lsst.meas.algorithms
13.0-23-gb99accf8
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Functions | |
def | splitId |
def | showSourceSet |
def | showPsfSpatialCells |
def | showPsfCandidates |
def | makeSubplots |
def | plotPsfSpatialModel |
def | showPsf |
def | showPsfMosaic |
def | showPsfResiduals |
def | saveSpatialCellSet |
Variables | |
keptPlots = False | |
def lsst.meas.algorithms.utils.makeSubplots | ( | fig, | |
nx = 2 , |
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ny = 2 , |
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Nx = 1 , |
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Ny = 1 , |
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plottingArea = (0.1, 0.1 , |
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pxgutter = 0.05 , |
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pygutter = 0.05 , |
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xgutter = 0.04 , |
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ygutter = 0.04 , |
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headroom = 0.0 , |
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panelBorderWeight = 0 , |
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panelColor = 'black' |
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) |
Return a generator of a set of subplots, a set of Nx*Ny panels of nx*ny plots. Each panel is fully filled by row (starting in the bottom left) before the next panel is started. If panelBorderWidth is greater than zero a border is drawn around each panel, adjusted to enclose the axis labels. E.g. subplots = makeSubplots(fig, 2, 2, Nx=1, Ny=1, panelColor='k') ax = subplots.next(); ax.text(0.3, 0.5, '[0, 0] (0,0)') ax = subplots.next(); ax.text(0.3, 0.5, '[0, 0] (1,0)') ax = subplots.next(); ax.text(0.3, 0.5, '[0, 0] (0,1)') ax = subplots.next(); ax.text(0.3, 0.5, '[0, 0] (1,1)') fig.show() @param fig The matplotlib figure to draw @param nx The number of plots in each row of each panel @param ny The number of plots in each column of each panel @param Nx The number of panels in each row of the figure @param Ny The number of panels in each column of the figure @param plottingArea (x0, y0, x1, y1) for the part of the figure containing all the panels @param pxgutter Spacing between columns of panels in units of (x1 - x0) @param pygutter Spacing between rows of panels in units of (y1 - y0) @param xgutter Spacing between columns of plots within a panel in units of (x1 - x0) @param ygutter Spacing between rows of plots within a panel in units of (y1 - y0) @param headroom Extra spacing above each plot for e.g. a title @param panelBorderWeight Width of border drawn around panels @param panelColor Colour of border around panels
def lsst.meas.algorithms.utils.plotPsfSpatialModel | ( | exposure, | |
psf, | |||
psfCellSet, | |||
showBadCandidates = True , |
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numSample = 128 , |
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matchKernelAmplitudes = False , |
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keepPlots = True |
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) |
def lsst.meas.algorithms.utils.saveSpatialCellSet | ( | psfCellSet, | |
fileName = "foo.fits" , |
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frame = None |
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) |
def lsst.meas.algorithms.utils.showPsf | ( | psf, | |
eigenValues = None , |
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XY = None , |
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normalize = True , |
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frame = None |
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) |
def lsst.meas.algorithms.utils.showPsfCandidates | ( | exposure, | |
psfCellSet, | |||
psf = None , |
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frame = None , |
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normalize = True , |
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showBadCandidates = True , |
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fitBasisComponents = False , |
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variance = None , |
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chi = None |
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) |
Display the PSF candidates. If psf is provided include PSF model and residuals; if normalize is true normalize the PSFs (and residuals) If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi If fitBasisComponents is true, also find the best linear combination of the PSF's components (if they exist)
def lsst.meas.algorithms.utils.showPsfMosaic | ( | exposure, | |
psf = None , |
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nx = 7 , |
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ny = None , |
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showCenter = True , |
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showEllipticity = False , |
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showFwhm = False , |
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stampSize = 0 , |
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frame = None , |
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title = None |
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) |
def lsst.meas.algorithms.utils.showPsfResiduals | ( | exposure, | |
sourceSet, | |||
magType = "psf" , |
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scale = 10 , |
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frame = None |
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) |
def lsst.meas.algorithms.utils.showPsfSpatialCells | ( | exposure, | |
psfCellSet, | |||
nMaxPerCell = -1 , |
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showChi2 = False , |
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showMoments = False , |
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symb = None , |
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ctype = None , |
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ctypeUnused = None , |
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ctypeBad = None , |
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size = 2 , |
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frame = None |
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) |
def lsst.meas.algorithms.utils.showSourceSet | ( | sSet, | |
xy0 = (0, 0 , |
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frame = 0 , |
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ctype = ds9.GREEN , |
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symb = "+" , |
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size = 2 |
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) |
def lsst.meas.algorithms.utils.splitId | ( | oid, | |
asDict = True |
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) |