Coverage for python/lsst/scarlet/lite/measure.py: 33%

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1# This file is part of scarlet_lite. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21 

22from typing import cast 

23 

24import numpy as np 

25 

26from .bbox import Box 

27from .image import Image 

28 

29 

30def calculate_snr( 

31 images: Image, 

32 variance: Image, 

33 psfs: np.ndarray, 

34 center: tuple[int, int], 

35) -> float: 

36 """Calculate the signal to noise for a point source 

37 

38 This is done by weighting the image with the PSF in each band 

39 and dividing by the PSF weighted variance. 

40 

41 Parameters 

42 ---------- 

43 images: 

44 The 3D (bands, y, x) image containing the data. 

45 variance: 

46 The variance of `images`. 

47 psfs: 

48 The PSF in each band. 

49 center: 

50 The center of the signal. 

51 

52 Returns 

53 ------- 

54 snr: 

55 The signal to noise of the source. 

56 """ 

57 py = psfs.shape[1] // 2 

58 px = psfs.shape[2] // 2 

59 bbox = Box(psfs[0].shape, origin=(-py + center[0], -px + center[1])) 

60 overlap = images.bbox & bbox 

61 noise = variance[overlap].data 

62 img = images[overlap].data 

63 _psfs = Image(psfs, bands=images.bands, yx0=cast(tuple[int, int], bbox.origin))[overlap].data 

64 numerator = img * _psfs 

65 denominator = (_psfs * noise) * _psfs 

66 return np.sum(numerator) / np.sqrt(np.sum(denominator))