drizzle_from_visit¶
- grizli.utils.drizzle_from_visit(visit, output=None, pixfrac=1.0, kernel='point', clean=True, include_saturated=True, keep_bits=None, dryrun=False, skip=None, extra_wfc3ir_badpix=True, verbose=True, scale_photom=True, context='jwst_1130.pmap', weight_type='jwst', rnoise_percentile=99, calc_wcsmap=False, niriss_ghost_kwargs={}, get_dbmask=True)[source]¶
Make drizzle mosaic from exposures in a visit dictionary
- Parameters
- visitdict
Visit dictionary with ‘product’ and ‘files’ keys
- output
WCS
,Header
,ImageHDU
Output frame definition. Can be a WCS object, header, or FITS HDU. If None, then generates a WCS with
grizli.utils.make_maximal_wcs
- pixfracfloat
Drizzle
pixfrac
- kernelstr
Drizzle
kernel
(e.g., ‘point’, ‘square’)- cleanbool
Remove exposure files after adding to the mosaic
- include_saturatedbool
Include pixels with saturated DQ flag
- keep_bitsint, None
Extra DQ bits to keep as valid
- dryrunbool
If True, don’t actually produce the output
- skipint
Slice skip to drizzle a subset of exposures
- extra_wfc3ir_badpixbool
Apply extra WFC3/IR bad pix to DQ
- verbosebool
Some verbose message printing
- scale_photombool
For JWST, apply photometry scale corrections from the
grizli/data/photom_correction.yml
table- weight_type‘err’, ‘median_err’, ‘time’, ‘jwst’
Exposure weighting strategy.
The default ‘err’ strategy uses the full uncertainty array defined in the
ERR
image extensions. The alternativeThe ‘median_err’ strategy uses the median of the
ERR
extensionThe ‘time’ strategy weights ‘median_err’ by the
TIME
extension, if availableFor the ‘jwst’ strategy, if ‘VAR_POISSON’ and ‘VAR_RNOISE’ extensions found, weight by VAR_RNOISE + median(VAR_POISSON). Fall back to ‘median_err’ otherwise.
- rnoise_percentilefloat
Percentile defining the upper limit of valid
VAR_RNOISE
values, if that extension is found in the exposure files(e.g., for JWST)- niriss_ghost_kwargsdict
Keyword arguments for
niriss_ghost_mask
- Returns