A useful visual grid of ProFound diagnostics. This is useful for checking if something very odd has occurred when running the code.
# S3 method for profound
plot(x, logR50 = TRUE, dmag=0.5, hist='sky', ...)Argument for the class dependent plot.profound function. An object of class profound as output by the profoundProFound function. This is the only structure that needs to be provided when executing plot(profound) class dependent plotting, which will use the plot.profound function.
Logical; specifies whether the bottom-centre panel uses a logarithmic y-axis for R50 (default is TRUE).
Numeric scaler; the magnitude binning scale to use (default 0.5 to reflect the axis binning). The magnitude histograms always use 0.5 magnitude bins, but this controls the y-axis scaling to give the correct normalisation as if the specified binning was used. I.e. the raw counts are scaled by an additional factor of 2 if dmag=1 is specified.
Character scalar; specifies the plot type for the bottom-left plot. Options are 'sky' (which is a sky pixel (image-sky)/skyRMS PDF using the objects_redo mask) or 'iters' (histogram of required iterations). Old default was 'iters', but now 'sky', since this is more useful in general.
Nothing to see here.
Run for the side effect of generating a grid of useful diagnostic plots:
Sky subracted image x$image-x$sky, where blue is negative, yellow is 0, and red is positive
Output segmentation map x$segim
Sky subracted and normalised image (x$image-x$sky)/x$skyRMS, with segment dilation extent shown in colour
Magnitude (x$segstats$mag) counts histogram (max in red), scaled to counts per square degree if x$header is present
Output x$sky, where blue is negative, yellow is 0, and red is positive
Output x$skyRMS, where dark is lower values and white larger values
Sky pixel (x$image-x$sky)/x$skyRMS PDF, or dilation iteration (x$segstats$iter) histogram (depends on hist)
Output mag (x$segstats$mag) versus R50 (x$segstats$R50)
Output mag (x$segstats$mag) versus axrat (x$segstats$axrat)
Run for the side effect of generating a grid of useful diagnostic plots.
# NOT RUN {
image=readFITS(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits', package="ProFound"))
profound=profoundProFound(image, skycut=1.5, magzero=30, verbose=TRUE, plot=TRUE)
plot(profound)
# }
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