cosmicray_median¶
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ccdproc.
cosmicray_median
(ccd, error_image=None, thresh=5, mbox=11, gbox=0, rbox=0)[source]¶ Identify cosmic rays through median technique. The median technique identifies cosmic rays by identifying pixels by subtracting a median image from the initial data array.
Parameters: ccd :
CCDData
or numpy.ndarray or numpy.MaskedAraryData to have cosmic ray cleaned
thresh : float
Threshold for detecting cosmic rays
error_image : None, float, or
ndarray
Error level. If None, the task will use the standard deviation of the data. If an ndarray, it should have the same shape as data.
mbox : int
Median box for detecting cosmic rays
gbox : int
Box size to grow cosmic rays. If zero, no growing will be done.
rbox : int
Median box for calculating replacement values. If zero, no pixels will be replaced.
{log}
Returns: 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.nccd :
ndarray
If an
ndarray
is provided as ccd, a boolean ndarray with the cosmic rays identified will also be returned.Notes
Similar implementation to crmedian in iraf.imred.crutil.crmedian
Examples
Given an numpy.ndarray object, the syntax for running cosmicray_median would be:
>>> newdata, mask = cosmicray_median(data, error_image=error, thresh=5, mbox=11, rbox=11, gbox=5)
where 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 cosmicray_median would be:>>> newccd = cosmicray_median(ccd, thresh=5, mbox=11, rbox=11, gbox=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.