Coverage for pyhiperta/leakage.py: 100%

1import numpy as np 

2 

3 

4def leakage(waveforms: np.ndarray, intensity: np.ndarray, leakage_mask: np.ndarray) -> np.ndarray: 

5 """Compute the leakage 1 and 2, and their intensity. 

6 

7 The leakage 1, respectively 2, is the number of pixels that are not 0 after cleaning on the outermost, 

8 respectively 2nd outermost, ring of the camera. 

9 The leakage intensity is the charge of the leakage pixels, so leakage 1, resp. 2, intensity is the sum of the signal 

10 in all pixels of the outermost, resp. 2nd outermost, ring of the camera. 

11 

12 Parameters 

13 ---------- 

14 waveforms : np.ndarray 

15 1D shower image(s). If a single image is provided the shape must be (N_pixels,). If a 

16 batch of images is provided, the shape should be (N_batch, N_pixels). 

17 Shape: ([N_batch,] N_pixels) 

18 intensity : np.ndarray 

19 Total charge of each waveform. 

20 Shape: ([B_batch,] 1) 

21 leakage_mask : np.ndarray 

22 Boolean array with value True for pixels on the outermost rings of the camera, False otherwise. 

23 leakage_mask[0, :] selects the outermost ring (leakage 1) while leakage_mask[1, :] selects the 

24 second outermost ring (leakage 2). 

25 Shape: (2, N_pixels) 

26 

27 Returns 

28 ------- 

29 np.ndarray 

30 Leakage array. This is a 1D array with shape (4,) if a single image was 

31 provided, or a 2D array with shape (N_batch, 4) if a batch of images was provided. The 

32 parameters are ordered like so: 

33 leakage[..., 0]: leakage 1: number of pixels with signal on the outermost ring of the camera, normalized by 

34 the number of pixels with signal. 

35 leakage[..., 1]: leakage 2: number of pixels with signal on the 1st and 2nd outermost ring of 

36 the camera, normalized by the number of pixels with signal. 

37 leakage[..., 3]: leakage 1 intensity: Waveform charge contained in the outermost ring of the camera, 

38 normalized by the total charge in the camera 

39 leakage[..., 4]: leakage 2 intensity: Waveform charge contained in the 1st and 2nd outermost ring of the camera, 

40 normalized by the total charge in the camera 

41 """ 

42 # number of pixels for normalization 

43 nb_pixels = np.count_nonzero(waveforms, axis=-1, keepdims=True).astype(np.float32) 

44 # apply leakage mask (both leakage 1 and leakage 2 with broadcasting) 

45 leakage_charge = waveforms[..., np.newaxis, :] * leakage_mask 

46 # normalized number of pixels with signal in leakage for both leakages 

47 leakage = np.count_nonzero(leakage_charge, axis=-1).astype(np.float32) / nb_pixels 

48 # normalized leakage intensity for both leakage (add axis to intensity for broadcasting!) 

49 leakage_intensity = leakage_charge.sum(axis=-1, dtype=np.float32) / intensity[..., np.newaxis] 

50 return np.concatenate([leakage, leakage_intensity], axis=-1) 

51 # return np.stack([leakage[..., 0], leakage[..., 1], leakage_intensity[..., 0], leakage_intensity[..., 1]], axis=-1)