Parameters¶

All pipelines and steps have parameters that can be set to change various aspects of how they execute. To see what parameters are available for any given pipeline or step, use the -h option on strun. Some examples are:

$ strun roman_elp -h
$ strun romancal.dq_init.DQInitStep -h

$strun roman_hlp -h
$strun romancal.skymatch.SkyMatchStep -h

To set a parameter, simply specify it on the command line. For example, to have roman_elp save the calibrated ramp files, the strun command would be as follows:

$ strun roman_elp r0008308002010007027_06311_0019_WFI01_uncal.asdf --save_calibrated_ramp=true

To specify parameter values for an individual step when running a pipeline use the syntax --steps.<step_name>.<parameter>=value. For example, to override the default selection of a dark current reference file from CRDS when running a pipeline:

$ strun roman_elp r0008308002010007027_06311_0019_WFI01_uncal.asdf
      --steps.dark_current.override_dark='my_dark.asdf'

Universal Parameters¶

The set of parameters that are common to all pipelines and steps are referred to as universal parameters and are described below.

Output Directory¶

By default, all pipeline and step outputs will drop into the current working directory, i.e., the directory in which the process is running. To change this, use the output_dir parameter. For example, to have all output from roman_elp, including any saved intermediate steps, appear in the sub-directory calibrated, use

$ strun roman_elp r0008308002010007027_06311_0019_WFI01_uncal.asdf
    --output_dir=calibrated

output_dir can be specified at the step level, overriding what was specified for the pipeline. From the example above, to change the name and location of the dark_current step, use the following

$ strun roman_elp r0008308002010007027_06311_0019_WFI01_uncal.asdf
    --output_dir=calibrated
    --steps.dark_current.output_file='dark_sub.asdf'
    --steps.dark_current.output_dir='dark_calibrated'

Output File¶

When running a pipeline, the stpipe infrastructure automatically passes the output data model from one step to the input of the next step, without saving any intermediate results to disk.