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# 

# LSST Data Management System 

# Copyright 2008-2017 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# 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 LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <http://www.lsstcorp.org/LegalNotices/>. 

# 

 

import unittest 

 

import numpy as np 

 

import lsst.geom 

import lsst.afw.geom 

import lsst.meas.base 

import lsst.utils.tests 

from lsst.meas.base.tests import (AlgorithmTestCase, FluxTransformTestCase, 

SingleFramePluginTransformSetupHelper) 

 

 

class GaussianFluxTestCase(AlgorithmTestCase, lsst.utils.tests.TestCase): 

 

def setUp(self): 

self.bbox = lsst.geom.Box2I(lsst.geom.Point2I(-20, -30), 

lsst.geom.Extent2I(240, 1600)) 

self.dataset = lsst.meas.base.tests.TestDataset(self.bbox) 

# first source is a point 

self.dataset.addSource(100000.0, lsst.geom.Point2D(50.1, 49.8)) 

# second source is extended 

self.dataset.addSource(100000.0, lsst.geom.Point2D(149.9, 50.3), 

lsst.afw.geom.Quadrupole(8, 9, 3)) 

 

def tearDown(self): 

del self.bbox 

del self.dataset 

 

def makeAlgorithm(self, ctrl=None): 

"""Construct an algorithm (finishing a schema in the process), and return both.""" 

53 ↛ 55line 53 didn't jump to line 55, because the condition on line 53 was never false if ctrl is None: 

ctrl = lsst.meas.base.GaussianFluxControl() 

schema = lsst.meas.base.tests.TestDataset.makeMinimalSchema() 

algorithm = lsst.meas.base.GaussianFluxAlgorithm(ctrl, "base_GaussianFlux", schema) 

return algorithm, schema 

 

def testGaussians(self): 

"""Test that we get correct fluxes when measuring Gaussians with known positions and shapes.""" 

task = self.makeSingleFrameMeasurementTask("base_GaussianFlux") 

# Results are RNG dependent; we choose a seed that is known to pass. 

exposure, catalog = self.dataset.realize(10.0, task.schema, randomSeed=0) 

task.run(catalog, exposure) 

for measRecord in catalog: 

self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_flux"), 

measRecord.get("truth_flux"), rtol=3E-3) 

 

def testMonteCarlo(self): 

"""Test that we get exactly the right answer on an ideal sim with no noise, and that 

the reported uncertainty agrees with a Monte Carlo test of the noise. 

""" 

algorithm, schema = self.makeAlgorithm() 

# Results are RNG dependent; we choose a seed that is known to pass. 

exposure, catalog = self.dataset.realize(1E-8, schema, randomSeed=1) 

record = catalog[0] 

flux = record.get("truth_flux") 

algorithm.measure(record, exposure) 

self.assertFloatsAlmostEqual(record.get("base_GaussianFlux_flux"), flux, rtol=1E-3) 

self.assertLess(record.get("base_GaussianFlux_fluxErr"), 1E-3) 

for noise in (0.001, 0.01, 0.1): 

fluxes = [] 

fluxErrs = [] 

nSamples = 1000 

for repeat in range(nSamples): 

# By using ``repeat`` to seed the RNG, we get results which fall within the tolerances 

# defined below. If we allow this test to be truly random, passing becomes RNG-dependent. 

exposure, catalog = self.dataset.realize(noise*flux, schema, randomSeed=repeat) 

record = catalog[1] 

algorithm.measure(record, exposure) 

fluxes.append(record.get("base_GaussianFlux_flux")) 

fluxErrs.append(record.get("base_GaussianFlux_fluxErr")) 

fluxMean = np.mean(fluxes) 

fluxErrMean = np.mean(fluxErrs) 

fluxStandardDeviation = np.std(fluxes) 

self.assertFloatsAlmostEqual(fluxErrMean, fluxStandardDeviation, rtol=0.10) 

self.assertLess(fluxMean - flux, 2.0*fluxErrMean / nSamples**0.5) 

 

def testForcedPlugin(self): 

task = self.makeForcedMeasurementTask("base_GaussianFlux") 

# Results of this test are RNG dependent: we choose seeds that are known to pass. 

measWcs = self.dataset.makePerturbedWcs(self.dataset.exposure.getWcs(), randomSeed=2) 

measDataset = self.dataset.transform(measWcs) 

exposure, truthCatalog = measDataset.realize(10.0, measDataset.makeMinimalSchema(), randomSeed=2) 

refWcs = self.dataset.exposure.getWcs() 

refCat = self.dataset.catalog 

measCat = task.generateMeasCat(exposure, refCat, refWcs) 

task.attachTransformedFootprints(measCat, refCat, exposure, refWcs) 

task.run(measCat, exposure, refCat, refWcs) 

for measRecord, truthRecord in zip(measCat, truthCatalog): 

# Centroid tolerances set to ~ single precision epsilon 

self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_x"), 

truthRecord.get("truth_x"), rtol=1E-7) 

self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_y"), 

truthRecord.get("truth_y"), rtol=1E-7) 

self.assertFalse(measRecord.get("base_GaussianFlux_flag")) 

# GaussianFlux isn't designed to do a good job in forced mode, because it doesn't account 

# for changes in the PSF (and in fact confuses them with changes in the WCS). Hence, this 

# is really just a regression test, with the initial threshold set to just a bit more than 

# what it was found to be at one point. 

self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_flux"), 

truthCatalog.get("truth_flux"), rtol=0.3) 

self.assertLess(measRecord.get("base_GaussianFlux_fluxErr"), 500.0) 

 

 

class GaussianFluxTransformTestCase(FluxTransformTestCase, SingleFramePluginTransformSetupHelper, 

lsst.utils.tests.TestCase): 

controlClass = lsst.meas.base.GaussianFluxControl 

algorithmClass = lsst.meas.base.GaussianFluxAlgorithm 

transformClass = lsst.meas.base.GaussianFluxTransform 

singleFramePlugins = ('base_GaussianFlux',) 

forcedPlugins = ('base_GaussianFlux',) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

143 ↛ 144line 143 didn't jump to line 144, because the condition on line 143 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()