def _runWep(self, imgIntraName, imgExtraName, offset, model):
# Cut the donut image from input files
centroidFindType = CentroidFindType.Otsu
imgIntra = Image(centroidFindType=centroidFindType)
imgExtra = Image(centroidFindType=centroidFindType)
imgIntraPath = os.path.join(self.testImgDir, imgIntraName)
imgExtraPath = os.path.join(self.testImgDir, imgExtraName)
imgIntra.setImg(imageFile=imgIntraPath)
imgExtra.setImg(imageFile=imgExtraPath)
xIntra, yIntra, _ = imgIntra.getCenterAndR()
imgIntraArray = imgIntra.getImg()[int(yIntra) - offset:int(yIntra) +
offset,
int(xIntra) - offset:int(xIntra) +
offset, ]
xExtra, yExtra, _ = imgExtra.getCenterAndR()
imgExtraArray = imgExtra.getImg()[int(yExtra) - offset:int(yExtra) +
offset,
int(xExtra) - offset:int(xExtra) +
offset, ]
# Set the images
fieldXY = (0, 0)
imgCompIntra = CompensableImage(centroidFindType=centroidFindType)
imgCompIntra.setImg(fieldXY, DefocalType.Intra, image=imgIntraArray)
imgCompExtra = CompensableImage(centroidFindType=centroidFindType)
imgCompExtra.setImg(fieldXY, DefocalType.Extra, image=imgExtraArray)
# Calculate the wavefront error
# Set the instrument
instDir = os.path.join(getConfigDir(), "cwfs", "instData")
instAuxTel = Instrument(instDir)
instAuxTel.config(CamType.AuxTel,
imgCompIntra.getImgSizeInPix(),
announcedDefocalDisInMm=0.8)
# Set the algorithm
algoFolderPath = os.path.join(getConfigDir(), "cwfs", "algo")
algoAuxTel = Algorithm(algoFolderPath)
algoAuxTel.config("exp", instAuxTel)
algoAuxTel.runIt(imgCompIntra, imgCompExtra, model)
return algoAuxTel.getZer4UpInNm()
def testCompensate(self):
# Generate a fake algorithm class
algo = TempAlgo()
# Test the function of image compensation
boundaryT = 8
offAxisCorrOrder = 10
zcCol = np.zeros(22)
zcCol[3:] = self.zcCol * 1e-9
wfsImgIntra = CompensableImage()
wfsImgExtra = CompensableImage()
wfsImgIntra.setImg(
self.fieldXY,
DefocalType.Intra,
imageFile=self.imgFilePathIntra,
)
wfsImgExtra.setImg(self.fieldXY,
DefocalType.Extra,
imageFile=self.imgFilePathExtra)
for wfsImg in [wfsImgIntra, wfsImgExtra]:
wfsImg.makeMask(self.inst, self.opticalModel, boundaryT, 1)
wfsImg.setOffAxisCorr(self.inst, offAxisCorrOrder)
wfsImg.imageCoCenter(self.inst)
wfsImg.compensate(self.inst, algo, zcCol, self.opticalModel)
# Get the common region
intraImg = wfsImgIntra.getImg()
extraImg = wfsImgExtra.getImg()
centroid = CentroidRandomWalk()
binaryImgIntra = centroid.getImgBinary(intraImg)
binaryImgExtra = centroid.getImgBinary(extraImg)
binaryImg = binaryImgIntra + binaryImgExtra
binaryImg[binaryImg < 2] = 0
binaryImg = binaryImg / 2
# Calculate the difference
res = np.sum(np.abs(intraImg - extraImg) * binaryImg)
self.assertLess(res, 500)
class TestCompensableImage(unittest.TestCase):
"""Test the CompensableImage class."""
def setUp(self):
# Get the path of module
modulePath = getModulePath()
# Define the instrument folder
instDir = os.path.join(getConfigDir(), "cwfs", "instData")
# Define the instrument name
dimOfDonutOnSensor = 120
self.inst = Instrument(instDir)
self.inst.config(CamType.LsstCam,
dimOfDonutOnSensor,
announcedDefocalDisInMm=1.0)
# Define the image folder and image names
# Image data -- Don't know the final image format.
# It is noted that image.readFile inuts is based on the txt file
imageFolderPath = os.path.join(modulePath, "tests", "testData",
"testImages", "LSST_NE_SN25")
intra_image_name = "z11_0.25_intra.txt"
extra_image_name = "z11_0.25_extra.txt"
self.imgFilePathIntra = os.path.join(imageFolderPath, intra_image_name)
self.imgFilePathExtra = os.path.join(imageFolderPath, extra_image_name)
# This is the position of donut on the focal plane in degree
self.fieldXY = (1.185, 1.185)
# Define the optical model: "paraxial", "onAxis", "offAxis"
self.opticalModel = "offAxis"
# Get the true Zk
zcAnsFilePath = os.path.join(
modulePath,
"tests",
"testData",
"testImages",
"validation",
"simulation",
"LSST_NE_SN25_z11_0.25_exp.txt",
)
self.zcCol = np.loadtxt(zcAnsFilePath)
self.wfsImg = CompensableImage()
def testGetDefocalType(self):
defocalType = self.wfsImg.getDefocalType()
self.assertEqual(defocalType, DefocalType.Intra)
def testGetImgObj(self):
imgObj = self.wfsImg.getImgObj()
self.assertTrue(isinstance(imgObj, Image))
def testGetImg(self):
img = self.wfsImg.getImg()
self.assertTrue(isinstance(img, np.ndarray))
self.assertEqual(len(img), 0)
def testGetImgSizeInPix(self):
imgSizeInPix = self.wfsImg.getImgSizeInPix()
self.assertEqual(imgSizeInPix, 0)
def testGetOffAxisCoeff(self):
offAxisCoeff, offAxisOffset = self.wfsImg.getOffAxisCoeff()
self.assertTrue(isinstance(offAxisCoeff, np.ndarray))
self.assertEqual(len(offAxisCoeff), 0)
self.assertEqual(offAxisOffset, 0.0)
def testGetImgInit(self):
imgInit = self.wfsImg.getImgInit()
self.assertEqual(imgInit, None)
def testIsCaustic(self):
self.assertFalse(self.wfsImg.isCaustic())
def testGetPaddedMask(self):
pMask = self.wfsImg.getPaddedMask()
self.assertEqual(len(pMask), 0)
self.assertEqual(pMask.dtype, int)
def testGetNonPaddedMask(self):
cMask = self.wfsImg.getNonPaddedMask()
self.assertEqual(len(cMask), 0)
self.assertEqual(cMask.dtype, int)
def testGetFieldXY(self):
fieldX, fieldY = self.wfsImg.getFieldXY()
self.assertEqual(fieldX, 0)
self.assertEqual(fieldY, 0)
def testSetImg(self):
self._setIntraImg()
self.assertEqual(self.wfsImg.getImg().shape, (120, 120))
def _setIntraImg(self):
self.wfsImg.setImg(self.fieldXY,
DefocalType.Intra,
imageFile=self.imgFilePathIntra)
def testUpdateImage(self):
self._setIntraImg()
newImg = np.random.rand(5, 5)
self.wfsImg.updateImage(newImg)
self.assertTrue(np.all(self.wfsImg.getImg() == newImg))
def testUpdateImgInit(self):
self._setIntraImg()
self.wfsImg.updateImgInit()
delta = np.sum(np.abs(self.wfsImg.getImgInit() - self.wfsImg.getImg()))
self.assertEqual(delta, 0)
def testImageCoCenter(self):
self._setIntraImg()
self.wfsImg.imageCoCenter(self.inst)
xc, yc = self.wfsImg.getImgObj().getCenterAndR()[0:2]
self.assertEqual(int(xc), 63)
self.assertEqual(int(yc), 63)
def testCompensate(self):
# Generate a fake algorithm class
algo = TempAlgo()
# Test the function of image compensation
boundaryT = 8
offAxisCorrOrder = 10
zcCol = np.zeros(22)
zcCol[3:] = self.zcCol * 1e-9
wfsImgIntra = CompensableImage()
wfsImgExtra = CompensableImage()
wfsImgIntra.setImg(
self.fieldXY,
DefocalType.Intra,
imageFile=self.imgFilePathIntra,
)
wfsImgExtra.setImg(self.fieldXY,
DefocalType.Extra,
imageFile=self.imgFilePathExtra)
for wfsImg in [wfsImgIntra, wfsImgExtra]:
wfsImg.makeMask(self.inst, self.opticalModel, boundaryT, 1)
wfsImg.setOffAxisCorr(self.inst, offAxisCorrOrder)
wfsImg.imageCoCenter(self.inst)
wfsImg.compensate(self.inst, algo, zcCol, self.opticalModel)
# Get the common region
intraImg = wfsImgIntra.getImg()
extraImg = wfsImgExtra.getImg()
centroid = CentroidRandomWalk()
binaryImgIntra = centroid.getImgBinary(intraImg)
binaryImgExtra = centroid.getImgBinary(extraImg)
binaryImg = binaryImgIntra + binaryImgExtra
binaryImg[binaryImg < 2] = 0
binaryImg = binaryImg / 2
# Calculate the difference
res = np.sum(np.abs(intraImg - extraImg) * binaryImg)
self.assertLess(res, 500)
def testCenterOnProjection(self):
template = self._prepareGaussian2D(100, 1)
dx = 2
dy = 8
img = np.roll(np.roll(template, dx, axis=1), dy, axis=0)
np.roll(np.roll(img, dx, axis=1), dy, axis=0)
self.assertGreater(np.sum(np.abs(img - template)), 29)
imgRecenter = self.wfsImg.centerOnProjection(img, template, window=20)
self.assertLess(np.sum(np.abs(imgRecenter - template)), 1e-7)
def _prepareGaussian2D(self, imgSize, sigma):
x = np.linspace(-10, 10, imgSize)
y = np.linspace(-10, 10, imgSize)
xx, yy = np.meshgrid(x, y)
return (1 / (2 * np.pi * sigma**2) *
np.exp(-(xx**2 / (2 * sigma**2) + yy**2 / (2 * sigma**2))))
def testSetOffAxisCorr(self):
self._setIntraImg()
offAxisCorrOrder = 10
self.wfsImg.setOffAxisCorr(self.inst, offAxisCorrOrder)
offAxisCoeff, offAxisOffset = self.wfsImg.getOffAxisCoeff()
self.assertEqual(offAxisCoeff.shape, (4, 66))
self.assertAlmostEqual(offAxisCoeff[0, 0], -2.6362089 * 1e-3)
self.assertEqual(offAxisOffset, 0.001)
def testMakeMaskListOfParaxial(self):
self._setIntraImg()
model = "paraxial"
masklist = self.wfsImg.makeMaskList(self.inst, model)
masklistAns = np.array([[0, 0, 1, 1], [0, 0, 0.61, 0]])
self.assertEqual(np.sum(np.abs(masklist - masklistAns)), 0)
def testMakeMaskListOfOffAxis(self):
self._setIntraImg()
model = "offAxis"
masklist = self.wfsImg.makeMaskList(self.inst, model)
masklistAns = np.array([
[0, 0, 1, 1],
[0, 0, 0.61, 0],
[-0.21240585, -0.21240585, 1.2300922, 1],
[-0.08784336, -0.08784336, 0.55802573, 0],
])
self.assertAlmostEqual(np.sum(np.abs(masklist - masklistAns)), 0)
def testMakeMask(self):
self._setIntraImg()
boundaryT = 8
maskScalingFactorLocal = 1
model = "offAxis"
self.wfsImg.makeMask(self.inst, model, boundaryT,
maskScalingFactorLocal)
image = self.wfsImg.getImg()
pMask = self.wfsImg.getPaddedMask()
cMask = self.wfsImg.getNonPaddedMask()
self.assertEqual(pMask.shape, image.shape)
self.assertEqual(cMask.shape, image.shape)
self.assertEqual(np.sum(np.abs(cMask - pMask)), 3001)
def runWEP(instDir, algoFolderPath, useAlgorithm, imageFolderPath,
intra_image_name, extra_image_name, fieldXY, opticalModel,
showFig=False):
"""Calculate the coefficients of normal/ annular Zernike polynomials based
on the provided instrument, algorithm, and optical model.
Parameters
----------
instDir : str
Path to instrument folder.
algoFolderPath : str
Path to algorithm folder.
useAlgorithm : str
Algorithm to solve the Poisson's equation in the transport of intensity
equation (TIE). It can be "fft" or "exp" here.
imageFolderPath : str
Path to image folder.
intra_image_name : str
File name of intra-focal image.
extra_image_name : str
File name of extra-focal image.
fieldXY : tuple
Position of donut on the focal plane in degree for intra- and
extra-focal images.
opticalModel : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
showFig : bool, optional
Show the wavefront image and compenstated image or not. (the default is
False.)
Returns
-------
numpy.ndarray
Coefficients of Zernike polynomials (z4 - z22).
"""
# Image files Path
intra_image_file = os.path.join(imageFolderPath, intra_image_name)
extra_image_file = os.path.join(imageFolderPath, extra_image_name)
# There is the difference between intra and extra images
# I1: intra_focal images, I2: extra_focal Images
I1 = CompensableImage()
I2 = CompensableImage()
I1.setImg(fieldXY, DefocalType.Intra, imageFile=intra_image_file)
I2.setImg(fieldXY, DefocalType.Extra, imageFile=extra_image_file)
# Set the instrument
inst = Instrument(instDir)
inst.config(CamType.LsstCam, I1.getImgSizeInPix(),
announcedDefocalDisInMm=1.0)
# Define the algorithm to be used.
algo = Algorithm(algoFolderPath)
algo.config(useAlgorithm, inst, debugLevel=0)
# Plot the original wavefront images
if (showFig):
plotImage(I1.image, title="intra image")
plotImage(I2.image, title="extra image")
# Run it
algo.runIt(I1, I2, opticalModel, tol=1e-3)
# Show the Zernikes Zn (n>=4)
algo.outZer4Up(showPlot=False)
# Plot the final conservated images and wavefront
if (showFig):
plotImage(I1.image, title="Compensated intra image")
plotImage(I2.image, title="Compensated extra image")
# Plot the Wavefront
plotImage(algo.wcomp, title="Final wavefront")
plotImage(algo.wcomp, title="Final wavefront with pupil mask applied",
mask=algo.pMask)
# Return the Zernikes Zn (n>=4)
return algo.getZer4UpInNm()
def _runWEP(
self,
instDir,
algoFolderPath,
useAlgorithm,
imageFolderPath,
intra_image_name,
extra_image_name,
fieldXY,
opticalModel,
showFig=False,
):
# Image files Path
intra_image_file = os.path.join(imageFolderPath, intra_image_name)
extra_image_file = os.path.join(imageFolderPath, extra_image_name)
# There is the difference between intra and extra images
# I1: intra_focal images, I2: extra_focal Images
I1 = CompensableImage()
I2 = CompensableImage()
I1.setImg(fieldXY, DefocalType.Intra, imageFile=intra_image_file)
I2.setImg(fieldXY, DefocalType.Extra, imageFile=extra_image_file)
# Set the instrument
inst = Instrument(instDir)
inst.config(CamType.LsstCam,
I1.getImgSizeInPix(),
announcedDefocalDisInMm=1.0)
# Define the algorithm to be used.
algo = Algorithm(algoFolderPath)
algo.config(useAlgorithm, inst, debugLevel=0)
# Plot the original wavefront images
if showFig:
plotImage(I1.image, title="intra image")
plotImage(I2.image, title="extra image")
# Run it
algo.runIt(I1, I2, opticalModel, tol=1e-3)
# Show the Zernikes Zn (n>=4)
algo.outZer4Up(showPlot=False)
# Plot the final conservated images and wavefront
if showFig:
plotImage(I1.image, title="Compensated intra image")
plotImage(I2.image, title="Compensated extra image")
# Plot the Wavefront
plotImage(algo.wcomp, title="Final wavefront")
plotImage(
algo.wcomp,
title="Final wavefront with pupil mask applied",
mask=algo.pMask,
)
# Return the Zernikes Zn (n>=4)
return algo.getZer4UpInNm()
class TestAlgorithm(unittest.TestCase):
"""Test the Algorithm class."""
def setUp(self):
# Get the path of module
self.modulePath = getModulePath()
# Define the image folder and image names
# Image data -- Don't know the final image format.
# It is noted that image.readFile inuts is based on the txt file
imageFolderPath = os.path.join(self.modulePath, "tests", "testData",
"testImages", "LSST_NE_SN25")
intra_image_name = "z11_0.25_intra.txt"
extra_image_name = "z11_0.25_extra.txt"
# Define fieldXY: [1.185, 1.185] or [0, 0]
# This is the position of donut on the focal plane in degree
fieldXY = [1.185, 1.185]
# Define the optical model: "paraxial", "onAxis", "offAxis"
self.opticalModel = "offAxis"
# Image files Path
intra_image_file = os.path.join(imageFolderPath, intra_image_name)
extra_image_file = os.path.join(imageFolderPath, extra_image_name)
# Theree is the difference between intra and extra images
# I1: intra_focal images, I2: extra_focal Images
self.I1 = CompensableImage()
self.I2 = CompensableImage()
self.I1.setImg(fieldXY, DefocalType.Intra, imageFile=intra_image_file)
self.I2.setImg(fieldXY, DefocalType.Extra, imageFile=extra_image_file)
# Set up the instrument
cwfsConfigDir = os.path.join(getConfigDir(), "cwfs")
instDir = os.path.join(cwfsConfigDir, "instData")
self.inst = Instrument(instDir)
self.inst.config(CamType.LsstCam,
self.I1.getImgSizeInPix(),
announcedDefocalDisInMm=1.0)
# Set up the algorithm
algoDir = os.path.join(cwfsConfigDir, "algo")
self.algoExp = Algorithm(algoDir)
self.algoExp.config("exp", self.inst)
self.algoFft = Algorithm(algoDir)
self.algoFft.config("fft", self.inst)
def testGetDebugLevel(self):
self.assertEqual(self.algoExp.getDebugLevel(), 0)
def testSetDebugLevel(self):
self.algoExp.config("exp", self.inst, debugLevel=3)
self.assertEqual(self.algoExp.getDebugLevel(), 3)
self.algoExp.setDebugLevel(0)
self.assertEqual(self.algoExp.getDebugLevel(), 0)
def testGetZer4UpInNm(self):
zer4UpNm = self.algoExp.getZer4UpInNm()
self.assertTrue(isinstance(zer4UpNm, np.ndarray))
def testGetPoissonSolverName(self):
self.assertEqual(self.algoExp.getPoissonSolverName(), "exp")
self.assertEqual(self.algoFft.getPoissonSolverName(), "fft")
def testGetNumOfZernikes(self):
self.assertEqual(self.algoExp.getNumOfZernikes(), 22)
self.assertEqual(self.algoFft.getNumOfZernikes(), 22)
def testGetZernikeTerms(self):
zTerms = self.algoExp.getZernikeTerms()
self.assertTrue(type(zTerms[0]), int)
self.assertEqual(len(zTerms), self.algoExp.getNumOfZernikes())
self.assertEqual(zTerms[1], 1)
self.assertEqual(zTerms[-1], self.algoExp.getNumOfZernikes() - 1)
zTerms = self.algoFft.getZernikeTerms()
self.assertTrue(type(zTerms[0]), int)
self.assertEqual(len(zTerms), self.algoExp.getNumOfZernikes())
def testGetObsOfZernikes(self):
self.assertEqual(self.algoExp.getObsOfZernikes(),
self.inst.getObscuration())
self.assertEqual(self.algoFft.getObsOfZernikes(),
self.inst.getObscuration())
def testGetNumOfOuterItr(self):
self.assertEqual(self.algoExp.getNumOfOuterItr(), 14)
self.assertEqual(self.algoFft.getNumOfOuterItr(), 14)
def testGetNumOfInnerItr(self):
self.assertEqual(self.algoFft.getNumOfInnerItr(), 6)
def testGetFeedbackGain(self):
self.assertEqual(self.algoExp.getFeedbackGain(), 0.6)
self.assertEqual(self.algoFft.getFeedbackGain(), 0.6)
def testGetOffAxisPolyOrder(self):
self.assertEqual(self.algoExp.getOffAxisPolyOrder(), 10)
self.assertEqual(self.algoFft.getOffAxisPolyOrder(), 10)
def testGetCompensatorMode(self):
self.assertEqual(self.algoExp.getCompensatorMode(), "zer")
self.assertEqual(self.algoFft.getCompensatorMode(), "zer")
def testGetCompSequence(self):
compSequence = self.algoExp.getCompSequence()
self.assertTrue(isinstance(compSequence, np.ndarray))
self.assertEqual(compSequence.dtype, int)
self.assertEqual(len(compSequence), self.algoExp.getNumOfOuterItr())
self.assertEqual(compSequence[0], 4)
self.assertEqual(compSequence[-1], 22)
compSequence = self.algoFft.getCompSequence()
self.assertEqual(len(compSequence), self.algoFft.getNumOfOuterItr())
def testGetBoundaryThickness(self):
self.assertEqual(self.algoExp.getBoundaryThickness(), 8)
self.assertEqual(self.algoFft.getBoundaryThickness(), 1)
def testGetFftDimension(self):
self.assertEqual(self.algoFft.getFftDimension(), 128)
def testGetSignalClipSequence(self):
sumclipSequence = self.algoFft.getSignalClipSequence()
self.assertTrue(isinstance(sumclipSequence, np.ndarray))
self.assertEqual(len(sumclipSequence),
self.algoExp.getNumOfOuterItr() + 1)
self.assertEqual(sumclipSequence[0], 0.33)
self.assertEqual(sumclipSequence[-1], 0.51)
def testGetMaskScalingFactor(self):
self.assertAlmostEqual(self.algoExp.getMaskScalingFactor(),
1.0939,
places=4)
self.assertAlmostEqual(self.algoFft.getMaskScalingFactor(),
1.0939,
places=4)
def testGetWavefrontMapEstiInIter0(self):
self.assertRaises(RuntimeError, self.algoExp.getWavefrontMapEsti)
def testGetWavefrontMapEstiAndResidual(self):
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
wavefrontMapEsti = self.algoExp.getWavefrontMapEsti()
wavefrontMapEsti[np.isnan(wavefrontMapEsti)] = 0
self.assertGreater(np.sum(np.abs(wavefrontMapEsti)), 4.8e-4)
wavefrontMapResidual = self.algoExp.getWavefrontMapResidual()
wavefrontMapResidual[np.isnan(wavefrontMapResidual)] = 0
self.assertLess(np.sum(np.abs(wavefrontMapResidual)), 2.5e-6)
def testItr0(self):
self.algoExp.itr0(self.I1, self.I2, self.opticalModel)
zer4UpNm = self.algoExp.getZer4UpInNm()
self.assertEqual(
np.sum(np.abs(np.rint(zer4UpNm) - self._getAnsItr0())), 0)
def _getAnsItr0(self):
return [
31,
-69,
-21,
84,
44,
-53,
48,
-146,
6,
10,
13,
-5,
1,
-12,
-8,
7,
0,
-6,
11,
]
def testNextItrWithOneIter(self):
self.algoExp.nextItr(self.I1, self.I2, self.opticalModel, nItr=1)
zer4UpNm = self.algoExp.getZer4UpInNm()
self.assertEqual(
np.sum(np.abs(np.rint(zer4UpNm) - self._getAnsItr0())), 0)
def testNextItrWithTwoIter(self):
self.algoExp.nextItr(self.I1, self.I2, self.opticalModel, nItr=2)
zer4UpNm = self.algoExp.getZer4UpInNm()
ansRint = [
40,
-80,
-18,
92,
44.0,
-52,
54,
-146,
5,
10,
15,
-3,
-0,
-12,
-8,
7,
1,
-3,
12,
]
self.assertEqual(np.sum(np.abs(np.rint(zer4UpNm) - ansRint)), 0)
def testIter0AndNextIterToCheckReset(self):
self.algoExp.itr0(self.I1, self.I2, self.opticalModel)
tmp1 = self.algoExp.getZer4UpInNm()
self.algoExp.nextItr(self.I1, self.I2, self.opticalModel, nItr=2)
# itr0() should reset the images and ignore the effect from nextItr()
self.algoExp.itr0(self.I1, self.I2, self.opticalModel)
tmp2 = self.algoExp.getZer4UpInNm()
difference = np.sum(np.abs(tmp1 - tmp2))
self.assertEqual(difference, 0)
def testRunItOfExp(self):
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
# Check the value
zk = self.algoExp.getZer4UpInNm()
self.assertEqual(int(zk[7]), -192)
def testResetAfterFullCalc(self):
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
# Reset and check the calculation again
fieldXY = [self.I1.fieldX, self.I1.fieldY]
self.I1.setImg(fieldXY,
self.I1.getDefocalType(),
image=self.I1.getImgInit())
self.I2.setImg(fieldXY,
self.I2.getDefocalType(),
image=self.I2.getImgInit())
self.algoExp.reset()
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
zk = self.algoExp.getZer4UpInNm()
self.assertEqual(int(zk[7]), -192)
def testRunItOfFft(self):
self.algoFft.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
zk = self.algoFft.getZer4UpInNm()
self.assertEqual(int(zk[7]), -192)
def makeTemplate(
self,
sensorName,
defocalType,
imageSize,
camType=CamType.LsstCam,
opticalModel="offAxis",
pixelScale=0.2,
):
"""Make the donut template image.
Parameters
----------
sensorName : str
The camera detector for which we want to make a template. Should
be in "Rxx_Sxx" format.
defocalType : enum 'DefocalType'
The defocal state of the sensor.
imageSize : int
Size of template in pixels. The template will be a square.
camType : enum 'CamType', optional
Camera type. (The default is CamType.LsstCam)
opticalModel : str, optional
Optical model. It can be "paraxial", "onAxis", or "offAxis".
(The default is "offAxis")
pixelScale : float, optional
The pixels to arcseconds conversion factor. (The default is 0.2)
Returns
-------
numpy.ndarray [int]
The donut template as a binary image.
Raises
------
ValueError
Camera type is not supported.
"""
configDir = getConfigDir()
# Load Instrument parameters
instDir = os.path.join(configDir, "cwfs", "instData")
inst = Instrument(instDir)
if camType in (CamType.LsstCam, CamType.LsstFamCam, CamType.ComCam):
inst.config(camType, imageSize)
focalPlaneLayout = readPhoSimSettingData(configDir,
"focalplanelayout.txt",
"fieldCenter")
pixelSizeInUm = float(focalPlaneLayout[sensorName][2])
sensorXMicron, sensorYMicron = np.array(
focalPlaneLayout[sensorName][:2], dtype=float)
elif camType == CamType.AuxTel:
# AuxTel only works with onAxis sources
if opticalModel != "onAxis":
raise ValueError(
str(f"Optical Model {opticalModel} not supported with AuxTel. "
+ "Must use 'onAxis'."))
# Defocal distance for Latiss in mm
# for LsstCam can use the default
# hence only need to set here
announcedDefocalDisInMm = getDefocalDisInMm("auxTel")
inst.config(camType, imageSize, announcedDefocalDisInMm)
# load the info for auxTel
pixelSizeInMeters = inst.getCamPixelSize() # pixel size in meters.
pixelSizeInUm = pixelSizeInMeters * 1e6
camera = obs_lsst.Latiss.getCamera()
sensorName = list(
camera.getNameIter())[0] # only one detector in latiss
detector = camera.get(sensorName)
xp, yp = detector.getCenter(
cameraGeom.FOCAL_PLANE) # center of CCD in mm
# multiply by 1000 to for mm --> microns conversion
sensorXMicron = yp * 1000
sensorYMicron = xp * 1000
else:
raise ValueError("Camera type (%s) is not supported." % camType)
# Create image for mask
img = CompensableImage()
# Convert pixel locations to degrees
sensorXPixel = float(sensorXMicron) / pixelSizeInUm
sensorYPixel = float(sensorYMicron) / pixelSizeInUm
# Multiply by pixelScale then divide by 3600 for arcsec->deg conversion
sensorXDeg = sensorXPixel * pixelScale / 3600
sensorYDeg = sensorYPixel * pixelScale / 3600
fieldXY = [sensorXDeg, sensorYDeg]
# Define position of donut at center of current sensor in degrees
boundaryT = 0
maskScalingFactorLocal = 1
img.setImg(fieldXY,
defocalType,
image=np.zeros((imageSize, imageSize)))
img.makeMask(inst, opticalModel, boundaryT, maskScalingFactorLocal)
return img.getNonPaddedMask()
def calcWfErr(
self,
centroidFindType,
fieldXY,
camType,
algoName,
announcedDefocalDisInMm,
opticalModel,
imageIntra=None,
imageExtra=None,
imageFileIntra=None,
imageFileExtra=None,
):
"""Calculate the wavefront error.
Parameters
----------
centroidFindType : enum 'CentroidFindType'
Algorithm to find the centroid of donut.
fieldXY : tuple or list
Position of donut on the focal plane in degree (field x, field y).
camType : enum 'CamType'
Camera type.
algoName : str
Algorithm configuration file to solve the Poisson's equation in the
transport of intensity equation (TIE). It can be "fft" or "exp"
here.
announcedDefocalDisInMm : float
Announced defocal distance in mm. It is noted that the defocal
distance offset used in calculation might be different from this
value.
opticalModel : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
imageIntra : numpy.ndarray, optional
Array of intra-focal image. (the default is None.)
imageExtra : numpy.ndarray, optional
Array of extra-focal image. (the default is None.)
imageFileIntra : str, optional
Path of intra-focal image file. (the default is None.)
imageFileExtra : str, optional
Path of extra-focal image file. (the default is None.)
Returns
-------
numpy.ndarray
Zernike polynomials of z4-zn in nm.
"""
# Set the defocal images
imgIntra = CompensableImage(centroidFindType=centroidFindType)
imgExtra = CompensableImage(centroidFindType=centroidFindType)
imgIntra.setImg(
fieldXY, DefocalType.Intra, image=imageIntra, imageFile=imageFileIntra
)
imgExtra.setImg(
fieldXY, DefocalType.Extra, image=imageExtra, imageFile=imageFileExtra
)
# Set the instrument
instDir = os.path.join(getConfigDir(), "cwfs", "instData")
inst = Instrument(instDir)
inst.config(
camType,
imgIntra.getImgSizeInPix(),
announcedDefocalDisInMm=announcedDefocalDisInMm,
)
# Define the algorithm to be used.
algoFolderPath = os.path.join(getConfigDir(), "cwfs", "algo")
algo = Algorithm(algoFolderPath)
algo.config(algoName, inst, debugLevel=0)
# Run it
algo.runIt(imgIntra, imgExtra, opticalModel, tol=1e-3)
# Return the Zernikes Zn (n>=4)
return algo.getZer4UpInNm()
class TestAlgorithm(unittest.TestCase):
"""Test the Algorithm class."""
def setUp(self):
# Get the path of module
self.modulePath = getModulePath()
# Define the image folder and image names
# Image data -- Don't know the final image format.
# It is noted that image.readFile inuts is based on the txt file
imageFolderPath = os.path.join(self.modulePath, "tests", "testData",
"testImages", "LSST_NE_SN25")
intra_image_name = "z11_0.25_intra.txt"
extra_image_name = "z11_0.25_extra.txt"
# Define fieldXY: [1.185, 1.185] or [0, 0]
# This is the position of donut on the focal plane in degree
fieldXY = [1.185, 1.185]
# Define the optical model: "paraxial", "onAxis", "offAxis"
self.opticalModel = "offAxis"
# Image files Path
intra_image_file = os.path.join(imageFolderPath, intra_image_name)
extra_image_file = os.path.join(imageFolderPath, extra_image_name)
# Theree is the difference between intra and extra images
# I1: intra_focal images, I2: extra_focal Images
self.I1 = CompensableImage()
self.I2 = CompensableImage()
self.I1.setImg(fieldXY, DefocalType.Intra, imageFile=intra_image_file)
self.I2.setImg(fieldXY, DefocalType.Extra, imageFile=extra_image_file)
# Set up the instrument
cwfsConfigDir = os.path.join(getConfigDir(), "cwfs")
instDir = os.path.join(cwfsConfigDir, "instData")
self.inst = Instrument(instDir)
self.inst.config(CamType.LsstCam, self.I1.getImgSizeInPix(),
announcedDefocalDisInMm=1.0)
# Set up the algorithm
algoDir = os.path.join(cwfsConfigDir, "algo")
self.algoExp = Algorithm(algoDir)
self.algoExp.config("exp", self.inst)
self.algoFft = Algorithm(algoDir)
self.algoFft.config("fft", self.inst)
def testGetDebugLevel(self):
self.assertEqual(self.algoExp.getDebugLevel(), 0)
def testSetDebugLevel(self):
self.algoExp.config("exp", self.inst, debugLevel=3)
self.assertEqual(self.algoExp.getDebugLevel(), 3)
self.algoExp.setDebugLevel(0)
self.assertEqual(self.algoExp.getDebugLevel(), 0)
def testGetZer4UpInNm(self):
zer4UpNm = self.algoExp.getZer4UpInNm()
self.assertTrue(isinstance(zer4UpNm, np.ndarray))
def testGetPoissonSolverName(self):
self.assertEqual(self.algoExp.getPoissonSolverName(), "exp")
self.assertEqual(self.algoFft.getPoissonSolverName(), "fft")
def testGetNumOfZernikes(self):
self.assertEqual(self.algoExp.getNumOfZernikes(), 22)
self.assertEqual(self.algoFft.getNumOfZernikes(), 22)
def testGetZernikeTerms(self):
zTerms = self.algoExp.getZernikeTerms()
self.assertTrue(zTerms.dtype, int)
self.assertEqual(len(zTerms), self.algoExp.getNumOfZernikes())
self.assertEqual(zTerms[0], 1)
self.assertEqual(zTerms[-1], self.algoExp.getNumOfZernikes())
zTerms = self.algoFft.getZernikeTerms()
self.assertTrue(zTerms.dtype, int)
self.assertEqual(len(zTerms), self.algoExp.getNumOfZernikes())
def testGetObsOfZernikes(self):
self.assertEqual(self.algoExp.getObsOfZernikes(),
self.inst.getObscuration())
self.assertEqual(self.algoFft.getObsOfZernikes(),
self.inst.getObscuration())
def testGetNumOfOuterItr(self):
self.assertEqual(self.algoExp.getNumOfOuterItr(), 14)
self.assertEqual(self.algoFft.getNumOfOuterItr(), 14)
def testGetNumOfInnerItr(self):
self.assertEqual(self.algoFft.getNumOfInnerItr(), 6)
def testGetFeedbackGain(self):
self.assertEqual(self.algoExp.getFeedbackGain(), 0.6)
self.assertEqual(self.algoFft.getFeedbackGain(), 0.6)
def testGetOffAxisPolyOrder(self):
self.assertEqual(self.algoExp.getOffAxisPolyOrder(), 10)
self.assertEqual(self.algoFft.getOffAxisPolyOrder(), 10)
def testGetCompensatorMode(self):
self.assertEqual(self.algoExp.getCompensatorMode(), "zer")
self.assertEqual(self.algoFft.getCompensatorMode(), "zer")
def testGetCompSequence(self):
compSequence = self.algoExp.getCompSequence()
self.assertTrue(isinstance(compSequence, np.ndarray))
self.assertEqual(compSequence.dtype, int)
self.assertEqual(len(compSequence), self.algoExp.getNumOfOuterItr())
self.assertEqual(compSequence[0], 4)
self.assertEqual(compSequence[-1], 22)
compSequence = self.algoFft.getCompSequence()
self.assertEqual(len(compSequence), self.algoFft.getNumOfOuterItr())
def testGetBoundaryThickness(self):
self.assertEqual(self.algoExp.getBoundaryThickness(), 8)
self.assertEqual(self.algoFft.getBoundaryThickness(), 1)
def testGetFftDimension(self):
self.assertEqual(self.algoFft.getFftDimension(), 128)
def testGetSignalClipSequence(self):
sumclipSequence = self.algoFft.getSignalClipSequence()
self.assertTrue(isinstance(sumclipSequence, np.ndarray))
self.assertEqual(len(sumclipSequence), self.algoExp.getNumOfOuterItr()+1)
self.assertEqual(sumclipSequence[0], 0.33)
self.assertEqual(sumclipSequence[-1], 0.51)
def testGetMaskScalingFactor(self):
self.assertAlmostEqual(self.algoExp.getMaskScalingFactor(), 1.0939,
places=4)
self.assertAlmostEqual(self.algoFft.getMaskScalingFactor(), 1.0939,
places=4)
def testRunItOfExp(self):
# Test functions: itr0() and nextItr()
self.algoExp.itr0(self.I1, self.I2, self.opticalModel)
tmp1 = self.algoExp.getZer4UpInNm()
self.algoExp.nextItr(self.I1, self.I2, self.opticalModel, nItr=2)
self.algoExp.itr0(self.I1, self.I2, self.opticalModel)
tmp2 = self.algoExp.getZer4UpInNm()
difference = np.sum(np.abs(tmp1-tmp2))
self.assertEqual(difference, 0)
# Run it
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
# Check the value
Zk = self.algoExp.getZer4UpInNm()
self.assertEqual(int(Zk[7]), -192)
# Reset and check the calculation again
fieldXY = [self.I1.fieldX, self.I1.fieldY]
self.I1.setImg(fieldXY, self.I1.getDefocalType(), image=self.I1.getImgInit())
self.I2.setImg(fieldXY, self.I2.getDefocalType(), image=self.I2.getImgInit())
self.algoExp.reset()
self.algoExp.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
Zk = self.algoExp.getZer4UpInNm()
self.assertEqual(int(Zk[7]), -192)
def testRunItOfFft(self):
self.algoFft.runIt(self.I1, self.I2, self.opticalModel, tol=1e-3)
zk = self.algoFft.getZer4UpInNm()
self.assertEqual(int(zk[7]), -192)
def makeTemplate(
self,
sensorName,
defocalType,
imageSize,
camType=CamType.LsstCam,
opticalModel="offAxis",
pixelScale=0.2,
):
"""Make the donut template image.
Parameters
----------
sensorName : str
The camera detector for which we want to make a template. Should
be in "Rxx_Sxx" format.
defocalType : enum 'DefocalType'
The defocal state of the sensor.
imageSize : int
Size of template in pixels. The template will be a square.
camType : enum 'CamType', optional
Camera type. (Default is CamType.LsstCam)
model : str, optional
Optical model. It can be "paraxial", "onAxis", or "offAxis".
(The default is "offAxis")
pixelScale : float, optional
The pixels to arcseconds conversion factor. (The default is 0.2)
Returns
-------
numpy.ndarray [int]
The donut template as a binary image.
"""
configDir = getConfigDir()
focalPlaneLayout = readPhoSimSettingData(configDir,
"focalplanelayout.txt",
"fieldCenter")
pixelSizeInUm = float(focalPlaneLayout[sensorName][2])
sizeXinPixel = int(focalPlaneLayout[sensorName][3])
sensorXMicron, sensorYMicron = np.array(
focalPlaneLayout[sensorName][:2], dtype=float)
# Correction for wavefront sensors
if sensorName in ("R44_S00_C0", "R00_S22_C1"):
# Shift center to +x direction
sensorXMicron = sensorXMicron + sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R44_S00_C1", "R00_S22_C0"):
# Shift center to -x direction
sensorXMicron = sensorXMicron - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C1", "R40_S02_C0"):
# Shift center to -y direction
sensorYMicron = sensorYMicron - sizeXinPixel / 2 * pixelSizeInUm
elif sensorName in ("R04_S20_C0", "R40_S02_C1"):
# Shift center to +y direction
sensorYMicron = sensorYMicron + sizeXinPixel / 2 * pixelSizeInUm
# Load Instrument parameters
instDir = os.path.join(configDir, "cwfs", "instData")
inst = Instrument(instDir)
inst.config(camType, imageSize)
# Create image for mask
img = CompensableImage()
# Convert pixel locations to degrees
sensorXPixel = float(sensorXMicron) / pixelSizeInUm
sensorYPixel = float(sensorYMicron) / pixelSizeInUm
# Multiply by pixelScale then divide by 3600 for arcsec -> deg conversion
sensorXDeg = sensorXPixel * pixelScale / 3600
sensorYDeg = sensorYPixel * pixelScale / 3600
fieldXY = [sensorXDeg, sensorYDeg]
# Define position of donut at center of current sensor in degrees
boundaryT = 0
maskScalingFactorLocal = 1
img.setImg(fieldXY,
defocalType,
image=np.zeros((imageSize, imageSize)))
img.makeMask(inst, opticalModel, boundaryT, maskScalingFactorLocal)
return img.getNonPaddedMask()