cosmicray_lacosmic¶

ccdproc.
cosmicray_lacosmic
(ccd, sigclip=4.5, sigfrac=0.3, objlim=5.0, gain=1.0, readnoise=6.5, satlevel=65535.0, pssl=0.0, niter=4, sepmed=True, cleantype='meanmask', fsmode='median', psfmodel='gauss', psffwhm=2.5, psfsize=7, psfk=None, psfbeta=4.765, verbose=False)[source]¶ Identify cosmic rays through the lacosmic technique. The lacosmic technique identifies cosmic rays by identifying pixels based on a variation of the Laplacian edge detection. The algorithm is an implementation of the code describe in van Dokkum (2001) [R3] as implemented by McCully (2014) [R4]. If you use this algorithm, please cite these two works.
Parameters: ccd :
CCDData
ornumpy.ndarray
Data to have cosmic ray cleaned.
sigclip : float, optional
Laplaciantonoise limit for cosmic ray detection. Lower values will flag more pixels as cosmic rays. Default: 4.5.
sigfrac : float, optional
Fractional detection limit for neighboring pixels. For cosmic ray neighbor pixels, a Laplaciantonoise detection limit of sigfrac * sigclip will be used. Default: 0.3.
objlim : float, optional
Minimum 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.
pssl : float, optional
Previously subtracted sky level in ADU. We always need to work in electrons for cosmic ray detection, so we need to know the sky level that has been subtracted so we can add it back in. Default: 0.0.
gain : float, optional
Gain of the image (electrons / ADU). We always need to work in electrons for cosmic ray detection. Default: 1.0
readnoise : float, optional
Read noise of the image (electrons). Used to generate the noise model of the image. Default: 6.5.
satlevel : float, optional
Saturation 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.
niter : int, optional
Number of iterations of the LA Cosmic algorithm to perform. Default: 4.
sepmed : bool, optional
Use 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 and the separable median filter is significantly faster and still detects cosmic rays well. Default: True
cleantype : str, optional
Set which clean algorithm is used:
"median"
: An unmasked 5x5 median filter."medmask"
: A masked 5x5 median filter."meanmask"
: A masked 5x5 mean filter."idw"
: A masked 5x5 inverse distance weighted interpolation.
Default:
"meanmask"
.fsmode : str, optional
Method to build the fine structure image:
"median"
: Use the median filter in the standard LA Cosmic algorithm."convolve"
: Convolve the image with the psf kernel to calculate the fine structure image.
Default:
"median"
.psfmodel : str, optional
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"
.psffwhm : float, optional
Full Width Half Maximum of the PSF to use to generate the kernel. Default: 2.5.
psfsize : int, optional
Size of the kernel to calculate. Returned kernel will have size psfsize x psfsize. psfsize should be odd. Default: 7.
psfk :
numpy.ndarray
(with float dtype) or None, optionalPSF kernel array to use for the fine structure image if
fsmode == 'convolve'
. If None andfsmode == 'convolve'
, we calculate the psf kernel usingpsfmodel
. Default: None.psfbeta : float, optional
Moffat beta parameter. Only used if
fsmode=='convolve'
andpsfmodel=='moffat'
. Default: 4.765.verbose : bool, optional
Print to the screen or not. Default: False.
Returns: nccd :
CCDData
ornumpy.ndarray
An object of the same type as ccd is returned. If it is a
CCDData
, the mask attribute will also be updated with areas identified with cosmic rays masked.crmask :
numpy.ndarray
If an
numpy.ndarray
is provided as ccd, a boolean ndarray with the cosmic rays identified will also be returned.Notes
Implementation of the cosmic ray identification L.A.Cosmic: http://www.astro.yale.edu/dokkum/lacosmic/
References
[R3] (1, 2) van Dokkum, P; 2001, “CosmicRay Rejection by Laplacian Edge Detection”. The Publications of the Astronomical Society of the Pacific, Volume 113, Issue 789, pp. 14201427. doi: 10.1086/323894 [R4] (1, 2) McCully, C., 2014, “AstroSCRAPPY”, https://github.com/astropy/astroscrappy Examples
Given an numpy.ndarray object, the syntax for running cosmicrar_lacosmic would be:
>>> newdata, mask = cosmicray_lacosmic(data, sigclip=5)
where the error is an array that is the same shape as data but includes the pixel error. This would return a data array, newdata, with the bad pixels replaced by the local median from a box of 11 pixels; and it would return a mask indicating the bad pixels.
Given an
CCDData
object with an uncertainty frame, the syntax for running cosmicrar_lacosmic would be:>>> newccd = cosmicray_lacosmic(ccd, sigclip=5)
The newccd object will have bad pixels in its data array replace and the mask of the object will be created if it did not previously exist or be updated with the detected cosmic rays.