SYNOPSIS
mFixNaN [-d level] [-v NaN-value] in.fits out.fits [minblank maxblank]
DESCRIPTION
Converts NaNs found in the image to some other value (given by the user), or converts a range of supplied values into NaNs.
OPTIONS
- -d level
- Turn on debugging to the specified level (1-3)
- -v NaN-value
-
Value to use in place of any NaNs
ARGUMENTS
- in.fits
- Input FITS image file
- out.fits
- Path of output FITS file. To run in "count" mode without creating an output file, use a dash ("-") for this argument.
- minblank maxblank
-
If the "-v" switch is not used, mFixNaN will replace all pixel values between minblank and maxblank with NaN.
RESULT
[struct stat="OK", rangeCount=rangeCount, nanCount=nanCount]rangeCount is the number of pixels that were found between minblank and maxblank, if they were specified. If not (i.e., NaNs were removed and replaced with value), nanCount is the number of NaNs removed.
MESSAGES
- OK
- [struct stat="OK", rangeCount=rangeCount, nanCount=nanCount"]
- ERROR
- No debug level given
- ERROR
- Debug level string is invalid: level
- ERROR
- Debug level string is invalid: level
- ERROR
- Debug level string cannot be negative
- ERROR
- No value given for NaN conversion
- ERROR
- NaN conversion value string is invalid: 'NaN-value'
- ERROR
- Invalid input file 'in.fits']
- ERROR
- min blank value string is not a number
- ERROR
- max blank value string is not a number
- ERROR
- Image file in.fits missing or invalid FITS
- ERROR
-
FITS library error
EXAMPLES
A FITS image with BITPIX -64 (double-precision floating point) was generated without using NaNs; all "blank" pixels are represented by very small negative numbers. This can throw off initial attempts to display the image with a proper stretch, and does not conform to the FITS standard. To replace all those "blank" pixels with NaNs:
- mFixNaN original.fits NaN.fits -4.61169e32 -4.61169e10
- [struct stat="OK", rangeCount=1321, nanCount=0]
To convert those NaNs back into a single pixel value:
- mFixNaN -v -4.6e32 NaN.fits blankval.fits
-
[struct stat="OK", rangeCount=0, nanCount=1321]
BUGS
The drizzle algorithm has been implemented but has not been tested in this release.If a header template contains carriage returns (i.e., created/modified on a Windows machine), the cfitsio library will be unable to read it properly, resulting in the error: [struct stat="ERROR", status=207, msg="illegal character in keyword"]
It is best for the background correction algorithms if the area described in the header template completely encloses all of the input images in their entirety. If parts of input images are "chopped off" by the header template, the background correction will be affected. We recommend you use an expanded header for the reprojection and background modeling steps, returning to the originally desired header size for the final coaddition. The default background matching assumes that there are no non-linear background variations in the individual images (and therefore in the overlap differences). If there is any uncertainty in this regard, it is safer to turn on the "level only" background matching (the "-l" flag in mBgModel.
COPYRIGHT
2001-2015 California Institute of Technology, Pasadena, CaliforniaIf your research uses Montage, please include the following acknowledgement: "This research made use of Montage. It is funded by the National Science Foundation under Grant Number ACI-1440620, and was previously funded by the National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Institute of Technology."
The Montage distribution includes an adaptation of the MOPEX algorithm developed at the Spitzer Science Center.