plot.profound: ProFound Diagnostic Grid

In asgr/ProFound: Photometry Tools

ProFound Diagnostic Grid

Description

A useful visual grid of ProFound diagnostics. This is useful for checking if something very odd has occurred when running the code.

Usage

## S3 method for class 'profound'
plot(x, logR50 = TRUE, dmag=0.5, hist='sky', ...)

Arguments

x	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.
logR50	Logical; specifies whether the bottom-centre panel uses a logarithmic y-axis for R50 (default is TRUE).
dmag	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.
hist	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.

Details

Run for the side effect of generating a grid of useful diagnostic plots.

Value

Run for the side effect of generating a grid of useful diagnostic plots:

Top-left	Sky subracted image x$image-x$sky, where blue is negative, yellow is 0, and red is positive. Black outline shows the extracted objects.
Top-centre	Output segmentation map x$segim using profoundSegimPlot.
Top-right	Sky subracted and normalised image (x$image-x$sky)/x$skyRMS, with segment dilation extent shown in colour.
Middle-left	Magnitude (x$segstats$mag) counts histogram (max in red), scaled to counts per square degree if x$header is present.
Middle-centre	Output x$sky, where blue is negative, yellow is 0, and red is positive. Black outline shows the extracted objects, where we want to be alert to suspicious association between the map and the objects. x$sky stats are in magenta.
Middle-right	Output x$skyRMS, where dark is lower values and white larger values. Orange outline shows the extracted objects, where we want to be alert to suspicious association between the map and the objects. x$skyRMS stats are in magenta.
Bottom-left	For hist = 'sky': black line shows the sky pixels PDF normalised by x$image-x$sky)/x$skyRMS; the green dashed is the idealised Normal distribution, where we might hope the negative wing agrees well with the black line; the red line shows the PDF of the x$sky map for pixels inside the object mask; the blue line shows the PDF of the x$sky map for pixels outside the object mask which should nominally be 'sky' (we might hope that the blue and red distributions look visually similar, else we might be associating object flux with 'sky'). For hist = 'iters': dilation iteration (x$segstats$iter) histogram.
Bottom-centre	Output mag (x$segstats$mag) versus R50 (x$segstats$R50).
Bottom-right	Output mag (x$segstats$mag) versus axrat (x$segstats$axrat).

Author(s)

Aaron Robotham

See Also

profoundProFound, profoundSegimPlot

Examples

## 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)

## End(Not run)

asgr/ProFound documentation built on Feb. 10, 2024, 9:04 p.m.