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# This file is part of ip_diffim. 

# 

# LSST Data Management System 

# This product includes software developed by the 

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

# See COPYRIGHT file at the top of the source tree. 

# 

# 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 <https://www.lsstcorp.org/LegalNotices/>. 

 

from astropy import units as u 

import numpy as np 

from scipy import ndimage 

import unittest 

 

from lsst.afw.coord import Observatory, Weather 

import lsst.afw.geom as afwGeom 

import lsst.afw.image as afwImage 

import lsst.afw.math as afwMath 

from lsst.geom import arcseconds, degrees, radians 

from lsst.ip.diffim.dcrModel import DcrModel, calculateDcr, calculateImageParallacticAngle, applyDcr 

from lsst.meas.algorithms.testUtils import plantSources 

import lsst.utils.tests 

 

 

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

"""A test case for the DCR-aware image coaddition algorithm. 

 

Attributes 

---------- 

bbox : `lsst.afw.geom.Box2I` 

Bounding box of the test model. 

bufferSize : `int` 

Distance from the inner edge of the bounding box 

to avoid placing test sources in the model images. 

dcrNumSubfilters : int 

Number of sub-filters used to model chromatic effects within a band. 

lambdaEff : `float` 

Effective wavelength of the full band. 

lambdaMax : `float` 

Maximum wavelength where the relative throughput 

of the band is greater than 1%. 

lambdaMin : `float` 

Minimum wavelength where the relative throughput 

of the band is greater than 1%. 

mask : `lsst.afw.image.Mask` 

Reference mask of the unshifted model. 

""" 

 

def setUp(self): 

"""Define the filter, DCR parameters, and the bounding box for the tests. 

""" 

self.dcrNumSubfilters = 3 

self.lambdaEff = 476.31 # Use LSST g band values for the test. 

self.lambdaMin = 405. 

self.lambdaMax = 552. 

self.bufferSize = 5 

xSize = 40 

ySize = 42 

x0 = 12345 

y0 = 67890 

self.bbox = afwGeom.Box2I(afwGeom.Point2I(x0, y0), afwGeom.Extent2I(xSize, ySize)) 

 

def makeTestImages(self, seed=5, nSrc=5, psfSize=2., noiseLevel=5., 

detectionSigma=5., sourceSigma=20., fluxRange=2.): 

"""Make reproduceable PSF-convolved masked images for testing. 

 

Parameters 

---------- 

seed : `int`, optional 

Seed value to initialize the random number generator. 

nSrc : `int`, optional 

Number of sources to simulate. 

psfSize : `float`, optional 

Width of the PSF of the simulated sources, in pixels. 

noiseLevel : `float`, optional 

Standard deviation of the noise to add to each pixel. 

detectionSigma : `float`, optional 

Threshold amplitude of the image to set the "DETECTED" mask. 

sourceSigma : `float`, optional 

Average amplitude of the simulated sources, 

relative to ``noiseLevel`` 

fluxRange : `float`, optional 

Range in flux amplitude of the simulated sources. 

 

Returns 

------- 

modelImages : `list` of `lsst.afw.image.maskedImage` 

A list of masked images, each containing the model for one subfilter 

""" 

rng = np.random.RandomState(seed) 

x0, y0 = self.bbox.getBegin() 

xSize, ySize = self.bbox.getDimensions() 

xLoc = rng.rand(nSrc)*(xSize - 2*self.bufferSize) + self.bufferSize + x0 

yLoc = rng.rand(nSrc)*(ySize - 2*self.bufferSize) + self.bufferSize + y0 

modelImages = [] 

 

imageSum = np.zeros((ySize, xSize)) 

for subfilter in range(self.dcrNumSubfilters): 

flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*sourceSigma*noiseLevel 

sigmas = [psfSize for src in range(nSrc)] 

coordList = list(zip(xLoc, yLoc, flux, sigmas)) 

model = plantSources(self.bbox, 10, 0, coordList, addPoissonNoise=False) 

model.image.array += rng.rand(ySize, xSize)*noiseLevel 

imageSum += model.image.array 

model.mask.addMaskPlane("CLIPPED") 

modelImages.append(model.maskedImage) 

maskVals = np.zeros_like(imageSum) 

maskVals[imageSum > detectionSigma*noiseLevel] = afwImage.Mask.getPlaneBitMask('DETECTED') 

for model in modelImages: 

model.mask.array[:] = maskVals 

self.mask = modelImages[0].mask 

return modelImages 

 

def makeDummyWcs(self, rotAngle, pixelScale, crval): 

"""Make a World Coordinate System object for testing. 

 

Parameters 

---------- 

rotAngle : `lsst.geom.Angle` 

rotation of the CD matrix, East from North 

pixelScale : `lsst.geom.Angle` 

Pixel scale of the projection. 

crval : `lsst.afw.geom.SpherePoint` 

Coordinates of the reference pixel of the wcs. 

 

Returns 

------- 

`lsst.afw.geom.skyWcs.SkyWcs` 

A wcs that matches the inputs. 

""" 

crpix = afwGeom.Box2D(self.bbox).getCenter() 

cdMatrix = afwGeom.makeCdMatrix(scale=pixelScale, orientation=rotAngle, flipX=True) 

wcs = afwGeom.makeSkyWcs(crpix=crpix, crval=crval, cdMatrix=cdMatrix) 

return wcs 

 

def makeDummyVisitInfo(self, azimuth, elevation): 

"""Make a self-consistent visitInfo object for testing. 

 

For simplicity, the simulated observation is assumed 

to be taken on the local meridian. 

 

Parameters 

---------- 

azimuth : `lsst.geom.Angle` 

Azimuth angle of the simulated observation. 

elevation : `lsst.geom.Angle` 

Elevation angle of the simulated observation. 

 

Returns 

------- 

`lsst.afw.image.VisitInfo` 

VisitInfo for the exposure. 

""" 

lsstLat = -30.244639*degrees 

lsstLon = -70.749417*degrees 

lsstAlt = 2663. 

lsstTemperature = 20.*u.Celsius # in degrees Celcius 

lsstHumidity = 40. # in percent 

lsstPressure = 73892.*u.pascal 

lsstWeather = Weather(lsstTemperature.value, lsstPressure.value, lsstHumidity) 

lsstObservatory = Observatory(lsstLon, lsstLat, lsstAlt) 

airmass = 1.0/np.sin(elevation.asRadians()) 

era = 0.*radians # on the meridian 

zenithAngle = 90.*degrees - elevation 

ra = lsstLon + np.sin(azimuth.asRadians())*zenithAngle/np.cos(lsstLat.asRadians()) 

dec = lsstLat + np.cos(azimuth.asRadians())*zenithAngle 

visitInfo = afwImage.VisitInfo(era=era, 

boresightRaDec=afwGeom.SpherePoint(ra, dec), 

boresightAzAlt=afwGeom.SpherePoint(azimuth, elevation), 

boresightAirmass=airmass, 

boresightRotAngle=0.*radians, 

observatory=lsstObservatory, 

weather=lsstWeather 

) 

return visitInfo 

 

def testDcrCalculation(self): 

"""Test that the shift in pixels due to DCR is consistently computed. 

 

The shift is compared to pre-computed values. 

""" 

dcrNumSubfilters = 3 

afwImage.utils.defineFilter("gTest", self.lambdaEff, 

lambdaMin=self.lambdaMin, lambdaMax=self.lambdaMax) 

filterInfo = afwImage.Filter("gTest") 

rotAngle = 0.*radians 

azimuth = 30.*degrees 

elevation = 65.*degrees 

pixelScale = 0.2*arcseconds 

visitInfo = self.makeDummyVisitInfo(azimuth, elevation) 

wcs = self.makeDummyWcs(rotAngle, pixelScale, crval=visitInfo.getBoresightRaDec()) 

dcrShift = calculateDcr(visitInfo, wcs, filterInfo, dcrNumSubfilters) 

refShift = [afwGeom.Extent2D(-0.5363512808, -0.3103517169), 

afwGeom.Extent2D(0.001887293861, 0.001092054612), 

afwGeom.Extent2D(0.3886592703, 0.2248919247)] 

for shiftOld, shiftNew in zip(refShift, dcrShift): 

self.assertFloatsAlmostEqual(shiftOld.getX(), shiftNew.getX(), rtol=1e-6, atol=1e-8) 

self.assertFloatsAlmostEqual(shiftOld.getY(), shiftNew.getY(), rtol=1e-6, atol=1e-8) 

 

def testRotationAngle(self): 

"""Test that the sky rotation angle is consistently computed. 

 

The rotation is compared to pre-computed values. 

""" 

cdRotAngle = 0.*radians 

azimuth = 130.*afwGeom.degrees 

elevation = 70.*afwGeom.degrees 

pixelScale = 0.2*afwGeom.arcseconds 

visitInfo = self.makeDummyVisitInfo(azimuth, elevation) 

wcs = self.makeDummyWcs(cdRotAngle, pixelScale, crval=visitInfo.getBoresightRaDec()) 

rotAngle = calculateImageParallacticAngle(visitInfo, wcs) 

refAngle = -0.9344289857053072*radians 

self.assertAnglesAlmostEqual(refAngle, rotAngle, maxDiff=1e-6*radians) 

 

def testConditionDcrModelNoChange(self): 

"""Conditioning should not change the model if it equals the reference. 

 

This additionally tests that the variance and mask planes do not change. 

""" 

dcrModels = DcrModel(modelImages=self.makeTestImages()) 

newModels = [model.clone() for model in dcrModels] 

dcrModels.conditionDcrModel(newModels, self.bbox, gain=1.) 

for refModel, newModel in zip(dcrModels, newModels): 

self.assertMaskedImagesEqual(refModel, newModel) 

 

def testConditionDcrModelNoChangeHighGain(self): 

"""Conditioning should not change the model if it equals the reference. 

 

This additionally tests that the variance and mask planes do not change. 

""" 

dcrModels = DcrModel(modelImages=self.makeTestImages()) 

newModels = [model.clone() for model in dcrModels] 

dcrModels.conditionDcrModel(newModels, self.bbox, gain=2.5) 

for refModel, newModel in zip(dcrModels, newModels): 

self.assertMaskedImagesAlmostEqual(refModel, newModel) 

 

def testConditionDcrModelWithChange(self): 

"""Verify conditioning when the model changes by a known amount. 

 

This additionally tests that the variance and mask planes do not change. 

""" 

dcrModels = DcrModel(modelImages=self.makeTestImages()) 

newModels = [model.clone() for model in dcrModels] 

for model in newModels: 

model.image.array[:] *= 3. 

dcrModels.conditionDcrModel(newModels, self.bbox, gain=1.) 

for refModel, newModel in zip(dcrModels, newModels): 

refModel.image.array[:] *= 2. 

self.assertMaskedImagesAlmostEqual(refModel, newModel) 

 

def testRegularizationLargeClamp(self): 

"""Frequency regularization should leave the models unchanged if the clamp factor is large. 

""" 

clampFrequency = 3. 

regularizationWidth = 2 

dcrModels = DcrModel(modelImages=self.makeTestImages()) 

newModels = [model.clone() for model in dcrModels] 

dcrModels.regularizeModelFreq(newModels, self.bbox, clampFrequency, regularizationWidth) 

for model, refModel in zip(newModels, dcrModels): 

self.assertMaskedImagesEqual(model, refModel) 

 

def testRegularizationSmallClamp(self): 

"""Test that large variations between model planes are reduced. 

 

This also tests that noise-like pixels are not regularized. 

""" 

clampFrequency = 1.1 

regularizationWidth = 2 

fluxRange = 10. 

dcrModels = DcrModel(modelImages=self.makeTestImages(fluxRange=fluxRange)) 

newModels = [model.clone() for model in dcrModels] 

templateImage = dcrModels.getReferenceImage(self.bbox) 

 

dcrModels.regularizeModelFreq(newModels, self.bbox, clampFrequency, regularizationWidth) 

for model, refModel in zip(newModels, dcrModels): 

# The mask and variance planes should be unchanged 

self.assertFloatsEqual(model.mask.array, refModel.mask.array) 

self.assertFloatsEqual(model.variance.array, refModel.variance.array) 

# Make sure the test parameters do reduce the outliers 

self.assertGreater(np.max(refModel.image.array - templateImage), 

np.max(model.image.array - templateImage)) 

highThreshold = templateImage*clampFrequency 

highPix = model.image.array > highThreshold 

highPix = ndimage.morphology.binary_opening(highPix, iterations=regularizationWidth) 

self.assertFalse(np.all(highPix)) 

lowThreshold = templateImage/clampFrequency 

lowPix = model.image.array < lowThreshold 

lowPix = ndimage.morphology.binary_opening(lowPix, iterations=regularizationWidth) 

self.assertFalse(np.all(lowPix)) 

 

def testRegularizationSidelobes(self): 

"""Test that artificial chromatic sidelobes are suppressed. 

""" 

warpCtrl = afwMath.WarpingControl("lanczos3", "bilinear", 

cacheSize=0, interpLength=max(self.bbox.getDimensions())) 

clampFrequency = 2. 

regularizationWidth = 2 

noiseLevel = 0.1 

sourceAmplitude = 100. 

modelImages = self.makeTestImages(seed=5, nSrc=5, psfSize=3., noiseLevel=noiseLevel, 

detectionSigma=5., sourceSigma=sourceAmplitude, fluxRange=2.) 

templateImage = np.mean([model.image.array for model in modelImages], axis=0) 

sidelobeImages = self.makeTestImages(seed=5, nSrc=5, psfSize=1.5, noiseLevel=noiseLevel/10., 

detectionSigma=5., sourceSigma=sourceAmplitude*5., fluxRange=2.) 

signList = [-1., 0., 1.] 

sidelobeShift = afwGeom.Extent2D(4., 0.) 

for model, sidelobe, sign in zip(modelImages, sidelobeImages, signList): 

sidelobe.image.array *= sign 

model += applyDcr(sidelobe, sidelobeShift, warpCtrl, useInverse=False) 

model += applyDcr(sidelobe, sidelobeShift, warpCtrl, useInverse=True) 

 

dcrModels = DcrModel(modelImages=modelImages) 

refModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)] 

 

dcrModels.regularizeModelFreq(modelImages, self.bbox, clampFrequency, 

regularizationWidth=regularizationWidth) 

for model, refModel, sign in zip(modelImages, refModels, signList): 

# The mask and variance planes should be unchanged 

self.assertFloatsEqual(model.mask.array, refModel.mask.array) 

self.assertFloatsEqual(model.variance.array, refModel.variance.array) 

if sign == 0: 

# The center subfilter does not have sidelobes, and should be unaffected. 

self.assertFloatsEqual(model.image.array, refModel.image.array) 

else: 

# Make sure the test parameters do reduce the outliers 

self.assertGreater(np.sum(np.abs(refModel.image.array - templateImage)), 

np.sum(np.abs(model.image.array - templateImage))) 

highThreshold = templateImage*clampFrequency 

highPix = model.image.array > highThreshold 

highPix = ndimage.morphology.binary_opening(highPix, iterations=regularizationWidth) 

self.assertFalse(np.all(highPix)) 

lowThreshold = templateImage/clampFrequency 

lowPix = model.image.array < lowThreshold 

lowPix = ndimage.morphology.binary_opening(lowPix, iterations=regularizationWidth) 

self.assertFalse(np.all(lowPix)) 

 

def testRegularizeModelIter(self): 

"""Test that large amplitude changes between iterations are restricted. 

 

This also tests that noise-like pixels are not regularized. 

""" 

modelClampFactor = 2. 

regularizationWidth = 2 

subfilter = 0 

dcrModels = DcrModel(modelImages=self.makeTestImages()) 

seed = 5 

rng = np.random.RandomState(seed) 

oldModel = dcrModels[0] 

xSize, ySize = self.bbox.getDimensions() 

newModel = oldModel.clone() 

newModel.image.array[:] += rng.rand(ySize, xSize)*np.max(oldModel.image.array) 

newModelRef = newModel.clone() 

 

dcrModels.regularizeModelIter(subfilter, newModel, self.bbox, modelClampFactor, regularizationWidth) 

 

# The mask and variance planes should be unchanged 

self.assertFloatsEqual(newModel.mask.array, oldModel.mask.array) 

self.assertFloatsEqual(newModel.variance.array, oldModel.variance.array) 

# Make sure the test parameters do reduce the outliers 

self.assertGreater(np.max(newModelRef.image.array), 

np.max(newModel.image.array - oldModel.image.array)) 

# Check that all of the outliers are clipped 

highThreshold = oldModel.image.array*modelClampFactor 

highPix = newModel.image.array > highThreshold 

highPix = ndimage.morphology.binary_opening(highPix, iterations=regularizationWidth) 

self.assertFalse(np.all(highPix)) 

lowThreshold = oldModel.image.array/modelClampFactor 

lowPix = newModel.image.array < lowThreshold 

lowPix = ndimage.morphology.binary_opening(lowPix, iterations=regularizationWidth) 

self.assertFalse(np.all(lowPix)) 

 

def testIterateModel(self): 

"""Test that the DcrModel is iterable, and has the right values. 

""" 

testModels = self.makeTestImages() 

refVals = [np.sum(model.image.array) for model in testModels] 

dcrModels = DcrModel(modelImages=testModels) 

for refVal, model in zip(refVals, dcrModels): 

self.assertFloatsEqual(refVal, np.sum(model.image.array)) 

# Negative indices are allowed, so check that those return models from the end. 

self.assertFloatsEqual(refVals[-1], np.sum(dcrModels[-1].image.array)) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

404 ↛ 405line 404 didn't jump to line 405, because the condition on line 404 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()