DarkPipeline

class jwst.pipeline.DarkPipeline(*args, **kwargs)[source]

Produce a 4-D corrected ramp product from a raw JWST dark ramp.

Included steps are: group_scale, dq_init, saturation, ipc, superbias, refpix, rscd, lastframe, and linearity.

See Step for the parameters.

Attributes Summary

`class_alias`
`step_defs`

Methods Summary

process(input_data)

Run the calwebb_dark pipeline on the input data.

Attributes Documentation

step_defs: ClassVar = {'dq_init': <class 'jwst.dq_init.dq_init_step.DQInitStep'>, 'emicorr': <class 'jwst.emicorr.emicorr_step.EmiCorrStep'>, 'firstframe': <class 'jwst.firstframe.firstframe_step.FirstFrameStep'>, 'group_scale': <class 'jwst.group_scale.group_scale_step.GroupScaleStep'>, 'ipc': <class 'jwst.ipc.ipc_step.IPCStep'>, 'lastframe': <class 'jwst.lastframe.lastframe_step.LastFrameStep'>, 'linearity': <class 'jwst.linearity.linearity_step.LinearityStep'>, 'refpix': <class 'jwst.refpix.refpix_step.RefPixStep'>, 'reset': <class 'jwst.reset.reset_step.ResetStep'>, 'rscd': <class 'jwst.rscd.rscd_step.RscdStep'>, 'saturation': <class 'jwst.saturation.saturation_step.SaturationStep'>, 'superbias': <class 'jwst.superbias.superbias_step.SuperBiasStep'>}

Methods Documentation

process(input_data)[source]

Run the calwebb_dark pipeline on the input data.

Parameters:

input_datastr or RampModel: The input data to process. If a string, it is assumed to be a filename pointing to a RampModel.

Returns: