For more details about step arguments (including datatypes, possible values and defaults) see romancal.outlier_detection.OutlierDetectionStep.spec.

Step Arguments

The outlier_detection step has the following optional arguments that control the behavior of the processing:

--weight_type

The type of data weighting to use during resampling the images for creating the median image used for detecting outliers; options are 'ivm', 'exptime', and None (see Weighting types for details).

--pixfrac

Fraction by which input pixels are “shrunk” before being drizzled onto the output image grid, given as a real number between 0 and 1. This specifies the size of the footprint, or “dropsize”, of a pixel in units of the input pixel size. If pixfrac is set to less than 0.001, the kernel parameter will be reset to 'point'` for more efficient processing. In the step of drizzling each input image onto a separate output image, the default value of 1.0 is best in order to ensure that each output drizzled image is fully populated with pixels from the input image. Valid values range from 0.0 to 1.0.

--kernel

This parameter specifies the form of the kernel function used to distribute flux onto the separate output images, for the initial separate drizzling operation only. The value options for this parameter include:

  • 'square': original classic drizzling kernel

  • 'tophat': this kernel is a circular “top hat” shape of width pixfrac. It effects only output pixels within a radius of pixfrac/2 from the output position.

  • 'lanczos3': a Lanczos style kernel, extending a radius of 3 pixels from the center of the detection. The Lanczos kernel is a damped and bounded form of the “sinc” interpolator, and is very effective for resampling single images when scale=pixfrac=1. It leads to less resolution loss than other kernels, and typically results in reduced correlated noise in outputs.

Warning

The 'lanczos3' kernel tends to result in much slower processing as compared to other kernel options. This option should never be used for pixfrac != 1.0, and is not recommended for scale!=1.0.

--fillval

The value for this parameter is to be assigned to the output pixels that have zero weight or which do not receive flux from any input pixels during drizzling. This parameter corresponds to the fillval parameter of the drizzle task and will be converted to a float.

--maskpt

Percentage of weight image values below which they are flagged as bad and rejected from the median image. Valid values range from 0.0 to 1.0.

--snr

The signal-to-noise values to use for bad pixel identification. Since cosmic rays often extend across several pixels the user must specify two cut-off values for determining whether a pixel should be masked: the first for detecting the primary cosmic ray, and the second (typically lower threshold) for masking lower-level bad pixels adjacent to those found in the first pass. Valid values are a pair of floating-point values in a single string (for example “5.0 4.0”).

--scale

The scaling factor applied to derivative used to identify bad pixels. Since cosmic rays often extend across several pixels the user must specify two cut-off values for determining whether a pixel should be masked: the first for detecting the primary cosmic ray, and the second (typically lower threshold) for masking lower-level bad pixels adjacent to those found in the first pass. Valid values are a pair of floating-point values in a single string (for example “1.2 0.7”).

--backg

User-specified background value (scalar) to subtract during final identification step of outliers in driz_cr computation.

--save_intermediate_results

Boolean specifying whether or not to write out intermediate products such as median image or resampled individual input exposures to disk. Typically, only used to track down problems with final results when too many or too few pixels are flagged as outliers.

--resample_data

Boolean specifying whether or not to resample the input images when performing outlier detection.

--resample_on_skycell

If input association contains skycell information use it for the resampling wcs. If False (or if the association contains no skycell information) the resampled wcs will be the combined wcs of all input models.

--good_bits

The DQ bit values from the input image DQ arrays that should be considered ‘good’ when creating masks of bad pixels during outlier detection when resampling the data. See Roman’s Data Quality Flags for details.

--in_memory

Boolean specifying whether or not to keep all intermediate products and datamodels in memory at the same time during the processing of this step. If set to False, any ModelLibrary opened by this step will use on_disk=True and use temporary files to store model modifications. Additionally any resampled images will be kept in memory (as long as needed). This can result in much lower memory usage (at the expense of file I/O) to process large associations.

Weighting types

weight_type specifies the type of weighting image to apply with the bad pixel mask for the final drizzle step. The options for this parameter include:

  • ivm: allows the user to either supply their own inverse-variance weighting map, or allow drizzle to generate one automatically on-the-fly during the final drizzle step. This parameter option may be necessary for specific purposes. For example, to create a drizzled weight file for software such as SExtractor, it is expected that a weight image containing all of the background noise sources (sky level, read-noise, dark current, etc), but not the Poisson noise from the objects themselves will be available. The user can create the inverse variance images and then specify their names using the input parameter for drizzle to specify an ‘@file’. This would be a single ASCII file containing the list of input calibrated exposure filenames (one per line), with a second column containing the name of the IVM file corresponding to each calibrated exposure. Each IVM file must have the same file format as the input file.

  • exptime: the images will be weighted according to their exposure time, which is the standard behavior for drizzle. This weighting is a good approximation in the regime where the noise is dominated by photon counts from the sources, while contributions from sky background, read-noise and dark current are negligible. This option is provided as the default since it produces reliable weighting for all types of data.

  • None: In this case, a bit mask will be generated based on the DQ array and a bit flag set to 0 (i.e. GOOD; see Roman’s Data Quality Flags for details).