 0 |
|
|
527 | 526 | if (self.max_hei == None): |
|
|
528 | 527 | self.max_hei = dataOut.heightList[-1] |
|
|
529 | 528 | self.hval = ((self.max_hei>=self.heights) & (self.heights >= self.min_hei)).nonzero() |
|
|
530 | 529 | self.heightsClean = self.heights[self.hval] #alturas filtradas |
|
|
531 | 530 | self.hval = self.hval[0] # forma (N,), an solo N elementos -> Indices de alturas |
|
|
532 | 531 | self.nHeightsClean = len(self.heightsClean) |
|
|
533 | 532 | self.channels = dataOut.channelList |
|
|
534 | 533 | self.nChan = len(self.channels) |
|
|
535 | 534 | self.nIncohInt = dataOut.nIncohInt |
|
|
536 | 535 | self.__initime = dataOut.utctime |
|
|
537 | 536 | self.maxAltInd = self.hval[-1]+1 |
|
|
538 | 537 | self.minAltInd = self.hval[0] |
|
|
539 | 538 | |
|
|
540 | 539 | self.crosspairs = dataOut.pairsList |
|
|
541 | 540 | self.nPairs = len(self.crosspairs) |
|
|
542 | 541 | self.normFactor = dataOut.normFactor |
|
|
543 | 542 | self.nFFTPoints = dataOut.nFFTPoints |
|
|
544 | 543 | self.ippSeconds = dataOut.ippSeconds |
|
|
545 | 544 | self.currentTime = self.__initime |
|
|
546 | 545 | self.pairsArray = numpy.array(dataOut.pairsList) |
|
|
547 | 546 | self.factor_stdv = factor_stdv |
|
|
548 | ||
|
|
549 | ||
|
|
547 | print("CHANNELS: ",[x for x in self.channels]) | |
|
|
550 | 548 | |
|
|
551 | 549 | if n != None : |
|
|
552 | 550 | self.byProfiles = True |
|
|
553 | 551 | self.nIntProfiles = n |
|
|
554 | 552 | else: |
|
|
555 | 553 | self.__integrationtime = timeInterval |
|
|
556 | 554 | |
|
|
557 | 555 | self.__dataReady = False |
|
|
558 | 556 | self.isConfig = True |
|
|
559 | 557 | |
|
|
560 | 558 | |
|
|
561 | 559 | |
|
|
562 | 560 | def run(self, dataOut,min_hei=None,max_hei=None, n=None, timeInterval=10,factor_stdv=2.5): |
|
|
563 | 561 | #print (dataOut.utctime) |
|
|
564 | 562 | if not self.isConfig : |
|
|
565 | 563 | #print("Setting config") |
|
|
566 | 564 | self.setup(dataOut, min_hei,max_hei,n,timeInterval,factor_stdv) |
|
|
567 | 565 | #print("Config Done") |
|
|
568 | 566 | tini=dataOut.utctime |
|
|
569 | 567 | |
|
|
570 | 568 | if self.byProfiles: |
|
|
571 | 569 | if self.__profIndex == self.nIntProfiles: |
|
|
572 | 570 | self.__dataReady = True |
|
|
573 | 571 | else: |
|
|
574 | 572 | if (tini - self.__initime) >= self.__integrationtime: |
|
|
575 | 573 | #print(tini - self.__initime,self.__profIndex) |
|
|
576 | 574 | self.__dataReady = True |
|
|
577 | 575 | self.__initime = tini |
|
|
578 | 576 | |
|
|
579 | 577 | #if (tini.tm_min % 2) == 0 and (tini.tm_sec < 5 and self.fint==0): |
|
|
580 | 578 | |
|
|
581 | 579 | if self.__dataReady: |
|
|
582 | 580 | print("Data ready",self.__profIndex) |
|
|
583 | 581 | self.__profIndex = 0 |
|
|
584 | 582 | jspc = self.buffer |
|
|
585 | 583 | jcspc = self.buffer2 |
|
|
586 | 584 | #jnoise = self.buffer3 |
|
|
587 | 585 | self.buffer = dataOut.data_spc |
|
|
588 | 586 | self.buffer2 = dataOut.data_cspc |
|
|
589 | 587 | #self.buffer3 = dataOut.noise |
|
|
590 | 588 | self.currentTime = dataOut.utctime |
|
|
591 | 589 | if numpy.any(jspc) : |
|
|
592 | 590 | #print( jspc.shape, jcspc.shape) |
|
|
593 | 591 | jspc = numpy.reshape(jspc,(int(len(jspc)/self.nChannels),self.nChannels,self.nFFTPoints,self.nHeights)) |
|
|
594 | 592 | jcspc= numpy.reshape(jcspc,(int(len(jcspc)/self.nPairs),self.nPairs,self.nFFTPoints,self.nHeights)) |
|
|
595 | 593 | self.__dataReady = False |
|
|
596 | 594 | #print( jspc.shape, jcspc.shape) |
|
|
597 | 595 | dataOut.flagNoData = False |
|
|
598 | 596 | else: |
|
|
599 | 597 | dataOut.flagNoData = True |
|
|
600 | 598 | self.__dataReady = False |
|
|
601 | 599 | return dataOut |
|
|
602 | 600 | else: |
|
|
603 | 601 | #print( len(self.buffer)) |
|
|
604 | 602 | if numpy.any(self.buffer): |
|
|
605 | 603 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) |
|
|
606 | 604 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) |
|
|
607 | 605 | self.buffer3 += dataOut.data_dc |
|
|
608 | 606 | else: |
|
|
609 | 607 | self.buffer = dataOut.data_spc |
|
|
610 | 608 | self.buffer2 = dataOut.data_cspc |
|
|
611 | 609 | self.buffer3 = dataOut.data_dc |
|
|
612 | 610 | #print self.index, self.fint |
|
|
613 | 611 | #print self.buffer2.shape |
|
|
614 | 612 | dataOut.flagNoData = True ## NOTE: ?? revisar LUEGO |
|
|
615 | 613 | self.__profIndex += 1 |
|
|
616 | 614 | return dataOut ## NOTE: REV |
|
|
617 | 615 | |
|
|
618 | 616 | # if self.index == 0 and self.fint == 1 : |
|
|
619 | 617 | # if jspc != None: |
|
|
620 | 618 | # print len(jspc), jspc.shape |
|
|
621 | 619 | # jspc= numpy.reshape(jspc,(4,128,63,len(jspc)/4)) |
|
|
622 | 620 | # print jspc.shape |
|
|
623 | 621 | # dataOut.flagNoData = True |
|
|
624 | 622 | # return dataOut |
|
|
625 | 623 | # if path != None: |
|
|
626 | 624 | # sys.path.append(path) |
|
|
627 | 625 | # self.library = importlib.import_module(file) |
|
|
628 | 626 | # |
|
|
629 | 627 | # To be inserted as a parameter |
|
|
630 | 628 | |
|
|
631 | 629 | #Set constants |
|
|
632 | 630 | #constants = self.library.setConstants(dataOut) |
|
|
633 | 631 | #dataOut.constants = constants |
|
|
634 | 632 | |
|
|
635 | 633 | #snrth= 20 |
|
|
636 | 634 | |
|
|
637 | 635 | #crosspairs = dataOut.groupList |
|
|
638 | 636 | #noise = dataOut.noise |
|
|
639 | 637 | #print( nProf,heights) |
|
|
640 | 638 | #print( jspc.shape, jspc.shape[0]) |
|
|
641 | 639 | #print noise |
|
|
642 | 640 | #print jnoise[len(jnoise)-1,:], numpy.nansum(jnoise,axis=0)/len(jnoise) |
|
|
643 | 641 | #jnoise = jnoise/N |
|
|
644 | 642 | #noise = numpy.nansum(jnoise,axis=0)#/len(jnoise) |
|
|
645 | 643 | #print( noise) |
|
|
646 | 644 | #power = numpy.sum(spectra, axis=1) |
|
|
647 | 645 | #print power[0,:] |
|
|
648 | 646 | #print("CROSSPAIRS",crosspairs) |
|
|
649 | 647 | #nPairs = len(crosspairs) |
|
|
650 | 648 | #print(numpy.shape(dataOut.data_spc)) |
|
|
651 | 649 | |
|
|
652 | 650 | #absc = dataOut.abscissaList[:-1] |
|
|
653 | 651 | |
|
|
654 | 652 | #print absc.shape |
|
|
655 | 653 | #nBlocks=149 |
|
|
656 | 654 | #print('spectra', spectra.shape) |
|
|
657 | 655 | #print('noise print', crosspairs) |
|
|
658 | 656 | #print('spectra', spectra.shape) |
|
|
659 | 657 | #print('cspectra', cspectra.shape) |
|
|
660 | 658 | #print numpy.array(dataOut.data_pre[1]).shape |
|
|
661 | 659 | #spec, cspec = self.__DiffCoherent(snrth, spectra, cspectra, nProf, heights,nChan, nHei, nPairs, channels, noise*nProf, crosspairs) |
|
|
662 | 660 | |
|
|
663 | 661 | |
|
|
664 | 662 | #index = tini.tm_hour*12+tini.tm_min/5 |
|
|
665 | 663 | # jspc = jspc/self.nFFTPoints/self.normFactor |
|
|
666 | 664 | # jcspc = jcspc/self.nFFTPoints/self.normFactor |
|
|
667 | 665 | |
|
|
668 | 666 | |
|
|
669 | 667 | |
|
|
670 | 668 | #dataOut.data_spc,dataOut.data_cspc = self.CleanRayleigh(dataOut,jspc,jcspc,crosspairs,heights,channels,nProf,nHei,nChan,nPairs,nIncohInt,nBlocks=nBlocks) |
|
|
671 | 669 | #tmp_spectra,tmp_cspectra,sat_spectra,sat_cspectra = self.cleanRayleigh(dataOut,jspc,jcspc,self.min_hei,self.max_hei) |
|
|
672 | 670 | tmp_spectra,tmp_cspectra = self.cleanRayleigh(dataOut,jspc,jcspc,self.factor_stdv) |
|
|
673 | 671 | #jspectra = tmp_spectra*len(jspc[:,0,0,0]) |
|
|
674 | 672 | #jcspectra = tmp_cspectra*len(jspc[:,0,0,0]) |
|
|
675 | 673 | |
|
|
676 | 674 | dataOut.data_spc = tmp_spectra |
|
|
677 | 675 | dataOut.data_cspc = tmp_cspectra |
|
|
678 | 676 | dataOut.data_dc = self.buffer3 |
|
|
679 | 677 | dataOut.nIncohInt *= self.nIntProfiles |
|
|
680 | 678 | dataOut.utctime = self.currentTime #tiempo promediado |
|
|
681 | print("Time: ",time.localtime(dataOut.utctime)) | |
|
|
679 | #print("Time: ",time.localtime(dataOut.utctime)) | |
|
|
682 | 680 | # dataOut.data_spc = sat_spectra |
|
|
683 | 681 | # dataOut.data_cspc = sat_cspectra |
|
|
684 | 682 | self.buffer = 0 |
|
|
685 | 683 | self.buffer2 = 0 |
|
|
686 | 684 | self.buffer3 = 0 |
|
|
687 | 685 | |
|
|
688 | 686 | return dataOut |
|
|
689 | 687 | |
|
|
690 | 688 | def cleanRayleigh(self,dataOut,spectra,cspectra,factor_stdv): |
|
|
691 | 689 | print("OP cleanRayleigh") |
|
|
692 | 690 | #import matplotlib.pyplot as plt |
|
|
693 | 691 | #for k in range(149): |
|
|
694 | 692 | |
|
|
695 | 693 | rfunc = cspectra.copy() #self.bloques |
|
|
696 | 694 | val_spc = spectra*0.0 #self.bloque0*0.0 |
|
|
697 | 695 | val_cspc = cspectra*0.0 #self.bloques*0.0 |
|
|
698 | 696 | in_sat_spectra = spectra.copy() #self.bloque0 |
|
|
699 | 697 | in_sat_cspectra = cspectra.copy() #self.bloques |
|
|
700 | 698 | |
|
|
701 | 699 | raxs = math.ceil(math.sqrt(self.nPairs)) |
|
|
702 | 700 | caxs = math.ceil(self.nPairs/raxs) |
|
|
703 | 701 | |
|
|
704 | 702 | #print(self.hval) |
|
|
705 | 703 | #print numpy.absolute(rfunc[:,0,0,14]) |
|
|
706 | 704 | for ih in range(self.minAltInd,self.maxAltInd): |
|
|
707 | 705 | for ifreq in range(self.nFFTPoints): |
|
|
708 | 706 | # fig, axs = plt.subplots(raxs, caxs) |
|
|
709 | 707 | # fig2, axs2 = plt.subplots(raxs, caxs) |
|
|
710 | 708 | col_ax = 0 |
|
|
711 | 709 | row_ax = 0 |
|
|
712 | 710 | for ii in range(self.nPairs): #PARES DE CANALES SELF y CROSS |
|
|
713 | 711 | #print("ii: ",ii) |
|
|
714 | 712 | if (col_ax%caxs==0 and col_ax!=0): |
|
|
715 | 713 | col_ax = 0 |
|
|
716 | 714 | row_ax += 1 |
|
|
717 | 715 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) #Potencia? |
|
|
718 | 716 | #print(func2clean.shape) |
|
|
719 | 717 | val = (numpy.isfinite(func2clean)==True).nonzero() |
|
|
720 | 718 | |
|
|
721 | 719 | if len(val)>0: #limitador |
|
|
722 | 720 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) |
|
|
723 | 721 | if min_val <= -40 : |
|
|
724 | 722 | min_val = -40 |
|
|
725 | 723 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 |
|
|
726 | 724 | if max_val >= 200 : |
|
|
727 | 725 | max_val = 200 |
|
|
728 | 726 | #print min_val, max_val |
|
|
729 | 727 | step = 1 |
|
|
730 | 728 | #print("Getting bins and the histogram") |
|
|
731 | 729 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step |
|
|
732 | 730 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
|
733 | 731 | #print(len(y_dist),len(binstep[:-1])) |
|
|
734 | 732 | #print(row_ax,col_ax, " ..") |
|
|
735 | 733 | #print(self.pairsArray[ii][0],self.pairsArray[ii][1]) |
|
|
736 | 734 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) |
|
|
737 | 735 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) |
|
|
738 | 736 | parg = [numpy.amax(y_dist),mean,sigma] |
|
|
739 | 737 | gauss_fit, covariance = None, None |
|
|
740 | 738 | newY = None |
|
|
741 | 739 | try : |
|
|
742 | 740 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) |
|
|
743 | 741 | mode = gauss_fit[1] |
|
|
744 | 742 | stdv = gauss_fit[2] |
|
|
745 | 743 | #print(" FIT OK",gauss_fit) |
|
|
746 | 744 | ''' |
|
|
747 | 745 | newY = fit_func(x_dist,gauss_fit[0],gauss_fit[1],gauss_fit[2]) |
|
|
748 | 746 | axs[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
|
|
749 | 747 | axs[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
|
|
750 | 748 | axs[row_ax,col_ax].set_title("Pair "+str(self.crosspairs[ii]))''' |
|
|
751 | 749 | except: |
|
|
752 | 750 | mode = mean |
|
|
753 | 751 | stdv = sigma |
|
|
754 | 752 | #print("FIT FAIL") |
|
|
755 | 753 | |
|
|
756 | 754 | |
|
|
757 | 755 | #print(mode,stdv) |
|
|
758 | 756 | #Removing echoes greater than mode + 3*stdv |
|
|
759 | 757 | #factor_stdv = 2 |
|
|
760 | 758 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() |
|
|
761 | 759 | #noval tiene los indices que se van a remover |
|
|
762 | 760 | #print("Pair ",ii," novals: ",len(noval[0])) |
|
|
763 | 761 | if len(noval[0]) > 0: #forma de array (N,) es igual a longitud (N) |
|
|
764 | 762 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() |
|
|
765 | 763 | #print(novall) |
|
|
766 | #print(" ") | |
|
|
764 | #print(" ",self.pairsArray[ii]) | |
|
|
767 | 765 | cross_pairs = self.pairsArray[ii] |
|
|
768 | 766 | #Getting coherent echoes which are removed. |
|
|
769 | if len(novall[0]) > 0: | |
|
|
770 | ||
|
|
771 | val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 | |
|
|
772 | val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 | |
|
|
773 | val_cspc[novall[0],ii,ifreq,ih] = 1 | |
|
|
767 | # if len(novall[0]) > 0: | |
|
|
768 | # | |
|
|
769 | # val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 | |
|
|
770 | # val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 | |
|
|
771 | # val_cspc[novall[0],ii,ifreq,ih] = 1 | |
|
|
774 | 772 | #print("OUT NOVALL 1") |
|
|
775 | 773 | #Removing coherent from ISR data |
|
|
774 | chA = self.channels.index(cross_pairs[0]) | |
|
|
775 | chB = self.channels.index(cross_pairs[1]) | |
|
|
776 | 776 | |
|
|
777 | #print(spectra[:,ii,ifreq,ih]) | |
|
|
778 | 777 | new_a = numpy.delete(cspectra[:,ii,ifreq,ih], noval[0]) |
|
|
779 | 778 | mean_cspc = numpy.mean(new_a) |
|
|
780 |
new_b = numpy.delete(spectra[:,c |
|
|
|
779 | new_b = numpy.delete(spectra[:,chA,ifreq,ih], noval[0]) | |
|
|
781 | 780 | mean_spc0 = numpy.mean(new_b) |
|
|
782 |
new_c = numpy.delete(spectra[:,c |
|
|
|
781 | new_c = numpy.delete(spectra[:,chB,ifreq,ih], noval[0]) | |
|
|
783 | 782 | mean_spc1 = numpy.mean(new_c) |
|
|
784 |
spectra[noval,c |
|
|
|
785 |
spectra[noval,c |
|
|
|
783 | spectra[noval,chA,ifreq,ih] = mean_spc0 | |
|
|
784 | spectra[noval,chB,ifreq,ih] = mean_spc1 | |
|
|
786 | 785 | cspectra[noval,ii,ifreq,ih] = mean_cspc |
|
|
787 | 786 | |
|
|
788 | 787 | ''' |
|
|
789 | 788 | func2clean = 10*numpy.log10(numpy.absolute(cspectra[:,ii,ifreq,ih])) |
|
|
790 | 789 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
|
791 | 790 | axs2[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
|
|
792 | 791 | axs2[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
|
|
793 | 792 | axs2[row_ax,col_ax].set_title("Pair "+str(self.crosspairs[ii])) |
|
|
794 | 793 | ''' |
|
|
795 | 794 | |
|
|
796 | 795 | col_ax += 1 #contador de ploteo columnas |
|
|
797 | 796 | ##print(col_ax) |
|
|
798 | 797 | ''' |
|
|
799 | 798 | title = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km" |
|
|
800 | 799 | title2 = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km CLEANED" |
|
|
801 | 800 | fig.suptitle(title) |
|
|
802 | 801 | fig2.suptitle(title2) |
|
|
803 | 802 | plt.show()''' |
|
|
804 | 803 | |
|
|
805 | 804 | ''' channels = channels |
|
|
806 | 805 | cross_pairs = cross_pairs |
|
|
807 | 806 | #print("OUT NOVALL 2") |
|
|
808 | 807 | |
|
|
809 | 808 | vcross0 = (cross_pairs[0] == channels[ii]).nonzero() |
|
|
810 | 809 | vcross1 = (cross_pairs[1] == channels[ii]).nonzero() |
|
|
811 | 810 | vcross = numpy.concatenate((vcross0,vcross1),axis=None) |
|
|
812 | 811 | #print('vcros =', vcross) |
|
|
813 | 812 | |
|
|
814 | 813 | #Getting coherent echoes which are removed. |
|
|
815 | 814 | if len(novall) > 0: |
|
|
816 | 815 | #val_spc[novall,ii,ifreq,ih] = 1 |
|
|
817 | 816 | val_spc[ii,ifreq,ih,novall] = 1 |
|
|
818 | 817 | if len(vcross) > 0: |
|
|
819 | 818 | val_cspc[vcross,ifreq,ih,novall] = 1 |
|
|
820 | 819 | |
|
|
821 | 820 | #Removing coherent from ISR data. |
|
|
822 | 821 | self.bloque0[ii,ifreq,ih,noval] = numpy.nan |
|
|
823 | 822 | if len(vcross) > 0: |
|
|
824 | 823 | self.bloques[vcross,ifreq,ih,noval] = numpy.nan |
|
|
825 | 824 | ''' |
|
|
826 | 825 | |
|
|
827 | 826 | print("Getting average of the spectra and cross-spectra from incoherent echoes.") |
|
|
828 | 827 | out_spectra = numpy.zeros([self.nChan,self.nFFTPoints,self.nHeights], dtype=float) #+numpy.nan |
|
|
829 | 828 | out_cspectra = numpy.zeros([self.nPairs,self.nFFTPoints,self.nHeights], dtype=complex) #+numpy.nan |
|
|
830 | 829 | for ih in range(self.nHeights): |
|
|
831 | 830 | for ifreq in range(self.nFFTPoints): |
|
|
832 | 831 | for ich in range(self.nChan): |
|
|
833 | 832 | tmp = spectra[:,ich,ifreq,ih] |
|
|
834 | 833 | valid = (numpy.isfinite(tmp[:])==True).nonzero() |
|
|
835 | 834 | # if ich == 0 and ifreq == 0 and ih == 17 : |
|
|
836 | 835 | # print tmp |
|
|
837 | 836 | # print valid |
|
|
838 | 837 | # print len(valid[0]) |
|
|
839 | 838 | #print('TMP',tmp) |
|
|
840 | 839 | if len(valid[0]) >0 : |
|
|
841 | 840 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0]) |
|
|
842 | 841 | #for icr in range(nPairs): |
|
|
843 | 842 | for icr in range(self.nPairs): |
|
|
844 | 843 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) |
|
|
845 | 844 | valid = (numpy.isfinite(tmp)==True).nonzero() |
|
|
846 | 845 | if len(valid[0]) > 0: |
|
|
847 | 846 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0]) |
|
|
848 | 847 | ''' |
|
|
849 | 848 | # print('##########################################################') |
|
|
850 | 849 | print("Removing fake coherent echoes (at least 4 points around the point)") |
|
|
851 | 850 | |
|
|
852 | 851 | val_spectra = numpy.sum(val_spc,0) |
|
|
853 | 852 | val_cspectra = numpy.sum(val_cspc,0) |
|
|
854 | 853 | |
|
|
855 | 854 | val_spectra = self.REM_ISOLATED_POINTS(val_spectra,4) |
|
|
856 | 855 | val_cspectra = self.REM_ISOLATED_POINTS(val_cspectra,4) |
|
|
857 | 856 | |
|
|
858 | 857 | for i in range(nChan): |
|
|
859 | 858 | for j in range(nProf): |
|
|
860 | 859 | for k in range(nHeights): |
|
|
861 | 860 | if numpy.isfinite(val_spectra[i,j,k]) and val_spectra[i,j,k] < 1 : |
|
|
862 | 861 | val_spc[:,i,j,k] = 0.0 |
|
|
863 | 862 | for i in range(nPairs): |
|
|
864 | 863 | for j in range(nProf): |
|
|
865 | 864 | for k in range(nHeights): |
|
|
866 | 865 | if numpy.isfinite(val_cspectra[i,j,k]) and val_cspectra[i,j,k] < 1 : |
|
|
867 | 866 | val_cspc[:,i,j,k] = 0.0 |
|
|
868 | 867 | |
|
|
869 | 868 | # val_spc = numpy.reshape(val_spc, (len(spectra[:,0,0,0]),nProf*nHeights*nChan)) |
|
|
870 | 869 | # if numpy.isfinite(val_spectra)==str(True): |
|
|
871 | 870 | # noval = (val_spectra<1).nonzero() |
|
|
872 | 871 | # if len(noval) > 0: |
|
|
873 | 872 | # val_spc[:,noval] = 0.0 |
|
|
874 | 873 | # val_spc = numpy.reshape(val_spc, (149,nChan,nProf,nHeights)) |
|
|
875 | 874 | |
|
|
876 | 875 | #val_cspc = numpy.reshape(val_spc, (149,nChan*nHeights*nProf)) |
|
|
877 | 876 | #if numpy.isfinite(val_cspectra)==str(True): |
|
|
878 | 877 | # noval = (val_cspectra<1).nonzero() |
|
|
879 | 878 | # if len(noval) > 0: |
|
|
880 | 879 | # val_cspc[:,noval] = 0.0 |
|
|
881 | 880 | # val_cspc = numpy.reshape(val_cspc, (149,nChan,nProf,nHeights)) |
|
|
882 | 881 | tmp_sat_spectra = spectra.copy() |
|
|
883 | 882 | tmp_sat_spectra = tmp_sat_spectra*numpy.nan |
|
|
884 | 883 | tmp_sat_cspectra = cspectra.copy() |
|
|
885 | 884 | tmp_sat_cspectra = tmp_sat_cspectra*numpy.nan |
|
|
886 | 885 | ''' |
|
|
887 | 886 | # fig = plt.figure(figsize=(6,5)) |
|
|
888 | 887 | # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8 |
|
|
889 | 888 | # ax = fig.add_axes([left, bottom, width, height]) |
|
|
890 | 889 | # cp = ax.contour(10*numpy.log10(numpy.absolute(spectra[0,0,:,:]))) |
|
|
891 | 890 | # ax.clabel(cp, inline=True,fontsize=10) |
|
|
892 | 891 | # plt.show() |
|
|
893 | 892 | ''' |
|
|
894 | 893 | val = (val_spc > 0).nonzero() |
|
|
895 | 894 | if len(val[0]) > 0: |
|
|
896 | 895 | tmp_sat_spectra[val] = in_sat_spectra[val] |
|
|
897 | 896 | val = (val_cspc > 0).nonzero() |
|
|
898 | 897 | if len(val[0]) > 0: |
|
|
899 | 898 | tmp_sat_cspectra[val] = in_sat_cspectra[val] |
|
|
900 | 899 | |
|
|
901 | 900 | print("Getting average of the spectra and cross-spectra from incoherent echoes 2") |
|
|
902 | 901 | sat_spectra = numpy.zeros((nChan,nProf,nHeights), dtype=float) |
|
|
903 | 902 | sat_cspectra = numpy.zeros((nPairs,nProf,nHeights), dtype=complex) |
|
|
904 | 903 | for ih in range(nHeights): |
|
|
905 | 904 | for ifreq in range(nProf): |
|
|
906 | 905 | for ich in range(nChan): |
|
|
907 | 906 | tmp = numpy.squeeze(tmp_sat_spectra[:,ich,ifreq,ih]) |
|
|
908 | 907 | valid = (numpy.isfinite(tmp)).nonzero() |
|
|
909 | 908 | if len(valid[0]) > 0: |
|
|
910 | 909 | sat_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
|
911 | 910 | |
|
|
912 | 911 | for icr in range(nPairs): |
|
|
913 | 912 | tmp = numpy.squeeze(tmp_sat_cspectra[:,icr,ifreq,ih]) |
|
|
914 | 913 | valid = (numpy.isfinite(tmp)).nonzero() |
|
|
915 | 914 | if len(valid[0]) > 0: |
|
|
916 | 915 | sat_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
|
917 | 916 | ''' |
|
|
918 | 917 | #self.__dataReady= True |
|
|
919 | 918 | #sat_spectra, sat_cspectra= sat_spectra, sat_cspectra |
|
|
920 | 919 | #if not self.__dataReady: |
|
|
921 | 920 | #return None, None |
|
|
922 | 921 | #return out_spectra, out_cspectra ,sat_spectra,sat_cspectra |
|
|
923 | 922 | return out_spectra, out_cspectra |
|
|
924 | 923 | |
|
|
925 | 924 | def REM_ISOLATED_POINTS(self,array,rth): |
|
|
926 | 925 | # import matplotlib.pyplot as plt |
|
|
927 | 926 | if rth == None : |
|
|
928 | 927 | rth = 4 |
|
|
929 | 928 | print("REM ISO") |
|
|
930 | 929 | num_prof = len(array[0,:,0]) |
|
|
931 | 930 | num_hei = len(array[0,0,:]) |
|
|
932 | 931 | n2d = len(array[:,0,0]) |
|
|
933 | 932 | |
|
|
934 | 933 | for ii in range(n2d) : |
|
|
935 | 934 | #print ii,n2d |
|
|
936 | 935 | tmp = array[ii,:,:] |
|
|
937 | 936 | #print tmp.shape, array[ii,101,:],array[ii,102,:] |
|
|
938 | 937 | |
|
|
939 | 938 | # fig = plt.figure(figsize=(6,5)) |
|
|
940 | 939 | # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8 |
|
|
941 | 940 | # ax = fig.add_axes([left, bottom, width, height]) |
|
|
942 | 941 | # x = range(num_prof) |
|
|
943 | 942 | # y = range(num_hei) |
|
|
944 | 943 | # cp = ax.contour(y,x,tmp) |
|
|
945 | 944 | # ax.clabel(cp, inline=True,fontsize=10) |
|
|
946 | 945 | # plt.show() |
|
|
947 | 946 | |
|
|
948 | 947 | #indxs = WHERE(FINITE(tmp) AND tmp GT 0,cindxs) |
|
|
949 | 948 | tmp = numpy.reshape(tmp,num_prof*num_hei) |
|
|
950 | 949 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() |
|
|
951 | 950 | indxs2 = (tmp > 0).nonzero() |
|
|
952 | 951 | |
|
|
953 | 952 | indxs1 = (indxs1[0]) |
|
|
954 | 953 | indxs2 = indxs2[0] |
|
|
955 | 954 | #indxs1 = numpy.array(indxs1[0]) |
|
|
956 | 955 | #indxs2 = numpy.array(indxs2[0]) |
|
|
957 | 956 | indxs = None |
|
|
958 | 957 | #print indxs1 , indxs2 |
|
|
959 | 958 | for iv in range(len(indxs2)): |
|
|
960 | 959 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) |
|
|
961 | 960 | #print len(indxs2), indv |
|
|
962 | 961 | if len(indv[0]) > 0 : |
|
|
963 | 962 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) |
|
|
964 | 963 | # print indxs |
|
|
965 | 964 | indxs = indxs[1:] |
|
|
966 | 965 | #print(indxs, len(indxs)) |
|
|
967 | 966 | if len(indxs) < 4 : |
|
|
968 | 967 | array[ii,:,:] = 0. |
|
|
969 | 968 | return |
|
|
970 | 969 | |
|
|
971 | 970 | xpos = numpy.mod(indxs ,num_hei) |
|
|
972 | 971 | ypos = (indxs / num_hei) |
|
|
973 | 972 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) |
|
|
974 | 973 | #print sx |
|
|
975 | 974 | xpos = xpos[sx] |
|
|
976 | 975 | ypos = ypos[sx] |
|
|
977 | 976 | |
|
|
978 | 977 | # *********************************** Cleaning isolated points ********************************** |
|
|
979 | 978 | ic = 0 |
|
|
980 | 979 | while True : |
|
|
981 | 980 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) |
|
|
982 | 981 | #no_coh = WHERE(FINITE(r) AND (r LE rth),cno_coh) |
|
|
983 | 982 | #plt.plot(r) |
|
|
984 | 983 | #plt.show() |
|
|
985 | 984 | no_coh1 = (numpy.isfinite(r)==True).nonzero() |
|
|
986 | 985 | no_coh2 = (r <= rth).nonzero() |
|
|
987 | 986 | #print r, no_coh1, no_coh2 |
|
|
988 | 987 | no_coh1 = numpy.array(no_coh1[0]) |
|
|
989 | 988 | no_coh2 = numpy.array(no_coh2[0]) |
|
|
990 | 989 | no_coh = None |
|
|
991 | 990 | #print valid1 , valid2 |
|
|
992 | 991 | for iv in range(len(no_coh2)): |
|
|
993 | 992 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) |
|
|
994 | 993 | if len(indv[0]) > 0 : |
|
|
995 | 994 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) |
|
|
996 | 995 | no_coh = no_coh[1:] |
|
|
997 | 996 | #print len(no_coh), no_coh |
|
|
998 | 997 | if len(no_coh) < 4 : |
|
|
999 | 998 | #print xpos[ic], ypos[ic], ic |
|
|
1000 | 999 | # plt.plot(r) |
|
|
1001 | 1000 | # plt.show() |
|
|
1002 | 1001 | xpos[ic] = numpy.nan |
|
|
1003 | 1002 | ypos[ic] = numpy.nan |
|
|
1004 | 1003 | |
|
|
1005 | 1004 | ic = ic + 1 |
|
|
1006 | 1005 | if (ic == len(indxs)) : |
|
|
1007 | 1006 | break |
|
|
1008 | 1007 | #print( xpos, ypos) |
|
|
1009 | 1008 | |
|
|
1010 | 1009 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() |
|
|
1011 | 1010 | #print indxs[0] |
|
|
1012 | 1011 | if len(indxs[0]) < 4 : |
|
|
1013 | 1012 | array[ii,:,:] = 0. |
|
|
1014 | 1013 | return |
|
|
1015 | 1014 | |
|
|
1016 | 1015 | xpos = xpos[indxs[0]] |
|
|
1017 | 1016 | ypos = ypos[indxs[0]] |
|
|
1018 | 1017 | for i in range(0,len(ypos)): |
|
|
1019 | 1018 | ypos[i]=int(ypos[i]) |
|
|
1020 | 1019 | junk = tmp |
|
|
1021 | 1020 | tmp = junk*0.0 |
|
|
1022 | 1021 | |
|
|
1023 | 1022 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] |
|
|
1024 | 1023 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) |
|
|
1025 | 1024 | |
|
|
1026 | 1025 | #print array.shape |
|
|
1027 | 1026 | #tmp = numpy.reshape(tmp,(num_prof,num_hei)) |
|
|
1028 | 1027 | #print tmp.shape |
|
|
1029 | 1028 | |
|
|
1030 | 1029 | # fig = plt.figure(figsize=(6,5)) |
|
|
1031 | 1030 | # left, bottom, width, height = 0.1, 0.1, 0.8, 0.8 |
|
|
1032 | 1031 | # ax = fig.add_axes([left, bottom, width, height]) |
|
|
1033 | 1032 | # x = range(num_prof) |
|
|
1034 | 1033 | # y = range(num_hei) |
|
|
1035 | 1034 | # cp = ax.contour(y,x,array[ii,:,:]) |
|
|
1036 | 1035 | # ax.clabel(cp, inline=True,fontsize=10) |
|
|
1037 | 1036 | # plt.show() |
|
|
1038 | 1037 | return array |
|
|
1039 | 1038 | |
|
|
1040 | 1039 | class removeInterference(Operation): |
|
|
1041 | 1040 | |
|
|
1042 | 1041 | def removeInterference2(self): |
|
|
1043 | 1042 | |
|
|
1044 | 1043 | cspc = self.dataOut.data_cspc |
|
|
1045 | 1044 | spc = self.dataOut.data_spc |
|
|
1046 | 1045 | Heights = numpy.arange(cspc.shape[2]) |
|
|
1047 | 1046 | realCspc = numpy.abs(cspc) |
|
|
1048 | 1047 | |
|
|
1049 | 1048 | for i in range(cspc.shape[0]): |
|
|
1050 | 1049 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
|
1051 | 1050 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
1052 | 1051 | SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ] |
|
|
1053 | 1052 | InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 ) |
|
|
1054 | 1053 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
1055 | 1054 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
|
1056 | 1055 | |
|
|
1057 | 1056 | |
|
|
1058 | 1057 | InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
|
1059 | 1058 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
|
1060 | 1059 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
|
1061 | 1060 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
|
1062 | 1061 | |
|
|
1063 | 1062 | self.dataOut.data_cspc = cspc |
|
|
1064 | 1063 | |
|
|
1065 | 1064 | def removeInterference(self, interf = 2, hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
1066 | 1065 | |
|
|
1067 | 1066 | jspectra = self.dataOut.data_spc |
|
|
1068 | 1067 | jcspectra = self.dataOut.data_cspc |
|
|
1069 | 1068 | jnoise = self.dataOut.getNoise() |
|
|
1070 | 1069 | num_incoh = self.dataOut.nIncohInt |
|
|
1071 | 1070 | |
|
|
1072 | 1071 | num_channel = jspectra.shape[0] |
|
|
1073 | 1072 | num_prof = jspectra.shape[1] |
|
|
1074 | 1073 | num_hei = jspectra.shape[2] |
|
|
1075 | 1074 | |
|
|
1076 | 1075 | # hei_interf |
|
|
1077 | 1076 | if hei_interf is None: |
|
|
1078 | 1077 | count_hei = int(num_hei / 2) |
|
|
1079 | 1078 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei |
|
|
1080 | 1079 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
1081 | 1080 | # nhei_interf |
|
|
1082 | 1081 | if (nhei_interf == None): |
|
|
1083 | 1082 | nhei_interf = 5 |
|
|
1084 | 1083 | if (nhei_interf < 1): |
|
|
1085 | 1084 | nhei_interf = 1 |
|
|
1086 | 1085 | if (nhei_interf > count_hei): |
|
|
1087 | 1086 | nhei_interf = count_hei |
|
|
1088 | 1087 | if (offhei_interf == None): |
|
|
1089 | 1088 | offhei_interf = 0 |
|
|
1090 | 1089 | |
|
|
1091 | 1090 | ind_hei = list(range(num_hei)) |
|
|
1092 | 1091 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
1093 | 1092 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
1094 | 1093 | mask_prof = numpy.asarray(list(range(num_prof))) |
|
|
1095 | 1094 | num_mask_prof = mask_prof.size |
|
|
1096 | 1095 | comp_mask_prof = [0, num_prof / 2] |
|
|
1097 | 1096 | |
|
|
1098 | 1097 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
1099 | 1098 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
1100 | 1099 | jnoise = numpy.nan |
|
|
1101 | 1100 | noise_exist = jnoise[0] < numpy.Inf |
|
|
1102 | 1101 | |
|
|
1103 | 1102 | # Subrutina de Remocion de la Interferencia |
|
|
1104 | 1103 | for ich in range(num_channel): |
|
|
1105 | 1104 | # Se ordena los espectros segun su potencia (menor a mayor) |
|
|
1106 | 1105 | power = jspectra[ich, mask_prof, :] |
|
|
1107 | 1106 | power = power[:, hei_interf] |
|
|
1108 | 1107 | power = power.sum(axis=0) |
|
|
1109 | 1108 | psort = power.ravel().argsort() |
|
|
1110 | 1109 | |
|
|
1111 | 1110 | # Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
1112 | 1111 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( |
|
|
1113 | 1112 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
1114 | 1113 | |
|
|
1115 | 1114 | if noise_exist: |
|
|
1116 | 1115 | # tmp_noise = jnoise[ich] / num_prof |
|
|
1117 | 1116 | tmp_noise = jnoise[ich] |
|
|
1118 | 1117 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
1119 | 1118 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
1120 | 1119 | |
|
|
1121 | 1120 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf |
|
|
1122 | 1121 | jspc_interf = jspc_interf.transpose() |
|
|
1123 | 1122 | # Calculando el espectro de interferencia promedio |
|
|
1124 | 1123 | noiseid = numpy.where( |
|
|
1125 | 1124 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) |
|
|
1126 | 1125 | noiseid = noiseid[0] |
|
|
1127 | 1126 | cnoiseid = noiseid.size |
|
|
1128 | 1127 | interfid = numpy.where( |
|
|
1129 | 1128 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) |
|
|
1130 | 1129 | interfid = interfid[0] |
|
|
1131 | 1130 | cinterfid = interfid.size |
|
|
1132 | 1131 | |
|
|
1133 | 1132 | if (cnoiseid > 0): |
|
|
1134 | 1133 | jspc_interf[noiseid] = 0 |
|
|
1135 | 1134 | |
|
|
1136 | 1135 | # Expandiendo los perfiles a limpiar |
|
|
1137 | 1136 | if (cinterfid > 0): |
|
|
1138 | 1137 | new_interfid = ( |
|
|
1139 | 1138 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof |
|
|
1140 | 1139 | new_interfid = numpy.asarray(new_interfid) |
|
|
1141 | 1140 | new_interfid = {x for x in new_interfid} |
|
|
1142 | 1141 | new_interfid = numpy.array(list(new_interfid)) |
|
|
1143 | 1142 | new_cinterfid = new_interfid.size |
|
|
1144 | 1143 | else: |
|
|
1145 | 1144 | new_cinterfid = 0 |
|
|
1146 | 1145 | |
|
|
1147 | 1146 | for ip in range(new_cinterfid): |
|
|
1148 | 1147 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() |
|
|
1149 | 1148 | jspc_interf[new_interfid[ip] |
|
|
1150 | 1149 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] |
|
|
1151 | 1150 | |
|
|
1152 | 1151 | jspectra[ich, :, ind_hei] = jspectra[ich, :, |
|
|
1153 | 1152 | ind_hei] - jspc_interf # Corregir indices |
|
|
1154 | 1153 | |
|
|
1155 | 1154 | # Removiendo la interferencia del punto de mayor interferencia |
|
|
1156 | 1155 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
1157 | 1156 | maxid = ListAux.index(max(ListAux)) |
|
|
1158 | 1157 | |
|
|
1159 | 1158 | if cinterfid > 0: |
|
|
1160 | 1159 | for ip in range(cinterfid * (interf == 2) - 1): |
|
|
1161 | 1160 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * |
|
|
1162 | 1161 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
1163 | 1162 | cind = len(ind) |
|
|
1164 | 1163 | |
|
|
1165 | 1164 | if (cind > 0): |
|
|
1166 | 1165 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ |
|
|
1167 | 1166 | (1 + (numpy.random.uniform(cind) - 0.5) / |
|
|
1168 | 1167 | numpy.sqrt(num_incoh)) |
|
|
1169 | 1168 | |
|
|
1170 | 1169 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
1171 | 1170 | xx = numpy.zeros([4, 4]) |
|
|
1172 | 1171 | |
|
|
1173 | 1172 | for id1 in range(4): |
|
|
1174 | 1173 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
1175 | 1174 | |
|
|
1176 | 1175 | xx_inv = numpy.linalg.inv(xx) |
|
|
1177 | 1176 | xx = xx_inv[:, 0] |
|
|
1178 | 1177 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
1179 | 1178 | yy = jspectra[ich, mask_prof[ind], :] |
|
|
1180 | 1179 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( |
|
|
1181 | 1180 | yy.transpose(), xx) |
|
|
1182 | 1181 | |
|
|
1183 | 1182 | indAux = (jspectra[ich, :, :] < tmp_noise * |
|
|
1184 | 1183 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
1185 | 1184 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ |
|
|
1186 | 1185 | (1 - 1 / numpy.sqrt(num_incoh)) |
|
|
1187 | 1186 | |
|
|
1188 | 1187 | # Remocion de Interferencia en el Cross Spectra |
|
|
1189 | 1188 | if jcspectra is None: |
|
|
1190 | 1189 | return jspectra, jcspectra |
|
|
1191 | 1190 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) |
|
|
1192 | 1191 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
1193 | 1192 | |
|
|
1194 | 1193 | for ip in range(num_pairs): |
|
|
1195 | 1194 | |
|
|
1196 | 1195 | #------------------------------------------- |
|
|
1197 | 1196 | |
|
|
1198 | 1197 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) |
|
|
1199 | 1198 | cspower = cspower[:, hei_interf] |
|
|
1200 | 1199 | cspower = cspower.sum(axis=0) |
|
|
1201 | 1200 | |
|
|
1202 | 1201 | cspsort = cspower.ravel().argsort() |
|
|
1203 | 1202 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( |
|
|
1204 | 1203 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
1205 | 1204 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
1206 | 1205 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf |
|
|
1207 | 1206 | |
|
|
1208 | 1207 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
1209 | 1208 | |
|
|
1210 | 1209 | median_real = int(numpy.median(numpy.real( |
|
|
1211 | 1210 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1212 | 1211 | median_imag = int(numpy.median(numpy.imag( |
|
|
1213 | 1212 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
1214 | 1213 | comp_mask_prof = [int(e) for e in comp_mask_prof] |
|
|
1215 | 1214 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( |
|
|
1216 | 1215 | median_real, median_imag) |
|
|
1217 | 1216 | |
|
|
1218 | 1217 | for iprof in range(num_prof): |
|
|
1219 | 1218 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() |
|
|
1220 | 1219 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] |
|
|
1221 | 1220 | |
|
|
1222 | 1221 | # Removiendo la Interferencia |
|
|
1223 | 1222 | jcspectra[ip, :, ind_hei] = jcspectra[ip, |
|
|
1224 | 1223 | :, ind_hei] - jcspc_interf |
|
|
1225 | 1224 | |
|
|
1226 | 1225 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
1227 | 1226 | maxid = ListAux.index(max(ListAux)) |
|
|
1228 | 1227 | |
|
|
1229 | 1228 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
1230 | 1229 | xx = numpy.zeros([4, 4]) |
|
|
1231 | 1230 | |
|
|
1232 | 1231 | for id1 in range(4): |
|
|
1233 | 1232 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
1234 | 1233 | |
|
|
1235 | 1234 | xx_inv = numpy.linalg.inv(xx) |
|
|
1236 | 1235 | xx = xx_inv[:, 0] |
|
|
1237 | 1236 | |
|
|
1238 | 1237 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
1239 | 1238 | yy = jcspectra[ip, mask_prof[ind], :] |
|
|
1240 | 1239 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) |
|
|
1241 | 1240 | |
|
|
1242 | 1241 | # Guardar Resultados |
|
|
1243 | 1242 | self.dataOut.data_spc = jspectra |
|
|
1244 | 1243 | self.dataOut.data_cspc = jcspectra |
|
|
1245 | 1244 | |
|
|
1246 | 1245 | return 1 |
|
|
1247 | 1246 | |
|
|
1248 | 1247 | def run(self, dataOut, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None, mode=1): |
|
|
1249 | 1248 | |
|
|
1250 | 1249 | self.dataOut = dataOut |
|
|
1251 | 1250 | |
|
|
1252 | 1251 | if mode == 1: |
|
|
1253 | 1252 | self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None) |
|
|
1254 | 1253 | elif mode == 2: |
|
|
1255 | 1254 | self.removeInterference2() |
|
|
1256 | 1255 | |
|
|
1257 | 1256 | return self.dataOut |
|
|
1258 | 1257 | |
|
|
1259 | 1258 | |
|
|
1260 | 1259 | class IncohInt(Operation): |
|
|
1261 | 1260 | |
|
|
1262 | 1261 | __profIndex = 0 |
|
|
1263 | 1262 | __withOverapping = False |
|
|
1264 | 1263 | |
|
|
1265 | 1264 | __byTime = False |
|
|
1266 | 1265 | __initime = None |
|
|
1267 | 1266 | __lastdatatime = None |
|
|
1268 | 1267 | __integrationtime = None |
|
|
1269 | 1268 | |
|
|
1270 | 1269 | __buffer_spc = None |
|
|
1271 | 1270 | __buffer_cspc = None |
|
|
1272 | 1271 | __buffer_dc = None |
|
|
1273 | 1272 | |
|
|
1274 | 1273 | __dataReady = False |
|
|
1275 | 1274 | |
|
|
1276 | 1275 | __timeInterval = None |
|
|
1277 | 1276 | |
|
|
1278 | 1277 | n = None |
|
|
1279 | 1278 | |
|
|
1280 | 1279 | def __init__(self): |
|
|
1281 | 1280 | |
|
|
1282 | 1281 | Operation.__init__(self) |
|
|
1283 | 1282 | |
|
|
1284 | 1283 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
1285 | 1284 | """ |
|
|
1286 | 1285 | Set the parameters of the integration class. |
|
|
1287 | 1286 | |
|
|
1288 | 1287 | Inputs: |
|
|
1289 | 1288 | |
|
|
1290 | 1289 | n : Number of coherent integrations |
|
|
1291 | 1290 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
1292 | 1291 | overlapping : |
|
|
1293 | 1292 | |
|
|
1294 | 1293 | """ |
|
|
1295 | 1294 | |
|
|
1296 | 1295 | self.__initime = None |
|
|
1297 | 1296 | self.__lastdatatime = 0 |
|
|
1298 | 1297 | |
|
|
1299 | 1298 | self.__buffer_spc = 0 |
|
|
1300 | 1299 | self.__buffer_cspc = 0 |
|
|
1301 | 1300 | self.__buffer_dc = 0 |
|
|
1302 | 1301 | |
|
|
1303 | 1302 | self.__profIndex = 0 |
|
|
1304 | 1303 | self.__dataReady = False |
|
|
1305 | 1304 | self.__byTime = False |
|
|
1306 | 1305 | |
|
|
1307 | 1306 | if n is None and timeInterval is None: |
|
|
1308 | 1307 | raise ValueError("n or timeInterval should be specified ...") |
|
|
1309 | 1308 | |
|
|
1310 | 1309 | if n is not None: |
|
|
1311 | 1310 | self.n = int(n) |
|
|
1312 | 1311 | else: |
|
|
1313 | 1312 | |
|
|
1314 | 1313 | self.__integrationtime = int(timeInterval) |
|
|
1315 | 1314 | self.n = None |
|
|
1316 | 1315 | self.__byTime = True |
|
|
1317 | 1316 | |
|
|
1318 | 1317 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
1319 | 1318 | """ |
|
|
1320 | 1319 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
1321 | 1320 | |
|
|
1322 | 1321 | """ |
|
|
1323 | 1322 | |
|
|
1324 | 1323 | self.__buffer_spc += data_spc |
|
|
1325 | 1324 | |
|
|
1326 | 1325 | if data_cspc is None: |
|
|
1327 | 1326 | self.__buffer_cspc = None |
|
|
1328 | 1327 | else: |
|
|
1329 | 1328 | self.__buffer_cspc += data_cspc |
|
|
1330 | 1329 | |
|
|
1331 | 1330 | if data_dc is None: |
|
|
1332 | 1331 | self.__buffer_dc = None |
|
|
1333 | 1332 | else: |
|
|
1334 | 1333 | self.__buffer_dc += data_dc |
|
|
1335 | 1334 | |
|
|
1336 | 1335 | self.__profIndex += 1 |
|
|
1337 | 1336 | |
|
|
1338 | 1337 | return |
|
|
1339 | 1338 | |
|
|
1340 | 1339 | def pushData(self): |
|
|
1341 | 1340 | """ |
|
|
1342 | 1341 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
1343 | 1342 | |
|
|
1344 | 1343 | Affected: |
|
|
1345 | 1344 | |
|
|
1346 | 1345 | self.__profileIndex |
|
|
1347 | 1346 | |
|
|
1348 | 1347 | """ |
|
|
1349 | 1348 | |
|
|
1350 | 1349 | data_spc = self.__buffer_spc |
|
|
1351 | 1350 | data_cspc = self.__buffer_cspc |
|
|
1352 | 1351 | data_dc = self.__buffer_dc |
|
|
1353 | 1352 | n = self.__profIndex |
|
|
1354 | 1353 | |
|
|
1355 | 1354 | self.__buffer_spc = 0 |
|
|
1356 | 1355 | self.__buffer_cspc = 0 |
|
|
1357 | 1356 | self.__buffer_dc = 0 |
|
|
1358 | 1357 | self.__profIndex = 0 |
|
|
1359 | 1358 | |
|
|
1360 | 1359 | return data_spc, data_cspc, data_dc, n |
|
|
1361 | 1360 | |
|
|
1362 | 1361 | def byProfiles(self, *args): |
|
|
1363 | 1362 | |
|
|
1364 | 1363 | self.__dataReady = False |
|
|
1365 | 1364 | avgdata_spc = None |
|
|
1366 | 1365 | avgdata_cspc = None |
|
|
1367 | 1366 | avgdata_dc = None |
|
|
1368 | 1367 | |
|
|
1369 | 1368 | self.putData(*args) |
|
|
1370 | 1369 | |
|
|
1371 | 1370 | if self.__profIndex == self.n: |
|
|
1372 | 1371 | |
|
|
1373 | 1372 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1374 | 1373 | self.n = n |
|
|
1375 | 1374 | self.__dataReady = True |
|
|
1376 | 1375 | |
|
|
1377 | 1376 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1378 | 1377 | |
|
|
1379 | 1378 | def byTime(self, datatime, *args): |
|
|
1380 | 1379 | |
|
|
1381 | 1380 | self.__dataReady = False |
|
|
1382 | 1381 | avgdata_spc = None |
|
|
1383 | 1382 | avgdata_cspc = None |
|
|
1384 | 1383 | avgdata_dc = None |
|
|
1385 | 1384 | |
|
|
1386 | 1385 | self.putData(*args) |
|
|
1387 | 1386 | |
|
|
1388 | 1387 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
1389 | 1388 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
1390 | 1389 | self.n = n |
|
|
1391 | 1390 | self.__dataReady = True |
|
|
1392 | 1391 | |
|
|
1393 | 1392 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1394 | 1393 | |
|
|
1395 | 1394 | def integrate(self, datatime, *args): |
|
|
1396 | 1395 | |
|
|
1397 | 1396 | if self.__profIndex == 0: |
|
|
1398 | 1397 | self.__initime = datatime |
|
|
1399 | 1398 | |
|
|
1400 | 1399 | if self.__byTime: |
|
|
1401 | 1400 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
1402 | 1401 | datatime, *args) |
|
|
1403 | 1402 | else: |
|
|
1404 | 1403 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
1405 | 1404 | |
|
|
1406 | 1405 | if not self.__dataReady: |
|
|
1407 | 1406 | return None, None, None, None |
|
|
1408 | 1407 | |
|
|
1409 | 1408 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
1410 | 1409 | |
|
|
1411 | 1410 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
1412 | 1411 | if n == 1: |
|
|
1413 | 1412 | return dataOut |
|
|
1414 | 1413 | |
|
|
1415 | 1414 | dataOut.flagNoData = True |
|
|
1416 | 1415 | |
|
|
1417 | 1416 | if not self.isConfig: |
|
|
1418 | 1417 | self.setup(n, timeInterval, overlapping) |
|
|
1419 | 1418 | self.isConfig = True |
|
|
1420 | 1419 | |
|
|
1421 | 1420 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
1422 | 1421 | dataOut.data_spc, |
|
|
1423 | 1422 | dataOut.data_cspc, |
|
|
1424 | 1423 | dataOut.data_dc) |
|
|
1425 | 1424 | |
|
|
1426 | 1425 | if self.__dataReady: |
|
|
1427 | 1426 | |
|
|
1428 | 1427 | dataOut.data_spc = avgdata_spc |
|
|
1429 | 1428 | dataOut.data_cspc = avgdata_cspc |
|
|
1430 | 1429 | dataOut.data_dc = avgdata_dc |
|
|
1431 | 1430 | dataOut.nIncohInt *= self.n |
|
|
1432 | 1431 | dataOut.utctime = avgdatatime |
|
|
1433 | 1432 | dataOut.flagNoData = False |
|
|
1434 | 1433 | |
|
|
1435 | 1434 | return dataOut |
|
|
1436 | 1435 | |
|
|
1437 | 1436 | class dopplerFlip(Operation): |
|
|
1438 | 1437 | |
|
|
1439 | 1438 | def run(self, dataOut): |
|
|
1440 | 1439 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
1441 | 1440 | self.dataOut = dataOut |
|
|
1442 | 1441 | # JULIA-oblicua, indice 2 |
|
|
1443 | 1442 | # arreglo 2: (num_profiles, num_heights) |
|
|
1444 | 1443 | jspectra = self.dataOut.data_spc[2] |
|
|
1445 | 1444 | jspectra_tmp = numpy.zeros(jspectra.shape) |
|
|
1446 | 1445 | num_profiles = jspectra.shape[0] |
|
|
1447 | 1446 | freq_dc = int(num_profiles / 2) |
|
|
1448 | 1447 | # Flip con for |
|
|
1449 | 1448 | for j in range(num_profiles): |
|
|
1450 | 1449 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
1451 | 1450 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
1452 | 1451 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
1453 | 1452 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
1454 | 1453 | # canal modificado es re-escrito en el arreglo de canales |
|
|
1455 | 1454 | self.dataOut.data_spc[2] = jspectra_tmp |
|
|
1456 | 1455 | |
|
|
1457 | 1456 | return self.dataOut |
| @@ -1,1624 +1,1624 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy,math |
|
|
3 | 3 | from scipy import interpolate |
|
|
4 | 4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
5 | 5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon |
|
|
6 | 6 | from schainpy.utils import log |
|
|
7 | 7 | from time import time |
|
|
8 | 8 | |
|
|
9 | 9 | |
|
|
10 | 10 | |
|
|
11 | 11 | class VoltageProc(ProcessingUnit): |
|
|
12 | 12 | |
|
|
13 | 13 | def __init__(self): |
|
|
14 | 14 | |
|
|
15 | 15 | ProcessingUnit.__init__(self) |
|
|
16 | 16 | |
|
|
17 | 17 | self.dataOut = Voltage() |
|
|
18 | 18 | self.flip = 1 |
|
|
19 | 19 | self.setupReq = False |
|
|
20 | 20 | |
|
|
21 | 21 | def run(self): |
|
|
22 | 22 | |
|
|
23 | 23 | if self.dataIn.type == 'AMISR': |
|
|
24 | 24 | self.__updateObjFromAmisrInput() |
|
|
25 | 25 | |
|
|
26 | 26 | if self.dataIn.type == 'Voltage': |
|
|
27 | 27 | self.dataOut.copy(self.dataIn) |
|
|
28 | 28 | |
|
|
29 | 29 | def __updateObjFromAmisrInput(self): |
|
|
30 | 30 | |
|
|
31 | 31 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
32 | 32 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
33 | 33 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
34 | 34 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
35 | 35 | |
|
|
36 | 36 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
|
37 | 37 | self.dataOut.data = self.dataIn.data |
|
|
38 | 38 | self.dataOut.utctime = self.dataIn.utctime |
|
|
39 | 39 | self.dataOut.channelList = self.dataIn.channelList |
|
|
40 | 40 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
41 | 41 | self.dataOut.heightList = self.dataIn.heightList |
|
|
42 | 42 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
43 | 43 | |
|
|
44 | 44 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
45 | 45 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
46 | 46 | self.dataOut.frequency = self.dataIn.frequency |
|
|
47 | 47 | |
|
|
48 | 48 | self.dataOut.azimuth = self.dataIn.azimuth |
|
|
49 | 49 | self.dataOut.zenith = self.dataIn.zenith |
|
|
50 | 50 | |
|
|
51 | 51 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
|
52 | 52 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
|
53 | 53 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
|
54 | 54 | |
|
|
55 | 55 | |
|
|
56 | 56 | class selectChannels(Operation): |
|
|
57 | 57 | |
|
|
58 | 58 | def run(self, dataOut, channelList): |
|
|
59 | 59 | |
|
|
60 | 60 | channelIndexList = [] |
|
|
61 | 61 | self.dataOut = dataOut |
|
|
62 | 62 | for channel in channelList: |
|
|
63 | 63 | if channel not in self.dataOut.channelList: |
|
|
64 | 64 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) |
|
|
65 | 65 | |
|
|
66 | 66 | index = self.dataOut.channelList.index(channel) |
|
|
67 | 67 | channelIndexList.append(index) |
|
|
68 | 68 | self.selectChannelsByIndex(channelIndexList) |
|
|
69 | 69 | return self.dataOut |
|
|
70 | 70 | |
|
|
71 | 71 | def selectChannelsByIndex(self, channelIndexList): |
|
|
72 | 72 | """ |
|
|
73 | 73 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
|
74 | 74 | |
|
|
75 | 75 | Input: |
|
|
76 | 76 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
|
77 | 77 | |
|
|
78 | 78 | Affected: |
|
|
79 | 79 | self.dataOut.data |
|
|
80 | 80 | self.dataOut.channelIndexList |
|
|
81 | 81 | self.dataOut.nChannels |
|
|
82 | 82 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
|
83 | 83 | self.dataOut.systemHeaderObj.numChannels |
|
|
84 | 84 | self.dataOut.m_ProcessingHeader.blockSize |
|
|
85 | 85 | |
|
|
86 | 86 | Return: |
|
|
87 | 87 | None |
|
|
88 | 88 | """ |
|
|
89 | 89 | |
|
|
90 | 90 | for channelIndex in channelIndexList: |
|
|
91 | 91 | if channelIndex not in self.dataOut.channelIndexList: |
|
|
92 | 92 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
|
93 | 93 | |
|
|
94 | 94 | if self.dataOut.type == 'Voltage': |
|
|
95 | 95 | if self.dataOut.flagDataAsBlock: |
|
|
96 | 96 | """ |
|
|
97 | 97 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
98 | 98 | """ |
|
|
99 | 99 | data = self.dataOut.data[channelIndexList,:,:] |
|
|
100 | 100 | else: |
|
|
101 | 101 | data = self.dataOut.data[channelIndexList,:] |
|
|
102 | 102 | |
|
|
103 | 103 | self.dataOut.data = data |
|
|
104 | 104 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
105 | 105 | self.dataOut.channelList = range(len(channelIndexList)) |
|
|
106 | 106 | |
|
|
107 | 107 | elif self.dataOut.type == 'Spectra': |
|
|
108 | 108 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
|
109 | 109 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
|
110 | 110 | |
|
|
111 | 111 | self.dataOut.data_spc = data_spc |
|
|
112 | 112 | self.dataOut.data_dc = data_dc |
|
|
113 | 113 | |
|
|
114 | 114 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
|
115 |
self.dataOut.channelList = |
|
|
|
115 | self.dataOut.channelList = channelIndexList | |
|
|
116 | 116 | self.__selectPairsByChannel(channelIndexList) |
|
|
117 | 117 | |
|
|
118 | 118 | return 1 |
|
|
119 | 119 | |
|
|
120 | 120 | def __selectPairsByChannel(self, channelList=None): |
|
|
121 | 121 | |
|
|
122 | 122 | if channelList == None: |
|
|
123 | 123 | return |
|
|
124 | 124 | |
|
|
125 | 125 | pairsIndexListSelected = [] |
|
|
126 | 126 | for pairIndex in self.dataOut.pairsIndexList: |
|
|
127 | 127 | # First pair |
|
|
128 | 128 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
|
129 | 129 | continue |
|
|
130 | 130 | # Second pair |
|
|
131 | 131 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
|
132 | 132 | continue |
|
|
133 | 133 | |
|
|
134 | 134 | pairsIndexListSelected.append(pairIndex) |
|
|
135 | 135 | |
|
|
136 | 136 | if not pairsIndexListSelected: |
|
|
137 | 137 | self.dataOut.data_cspc = None |
|
|
138 | 138 | self.dataOut.pairsList = [] |
|
|
139 | 139 | return |
|
|
140 | 140 | |
|
|
141 | 141 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
|
142 | 142 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
|
143 | 143 | for i in pairsIndexListSelected] |
|
|
144 | 144 | |
|
|
145 | 145 | return |
|
|
146 | 146 | |
|
|
147 | 147 | class selectHeights(Operation): |
|
|
148 | 148 | |
|
|
149 | 149 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
|
150 | 150 | """ |
|
|
151 | 151 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
|
152 | 152 | minHei <= height <= maxHei |
|
|
153 | 153 | |
|
|
154 | 154 | Input: |
|
|
155 | 155 | minHei : valor minimo de altura a considerar |
|
|
156 | 156 | maxHei : valor maximo de altura a considerar |
|
|
157 | 157 | |
|
|
158 | 158 | Affected: |
|
|
159 | 159 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
|
160 | 160 | |
|
|
161 | 161 | Return: |
|
|
162 | 162 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
163 | 163 | """ |
|
|
164 | 164 | |
|
|
165 | 165 | self.dataOut = dataOut |
|
|
166 | 166 | |
|
|
167 | 167 | if minHei and maxHei: |
|
|
168 | 168 | |
|
|
169 | 169 | if (minHei < self.dataOut.heightList[0]): |
|
|
170 | 170 | minHei = self.dataOut.heightList[0] |
|
|
171 | 171 | |
|
|
172 | 172 | if (maxHei > self.dataOut.heightList[-1]): |
|
|
173 | 173 | maxHei = self.dataOut.heightList[-1] |
|
|
174 | 174 | |
|
|
175 | 175 | minIndex = 0 |
|
|
176 | 176 | maxIndex = 0 |
|
|
177 | 177 | heights = self.dataOut.heightList |
|
|
178 | 178 | |
|
|
179 | 179 | inda = numpy.where(heights >= minHei) |
|
|
180 | 180 | indb = numpy.where(heights <= maxHei) |
|
|
181 | 181 | |
|
|
182 | 182 | try: |
|
|
183 | 183 | minIndex = inda[0][0] |
|
|
184 | 184 | except: |
|
|
185 | 185 | minIndex = 0 |
|
|
186 | 186 | |
|
|
187 | 187 | try: |
|
|
188 | 188 | maxIndex = indb[0][-1] |
|
|
189 | 189 | except: |
|
|
190 | 190 | maxIndex = len(heights) |
|
|
191 | 191 | |
|
|
192 | 192 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
|
193 | 193 | |
|
|
194 | 194 | return self.dataOut |
|
|
195 | 195 | |
|
|
196 | 196 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
197 | 197 | """ |
|
|
198 | 198 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
199 | 199 | minIndex <= index <= maxIndex |
|
|
200 | 200 | |
|
|
201 | 201 | Input: |
|
|
202 | 202 | minIndex : valor de indice minimo de altura a considerar |
|
|
203 | 203 | maxIndex : valor de indice maximo de altura a considerar |
|
|
204 | 204 | |
|
|
205 | 205 | Affected: |
|
|
206 | 206 | self.dataOut.data |
|
|
207 | 207 | self.dataOut.heightList |
|
|
208 | 208 | |
|
|
209 | 209 | Return: |
|
|
210 | 210 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
211 | 211 | """ |
|
|
212 | 212 | |
|
|
213 | 213 | if self.dataOut.type == 'Voltage': |
|
|
214 | 214 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
215 | 215 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
|
216 | 216 | |
|
|
217 | 217 | if (maxIndex >= self.dataOut.nHeights): |
|
|
218 | 218 | maxIndex = self.dataOut.nHeights |
|
|
219 | 219 | |
|
|
220 | 220 | #voltage |
|
|
221 | 221 | if self.dataOut.flagDataAsBlock: |
|
|
222 | 222 | """ |
|
|
223 | 223 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
224 | 224 | """ |
|
|
225 | 225 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
|
226 | 226 | else: |
|
|
227 | 227 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
|
228 | 228 | |
|
|
229 | 229 | # firstHeight = self.dataOut.heightList[minIndex] |
|
|
230 | 230 | |
|
|
231 | 231 | self.dataOut.data = data |
|
|
232 | 232 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
|
233 | 233 | |
|
|
234 | 234 | if self.dataOut.nHeights <= 1: |
|
|
235 | 235 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
|
236 | 236 | elif self.dataOut.type == 'Spectra': |
|
|
237 | 237 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
238 | 238 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
|
239 | 239 | minIndex, maxIndex)) |
|
|
240 | 240 | |
|
|
241 | 241 | if (maxIndex >= self.dataOut.nHeights): |
|
|
242 | 242 | maxIndex = self.dataOut.nHeights - 1 |
|
|
243 | 243 | |
|
|
244 | 244 | # Spectra |
|
|
245 | 245 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
|
246 | 246 | |
|
|
247 | 247 | data_cspc = None |
|
|
248 | 248 | if self.dataOut.data_cspc is not None: |
|
|
249 | 249 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
|
250 | 250 | |
|
|
251 | 251 | data_dc = None |
|
|
252 | 252 | if self.dataOut.data_dc is not None: |
|
|
253 | 253 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
|
254 | 254 | |
|
|
255 | 255 | self.dataOut.data_spc = data_spc |
|
|
256 | 256 | self.dataOut.data_cspc = data_cspc |
|
|
257 | 257 | self.dataOut.data_dc = data_dc |
|
|
258 | 258 | |
|
|
259 | 259 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
|
260 | 260 | |
|
|
261 | 261 | return 1 |
|
|
262 | 262 | |
|
|
263 | 263 | |
|
|
264 | 264 | class filterByHeights(Operation): |
|
|
265 | 265 | |
|
|
266 | 266 | def run(self, dataOut, window): |
|
|
267 | 267 | |
|
|
268 | 268 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
269 | 269 | |
|
|
270 | 270 | if window == None: |
|
|
271 | 271 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
|
272 | 272 | |
|
|
273 | 273 | newdelta = deltaHeight * window |
|
|
274 | 274 | r = dataOut.nHeights % window |
|
|
275 | 275 | newheights = (dataOut.nHeights-r)/window |
|
|
276 | 276 | |
|
|
277 | 277 | if newheights <= 1: |
|
|
278 | 278 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
|
279 | 279 | |
|
|
280 | 280 | if dataOut.flagDataAsBlock: |
|
|
281 | 281 | """ |
|
|
282 | 282 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
283 | 283 | """ |
|
|
284 | 284 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
|
285 | 285 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
|
286 | 286 | buffer = numpy.sum(buffer,3) |
|
|
287 | 287 | |
|
|
288 | 288 | else: |
|
|
289 | 289 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
|
290 | 290 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
|
291 | 291 | buffer = numpy.sum(buffer,2) |
|
|
292 | 292 | |
|
|
293 | 293 | dataOut.data = buffer |
|
|
294 | 294 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
|
295 | 295 | dataOut.windowOfFilter = window |
|
|
296 | 296 | |
|
|
297 | 297 | return dataOut |
|
|
298 | 298 | |
|
|
299 | 299 | |
|
|
300 | 300 | class setH0(Operation): |
|
|
301 | 301 | |
|
|
302 | 302 | def run(self, dataOut, h0, deltaHeight = None): |
|
|
303 | 303 | |
|
|
304 | 304 | if not deltaHeight: |
|
|
305 | 305 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
306 | 306 | |
|
|
307 | 307 | nHeights = dataOut.nHeights |
|
|
308 | 308 | |
|
|
309 | 309 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
|
310 | 310 | |
|
|
311 | 311 | dataOut.heightList = newHeiRange |
|
|
312 | 312 | |
|
|
313 | 313 | return dataOut |
|
|
314 | 314 | |
|
|
315 | 315 | |
|
|
316 | 316 | class deFlip(Operation): |
|
|
317 | 317 | |
|
|
318 | 318 | def run(self, dataOut, channelList = []): |
|
|
319 | 319 | |
|
|
320 | 320 | data = dataOut.data.copy() |
|
|
321 | 321 | |
|
|
322 | 322 | if dataOut.flagDataAsBlock: |
|
|
323 | 323 | flip = self.flip |
|
|
324 | 324 | profileList = list(range(dataOut.nProfiles)) |
|
|
325 | 325 | |
|
|
326 | 326 | if not channelList: |
|
|
327 | 327 | for thisProfile in profileList: |
|
|
328 | 328 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
329 | 329 | flip *= -1.0 |
|
|
330 | 330 | else: |
|
|
331 | 331 | for thisChannel in channelList: |
|
|
332 | 332 | if thisChannel not in dataOut.channelList: |
|
|
333 | 333 | continue |
|
|
334 | 334 | |
|
|
335 | 335 | for thisProfile in profileList: |
|
|
336 | 336 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
337 | 337 | flip *= -1.0 |
|
|
338 | 338 | |
|
|
339 | 339 | self.flip = flip |
|
|
340 | 340 | |
|
|
341 | 341 | else: |
|
|
342 | 342 | if not channelList: |
|
|
343 | 343 | data[:,:] = data[:,:]*self.flip |
|
|
344 | 344 | else: |
|
|
345 | 345 | for thisChannel in channelList: |
|
|
346 | 346 | if thisChannel not in dataOut.channelList: |
|
|
347 | 347 | continue |
|
|
348 | 348 | |
|
|
349 | 349 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
350 | 350 | |
|
|
351 | 351 | self.flip *= -1. |
|
|
352 | 352 | |
|
|
353 | 353 | dataOut.data = data |
|
|
354 | 354 | |
|
|
355 | 355 | return dataOut |
|
|
356 | 356 | |
|
|
357 | 357 | |
|
|
358 | 358 | class setAttribute(Operation): |
|
|
359 | 359 | ''' |
|
|
360 | 360 | Set an arbitrary attribute(s) to dataOut |
|
|
361 | 361 | ''' |
|
|
362 | 362 | |
|
|
363 | 363 | def __init__(self): |
|
|
364 | 364 | |
|
|
365 | 365 | Operation.__init__(self) |
|
|
366 | 366 | self._ready = False |
|
|
367 | 367 | |
|
|
368 | 368 | def run(self, dataOut, **kwargs): |
|
|
369 | 369 | |
|
|
370 | 370 | for key, value in kwargs.items(): |
|
|
371 | 371 | setattr(dataOut, key, value) |
|
|
372 | 372 | |
|
|
373 | 373 | return dataOut |
|
|
374 | 374 | |
|
|
375 | 375 | |
|
|
376 | 376 | @MPDecorator |
|
|
377 | 377 | class printAttribute(Operation): |
|
|
378 | 378 | ''' |
|
|
379 | 379 | Print an arbitrary attribute of dataOut |
|
|
380 | 380 | ''' |
|
|
381 | 381 | |
|
|
382 | 382 | def __init__(self): |
|
|
383 | 383 | |
|
|
384 | 384 | Operation.__init__(self) |
|
|
385 | 385 | |
|
|
386 | 386 | def run(self, dataOut, attributes): |
|
|
387 | 387 | |
|
|
388 | 388 | if isinstance(attributes, str): |
|
|
389 | 389 | attributes = [attributes] |
|
|
390 | 390 | for attr in attributes: |
|
|
391 | 391 | if hasattr(dataOut, attr): |
|
|
392 | 392 | log.log(getattr(dataOut, attr), attr) |
|
|
393 | 393 | |
|
|
394 | 394 | |
|
|
395 | 395 | class interpolateHeights(Operation): |
|
|
396 | 396 | |
|
|
397 | 397 | def run(self, dataOut, topLim, botLim): |
|
|
398 | 398 | #69 al 72 para julia |
|
|
399 | 399 | #82-84 para meteoros |
|
|
400 | 400 | if len(numpy.shape(dataOut.data))==2: |
|
|
401 | 401 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
|
402 | 402 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
|
403 | 403 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
404 | 404 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
|
405 | 405 | else: |
|
|
406 | 406 | nHeights = dataOut.data.shape[2] |
|
|
407 | 407 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
|
408 | 408 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
|
409 | 409 | f = interpolate.interp1d(x, y, axis = 2) |
|
|
410 | 410 | xnew = numpy.arange(botLim,topLim+1) |
|
|
411 | 411 | ynew = f(xnew) |
|
|
412 | 412 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
|
413 | 413 | |
|
|
414 | 414 | return dataOut |
|
|
415 | 415 | |
|
|
416 | 416 | |
|
|
417 | 417 | class CohInt(Operation): |
|
|
418 | 418 | |
|
|
419 | 419 | isConfig = False |
|
|
420 | 420 | __profIndex = 0 |
|
|
421 | 421 | __byTime = False |
|
|
422 | 422 | __initime = None |
|
|
423 | 423 | __lastdatatime = None |
|
|
424 | 424 | __integrationtime = None |
|
|
425 | 425 | __buffer = None |
|
|
426 | 426 | __bufferStride = [] |
|
|
427 | 427 | __dataReady = False |
|
|
428 | 428 | __profIndexStride = 0 |
|
|
429 | 429 | __dataToPutStride = False |
|
|
430 | 430 | n = None |
|
|
431 | 431 | |
|
|
432 | 432 | def __init__(self, **kwargs): |
|
|
433 | 433 | |
|
|
434 | 434 | Operation.__init__(self, **kwargs) |
|
|
435 | 435 | |
|
|
436 | 436 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
437 | 437 | """ |
|
|
438 | 438 | Set the parameters of the integration class. |
|
|
439 | 439 | |
|
|
440 | 440 | Inputs: |
|
|
441 | 441 | |
|
|
442 | 442 | n : Number of coherent integrations |
|
|
443 | 443 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
444 | 444 | overlapping : |
|
|
445 | 445 | """ |
|
|
446 | 446 | |
|
|
447 | 447 | self.__initime = None |
|
|
448 | 448 | self.__lastdatatime = 0 |
|
|
449 | 449 | self.__buffer = None |
|
|
450 | 450 | self.__dataReady = False |
|
|
451 | 451 | self.byblock = byblock |
|
|
452 | 452 | self.stride = stride |
|
|
453 | 453 | |
|
|
454 | 454 | if n == None and timeInterval == None: |
|
|
455 | 455 | raise ValueError("n or timeInterval should be specified ...") |
|
|
456 | 456 | |
|
|
457 | 457 | if n != None: |
|
|
458 | 458 | self.n = n |
|
|
459 | 459 | self.__byTime = False |
|
|
460 | 460 | else: |
|
|
461 | 461 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
462 | 462 | self.n = 9999 |
|
|
463 | 463 | self.__byTime = True |
|
|
464 | 464 | |
|
|
465 | 465 | if overlapping: |
|
|
466 | 466 | self.__withOverlapping = True |
|
|
467 | 467 | self.__buffer = None |
|
|
468 | 468 | else: |
|
|
469 | 469 | self.__withOverlapping = False |
|
|
470 | 470 | self.__buffer = 0 |
|
|
471 | 471 | |
|
|
472 | 472 | self.__profIndex = 0 |
|
|
473 | 473 | |
|
|
474 | 474 | def putData(self, data): |
|
|
475 | 475 | |
|
|
476 | 476 | """ |
|
|
477 | 477 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
478 | 478 | |
|
|
479 | 479 | """ |
|
|
480 | 480 | |
|
|
481 | 481 | if not self.__withOverlapping: |
|
|
482 | 482 | self.__buffer += data.copy() |
|
|
483 | 483 | self.__profIndex += 1 |
|
|
484 | 484 | return |
|
|
485 | 485 | |
|
|
486 | 486 | #Overlapping data |
|
|
487 | 487 | nChannels, nHeis = data.shape |
|
|
488 | 488 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
489 | 489 | |
|
|
490 | 490 | #If the buffer is empty then it takes the data value |
|
|
491 | 491 | if self.__buffer is None: |
|
|
492 | 492 | self.__buffer = data |
|
|
493 | 493 | self.__profIndex += 1 |
|
|
494 | 494 | return |
|
|
495 | 495 | |
|
|
496 | 496 | #If the buffer length is lower than n then stakcing the data value |
|
|
497 | 497 | if self.__profIndex < self.n: |
|
|
498 | 498 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
499 | 499 | self.__profIndex += 1 |
|
|
500 | 500 | return |
|
|
501 | 501 | |
|
|
502 | 502 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
503 | 503 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
504 | 504 | self.__buffer[self.n-1] = data |
|
|
505 | 505 | self.__profIndex = self.n |
|
|
506 | 506 | return |
|
|
507 | 507 | |
|
|
508 | 508 | |
|
|
509 | 509 | def pushData(self): |
|
|
510 | 510 | """ |
|
|
511 | 511 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
512 | 512 | |
|
|
513 | 513 | Affected: |
|
|
514 | 514 | |
|
|
515 | 515 | self.__profileIndex |
|
|
516 | 516 | |
|
|
517 | 517 | """ |
|
|
518 | 518 | |
|
|
519 | 519 | if not self.__withOverlapping: |
|
|
520 | 520 | data = self.__buffer |
|
|
521 | 521 | n = self.__profIndex |
|
|
522 | 522 | |
|
|
523 | 523 | self.__buffer = 0 |
|
|
524 | 524 | self.__profIndex = 0 |
|
|
525 | 525 | |
|
|
526 | 526 | return data, n |
|
|
527 | 527 | |
|
|
528 | 528 | #Integration with Overlapping |
|
|
529 | 529 | data = numpy.sum(self.__buffer, axis=0) |
|
|
530 | 530 | # print data |
|
|
531 | 531 | # raise |
|
|
532 | 532 | n = self.__profIndex |
|
|
533 | 533 | |
|
|
534 | 534 | return data, n |
|
|
535 | 535 | |
|
|
536 | 536 | def byProfiles(self, data): |
|
|
537 | 537 | |
|
|
538 | 538 | self.__dataReady = False |
|
|
539 | 539 | avgdata = None |
|
|
540 | 540 | # n = None |
|
|
541 | 541 | # print data |
|
|
542 | 542 | # raise |
|
|
543 | 543 | self.putData(data) |
|
|
544 | 544 | |
|
|
545 | 545 | if self.__profIndex == self.n: |
|
|
546 | 546 | avgdata, n = self.pushData() |
|
|
547 | 547 | self.__dataReady = True |
|
|
548 | 548 | |
|
|
549 | 549 | return avgdata |
|
|
550 | 550 | |
|
|
551 | 551 | def byTime(self, data, datatime): |
|
|
552 | 552 | |
|
|
553 | 553 | self.__dataReady = False |
|
|
554 | 554 | avgdata = None |
|
|
555 | 555 | n = None |
|
|
556 | 556 | |
|
|
557 | 557 | self.putData(data) |
|
|
558 | 558 | |
|
|
559 | 559 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
560 | 560 | avgdata, n = self.pushData() |
|
|
561 | 561 | self.n = n |
|
|
562 | 562 | self.__dataReady = True |
|
|
563 | 563 | |
|
|
564 | 564 | return avgdata |
|
|
565 | 565 | |
|
|
566 | 566 | def integrateByStride(self, data, datatime): |
|
|
567 | 567 | # print data |
|
|
568 | 568 | if self.__profIndex == 0: |
|
|
569 | 569 | self.__buffer = [[data.copy(), datatime]] |
|
|
570 | 570 | else: |
|
|
571 | 571 | self.__buffer.append([data.copy(),datatime]) |
|
|
572 | 572 | self.__profIndex += 1 |
|
|
573 | 573 | self.__dataReady = False |
|
|
574 | 574 | |
|
|
575 | 575 | if self.__profIndex == self.n * self.stride : |
|
|
576 | 576 | self.__dataToPutStride = True |
|
|
577 | 577 | self.__profIndexStride = 0 |
|
|
578 | 578 | self.__profIndex = 0 |
|
|
579 | 579 | self.__bufferStride = [] |
|
|
580 | 580 | for i in range(self.stride): |
|
|
581 | 581 | current = self.__buffer[i::self.stride] |
|
|
582 | 582 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
583 | 583 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
584 | 584 | # print data |
|
|
585 | 585 | self.__bufferStride.append((data, avgdatatime)) |
|
|
586 | 586 | |
|
|
587 | 587 | if self.__dataToPutStride: |
|
|
588 | 588 | self.__dataReady = True |
|
|
589 | 589 | self.__profIndexStride += 1 |
|
|
590 | 590 | if self.__profIndexStride == self.stride: |
|
|
591 | 591 | self.__dataToPutStride = False |
|
|
592 | 592 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
593 | 593 | # raise |
|
|
594 | 594 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
595 | 595 | |
|
|
596 | 596 | |
|
|
597 | 597 | return None, None |
|
|
598 | 598 | |
|
|
599 | 599 | def integrate(self, data, datatime=None): |
|
|
600 | 600 | |
|
|
601 | 601 | if self.__initime == None: |
|
|
602 | 602 | self.__initime = datatime |
|
|
603 | 603 | |
|
|
604 | 604 | if self.__byTime: |
|
|
605 | 605 | avgdata = self.byTime(data, datatime) |
|
|
606 | 606 | else: |
|
|
607 | 607 | avgdata = self.byProfiles(data) |
|
|
608 | 608 | |
|
|
609 | 609 | |
|
|
610 | 610 | self.__lastdatatime = datatime |
|
|
611 | 611 | |
|
|
612 | 612 | if avgdata is None: |
|
|
613 | 613 | return None, None |
|
|
614 | 614 | |
|
|
615 | 615 | avgdatatime = self.__initime |
|
|
616 | 616 | |
|
|
617 | 617 | deltatime = datatime - self.__lastdatatime |
|
|
618 | 618 | |
|
|
619 | 619 | if not self.__withOverlapping: |
|
|
620 | 620 | self.__initime = datatime |
|
|
621 | 621 | else: |
|
|
622 | 622 | self.__initime += deltatime |
|
|
623 | 623 | |
|
|
624 | 624 | return avgdata, avgdatatime |
|
|
625 | 625 | |
|
|
626 | 626 | def integrateByBlock(self, dataOut): |
|
|
627 | 627 | |
|
|
628 | 628 | times = int(dataOut.data.shape[1]/self.n) |
|
|
629 | 629 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
|
630 | 630 | |
|
|
631 | 631 | id_min = 0 |
|
|
632 | 632 | id_max = self.n |
|
|
633 | 633 | |
|
|
634 | 634 | for i in range(times): |
|
|
635 | 635 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
636 | 636 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
637 | 637 | id_min += self.n |
|
|
638 | 638 | id_max += self.n |
|
|
639 | 639 | |
|
|
640 | 640 | timeInterval = dataOut.ippSeconds*self.n |
|
|
641 | 641 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
642 | 642 | self.__dataReady = True |
|
|
643 | 643 | return avgdata, avgdatatime |
|
|
644 | 644 | |
|
|
645 | 645 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
646 | 646 | |
|
|
647 | 647 | if not self.isConfig: |
|
|
648 | 648 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
649 | 649 | self.isConfig = True |
|
|
650 | 650 | |
|
|
651 | 651 | if dataOut.flagDataAsBlock: |
|
|
652 | 652 | """ |
|
|
653 | 653 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
654 | 654 | """ |
|
|
655 | 655 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
656 | 656 | dataOut.nProfiles /= self.n |
|
|
657 | 657 | else: |
|
|
658 | 658 | if stride is None: |
|
|
659 | 659 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
660 | 660 | else: |
|
|
661 | 661 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
662 | 662 | |
|
|
663 | 663 | |
|
|
664 | 664 | # dataOut.timeInterval *= n |
|
|
665 | 665 | dataOut.flagNoData = True |
|
|
666 | 666 | |
|
|
667 | 667 | if self.__dataReady: |
|
|
668 | 668 | dataOut.data = avgdata |
|
|
669 | 669 | if not dataOut.flagCohInt: |
|
|
670 | 670 | dataOut.nCohInt *= self.n |
|
|
671 | 671 | dataOut.flagCohInt = True |
|
|
672 | 672 | dataOut.utctime = avgdatatime |
|
|
673 | 673 | # print avgdata, avgdatatime |
|
|
674 | 674 | # raise |
|
|
675 | 675 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
676 | 676 | dataOut.flagNoData = False |
|
|
677 | 677 | return dataOut |
|
|
678 | 678 | |
|
|
679 | 679 | class Decoder(Operation): |
|
|
680 | 680 | |
|
|
681 | 681 | isConfig = False |
|
|
682 | 682 | __profIndex = 0 |
|
|
683 | 683 | |
|
|
684 | 684 | code = None |
|
|
685 | 685 | |
|
|
686 | 686 | nCode = None |
|
|
687 | 687 | nBaud = None |
|
|
688 | 688 | |
|
|
689 | 689 | def __init__(self, **kwargs): |
|
|
690 | 690 | |
|
|
691 | 691 | Operation.__init__(self, **kwargs) |
|
|
692 | 692 | |
|
|
693 | 693 | self.times = None |
|
|
694 | 694 | self.osamp = None |
|
|
695 | 695 | # self.__setValues = False |
|
|
696 | 696 | self.isConfig = False |
|
|
697 | 697 | self.setupReq = False |
|
|
698 | 698 | def setup(self, code, osamp, dataOut): |
|
|
699 | 699 | |
|
|
700 | 700 | self.__profIndex = 0 |
|
|
701 | 701 | |
|
|
702 | 702 | self.code = code |
|
|
703 | 703 | |
|
|
704 | 704 | self.nCode = len(code) |
|
|
705 | 705 | self.nBaud = len(code[0]) |
|
|
706 | 706 | if (osamp != None) and (osamp >1): |
|
|
707 | 707 | self.osamp = osamp |
|
|
708 | 708 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
709 | 709 | self.nBaud = self.nBaud*self.osamp |
|
|
710 | 710 | |
|
|
711 | 711 | self.__nChannels = dataOut.nChannels |
|
|
712 | 712 | self.__nProfiles = dataOut.nProfiles |
|
|
713 | 713 | self.__nHeis = dataOut.nHeights |
|
|
714 | 714 | |
|
|
715 | 715 | if self.__nHeis < self.nBaud: |
|
|
716 | 716 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
717 | 717 | |
|
|
718 | 718 | #Frequency |
|
|
719 | 719 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
|
720 | 720 | |
|
|
721 | 721 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
722 | 722 | |
|
|
723 | 723 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
724 | 724 | |
|
|
725 | 725 | if dataOut.flagDataAsBlock: |
|
|
726 | 726 | |
|
|
727 | 727 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
728 | 728 | |
|
|
729 | 729 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
|
730 | 730 | |
|
|
731 | 731 | else: |
|
|
732 | 732 | |
|
|
733 | 733 | #Time |
|
|
734 | 734 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
735 | 735 | |
|
|
736 | 736 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
|
737 | 737 | |
|
|
738 | 738 | def __convolutionInFreq(self, data): |
|
|
739 | 739 | |
|
|
740 | 740 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
741 | 741 | |
|
|
742 | 742 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
743 | 743 | |
|
|
744 | 744 | conv = fft_data*fft_code |
|
|
745 | 745 | |
|
|
746 | 746 | data = numpy.fft.ifft(conv,axis=1) |
|
|
747 | 747 | |
|
|
748 | 748 | return data |
|
|
749 | 749 | |
|
|
750 | 750 | def __convolutionInFreqOpt(self, data): |
|
|
751 | 751 | |
|
|
752 | 752 | raise NotImplementedError |
|
|
753 | 753 | |
|
|
754 | 754 | def __convolutionInTime(self, data): |
|
|
755 | 755 | |
|
|
756 | 756 | code = self.code[self.__profIndex] |
|
|
757 | 757 | for i in range(self.__nChannels): |
|
|
758 | 758 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
759 | 759 | |
|
|
760 | 760 | return self.datadecTime |
|
|
761 | 761 | |
|
|
762 | 762 | def __convolutionByBlockInTime(self, data): |
|
|
763 | 763 | |
|
|
764 | 764 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
765 | 765 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
766 | 766 | junk = junk.flatten() |
|
|
767 | 767 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
768 | 768 | profilesList = range(self.__nProfiles) |
|
|
769 | 769 | |
|
|
770 | 770 | for i in range(self.__nChannels): |
|
|
771 | 771 | for j in profilesList: |
|
|
772 | 772 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
773 | 773 | return self.datadecTime |
|
|
774 | 774 | |
|
|
775 | 775 | def __convolutionByBlockInFreq(self, data): |
|
|
776 | 776 | |
|
|
777 | 777 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
778 | 778 | |
|
|
779 | 779 | |
|
|
780 | 780 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
781 | 781 | |
|
|
782 | 782 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
783 | 783 | |
|
|
784 | 784 | conv = fft_data*fft_code |
|
|
785 | 785 | |
|
|
786 | 786 | data = numpy.fft.ifft(conv,axis=2) |
|
|
787 | 787 | |
|
|
788 | 788 | return data |
|
|
789 | 789 | |
|
|
790 | 790 | |
|
|
791 | 791 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
792 | 792 | |
|
|
793 | 793 | if dataOut.flagDecodeData: |
|
|
794 | 794 | print("This data is already decoded, recoding again ...") |
|
|
795 | 795 | |
|
|
796 | 796 | if not self.isConfig: |
|
|
797 | 797 | |
|
|
798 | 798 | if code is None: |
|
|
799 | 799 | if dataOut.code is None: |
|
|
800 | 800 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
801 | 801 | |
|
|
802 | 802 | code = dataOut.code |
|
|
803 | 803 | else: |
|
|
804 | 804 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
805 | 805 | self.setup(code, osamp, dataOut) |
|
|
806 | 806 | |
|
|
807 | 807 | self.isConfig = True |
|
|
808 | 808 | |
|
|
809 | 809 | if mode == 3: |
|
|
810 | 810 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
811 | 811 | |
|
|
812 | 812 | if times != None: |
|
|
813 | 813 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
814 | 814 | |
|
|
815 | 815 | if self.code is None: |
|
|
816 | 816 | print("Fail decoding: Code is not defined.") |
|
|
817 | 817 | return |
|
|
818 | 818 | |
|
|
819 | 819 | self.__nProfiles = dataOut.nProfiles |
|
|
820 | 820 | datadec = None |
|
|
821 | 821 | |
|
|
822 | 822 | if mode == 3: |
|
|
823 | 823 | mode = 0 |
|
|
824 | 824 | |
|
|
825 | 825 | if dataOut.flagDataAsBlock: |
|
|
826 | 826 | """ |
|
|
827 | 827 | Decoding when data have been read as block, |
|
|
828 | 828 | """ |
|
|
829 | 829 | |
|
|
830 | 830 | if mode == 0: |
|
|
831 | 831 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
832 | 832 | if mode == 1: |
|
|
833 | 833 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
834 | 834 | else: |
|
|
835 | 835 | """ |
|
|
836 | 836 | Decoding when data have been read profile by profile |
|
|
837 | 837 | """ |
|
|
838 | 838 | if mode == 0: |
|
|
839 | 839 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
840 | 840 | |
|
|
841 | 841 | if mode == 1: |
|
|
842 | 842 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
843 | 843 | |
|
|
844 | 844 | if mode == 2: |
|
|
845 | 845 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
846 | 846 | |
|
|
847 | 847 | if datadec is None: |
|
|
848 | 848 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
849 | 849 | |
|
|
850 | 850 | dataOut.code = self.code |
|
|
851 | 851 | dataOut.nCode = self.nCode |
|
|
852 | 852 | dataOut.nBaud = self.nBaud |
|
|
853 | 853 | |
|
|
854 | 854 | dataOut.data = datadec |
|
|
855 | 855 | |
|
|
856 | 856 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
857 | 857 | |
|
|
858 | 858 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
859 | 859 | |
|
|
860 | 860 | if self.__profIndex == self.nCode-1: |
|
|
861 | 861 | self.__profIndex = 0 |
|
|
862 | 862 | return dataOut |
|
|
863 | 863 | |
|
|
864 | 864 | self.__profIndex += 1 |
|
|
865 | 865 | |
|
|
866 | 866 | return dataOut |
|
|
867 | 867 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
868 | 868 | |
|
|
869 | 869 | |
|
|
870 | 870 | class ProfileConcat(Operation): |
|
|
871 | 871 | |
|
|
872 | 872 | isConfig = False |
|
|
873 | 873 | buffer = None |
|
|
874 | 874 | |
|
|
875 | 875 | def __init__(self, **kwargs): |
|
|
876 | 876 | |
|
|
877 | 877 | Operation.__init__(self, **kwargs) |
|
|
878 | 878 | self.profileIndex = 0 |
|
|
879 | 879 | |
|
|
880 | 880 | def reset(self): |
|
|
881 | 881 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
882 | 882 | self.start_index = 0 |
|
|
883 | 883 | self.times = 1 |
|
|
884 | 884 | |
|
|
885 | 885 | def setup(self, data, m, n=1): |
|
|
886 | 886 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
887 | 887 | self.nHeights = data.shape[1]#.nHeights |
|
|
888 | 888 | self.start_index = 0 |
|
|
889 | 889 | self.times = 1 |
|
|
890 | 890 | |
|
|
891 | 891 | def concat(self, data): |
|
|
892 | 892 | |
|
|
893 | 893 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
894 | 894 | self.start_index = self.start_index + self.nHeights |
|
|
895 | 895 | |
|
|
896 | 896 | def run(self, dataOut, m): |
|
|
897 | 897 | dataOut.flagNoData = True |
|
|
898 | 898 | |
|
|
899 | 899 | if not self.isConfig: |
|
|
900 | 900 | self.setup(dataOut.data, m, 1) |
|
|
901 | 901 | self.isConfig = True |
|
|
902 | 902 | |
|
|
903 | 903 | if dataOut.flagDataAsBlock: |
|
|
904 | 904 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
905 | 905 | |
|
|
906 | 906 | else: |
|
|
907 | 907 | self.concat(dataOut.data) |
|
|
908 | 908 | self.times += 1 |
|
|
909 | 909 | if self.times > m: |
|
|
910 | 910 | dataOut.data = self.buffer |
|
|
911 | 911 | self.reset() |
|
|
912 | 912 | dataOut.flagNoData = False |
|
|
913 | 913 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
914 | 914 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
915 | 915 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
916 | 916 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
917 | 917 | dataOut.ippSeconds *= m |
|
|
918 | 918 | return dataOut |
|
|
919 | 919 | |
|
|
920 | 920 | class ProfileSelector(Operation): |
|
|
921 | 921 | |
|
|
922 | 922 | profileIndex = None |
|
|
923 | 923 | # Tamanho total de los perfiles |
|
|
924 | 924 | nProfiles = None |
|
|
925 | 925 | |
|
|
926 | 926 | def __init__(self, **kwargs): |
|
|
927 | 927 | |
|
|
928 | 928 | Operation.__init__(self, **kwargs) |
|
|
929 | 929 | self.profileIndex = 0 |
|
|
930 | 930 | |
|
|
931 | 931 | def incProfileIndex(self): |
|
|
932 | 932 | |
|
|
933 | 933 | self.profileIndex += 1 |
|
|
934 | 934 | |
|
|
935 | 935 | if self.profileIndex >= self.nProfiles: |
|
|
936 | 936 | self.profileIndex = 0 |
|
|
937 | 937 | |
|
|
938 | 938 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
939 | 939 | |
|
|
940 | 940 | if profileIndex < minIndex: |
|
|
941 | 941 | return False |
|
|
942 | 942 | |
|
|
943 | 943 | if profileIndex > maxIndex: |
|
|
944 | 944 | return False |
|
|
945 | 945 | |
|
|
946 | 946 | return True |
|
|
947 | 947 | |
|
|
948 | 948 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
949 | 949 | |
|
|
950 | 950 | if profileIndex not in profileList: |
|
|
951 | 951 | return False |
|
|
952 | 952 | |
|
|
953 | 953 | return True |
|
|
954 | 954 | |
|
|
955 | 955 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
956 | 956 | |
|
|
957 | 957 | """ |
|
|
958 | 958 | ProfileSelector: |
|
|
959 | 959 | |
|
|
960 | 960 | Inputs: |
|
|
961 | 961 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
962 | 962 | |
|
|
963 | 963 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
964 | 964 | |
|
|
965 | 965 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
966 | 966 | |
|
|
967 | 967 | """ |
|
|
968 | 968 | |
|
|
969 | 969 | if rangeList is not None: |
|
|
970 | 970 | if type(rangeList[0]) not in (tuple, list): |
|
|
971 | 971 | rangeList = [rangeList] |
|
|
972 | 972 | |
|
|
973 | 973 | dataOut.flagNoData = True |
|
|
974 | 974 | |
|
|
975 | 975 | if dataOut.flagDataAsBlock: |
|
|
976 | 976 | """ |
|
|
977 | 977 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
978 | 978 | """ |
|
|
979 | 979 | if profileList != None: |
|
|
980 | 980 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
981 | 981 | |
|
|
982 | 982 | if profileRangeList != None: |
|
|
983 | 983 | minIndex = profileRangeList[0] |
|
|
984 | 984 | maxIndex = profileRangeList[1] |
|
|
985 | 985 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
986 | 986 | |
|
|
987 | 987 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
988 | 988 | |
|
|
989 | 989 | if rangeList != None: |
|
|
990 | 990 | |
|
|
991 | 991 | profileList = [] |
|
|
992 | 992 | |
|
|
993 | 993 | for thisRange in rangeList: |
|
|
994 | 994 | minIndex = thisRange[0] |
|
|
995 | 995 | maxIndex = thisRange[1] |
|
|
996 | 996 | |
|
|
997 | 997 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
998 | 998 | |
|
|
999 | 999 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | dataOut.nProfiles = len(profileList) |
|
|
1002 | 1002 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
1003 | 1003 | dataOut.flagNoData = False |
|
|
1004 | 1004 | |
|
|
1005 | 1005 | return dataOut |
|
|
1006 | 1006 | |
|
|
1007 | 1007 | """ |
|
|
1008 | 1008 | data dimension = [nChannels, nHeis] |
|
|
1009 | 1009 | """ |
|
|
1010 | 1010 | |
|
|
1011 | 1011 | if profileList != None: |
|
|
1012 | 1012 | |
|
|
1013 | 1013 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
1014 | 1014 | |
|
|
1015 | 1015 | self.nProfiles = len(profileList) |
|
|
1016 | 1016 | dataOut.nProfiles = self.nProfiles |
|
|
1017 | 1017 | dataOut.profileIndex = self.profileIndex |
|
|
1018 | 1018 | dataOut.flagNoData = False |
|
|
1019 | 1019 | |
|
|
1020 | 1020 | self.incProfileIndex() |
|
|
1021 | 1021 | return dataOut |
|
|
1022 | 1022 | |
|
|
1023 | 1023 | if profileRangeList != None: |
|
|
1024 | 1024 | |
|
|
1025 | 1025 | minIndex = profileRangeList[0] |
|
|
1026 | 1026 | maxIndex = profileRangeList[1] |
|
|
1027 | 1027 | |
|
|
1028 | 1028 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1029 | 1029 | |
|
|
1030 | 1030 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
1031 | 1031 | dataOut.nProfiles = self.nProfiles |
|
|
1032 | 1032 | dataOut.profileIndex = self.profileIndex |
|
|
1033 | 1033 | dataOut.flagNoData = False |
|
|
1034 | 1034 | |
|
|
1035 | 1035 | self.incProfileIndex() |
|
|
1036 | 1036 | return dataOut |
|
|
1037 | 1037 | |
|
|
1038 | 1038 | if rangeList != None: |
|
|
1039 | 1039 | |
|
|
1040 | 1040 | nProfiles = 0 |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | for thisRange in rangeList: |
|
|
1043 | 1043 | minIndex = thisRange[0] |
|
|
1044 | 1044 | maxIndex = thisRange[1] |
|
|
1045 | 1045 | |
|
|
1046 | 1046 | nProfiles += maxIndex - minIndex + 1 |
|
|
1047 | 1047 | |
|
|
1048 | 1048 | for thisRange in rangeList: |
|
|
1049 | 1049 | |
|
|
1050 | 1050 | minIndex = thisRange[0] |
|
|
1051 | 1051 | maxIndex = thisRange[1] |
|
|
1052 | 1052 | |
|
|
1053 | 1053 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
1054 | 1054 | |
|
|
1055 | 1055 | self.nProfiles = nProfiles |
|
|
1056 | 1056 | dataOut.nProfiles = self.nProfiles |
|
|
1057 | 1057 | dataOut.profileIndex = self.profileIndex |
|
|
1058 | 1058 | dataOut.flagNoData = False |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | self.incProfileIndex() |
|
|
1061 | 1061 | |
|
|
1062 | 1062 | break |
|
|
1063 | 1063 | |
|
|
1064 | 1064 | return dataOut |
|
|
1065 | 1065 | |
|
|
1066 | 1066 | |
|
|
1067 | 1067 | if beam != None: #beam is only for AMISR data |
|
|
1068 | 1068 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
1069 | 1069 | dataOut.flagNoData = False |
|
|
1070 | 1070 | dataOut.profileIndex = self.profileIndex |
|
|
1071 | 1071 | |
|
|
1072 | 1072 | self.incProfileIndex() |
|
|
1073 | 1073 | |
|
|
1074 | 1074 | return dataOut |
|
|
1075 | 1075 | |
|
|
1076 | 1076 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
1077 | 1077 | |
|
|
1078 | 1078 | |
|
|
1079 | 1079 | class Reshaper(Operation): |
|
|
1080 | 1080 | |
|
|
1081 | 1081 | def __init__(self, **kwargs): |
|
|
1082 | 1082 | |
|
|
1083 | 1083 | Operation.__init__(self, **kwargs) |
|
|
1084 | 1084 | |
|
|
1085 | 1085 | self.__buffer = None |
|
|
1086 | 1086 | self.__nitems = 0 |
|
|
1087 | 1087 | |
|
|
1088 | 1088 | def __appendProfile(self, dataOut, nTxs): |
|
|
1089 | 1089 | |
|
|
1090 | 1090 | if self.__buffer is None: |
|
|
1091 | 1091 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
1092 | 1092 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | ini = dataOut.nHeights * self.__nitems |
|
|
1095 | 1095 | end = ini + dataOut.nHeights |
|
|
1096 | 1096 | |
|
|
1097 | 1097 | self.__buffer[:, ini:end] = dataOut.data |
|
|
1098 | 1098 | |
|
|
1099 | 1099 | self.__nitems += 1 |
|
|
1100 | 1100 | |
|
|
1101 | 1101 | return int(self.__nitems*nTxs) |
|
|
1102 | 1102 | |
|
|
1103 | 1103 | def __getBuffer(self): |
|
|
1104 | 1104 | |
|
|
1105 | 1105 | if self.__nitems == int(1./self.__nTxs): |
|
|
1106 | 1106 | |
|
|
1107 | 1107 | self.__nitems = 0 |
|
|
1108 | 1108 | |
|
|
1109 | 1109 | return self.__buffer.copy() |
|
|
1110 | 1110 | |
|
|
1111 | 1111 | return None |
|
|
1112 | 1112 | |
|
|
1113 | 1113 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
1114 | 1114 | |
|
|
1115 | 1115 | if shape is None and nTxs is None: |
|
|
1116 | 1116 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
1117 | 1117 | |
|
|
1118 | 1118 | if nTxs: |
|
|
1119 | 1119 | if nTxs < 0: |
|
|
1120 | 1120 | raise ValueError("nTxs should be greater than 0") |
|
|
1121 | 1121 | |
|
|
1122 | 1122 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
1123 | 1123 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
1124 | 1124 | |
|
|
1125 | 1125 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
1126 | 1126 | |
|
|
1127 | 1127 | return shape, nTxs |
|
|
1128 | 1128 | |
|
|
1129 | 1129 | if len(shape) != 2 and len(shape) != 3: |
|
|
1130 | 1130 | raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)) |
|
|
1131 | 1131 | |
|
|
1132 | 1132 | if len(shape) == 2: |
|
|
1133 | 1133 | shape_tuple = [dataOut.nChannels] |
|
|
1134 | 1134 | shape_tuple.extend(shape) |
|
|
1135 | 1135 | else: |
|
|
1136 | 1136 | shape_tuple = list(shape) |
|
|
1137 | 1137 | |
|
|
1138 | 1138 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
1139 | 1139 | |
|
|
1140 | 1140 | return shape_tuple, nTxs |
|
|
1141 | 1141 | |
|
|
1142 | 1142 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
1143 | 1143 | |
|
|
1144 | 1144 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
1145 | 1145 | |
|
|
1146 | 1146 | dataOut.flagNoData = True |
|
|
1147 | 1147 | profileIndex = None |
|
|
1148 | 1148 | |
|
|
1149 | 1149 | if dataOut.flagDataAsBlock: |
|
|
1150 | 1150 | |
|
|
1151 | 1151 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
1152 | 1152 | dataOut.flagNoData = False |
|
|
1153 | 1153 | |
|
|
1154 | 1154 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
1155 | 1155 | |
|
|
1156 | 1156 | else: |
|
|
1157 | 1157 | |
|
|
1158 | 1158 | if self.__nTxs < 1: |
|
|
1159 | 1159 | |
|
|
1160 | 1160 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
1161 | 1161 | new_data = self.__getBuffer() |
|
|
1162 | 1162 | |
|
|
1163 | 1163 | if new_data is not None: |
|
|
1164 | 1164 | dataOut.data = new_data |
|
|
1165 | 1165 | dataOut.flagNoData = False |
|
|
1166 | 1166 | |
|
|
1167 | 1167 | profileIndex = dataOut.profileIndex*nTxs |
|
|
1168 | 1168 | |
|
|
1169 | 1169 | else: |
|
|
1170 | 1170 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
1171 | 1171 | |
|
|
1172 | 1172 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1173 | 1173 | |
|
|
1174 | 1174 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
1175 | 1175 | |
|
|
1176 | 1176 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
1177 | 1177 | |
|
|
1178 | 1178 | dataOut.profileIndex = profileIndex |
|
|
1179 | 1179 | |
|
|
1180 | 1180 | dataOut.ippSeconds /= self.__nTxs |
|
|
1181 | 1181 | |
|
|
1182 | 1182 | return dataOut |
|
|
1183 | 1183 | |
|
|
1184 | 1184 | class SplitProfiles(Operation): |
|
|
1185 | 1185 | |
|
|
1186 | 1186 | def __init__(self, **kwargs): |
|
|
1187 | 1187 | |
|
|
1188 | 1188 | Operation.__init__(self, **kwargs) |
|
|
1189 | 1189 | |
|
|
1190 | 1190 | def run(self, dataOut, n): |
|
|
1191 | 1191 | |
|
|
1192 | 1192 | dataOut.flagNoData = True |
|
|
1193 | 1193 | profileIndex = None |
|
|
1194 | 1194 | |
|
|
1195 | 1195 | if dataOut.flagDataAsBlock: |
|
|
1196 | 1196 | |
|
|
1197 | 1197 | #nchannels, nprofiles, nsamples |
|
|
1198 | 1198 | shape = dataOut.data.shape |
|
|
1199 | 1199 | |
|
|
1200 | 1200 | if shape[2] % n != 0: |
|
|
1201 | 1201 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
1202 | 1202 | |
|
|
1203 | 1203 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
1204 | 1204 | |
|
|
1205 | 1205 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1206 | 1206 | dataOut.flagNoData = False |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
1209 | 1209 | |
|
|
1210 | 1210 | else: |
|
|
1211 | 1211 | |
|
|
1212 | 1212 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
1213 | 1213 | |
|
|
1214 | 1214 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1215 | 1215 | |
|
|
1216 | 1216 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
1217 | 1217 | |
|
|
1218 | 1218 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
1219 | 1219 | |
|
|
1220 | 1220 | dataOut.profileIndex = profileIndex |
|
|
1221 | 1221 | |
|
|
1222 | 1222 | dataOut.ippSeconds /= n |
|
|
1223 | 1223 | |
|
|
1224 | 1224 | return dataOut |
|
|
1225 | 1225 | |
|
|
1226 | 1226 | class CombineProfiles(Operation): |
|
|
1227 | 1227 | def __init__(self, **kwargs): |
|
|
1228 | 1228 | |
|
|
1229 | 1229 | Operation.__init__(self, **kwargs) |
|
|
1230 | 1230 | |
|
|
1231 | 1231 | self.__remData = None |
|
|
1232 | 1232 | self.__profileIndex = 0 |
|
|
1233 | 1233 | |
|
|
1234 | 1234 | def run(self, dataOut, n): |
|
|
1235 | 1235 | |
|
|
1236 | 1236 | dataOut.flagNoData = True |
|
|
1237 | 1237 | profileIndex = None |
|
|
1238 | 1238 | |
|
|
1239 | 1239 | if dataOut.flagDataAsBlock: |
|
|
1240 | 1240 | |
|
|
1241 | 1241 | #nchannels, nprofiles, nsamples |
|
|
1242 | 1242 | shape = dataOut.data.shape |
|
|
1243 | 1243 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
1244 | 1244 | |
|
|
1245 | 1245 | if shape[1] % n != 0: |
|
|
1246 | 1246 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
1247 | 1247 | |
|
|
1248 | 1248 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
1249 | 1249 | dataOut.flagNoData = False |
|
|
1250 | 1250 | |
|
|
1251 | 1251 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
1252 | 1252 | |
|
|
1253 | 1253 | else: |
|
|
1254 | 1254 | |
|
|
1255 | 1255 | #nchannels, nsamples |
|
|
1256 | 1256 | if self.__remData is None: |
|
|
1257 | 1257 | newData = dataOut.data |
|
|
1258 | 1258 | else: |
|
|
1259 | 1259 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
1260 | 1260 | |
|
|
1261 | 1261 | self.__profileIndex += 1 |
|
|
1262 | 1262 | |
|
|
1263 | 1263 | if self.__profileIndex < n: |
|
|
1264 | 1264 | self.__remData = newData |
|
|
1265 | 1265 | #continue |
|
|
1266 | 1266 | return |
|
|
1267 | 1267 | |
|
|
1268 | 1268 | self.__profileIndex = 0 |
|
|
1269 | 1269 | self.__remData = None |
|
|
1270 | 1270 | |
|
|
1271 | 1271 | dataOut.data = newData |
|
|
1272 | 1272 | dataOut.flagNoData = False |
|
|
1273 | 1273 | |
|
|
1274 | 1274 | profileIndex = dataOut.profileIndex/n |
|
|
1275 | 1275 | |
|
|
1276 | 1276 | |
|
|
1277 | 1277 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1278 | 1278 | |
|
|
1279 | 1279 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
1280 | 1280 | |
|
|
1281 | 1281 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
1282 | 1282 | |
|
|
1283 | 1283 | dataOut.profileIndex = profileIndex |
|
|
1284 | 1284 | |
|
|
1285 | 1285 | dataOut.ippSeconds *= n |
|
|
1286 | 1286 | |
|
|
1287 | 1287 | return dataOut |
|
|
1288 | 1288 | |
|
|
1289 | 1289 | class PulsePairVoltage(Operation): |
|
|
1290 | 1290 | ''' |
|
|
1291 | 1291 | Function PulsePair(Signal Power, Velocity) |
|
|
1292 | 1292 | The real component of Lag[0] provides Intensity Information |
|
|
1293 | 1293 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
1294 | 1294 | |
|
|
1295 | 1295 | Configuration Parameters: |
|
|
1296 | 1296 | nPRF = Number of Several PRF |
|
|
1297 | 1297 | theta = Degree Azimuth angel Boundaries |
|
|
1298 | 1298 | |
|
|
1299 | 1299 | Input: |
|
|
1300 | 1300 | self.dataOut |
|
|
1301 | 1301 | lag[N] |
|
|
1302 | 1302 | Affected: |
|
|
1303 | 1303 | self.dataOut.spc |
|
|
1304 | 1304 | ''' |
|
|
1305 | 1305 | isConfig = False |
|
|
1306 | 1306 | __profIndex = 0 |
|
|
1307 | 1307 | __initime = None |
|
|
1308 | 1308 | __lastdatatime = None |
|
|
1309 | 1309 | __buffer = None |
|
|
1310 | 1310 | noise = None |
|
|
1311 | 1311 | __dataReady = False |
|
|
1312 | 1312 | n = None |
|
|
1313 | 1313 | __nch = 0 |
|
|
1314 | 1314 | __nHeis = 0 |
|
|
1315 | 1315 | removeDC = False |
|
|
1316 | 1316 | ipp = None |
|
|
1317 | 1317 | lambda_ = 0 |
|
|
1318 | 1318 | |
|
|
1319 | 1319 | def __init__(self,**kwargs): |
|
|
1320 | 1320 | Operation.__init__(self,**kwargs) |
|
|
1321 | 1321 | |
|
|
1322 | 1322 | def setup(self, dataOut, n = None, removeDC=False): |
|
|
1323 | 1323 | ''' |
|
|
1324 | 1324 | n= Numero de PRF's de entrada |
|
|
1325 | 1325 | ''' |
|
|
1326 | 1326 | self.__initime = None |
|
|
1327 | 1327 | self.__lastdatatime = 0 |
|
|
1328 | 1328 | self.__dataReady = False |
|
|
1329 | 1329 | self.__buffer = 0 |
|
|
1330 | 1330 | self.__profIndex = 0 |
|
|
1331 | 1331 | self.noise = None |
|
|
1332 | 1332 | self.__nch = dataOut.nChannels |
|
|
1333 | 1333 | self.__nHeis = dataOut.nHeights |
|
|
1334 | 1334 | self.removeDC = removeDC |
|
|
1335 | 1335 | self.lambda_ = 3.0e8/(9345.0e6) |
|
|
1336 | 1336 | self.ippSec = dataOut.ippSeconds |
|
|
1337 | 1337 | self.nCohInt = dataOut.nCohInt |
|
|
1338 | 1338 | print("IPPseconds",dataOut.ippSeconds) |
|
|
1339 | 1339 | |
|
|
1340 | 1340 | print("ELVALOR DE n es:", n) |
|
|
1341 | 1341 | if n == None: |
|
|
1342 | 1342 | raise ValueError("n should be specified.") |
|
|
1343 | 1343 | |
|
|
1344 | 1344 | if n != None: |
|
|
1345 | 1345 | if n<2: |
|
|
1346 | 1346 | raise ValueError("n should be greater than 2") |
|
|
1347 | 1347 | |
|
|
1348 | 1348 | self.n = n |
|
|
1349 | 1349 | self.__nProf = n |
|
|
1350 | 1350 | |
|
|
1351 | 1351 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
1352 | 1352 | n, |
|
|
1353 | 1353 | dataOut.nHeights), |
|
|
1354 | 1354 | dtype='complex') |
|
|
1355 | 1355 | |
|
|
1356 | 1356 | def putData(self,data): |
|
|
1357 | 1357 | ''' |
|
|
1358 | 1358 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
1359 | 1359 | ''' |
|
|
1360 | 1360 | self.__buffer[:,self.__profIndex,:]= data |
|
|
1361 | 1361 | self.__profIndex += 1 |
|
|
1362 | 1362 | return |
|
|
1363 | 1363 | |
|
|
1364 | 1364 | def pushData(self,dataOut): |
|
|
1365 | 1365 | ''' |
|
|
1366 | 1366 | Return the PULSEPAIR and the profiles used in the operation |
|
|
1367 | 1367 | Affected : self.__profileIndex |
|
|
1368 | 1368 | ''' |
|
|
1369 | 1369 | #----------------- Remove DC----------------------------------- |
|
|
1370 | 1370 | if self.removeDC==True: |
|
|
1371 | 1371 | mean = numpy.mean(self.__buffer,1) |
|
|
1372 | 1372 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
|
1373 | 1373 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
|
1374 | 1374 | self.__buffer = self.__buffer - dc |
|
|
1375 | 1375 | #------------------Calculo de Potencia ------------------------ |
|
|
1376 | 1376 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
|
1377 | 1377 | pair0 = pair0.real |
|
|
1378 | 1378 | lag_0 = numpy.sum(pair0,1) |
|
|
1379 | 1379 | #------------------Calculo de Ruido x canal-------------------- |
|
|
1380 | 1380 | self.noise = numpy.zeros(self.__nch) |
|
|
1381 | 1381 | for i in range(self.__nch): |
|
|
1382 | 1382 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
|
1383 | 1383 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
|
1384 | 1384 | |
|
|
1385 | 1385 | self.noise = self.noise.reshape(self.__nch,1) |
|
|
1386 | 1386 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
|
1387 | 1387 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
|
1388 | 1388 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
|
1389 | 1389 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
1390 | 1390 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
1391 | 1391 | #-------------------- Power -------------------------------------------------- |
|
|
1392 | 1392 | data_power = lag_0/(self.n*self.nCohInt) |
|
|
1393 | 1393 | #------------------ Senal --------------------------------------------------- |
|
|
1394 | 1394 | data_intensity = pair0 - noise_buffer |
|
|
1395 | 1395 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
|
1396 | 1396 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
1397 | 1397 | for i in range(self.__nch): |
|
|
1398 | 1398 | for j in range(self.__nHeis): |
|
|
1399 | 1399 | if data_intensity[i][j] < 0: |
|
|
1400 | 1400 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
1401 | 1401 | |
|
|
1402 | 1402 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
1403 | 1403 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
|
1404 | 1404 | lag_1 = numpy.sum(pair1,1) |
|
|
1405 | 1405 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
|
1406 | 1406 | data_velocity = (self.lambda_/2.0)*data_freq |
|
|
1407 | 1407 | |
|
|
1408 | 1408 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
1409 | 1409 | lag_0 = lag_0/self.n |
|
|
1410 | 1410 | S = lag_0-self.noise |
|
|
1411 | 1411 | |
|
|
1412 | 1412 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
1413 | 1413 | lag_1 = lag_1/(self.n-1) |
|
|
1414 | 1414 | R1 = numpy.abs(lag_1) |
|
|
1415 | 1415 | |
|
|
1416 | 1416 | #---------------- Calculo del SNR---------------------------------- |
|
|
1417 | 1417 | data_snrPP = S/self.noise |
|
|
1418 | 1418 | for i in range(self.__nch): |
|
|
1419 | 1419 | for j in range(self.__nHeis): |
|
|
1420 | 1420 | if data_snrPP[i][j] < 1.e-20: |
|
|
1421 | 1421 | data_snrPP[i][j] = 1.e-20 |
|
|
1422 | 1422 | |
|
|
1423 | 1423 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
1424 | 1424 | L = S/R1 |
|
|
1425 | 1425 | L = numpy.where(L<0,1,L) |
|
|
1426 | 1426 | L = numpy.log(L) |
|
|
1427 | 1427 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
1428 | 1428 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
|
1429 | 1429 | n = self.__profIndex |
|
|
1430 | 1430 | |
|
|
1431 | 1431 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
|
1432 | 1432 | self.__profIndex = 0 |
|
|
1433 | 1433 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n |
|
|
1434 | 1434 | |
|
|
1435 | 1435 | |
|
|
1436 | 1436 | def pulsePairbyProfiles(self,dataOut): |
|
|
1437 | 1437 | |
|
|
1438 | 1438 | self.__dataReady = False |
|
|
1439 | 1439 | data_power = None |
|
|
1440 | 1440 | data_intensity = None |
|
|
1441 | 1441 | data_velocity = None |
|
|
1442 | 1442 | data_specwidth = None |
|
|
1443 | 1443 | data_snrPP = None |
|
|
1444 | 1444 | self.putData(data=dataOut.data) |
|
|
1445 | 1445 | if self.__profIndex == self.n: |
|
|
1446 | 1446 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) |
|
|
1447 | 1447 | self.__dataReady = True |
|
|
1448 | 1448 | |
|
|
1449 | 1449 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
|
1450 | 1450 | |
|
|
1451 | 1451 | |
|
|
1452 | 1452 | def pulsePairOp(self, dataOut, datatime= None): |
|
|
1453 | 1453 | |
|
|
1454 | 1454 | if self.__initime == None: |
|
|
1455 | 1455 | self.__initime = datatime |
|
|
1456 | 1456 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
|
1457 | 1457 | self.__lastdatatime = datatime |
|
|
1458 | 1458 | |
|
|
1459 | 1459 | if data_power is None: |
|
|
1460 | 1460 | return None, None, None,None,None,None |
|
|
1461 | 1461 | |
|
|
1462 | 1462 | avgdatatime = self.__initime |
|
|
1463 | 1463 | deltatime = datatime - self.__lastdatatime |
|
|
1464 | 1464 | self.__initime = datatime |
|
|
1465 | 1465 | |
|
|
1466 | 1466 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
|
1467 | 1467 | |
|
|
1468 | 1468 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
|
1469 | 1469 | |
|
|
1470 | 1470 | if not self.isConfig: |
|
|
1471 | 1471 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
|
1472 | 1472 | self.isConfig = True |
|
|
1473 | 1473 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
|
1474 | 1474 | dataOut.flagNoData = True |
|
|
1475 | 1475 | |
|
|
1476 | 1476 | if self.__dataReady: |
|
|
1477 | 1477 | dataOut.nCohInt *= self.n |
|
|
1478 | 1478 | dataOut.dataPP_POW = data_intensity # S |
|
|
1479 | 1479 | dataOut.dataPP_POWER = data_power # P |
|
|
1480 | 1480 | dataOut.dataPP_DOP = data_velocity |
|
|
1481 | 1481 | dataOut.dataPP_SNR = data_snrPP |
|
|
1482 | 1482 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
1483 | 1483 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
1484 | 1484 | dataOut.utctime = avgdatatime |
|
|
1485 | 1485 | dataOut.flagNoData = False |
|
|
1486 | 1486 | return dataOut |
|
|
1487 | 1487 | |
|
|
1488 | 1488 | |
|
|
1489 | 1489 | |
|
|
1490 | 1490 | # import collections |
|
|
1491 | 1491 | # from scipy.stats import mode |
|
|
1492 | 1492 | # |
|
|
1493 | 1493 | # class Synchronize(Operation): |
|
|
1494 | 1494 | # |
|
|
1495 | 1495 | # isConfig = False |
|
|
1496 | 1496 | # __profIndex = 0 |
|
|
1497 | 1497 | # |
|
|
1498 | 1498 | # def __init__(self, **kwargs): |
|
|
1499 | 1499 | # |
|
|
1500 | 1500 | # Operation.__init__(self, **kwargs) |
|
|
1501 | 1501 | # # self.isConfig = False |
|
|
1502 | 1502 | # self.__powBuffer = None |
|
|
1503 | 1503 | # self.__startIndex = 0 |
|
|
1504 | 1504 | # self.__pulseFound = False |
|
|
1505 | 1505 | # |
|
|
1506 | 1506 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
1507 | 1507 | # |
|
|
1508 | 1508 | # #Read data |
|
|
1509 | 1509 | # |
|
|
1510 | 1510 | # powerdB = dataOut.getPower(channel = channel) |
|
|
1511 | 1511 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
1512 | 1512 | # |
|
|
1513 | 1513 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
1514 | 1514 | # |
|
|
1515 | 1515 | # dataArray = numpy.array(self.__powBuffer) |
|
|
1516 | 1516 | # |
|
|
1517 | 1517 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
1518 | 1518 | # |
|
|
1519 | 1519 | # maxValue = numpy.nanmax(filteredPower) |
|
|
1520 | 1520 | # |
|
|
1521 | 1521 | # if maxValue < noisedB + 10: |
|
|
1522 | 1522 | # #No se encuentra ningun pulso de transmision |
|
|
1523 | 1523 | # return None |
|
|
1524 | 1524 | # |
|
|
1525 | 1525 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
1526 | 1526 | # |
|
|
1527 | 1527 | # if len(maxValuesIndex) < 2: |
|
|
1528 | 1528 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
1529 | 1529 | # return None |
|
|
1530 | 1530 | # |
|
|
1531 | 1531 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
1532 | 1532 | # |
|
|
1533 | 1533 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
1534 | 1534 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
1535 | 1535 | # |
|
|
1536 | 1536 | # if len(pulseIndex) < 2: |
|
|
1537 | 1537 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1538 | 1538 | # return None |
|
|
1539 | 1539 | # |
|
|
1540 | 1540 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
1541 | 1541 | # |
|
|
1542 | 1542 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
1543 | 1543 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
1544 | 1544 | # |
|
|
1545 | 1545 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
1546 | 1546 | # |
|
|
1547 | 1547 | # if len(realIndex) < 2: |
|
|
1548 | 1548 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
1549 | 1549 | # return None |
|
|
1550 | 1550 | # |
|
|
1551 | 1551 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
1552 | 1552 | # realPulseIndex = pulseIndex[realIndex] |
|
|
1553 | 1553 | # |
|
|
1554 | 1554 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
1555 | 1555 | # |
|
|
1556 | 1556 | # print "IPP = %d samples" %period |
|
|
1557 | 1557 | # |
|
|
1558 | 1558 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
1559 | 1559 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
1560 | 1560 | # |
|
|
1561 | 1561 | # return 1 |
|
|
1562 | 1562 | # |
|
|
1563 | 1563 | # |
|
|
1564 | 1564 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
1565 | 1565 | # |
|
|
1566 | 1566 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
1567 | 1567 | # maxlen = buffer_size*nSamples) |
|
|
1568 | 1568 | # |
|
|
1569 | 1569 | # bufferList = [] |
|
|
1570 | 1570 | # |
|
|
1571 | 1571 | # for i in range(nChannels): |
|
|
1572 | 1572 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
|
1573 | 1573 | # maxlen = buffer_size*nSamples) |
|
|
1574 | 1574 | # |
|
|
1575 | 1575 | # bufferList.append(bufferByChannel) |
|
|
1576 | 1576 | # |
|
|
1577 | 1577 | # self.__nSamples = nSamples |
|
|
1578 | 1578 | # self.__nChannels = nChannels |
|
|
1579 | 1579 | # self.__bufferList = bufferList |
|
|
1580 | 1580 | # |
|
|
1581 | 1581 | # def run(self, dataOut, channel = 0): |
|
|
1582 | 1582 | # |
|
|
1583 | 1583 | # if not self.isConfig: |
|
|
1584 | 1584 | # nSamples = dataOut.nHeights |
|
|
1585 | 1585 | # nChannels = dataOut.nChannels |
|
|
1586 | 1586 | # self.setup(nSamples, nChannels) |
|
|
1587 | 1587 | # self.isConfig = True |
|
|
1588 | 1588 | # |
|
|
1589 | 1589 | # #Append new data to internal buffer |
|
|
1590 | 1590 | # for thisChannel in range(self.__nChannels): |
|
|
1591 | 1591 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1592 | 1592 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
1593 | 1593 | # |
|
|
1594 | 1594 | # if self.__pulseFound: |
|
|
1595 | 1595 | # self.__startIndex -= self.__nSamples |
|
|
1596 | 1596 | # |
|
|
1597 | 1597 | # #Finding Tx Pulse |
|
|
1598 | 1598 | # if not self.__pulseFound: |
|
|
1599 | 1599 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
1600 | 1600 | # |
|
|
1601 | 1601 | # if indexFound == None: |
|
|
1602 | 1602 | # dataOut.flagNoData = True |
|
|
1603 | 1603 | # return |
|
|
1604 | 1604 | # |
|
|
1605 | 1605 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
|
1606 | 1606 | # self.__pulseFound = True |
|
|
1607 | 1607 | # self.__startIndex = indexFound |
|
|
1608 | 1608 | # |
|
|
1609 | 1609 | # #If pulse was found ... |
|
|
1610 | 1610 | # for thisChannel in range(self.__nChannels): |
|
|
1611 | 1611 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
1612 | 1612 | # #print self.__startIndex |
|
|
1613 | 1613 | # x = numpy.array(bufferByChannel) |
|
|
1614 | 1614 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
1615 | 1615 | # |
|
|
1616 | 1616 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
1617 | 1617 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
1618 | 1618 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
1619 | 1619 | # |
|
|
1620 | 1620 | # dataOut.data = self.__arrayBuffer |
|
|
1621 | 1621 | # |
|
|
1622 | 1622 | # self.__startIndex += self.__newNSamples |
|
|
1623 | 1623 | # |
|
|
1624 | 1624 | # return |
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