All the options described until now were from the first class of operations discussed above: those that treat the whole dataset as one. However, it often happens that the relative position of the dataset elements over the dataset is significant. For example, you do not want one median value for the whole input image, you want to know how the median changes over the image. For such operations, the input has to be tessellated (see Tessellation). Thus this class of options cannot currently be called along with the options above in one run of Statistics.
Do the respective single-valued calculation over one tile of the input dataset, not the whole dataset. This option must be called with at least one of the single valued options discussed above (for example, --mean or --quantile). The output will be a file in the same format as the input. If the --oneelempertile option is called, then one element/pixel will be used for each tile (see Processing options). Otherwise, the output will have the same size as the input, but each element will have the value corresponding to that tile’s value. If multiple single valued operations are called, then for each operation there will be one extension in the output FITS file.
Estimate the Sky value on each tile as fully described in Quantifying signal in a tile. As described in that section, several options are necessary to configure the Sky estimation which are listed below. The output file will have two extensions: the first is the Sky value and the second is the Sky standard deviation on each tile. Similar to --ontile, if the --oneelempertile option is called, then one element/pixel will be used for each tile (see Processing options).
The parameters for estimating the sky value can be set with the following options, except for the --sclipparams option (which is also used by the --sigmaclip), the rest are only used for the Sky value estimation.
File name of kernel to help in estimating the significance of signal in a tile, see Quantifying signal in a tile.
Kernel HDU to help in estimating the significance of signal in a tile, see Quantifying signal in a tile.
The maximum acceptable distance between the quantiles of the mean and median, see Quantifying signal in a tile. The initial Sky and its standard deviation estimates are measured on tiles where the quantiles of their mean and median are less distant than the value given to this option. For example, --meanmedqdiff=0.01 means that only tiles where the mean’s quantile is between 0.49 and 0.51 (recall that the median’s quantile is 0.5) will be used.
The \(\sigma\)-clipping parameters, see Sigma clipping.
This option takes two values which are separated by a comma (,).
Each value can either be written as a single number or as a fraction of two numbers (for example, 3,1/10).
The first value to this option is the multiple of \(\sigma\) that will be clipped (\(\alpha\) in that section).
The second value is the exit criteria.
If it is less than 1, then it is interpreted as tolerance and if it is larger than one it is a specific number.
Hence, in the latter case the value must be an integer.
The MAD-clipping parameters. This is very similar to --sclipparams above, see there for more.
\(\sigma\)-clipping parameters for the outlier rejection of the Sky value (similar to --sclipparams).
Outlier rejection is useful when the dataset contains a large and diffuse (almost flat within each tile) signal. The flatness of the profile will cause it to successfully pass the mean-median quantile difference test, so we will need to use the distribution of successful tiles for removing these false positive. For more, see the latter half of Quantifying signal in a tile.
Number of neighboring tiles to use for outlier rejection (mostly the wings of bright stars or galaxies). If this option is given a value of zero, no outlier rejection will take place. For more see the latter half of Quantifying signal in a tile.
Multiple of sigma to define an outlier in the Sky value estimation. If this option is given a value of zero, no outlier rejection will take place. For more see --outliersclip and the latter half of Quantifying signal in a tile.
Width of a flat kernel to convolve the interpolated tile values. Tile interpolation is done using the median of the --interpnumngb neighbors of each tile (see Processing options). If this option is given a value of zero or one, no smoothing will be done. Without smoothing, strong boundaries will probably be created between the values estimated for each tile. It is thus good to smooth the interpolated image so strong discontinuities do not show up in the final Sky values. The smoothing is done through convolution (see Convolution process) with a flat kernel, so the value to this option must be an odd number.
Do not set the input’s blank pixels to blank in the tiled outputs (for example, Sky and Sky standard deviation extensions of the output). This is only applicable when the tiled output has the same size as the input, in other words, when --oneelempertile is not called.
By default, blank values in the input (commonly on the edges which are outside the survey/field area) will be set to blank in the tiled outputs also. But in other scenarios this default behavior is not desired; for example, if you have masked something in the input, but want the tiled output under that also.
Create a multi-extension FITS file showing the steps that were used to estimate the Sky value over the input, see Quantifying signal in a tile. The file will have two extensions for each step (one for the Sky and one for the Sky standard deviation).
Similar to --checksky, but it will stop as soon as the outlier tiles have been identified and before it interpolates the values to cover the whole image.
This is useful when you want the good tile values before interpolation, and don’t want to slow down your pipeline with the extra computing that interpolation and smoothing require.
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GNU Astronomy Utilities 0.22 manual, February 2024.