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Linux64
meas_algorithms
11.0-2-gb8b8ce7
python
lsst
meas
algorithms
testUtils.py
Go to the documentation of this file.
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#!/usr/bin/env python
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#
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# LSST Data Management System
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# Copyright 2008-2013 LSST Corporation.
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#
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# This product includes software developed by the
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# LSST Project (http://www.lsst.org/).
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the LSST License Statement and
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# the GNU General Public License along with this program. If not,
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# see <http://www.lsstcorp.org/LegalNotices/>.
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#
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import
numpy
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import
lsst.afw.image
as
afwImage
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import
lsst.afw.geom
as
afwGeom
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from
.algorithmsLib
import
SingleGaussianPsf
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def
plantSources
(bbox, kwid, sky, coordList, addPoissonNoise=True):
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"""Make an exposure with stars (modelled as Gaussians)
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@param bbox: parent bbox of exposure
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@param kwid: kernel width (and height; kernel is square)
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@param sky: amount of sky background (counts)
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@param coordList: a list of [x, y, counts, sigma], where:
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* x,y are relative to exposure origin
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* counts is the integrated counts for the star
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* sigma is the Gaussian sigma in pixels
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@param addPoissonNoise: add Poisson noise to the exposure?
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"""
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# make an image with sources
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img = afwImage.ImageD(bbox)
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meanSigma = 0.0
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for
coord
in
coordList:
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x, y, counts, sigma = coord
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meanSigma += sigma
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# make a single gaussian psf
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psf = SingleGaussianPsf(kwid, kwid, sigma)
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# make an image of it and scale to the desired number of counts
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thisPsfImg = psf.computeImage(
afwGeom.PointD
(int(x), int(y)))
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thisPsfImg *= counts
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# bbox a window in our image and add the fake star image
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imgSeg = img.Factory(img, thisPsfImg.getBBox())
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imgSeg += thisPsfImg
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meanSigma /= len(coordList)
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img += sky
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# add Poisson noise
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if
(addPoissonNoise):
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numpy.random.seed(seed=1)
# make results reproducible
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imgArr = img.getArray()
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imgArr[:] = numpy.random.poisson(imgArr)
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# bundle into a maskedimage and an exposure
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mask = afwImage.MaskU(bbox)
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var = img.convertFloat()
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img -= sky
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mimg = afwImage.MaskedImageF(img.convertFloat(), mask, var)
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exposure =
afwImage.makeExposure
(mimg)
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# insert an approximate psf
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psf = SingleGaussianPsf(kwid, kwid, meanSigma)
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exposure.setPsf(psf)
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return
exposure
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lsst::afw::geom::Point< double, 2 >
lsst::meas::algorithms.testUtils.plantSources
def plantSources
Definition:
testUtils.py:30
lsst::afw::image::makeExposure
Exposure< ImagePixelT, MaskPixelT, VariancePixelT >::Ptr makeExposure(MaskedImage< ImagePixelT, MaskPixelT, VariancePixelT > &mimage, boost::shared_ptr< Wcs const > wcs=boost::shared_ptr< Wcs const >())
Definition:
Exposure.h:308
lsst::afw::geom
Definition:
AffineTransform.h:38
lsst::afw::image
Definition:
TanWcsFormatter.h:52
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