Algorithms for cleaning JWST data.

• SnowblindStep: mask cosmic ray showers and snowballs
• JumpPlusStep: Propagate JUMP_DET and SATURATED flags in GROUPDQ properly for frame-averaged groups
• PersistenceFlagStep: flag pixels effected by persistence exposure-to-exposure
• OpenPixelStep: flag new open pixels, hot pixels, or open adjacent pixels via self-cal

Installation

pip install snowblind

Usage

The steps in snowblind run like any other pipeline steps. From the command line you can run SnowblindStep (aliased as snowblind) on the result file from JumpStep:

strun snowblind jw001234_010203_00001_nrcalong_jump.fits --suffix=snowblind

Or you can run SnowblindStep and JumpPlusStep as post-hooks after JumpStep in a full pipeline, remembering to turn off the default snowball flagging.

strun calwebb_detector1 jw001234_010203_00001_nrcalong_uncal.fits --steps.jump.post_hooks="snowblind.SnowblindStep","snowblind.JumpPlusStep" --steps.jump.flag_large_events=False

In Python, we can insert SnowblindStep and JumpPlusStep after JumpStep as a post-hook:

from snowblind import SnowblindStep, JumpPlusStep
from jwst.pipeline import Detector1Pipeline


steps = {
    "jump": {
        "save_results": True,
        "flag_large_events": False,
        "post_hooks": [
            SnowblindStep,
            JumpPlusStep,
        ],
    },
}

Detector1Pipeline.call("jw001234_010203_00001_nrcalong_uncal.fits", steps=steps, save_results=True)

More to come on the other steps available.