LSST Applications  21.0.0-172-gfb10e10a+18fedfabac,22.0.0+297cba6710,22.0.0+80564b0ff1,22.0.0+8d77f4f51a,22.0.0+a28f4c53b1,22.0.0+dcf3732eb2,22.0.1-1-g7d6de66+2a20fdde0d,22.0.1-1-g8e32f31+297cba6710,22.0.1-1-geca5380+7fa3b7d9b6,22.0.1-12-g44dc1dc+2a20fdde0d,22.0.1-15-g6a90155+515f58c32b,22.0.1-16-g9282f48+790f5f2caa,22.0.1-2-g92698f7+dcf3732eb2,22.0.1-2-ga9b0f51+7fa3b7d9b6,22.0.1-2-gd1925c9+bf4f0e694f,22.0.1-24-g1ad7a390+a9625a72a8,22.0.1-25-g5bf6245+3ad8ecd50b,22.0.1-25-gb120d7b+8b5510f75f,22.0.1-27-g97737f7+2a20fdde0d,22.0.1-32-gf62ce7b1+aa4237961e,22.0.1-4-g0b3f228+2a20fdde0d,22.0.1-4-g243d05b+871c1b8305,22.0.1-4-g3a563be+32dcf1063f,22.0.1-4-g44f2e3d+9e4ab0f4fa,22.0.1-42-gca6935d93+ba5e5ca3eb,22.0.1-5-g15c806e+85460ae5f3,22.0.1-5-g58711c4+611d128589,22.0.1-5-g75bb458+99c117b92f,22.0.1-6-g1c63a23+7fa3b7d9b6,22.0.1-6-g50866e6+84ff5a128b,22.0.1-6-g8d3140d+720564cf76,22.0.1-6-gd805d02+cc5644f571,22.0.1-8-ge5750ce+85460ae5f3,master-g6e05de7fdc+babf819c66,master-g99da0e417a+8d77f4f51a,w.2021.48
LSST Data Management Base Package
Related Pages
Here is a list of all related documentation pages:
 How to use algorithms to manipulate ImagesAll of these algorithms require the inclusion of lsst/afw/image/ImageAlgorithm.h, and are in namespace lsst::afw::image
 How to display images
 How to manipulate images from python
 Image Iterators(Return to Images)
 MaskedImage Iterators(Return to Images)
 Image Locators(Return to Images)
 MaskedImage Locators(Return to Images)
 How Mask Planes are handled in @c afwThere is no universally-adopted standard on how the bits in a mask are to be interpreted, and accordingly the LSST code tries to be flexible
 imageStatistics
 splineInterpolate
 imageBackground
 Tables
 Table-Based Persistence
 Channel Attributes
 FitsChan Attributes
 Frame Attributes
 FrameSet Attributes
 LutMap Attributes
 Mapping Attributes
 MathMap Attributes
 Object Attributes
 PolyMap Attributes
 SkyFrame Attributes
 SpecFrame Attributes
 SphMap Attributes
 TimeFrame Attributes
 WcsMap Attributes
 XmlChan Attributes
 README
 README
 Truncated Gaussian MathThe implementation of the TruncatedGaussian class is a bit opaque due to the complex mathematics involved
 CModel MagnitudesThe CModel approach to model-fit galaxy photometry - also known as the "Sloan Swindle" - is an approximation to bulge+disk or Sersic model fitting that follows the following sequence:
 HTM Indexing
 Q3C Indexing
 Using lsstDebug to control debugging outputThe class lsstDebug can be used to turn on debugging output in a non-intrusive way
 Images
 Image/Mask/MaskedImage I/O
 Todo List
 Deprecated List
 Bug List