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# This file is part of meas_extensions_scarlet. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

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

# for details of code ownership. 

# 

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

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

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

# (at your option) any later version. 

# 

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

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

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

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

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

 

import unittest 

 

import numpy as np 

 

import lsst.utils.tests 

import lsst.afw.image as afwImage 

from lsst.meas.algorithms import SourceDetectionTask 

from lsst.meas.extensions.scarlet import ScarletDeblendTask 

from lsst.afw.table import SourceCatalog 

from lsst.afw.detection import MultibandFootprint 

from lsst.afw.image import Image, MultibandImage 

 

from utils import initData 

 

 

class TestDeblend(lsst.utils.tests.TestCase): 

def test_deblend_task(self): 

# Set the random seed so that the noise field is unaffected 

np.random.seed(0) 

# Test that executing the deblend task works 

# In the future we can have more detailed tests, 

# but for now this at least ensures that the task isn't broken 

shape = (5, 31, 55) 

coords = [(15, 25), (10, 30), (17, 38)] 

amplitudes = [80, 60, 90] 

result = initData(shape, coords, amplitudes) 

targetPsfImage, psfImages, images, channels, seds, morphs, targetPsf, psfs = result 

B, Ny, Nx = shape 

 

# Add some noise, otherwise the task will blow up due to 

# zero variance 

noise = 10*(np.random.rand(*images.shape).astype(np.float32)-.5) 

images += noise 

 

filters = "grizy" 

_images = afwImage.MultibandMaskedImage.fromArrays(filters, images.astype(np.float32), None, noise) 

coadds = [afwImage.Exposure(img, dtype=img.image.array.dtype) for img in _images] 

coadds = afwImage.MultibandExposure.fromExposures(filters, coadds) 

for b, coadd in enumerate(coadds): 

coadd.setPsf(psfs[b]) 

 

schema = SourceCatalog.Table.makeMinimalSchema() 

 

detectionTask = SourceDetectionTask(schema=schema) 

config = ScarletDeblendTask.ConfigClass() 

config.maxIter = 200 

deblendTask = ScarletDeblendTask(schema=schema, config=config) 

 

table = SourceCatalog.Table.make(schema) 

detectionResult = detectionTask.run(table, coadds["r"]) 

catalog = detectionResult.sources 

self.assertEqual(len(catalog), 1) 

_, result = deblendTask.run(coadds, catalog) 

 

# Changes to the internal workings of scarlet will change these results 

# however we include these tests just to track changes 

parent = result["r"][0] 

self.assertEqual(parent["iterations"], 11) 

self.assertEqual(parent["deblend_nChild"], 3) 

 

heavies = [] 

for k in range(1, len(result["g"])): 

heavy = MultibandFootprint(coadds.filters, [result[b][k].getFootprint() for b in filters]) 

heavies.append(heavy) 

 

seds = np.array([heavy.getImage(fill=0).image.array.sum(axis=(1, 2)) for heavy in heavies]) 

true_seds = np.array([ 

[1665.726318359375, 1745.5401611328125, 1525.91796875, 997.3868408203125, 0.0], 

[767.100341796875, 1057.0374755859375, 1312.89111328125, 1694.7535400390625, 2069.294921875], 

[8.08012580871582, 879.344970703125, 2246.90087890625, 4212.82470703125, 6987.0849609375] 

]) 

 

self.assertFloatsAlmostEqual(true_seds, seds, rtol=1e-4, atol=1e-4) 

 

bbox = parent.getFootprint().getBBox() 

data = coadds[:, bbox] 

model = MultibandImage.fromImages(coadds.filters, [ 

Image(bbox, dtype=np.float32) 

for b in range(len(filters)) 

]) 

for heavy in heavies: 

model[:, heavy.getBBox()].array += heavy.getImage(fill=0).image.array 

 

residual = data.image.array - model.array 

self.assertFloatsAlmostEqual(np.abs(residual).sum(), 11601.3867187500, rtol=1e-5, atol=1e-5) 

self.assertFloatsAlmostEqual(np.max(np.abs(residual)), 56.1048278809, rtol=1e-5, atol=1e-5) 

 

 

class MemoryTester(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

118 ↛ 119line 118 didn't jump to line 119, because the condition on line 118 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()