The cosmic rays and bad pixels are now identified by comparing the input images with the associated blotted,median-cleaned images created.
Mask blemishes in dithered data by comparison of an image with a model image and the derivative of the model image.
Authors: | |
---|---|
Warren Hack | |
License: | |
http://www.stsci.edu/resources/software_hardware/pyraf/LICENSE |
Create a _cor file with the same format as the original input image
The DQ array will be replaced with the mask array used to create the _cor file.
The blotted median images that are now transformed back into the original reference frame, get compared to the original input images to detect any spurious pixels (which may include pixels impacted by cosmic rays) in each input. Those spurious pixels then get flagged as ‘bad’ in the output cosmic ray mask files, which get used as input for the final combination so that they do not show up in the final product. The identified bad pixels get flagged by updating the input mask files. Optionally, copies of the original images with the bad pixels removed can be created through the use of the driz_cr_corr parameter.
Parameters: | |
---|---|
input : str or list of str (Default = None)
configObj : configObject (Default = None)
editpars : bool (Default = False)
inputDict : dict, optional
|
|
Other Parameters: | |
driz_cr : bool (Default = False)
driz_cr_corr : bool (Default = False)
driz_cr_snr : list of floats (Default = ‘3.5 3.0’)
driz_cr_grow : int (Default = 1)
driz_cr_ctegrow : int (Default = 0)
driz_cr_scale : str (Default = ‘1.2 0.7’)
|
Notes
These tasks are designed to work together seemlessly when run in the full AstroDrizzle interface. More advanced users may wish to create specialized scripts for their own datasets, making use of only a subset of the predefined AstroDrizzle tasks, or add additional processing, which may be usefull for their particular data. In these cases, individual access to the tasks is important.
Something to keep in mind is that the full AstroDrizzle interface will make backup copies of your original files and place them in the OrIg/ directory of your current working directory. If you are working with the stand alone interfaces, it is assumed that the user has already taken care of backing up their original datafiles as the input file with be directly altered.
Examples
Basic example of how to call drizCR yourself from a python command line using the default parameters for the task.
>>> from drizzlepac import drizCR
>>> drizCR.drizCR('*flt.fits')
return useful help from a file in the script directory called __taskname__.help
Print out syntax help for running astrodrizzle
Parameters: | |
---|---|
file : str (Default = None)
|
Return a dictionary of the default parameters which also been updated with the user overrides.