snowblind 0.2.0

Algorithms for cleaning JWST data.

• SnowblindStep: mask cosmic ray showers and snowballs
• JumpPlusStep: flag jumps and saturated pixels caused by cosmic rays properly when there are frame-averaged groups
• PersistenceFlagStep: flag pixels due to persistence between exposures
• RcSelfCalStep: flag new hot pixels

Installation

pip install snowblind

Usage

The steps in snowblind run like any other pipeline steps. From the command line:

strun snowblind jw001234_010203_00001_nrcalong_jump.fits --suffix=snowblind

In Python:

from snowblind import SnowblindStep
from jwst.pipeline import Detector1Pipeline
from jwst.step import RampFitStep
from jwst.step import GainScaleStep


steps = {
    "jump": {
        "save_results": True,
    },
    "ramp_fit": {
        "skip": True,
    },
    "gain_scale": {
        "skip": True,
    },
}

Detector1Pipeline.call("jw001234_010203_00001_nrcalong_uncal.fits", steps=steps)
SnowblindStep.call("jw001234_010203_00001_nrcalong_jump.fits", save_results=True, suffix="snowblind")
rate, rateints = RampFitStep.call("jw001234_010203_00001_nrcalong_snowblind.fits")
rate = GainScaleStep.call(rate)
rate.save(rate.meta.filename.replace("snowblind", "rate"))

More to come on the other steps available.