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# 

# LSST Data Management System 

# Copyright 2008-2016 LSST Corporation. 

# 

# 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/>. 

# 

 

"""Test Coadd class 

""" 

import unittest 

 

import numpy as np 

 

import lsst.utils.tests 

import lsst.afw.image as afwImage 

import lsst.afw.image.utils as imageUtils 

import lsst.afw.image.testUtils as afwTestUtils 

import lsst.coadd.chisquared as coaddChiSq 

 

doPlot = False 

 

37 ↛ 38line 37 didn't jump to line 38, because the condition on line 37 was never trueif doPlot: 

import matplotlib.pyplot as pyplot 

 

 

def makeHistogram(coadd, numBins, numImages): 

"""Generate a histogram for a given coadd maskedImage 

 

Inputs: 

- coadd: a chiSquared coadd MaskedImage 

- numBins: number of bins for histogram 

- numImages: number of images that went into the coadd 

 

Returns: 

- histX: x values for histogram of coadd data (counts) 

- histY: y values for histogram of coadd data (number of pixels) 

- chiSqY: chi squared distribution values corresponding to histX 

""" 

coaddData = coadd.getImage().getArray() 

# undo normalization 

coaddData *= float(numImages) 

# get rid of nans and infs 

goodData = np.extract(np.isfinite(coaddData.flat), coaddData.flat) 

goodData = np.extract(goodData < 50, goodData) 

 

# compute histogram 

histY, binEdges = np.histogram(goodData, bins=numBins) 

histX = binEdges[0:-1] 

histY = np.array(histY, dtype=float) # convert from int to float 

histY /= histY.sum() 

 

# compute chiSq probability distribution; chi squared order = numImages 

chiSqY = np.power(histX, (numImages / 2.0) - 1) * np.exp(-histX / 2.0) 

chiSqY /= chiSqY.sum() 

 

return (histX, histY, chiSqY) 

 

 

class CoaddTestCase(unittest.TestCase): 

 

def testNoiseCoadd(self): 

"""Build a coadd from noise images and compare the histogram to a chi 

squared distribution 

""" 

numImages = 4 

imShape = (150, 150) 

imSigma = 1.0 

imVariance = 1.0 

numBins = 200 

maxStdDevErr = 0.2 

maxMeanErr = 1.0e-12 

 

badMaskPlanes = ["EDGE"] 

 

np.random.seed(0) 

 

coadd = None 

for imInd in range(numImages): 

maskedImage = afwTestUtils.makeGaussianNoiseMaskedImage( 

dimensions=imShape, sigma=imSigma, variance=imVariance) 

exposure = afwImage.ExposureF(maskedImage) 

 

if not coadd: 

coadd = coaddChiSq.Coadd( 

bbox=exposure.getBBox(), 

wcs=exposure.getWcs(), 

badMaskPlanes=badMaskPlanes) 

 

coadd.addExposure(exposure) 

 

coadd.getWeightMap() 

coaddExposure = coadd.getCoadd() 

 

histX, histY, chiSqY = makeHistogram( 

coaddExposure.getMaskedImage(), numBins=numBins, numImages=numImages) 

 

if doPlot: 

pyplot.plot(histX, histY, drawstyle="steps") 

pyplot.plot(histX, chiSqY) 

pyplot.ylabel('frequency') 

pyplot.xlabel('sum of (counts/noise)^2') 

pyplot.show() 

 

errArr = (histY - chiSqY) * numBins # crude scaling 

# print "Mean error =", errArr.mean() 

# print "Std dev error = ", errArr.std() 

if errArr.std() > maxStdDevErr: 

self.fail("Standard deviation of error = %s > %s limit" % (errArr.std(), maxStdDevErr)) 

if errArr.mean() > maxMeanErr: 

self.fail("Mean of error = %s > %s limit" % (errArr.mean(), maxMeanErr)) 

 

def testFilters(self): 

"""Test that the coadd filter is set correctly 

""" 

imageUtils.defineFilter("g", 468.6) 

imageUtils.defineFilter("r", 616.5) 

 

unkFilter = afwImage.Filter() 

gFilter = afwImage.Filter("g") 

rFilter = afwImage.Filter("r") 

 

imShape = (150, 150) 

imSigma = 1.0 

imVariance = 1.0 

 

badMaskPlanes = ["EDGE"] 

 

np.random.seed(0) 

 

coadd = None 

maskedImage = afwTestUtils.makeGaussianNoiseMaskedImage( 

dimensions=imShape, sigma=imSigma, variance=imVariance) 

inExp = afwImage.ExposureF(maskedImage) 

 

coadd = coaddChiSq.Coadd( 

bbox=inExp.getBBox(), 

wcs=inExp.getWcs(), 

badMaskPlanes=badMaskPlanes, 

) 

 

inExp.setFilter(gFilter) 

coadd.addExposure(inExp) 

self.assertEqualFilters(coadd.getCoadd().getFilter(), gFilter) 

self.assertEqualFilterSets(coadd.getFilters(), (gFilter,)) 

coadd.addExposure(inExp) 

self.assertEqualFilters(coadd.getCoadd().getFilter(), gFilter) 

self.assertEqualFilterSets(coadd.getFilters(), (gFilter,)) 

 

inExp.setFilter(rFilter) 

coadd.addExposure(inExp) 

self.assertEqualFilters(coadd.getCoadd().getFilter(), unkFilter) 

self.assertEqualFilterSets(coadd.getFilters(), (gFilter, rFilter)) 

 

def assertEqualFilters(self, f1, f2): 

"""Compare two filters 

 

Right now compares only the name, but if == ever works for Filters 

(ticket #1744) then use == instead 

""" 

self.assertEqual(f1.getName(), f2.getName()) 

 

def assertEqualFilterSets(self, fs1, fs2): 

"""Assert that two collections of filters are equal, ignoring order 

""" 

self.assertEqual(set(f.getName() for f in fs1), set(f.getName() for f in fs2)) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

191 ↛ 192line 191 didn't jump to line 192, because the condition on line 191 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()