Mask cosmic ray showers (snowballs) in JWST data
Project description
Algorithms for cleaning JWST data.
SnowblindStep
: mask cosmic ray showers and snowballsJumpPlusStep
: flag jumps and saturated pixels caused by cosmic rays properly when there are frame-averaged groupsPersistenceFlagStep
: flag pixels due to persistence between exposuresRcSelfCalStep
: 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.
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