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# This file is part of jointcal. 

# 

# 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 unittest.mock 

 

import numpy as np 

 

import lsst.log 

import lsst.utils 

 

import lsst.jointcal 

from lsst.jointcal import MinimizeResult 

import lsst.jointcal.chi2 

import lsst.jointcal.testUtils 

 

 

# for MemoryTestCase 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

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

def setUp(self): 

struct = lsst.jointcal.testUtils.createTwoFakeCcdImages(100, 100) 

self.ccdImageList = struct.ccdImageList 

# so that countStars() returns nonzero results 

for ccdImage in self.ccdImageList: 

ccdImage.resetCatalogForFit() 

 

self.config = lsst.jointcal.jointcal.JointcalConfig() 

# disable both, so it doesn't configure any refObjLoaders 

self.config.doAstrometry = False 

self.config.doPhotometry = False 

self.jointcal = lsst.jointcal.JointcalTask(config=self.config) 

 

self.goodChi2 = lsst.jointcal.chi2.Chi2Statistic() 

# chi2/ndof == 2.0 should be non-bad 

self.goodChi2.chi2 = 200.0 

self.goodChi2.ndof = 100 

 

self.badChi2 = lsst.jointcal.chi2.Chi2Statistic() 

self.badChi2.chi2 = 600.0 

self.badChi2.ndof = 100 

 

self.nanChi2 = lsst.jointcal.chi2.Chi2Statistic() 

self.nanChi2.chi2 = np.nan 

self.nanChi2.ndof = 100 

 

self.maxSteps = 20 

self.name = "testing" 

self.whatToFit = "" # unneeded, since we're mocking the fitter 

 

# Mock the fitter, association manager, and model, so we can force particular 

# return values/exceptions. Default to "good" return values. 

self.fitter = unittest.mock.Mock(spec=lsst.jointcal.PhotometryFit) 

self.fitter.computeChi2.return_value = self.goodChi2 

self.fitter.minimize.return_value = MinimizeResult.Converged 

self.associations = unittest.mock.Mock(spec=lsst.jointcal.Associations) 

self.associations.getCcdImageList.return_value = self.ccdImageList 

self.model = unittest.mock.Mock(spec=lsst.jointcal.SimpleFluxModel) 

 

def test_iterateFit_success(self): 

chi2 = self.jointcal._iterate_fit(self.associations, self.fitter, 

self.maxSteps, self.name, self.whatToFit) 

self.assertEqual(chi2, self.goodChi2) 

# Once for the for loop, the second time for the rank update. 

self.assertEqual(self.fitter.minimize.call_count, 2) 

 

def test_iterateFit_failed(self): 

self.fitter.minimize.return_value = MinimizeResult.Failed 

 

with self.assertRaises(RuntimeError): 

self.jointcal._iterate_fit(self.associations, self.fitter, 

self.maxSteps, self.name, self.whatToFit) 

self.assertEqual(self.fitter.minimize.call_count, 1) 

 

def test_iterateFit_badFinalChi2(self): 

log = unittest.mock.Mock(spec=lsst.log.Log) 

self.jointcal.log = log 

self.fitter.computeChi2.return_value = self.badChi2 

 

chi2 = self.jointcal._iterate_fit(self.associations, self.fitter, 

self.maxSteps, self.name, self.whatToFit) 

self.assertEqual(chi2, self.badChi2) 

log.info.assert_called_with("%s %s", "Fit completed", self.badChi2) 

log.error.assert_called_with("Potentially bad fit: High chi-squared/ndof.") 

 

def test_iterateFit_exceedMaxSteps(self): 

log = unittest.mock.Mock(spec=lsst.log.Log) 

self.jointcal.log = log 

self.fitter.minimize.return_value = MinimizeResult.Chi2Increased 

maxSteps = 3 

 

chi2 = self.jointcal._iterate_fit(self.associations, self.fitter, 

maxSteps, self.name, self.whatToFit) 

self.assertEqual(chi2, self.goodChi2) 

self.assertEqual(self.fitter.minimize.call_count, maxSteps) 

log.error.assert_called_with("testing failed to converge after %s steps" % maxSteps) 

 

def test_invalid_model(self): 

self.model.validate.return_value = False 

with(self.assertRaises(ValueError)): 

self.jointcal._logChi2AndValidate(self.associations, self.fitter, self.model) 

 

def test_nonfinite_chi2(self): 

self.fitter.computeChi2.return_value = self.nanChi2 

with(self.assertRaises(FloatingPointError)): 

self.jointcal._logChi2AndValidate(self.associations, self.fitter, self.model) 

 

 

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

pass 

 

 

134 ↛ 135line 134 didn't jump to line 135, because the condition on line 134 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()