def testPersistence(self):
     im = ExposureF(10, 10)
     im.setPsf(self.psf)
     self.assertEqual(im.getPsf(), self.psf)
     with lsst.utils.tests.getTempFilePath(".fits") as tmpFile:
         im.writeFits(tmpFile)
         newIm = ExposureF(tmpFile)
         self.assertEqual(newIm.getPsf(), im.getPsf())
예제 #2
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 def testPersistence(self):
     for pgp in self.pgps:
         assert cppLib.isPersistable(pgp)
         im = ExposureF(10, 10)
         im.setPsf(pgp)
         self.assertEqual(im.getPsf(), pgp)
         with lsst.utils.tests.getTempFilePath(".fits") as tmpFile:
             im.writeFits(tmpFile)
             newIm = ExposureF(tmpFile)
             self.assertEqual(newIm.getPsf(), im.getPsf())
예제 #3
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    def readFits(cls, path):
        """Read an external detrended image and create an LSST Exposure object
        from it.

        Any significant processing (e.g. pixel interpolation) should probably
        be done in a ExternalIsrTask instead of here so it can be done once and
        saved, instead of being done every time the image is loaded.

        THIS METHOD IS INCOMPLETE; IT MUST BE MODIFIED ACCORDING TO THE
        FORMAT OF THE DATA BEING LOADED.
        """
        directory, filename = os.path.split(path)
        match = cls.EXTERNAL_REGEX.match(filename)
        camera = cls.getCameraFromVisit(match.group("visit"))

        # Customize the code below based on the camera determined above.
        # To support more than one camera it may be useful to delegate
        # to other methods that are specific to certain cameras.

        # Read the actual image in from the given path using e.g. astropy,
        # and use it to fill in various arrays below.

        bbox = Box2I(Point2I(0, 0), Extent2I(..., ...))  # width, height
        result = ExposureF(bbox)
        # main image, as a [y, x] numpy.float32 array
        result.image.array = ...
        # variance image, as a [y, x] numpy.float32 array
        result.variance.array = ...

        # This example includes masking NaN pixels as NO_DATA and pixels above
        # 1E5 counts as SAT.  External information about where bad pixels
        # should be preferred when available, and obviously that saturation
        # threshold is just an example (saturation should actually be
        # determined before flat-fielding, of course).
        # Interpolating these bad pixels is handled by ExternalIsrTask.
        noDataBitMask = result.mask.getPlaneBitMask("NO_DATA")
        satBitMask = result.mask.getPlaneBitMask("SAT")
        result.mask.array |= noDataBitMask * np.isnan(result.image.array)
        result.mask.array |= satBitMask * (result.image.array > 1E5)

        # If you have a better guess at the PSF, we can find a way to use it.
        # But it'd be a good idea to at least put this in with a guess at the
        # seeing (RMS in pixels).
        result.setPsf(SingleGaussianPsf(seeingRMS))

        # Add a guess for the WCS, in this case assuming it's in the FITS
        # header of the first HDU.  Need to have something here, even if it
        # isn't very good (e.g. whatever comes from the telescope).
        metadata = readMetadata(filename)
        wcs = SkyWcs(metadata)
        result.setWcs(wcs)

        return result
    def testMatchSingleGaussianPsf(self):
        """Test InstallGaussianPsfTask when the input exposure has a single Gaussian PSF."""
        config = InstallGaussianPsfTask.ConfigClass()
        task = InstallGaussianPsfTask(config=config)

        for desWidth, desHeight, desSigma in (
            (21, 23, 1.2),
            (23, 25, 3.5),
        ):
            exposure = ExposureF(100, 100)
            inPsf = SingleGaussianPsf(desWidth, desHeight, desSigma)
            exposure.setPsf(inPsf)
            task.run(exposure=exposure)
            self.assertTrue(exposure.hasPsf())
            psf = exposure.getPsf()
            psfIm = psf.computeImage()
            self.assertEqual(psfIm.getWidth(), desWidth)
            self.assertEqual(psfIm.getHeight(), desHeight)
            self.assertAlmostEqual(psf.computeShape().getDeterminantRadius(), desSigma, delta=1e-3)
    def testMatchSingleGaussianPsf(self):
        """Test InstallGaussianPsfTask when the input exposure has a single Gaussian PSF
        """
        config = InstallGaussianPsfTask.ConfigClass()
        task = InstallGaussianPsfTask(config=config)

        for desWidth, desHeight, desSigma in (
            (21, 23, 1.2),
            (23, 25, 3.5),
        ):
            exposure = ExposureF(100, 100)
            inPsf = SingleGaussianPsf(desWidth, desHeight, desSigma)
            exposure.setPsf(inPsf)
            task.run(exposure=exposure)
            self.assertTrue(exposure.hasPsf())
            psf = exposure.getPsf()
            psfIm = psf.computeImage()
            self.assertEqual(psfIm.getWidth(), desWidth)
            self.assertEqual(psfIm.getHeight(), desHeight)
            self.assertAlmostEqual(psf.computeShape().getDeterminantRadius(), desSigma, delta=1e-3)
    def testMatchDoubleGaussianPsf(self):
        """Test InstallGaussianPsfTask when the input exposure has a DoubleGaussian PSF."""
        config = InstallGaussianPsfTask.ConfigClass()
        task = InstallGaussianPsfTask(config=config)

        for doubleGaussParms in (
            # width, height, inner sigma, outer sigma, outer/inner peak amplitude
            (21, 23, 1.2, 3.5, 0.02),
            (23, 25, 3.5, 9.0, 0.02),
        ):
            exposure = ExposureF(100, 100)
            inPsf = DoubleGaussianPsf(*doubleGaussParms)
            exposure.setPsf(inPsf)
            desWidth, desHeight, innerSigma = doubleGaussParms[0:3]
            task.run(exposure=exposure)
            self.assertTrue(exposure.hasPsf())
            psf = exposure.getPsf()
            psfIm = psf.computeImage()
            self.assertEqual(psfIm.getWidth(), desWidth)
            self.assertEqual(psfIm.getHeight(), desHeight)
            self.assertAlmostEqual(psf.computeShape().getDeterminantRadius(), innerSigma, delta=0.1)
    def testMatchDoubleGaussianPsf(self):
        """Test InstallGaussianPsfTask when the input exposure has a DoubleGaussian PSF
        """
        config = InstallGaussianPsfTask.ConfigClass()
        task = InstallGaussianPsfTask(config=config)

        for doubleGaussParms in (
            # width, height, inner sigma, outer sigma, outer/inner peak amplitude
            (21, 23, 1.2, 3.5, 0.02),
            (23, 25, 3.5, 9.0, 0.02),
        ):
            exposure = ExposureF(100, 100)
            inPsf = DoubleGaussianPsf(*doubleGaussParms)
            exposure.setPsf(inPsf)
            desWidth, desHeight, innerSigma = doubleGaussParms[0:3]
            task.run(exposure=exposure)
            self.assertTrue(exposure.hasPsf())
            psf = exposure.getPsf()
            psfIm = psf.computeImage()
            self.assertEqual(psfIm.getWidth(), desWidth)
            self.assertEqual(psfIm.getHeight(), desHeight)
            self.assertAlmostEqual(psf.computeShape().getDeterminantRadius(), innerSigma, delta=0.1)