Coverage for python/lsst/scarlet/lite/measure.py: 33%
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« prev ^ index » next coverage.py v7.5.1, created at 2024-05-16 02:46 -0700
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/>.
22from typing import cast
24import numpy as np
26from .bbox import Box
27from .image import Image
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
38 This is done by weighting the image with the PSF in each band
39 and dividing by the PSF weighted variance.
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.
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))