5.36. cosmic_clean¶
Clears cosmic rays from the fits files.
- FitsArray.cosmic_clean(output: str | None = None, override: bool = False, sigclip: float = 4.5, sigfrac: float = 0.3, objlim: int = 5, gain: float = 1.0, readnoise: float = 6.5, satlevel: float = 65535.0, niter: int = 4, sepmed: bool = True, cleantype: str = 'meanmask', fsmode: str = 'median', psfmodel: str = 'gauss', psffwhm: float = 2.5, psfsize: int = 7, psfk: Any = None, psfbeta: float = 4.765, gain_apply: bool = True) Self¶
Clears cosmic rays from the fits files.
References [1]: https://ccdproc.readthedocs.io/en/latest/api/ccdproc.cosmicray_lacosmic.html
Parameters
outputstr, optionalPath of the new fits file.
overridebool, default=FalseIf True, will overwrite the new_path if a file already exists.
sigclipfloat, default=4.5Laplacian-to-noise limit for cosmic ray detection. Lower values will flag more pixels as cosmic rays. Default: 4.5. see [1]
sigfracfloat, default=0.3Fractional detection limit for neighboring pixels. For cosmic ray neighbor pixels, a Laplacian-to-noise detection limit of sigfrac * sigclip will be used. Default: 0.3. see [1]
objlimint, default=5Minimum contrast between Laplacian image and the fine structure image. Increase this value if cores of bright stars are flagged as cosmic rays. Default: 5.0. see [1]
gainfloat, default=1.0Gain of the image (electrons / ADU). We always need to work in electrons for cosmic ray detection. Default: 1.0 see [1]
readnoisefloat, default=6.5Read noise of the image (electrons). Used to generate the noise model of the image. Default: 6.5. see [1]
satlevelfloat, default=65535.0Saturation level of the image (electrons). This value is used to detect saturated stars and pixels at or above this level are added to the mask. Default: 65535.0. see [1]
niterint, default=4Number of iterations of the LA Cosmic algorithm to perform. Default: 4. see [1]
sepmedbool, default=TrueUse the separable median filter instead of the full median filter. The separable median is not identical to the full median filter, but they are approximately the same. The separable median filter is significantly faster, and still detects cosmic rays well. Note, this is a performance feature, and not part of the original L.A. Cosmic. Default: True. see [1]
cleantypestr, default=’meanmask’Set which clean algorithm is used: 1) “median”: An unmasked 5x5 median filter. 2) “medmask”: A masked 5x5 median filter. 3) “meanmask”: A masked 5x5 mean filter. 4) “idw”: A masked 5x5 inverse distance weighted interpolation. Default: “meanmask”. see [1]
fsmodestr, default=’median’Method to build the fine structure image: 1) “median”: Use the median filter in the standard LA Cosmic algorithm. 2) “convolve”: Convolve the image with the PSF kernel to calculate the fine structure image. Default: “median”. see [1]
psfmodelstr, default=’gauss’Model to use to generate the PSF kernel if fsmode == ‘convolve’ and psfk is None. The current choices are Gaussian and Moffat profiles: - “gauss” and “moffat” produce circular PSF kernels. - The “gaussx” and “gaussy” produce Gaussian kernels in the x and y directions respectively. Default: “gauss”. see [1]
psffwhmfloat, default=2.5Full Width Half Maximum of the PSF to use to generate the kernel. Default: 2.5. see [1]
psfsizeint, default=7Size of the kernel to calculate. Returned kernel will have size psfsize x psfsize. psfsize should be odd. Default: 7. see [1]
psfkAny, optionalPSF kernel array to use for the fine structure image if fsmode == ‘convolve’. If None and fsmode == ‘convolve’, we calculate the PSF kernel using psfmodel. Default: None. see [1]
psfbetafloat, default=4.765Moffat beta parameter. Only used if fsmode==’convolve’ and psfmodel==’moffat’. Default: 4.765.
gain_applybool, default=TrueIf True, return gain-corrected data, with correct units, otherwise do not gain-correct the data. Default is True to preserve backwards compatibility. see [1]
Returns
FitsArrayCleaned
FitsArray
5.36.1. Example:¶
from myraflib import FitsArray
fa = FitsArray.sample()
clean_fa = fits.cosmic_clean()