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

Installation

pip install snowblind

Usage

The steps in snowblind run like any other pipeline steps. From the command line you can run the step on the result file from JumpStep:

strun snowblind jw001234_010203_00001_nrcalong_jump.fits --suffix=snowblind

Or you can run it as a post-hook in a full pipeline

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

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.