| @@ -68,12 +68,23 class BLTRParametersProc(ProcessingUnit): | |||||
| 68 | self.dataOut.data_param = self.dataOut.data[mode] |
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68 | self.dataOut.data_param = self.dataOut.data[mode] | |
| 69 | self.dataOut.heightList = self.dataOut.height[0] |
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69 | self.dataOut.heightList = self.dataOut.height[0] | |
| 70 | self.dataOut.data_snr = self.dataOut.data_snr[mode] |
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70 | self.dataOut.data_snr = self.dataOut.data_snr[mode] | |
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71 | ||||
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72 | data_param = numpy.zeros([4, len(self.dataOut.heightList)]) | |||
| 71 |
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73 | |||
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74 | SNRavg = numpy.average(self.dataOut.data_snr, axis=0) | |||
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75 | SNRavgdB = 10*numpy.log10(SNRavg) | |||
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76 | # Censoring Data | |||
| 72 | if snr_threshold is not None: |
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77 | if snr_threshold is not None: | |
| 73 | SNRavg = numpy.average(self.dataOut.data_snr, axis=0) |
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| 74 | SNRavgdB = 10*numpy.log10(SNRavg) |
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| 75 | for i in range(3): |
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78 | for i in range(3): | |
| 76 | self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan |
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79 | self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan | |
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80 | # Including AvgSNR in data_param | |||
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81 | for i in range(data_param.shape[0]): | |||
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82 | if i == 3: | |||
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83 | data_param[i] = SNRavgdB | |||
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84 | else: | |||
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85 | data_param[i] = self.dataOut.data_param[i] | |||
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86 | ||||
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87 | self.dataOut.data_param = data_param | |||
| 77 |
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88 | |||
| 78 | # TODO |
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89 | # TODO | |
| 79 |
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90 | |||
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