Mercurial > public > atmospheric_lidar / file comparison
comparison: atmospheric_lidar/generic.py
atmospheric_lidar/generic.py
- changeset 174
- 16cf4d961d02
- parent 172
- 6bab03750268
- child 179
- 26551701d013
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| 173:9c29ffa49f2d | 174:16cf4d961d02 |
|---|---|
| 710 durations = [dt.seconds for dt in time_diff] | 710 durations = [dt.seconds for dt in time_diff] |
| 711 # Assume the last two profiles have the same duration | 711 # Assume the last two profiles have the same duration |
| 712 duration = np.array(durations) | 712 duration = np.array(durations) |
| 713 return duration | 713 return duration |
| 714 | 714 |
| 715 def calculate_rc(self, idx_min=4000, idx_max=5000): | 715 def calculate_rc(self, idx_min=-2000, idx_max=-500): |
| 716 """ Calculate range corrected signal. | 716 """ Calculate range corrected signal. |
| 717 | 717 |
| 718 The background signal is estimated as the mean signal between idx_min and idx_max. | 718 The background signal is estimated as the mean signal between idx_min and idx_max. |
| 719 | 719 |
| 720 The calculations is stored in the self.rc parameters. | 720 The calculations is stored in the self.rc parameters. |
| 724 idx_min : int | 724 idx_min : int |
| 725 Minimum index to calculate background signal. | 725 Minimum index to calculate background signal. |
| 726 idx_max : int | 726 idx_max : int |
| 727 Maximum index to calculate background signal. | 727 Maximum index to calculate background signal. |
| 728 """ | 728 """ |
| 729 background = np.mean(self.matrix[:, idx_min:idx_max], axis=1) # Calculate the background from 30000m and above | 729 background = np.mean(self.matrix[:, idx_min:idx_max], axis=1) |
| 730 self.rc = (self.matrix.transpose() - background).transpose() * (self.z ** 2) | 730 self.rc = (self.matrix.transpose() - background).transpose() * (self.z ** 2) |
| 731 | |
| 732 def noise_mask(self, idx_min=-2000, idx_max=-500, threshold=1.): | |
| 733 """ Calculate points that are probably noise. | |
| 734 | |
| 735 To calculate this, we find the max value of the background region. Then we reject all points that | |
| 736 are less than `threshold` times this value. | |
| 737 Parameters | |
| 738 ---------- | |
| 739 idx_min : int | |
| 740 Minimum index to calculate background signal. | |
| 741 idx_max : int | |
| 742 Maximum index to calculate background signal. | |
| 743 threshold : float | |
| 744 Threshold value. | |
| 745 """ | |
| 746 background_max = np.max(self.matrix[:, idx_min:idx_max], axis=1) | |
| 747 background_mean = np.mean(self.matrix[:, idx_min:idx_max], axis=1) | |
| 748 mean_to_max = background_max - background_mean | |
| 749 | |
| 750 noise_limit = background_mean + mean_to_max * threshold | |
| 751 | |
| 752 mask = self.matrix <= noise_limit[:, np.newaxis] | |
| 753 | |
| 754 return mask | |
| 731 | 755 |
| 732 def update(self): | 756 def update(self): |
| 733 """ | 757 """ |
| 734 Update the time parameters and data according to the raw input data. | 758 Update the time parameters and data according to the raw input data. |
| 735 """ | 759 """ |
