imcombine and minmax
sebas wrote on Jun 05, 2008
I'm reducing near-IR image data. All 86 frames are already registered and background (sky) subtracted, listed in @com with same badpix mask. I run Imcombine twice to create two output images using minmax with nlow=30 nhigh =30 and then with nlow=5 and nhigh =5. When I subtract the two resultant images with Imarith the final subtracted image is a null image. In other words the two images produced with Imcombine are identical. The minmax algorithm does not seem to be working the way I thought it would.
I was expecting that by running Imcombine with my parameter (below), the nlow and nhigh pixels would be excluded prior to combining. In other words for test1 the pixels of the 30 frames with the lowest values would be excluded as well as the pixels of the 30 frames with the highest values before combining (median). So I thought that the S/N of the second image should be higher than that of the first. It was therefore my expectation that the resultant image test2-test1 would not be null. I also tried to change "nkeep" because I'm using a badpix map but this didn't help. I also tried to use the task Combine but this also didn't help. I also used a different IRAF installation and this didn't help.
I am obviously doing something wrong. What should my input parameters really be in order to use the minmax rejection algorithm correctly?
----------------------------------------------
Here are the inputs used to create test1.fits
-----------------------------------------------
PACKAGE = immatch
TASK = imcombine
input = @com List of images to combine
output = test1 List of output images
(headers= ) List of header files (optional)
(bpmasks= ) List of bad pixel masks (optional)
(rejmask= ) List of rejection masks (optional)
(nrejmas= ) List of number rejected masks (optional)
(expmask= ) List of exposure masks (optional)
(sigmas = ) List of sigma images (optional)
(logfile= STDOUT) Log file
(combine= median) Type of combine operation
(reject = minmax) Type of rejection
(project= no) Project highest dimension of input images?
(outtype= real) Output image pixel datatype
(outlimi= ) Output limits (x1 x2 y1 y2 ...)
(offsets= none) Input image offsets
(masktyp= none) Mask type
(maskval= 0) Mask value
(blank = 0.) Value if there are no pixels
(scale = none) Image scaling
(zero = none) Image zero point offset
(weight = none) Image weights
(statsec= ) Image section for computing statistics
(expname= ) Image header exposure time keyword
(lthresh= INDEF) Lower threshold
(hthresh= INDEF) Upper threshold
(nlow = 30) minmax: Number of low pixels to reject
(nhigh = 30) minmax: Number of high pixels to reject
(nkeep = 1) Minimum to keep (pos) or maximum to reject (neg)
(mclip = yes) Use median in sigma clipping algorithms?
(lsigma = 3.) Lower sigma clipping factor
(hsigma = 3.) Upper sigma clipping factor
(rdnoise= 0.) ccdclip: CCD readout noise (electrons)
(gain = 1.) ccdclip: CCD gain (electrons/DN)
(snoise = 0.) ccdclip: Sensitivity noise (fraction)
(sigscal= 0.1) Tolerance for sigma clipping scaling corrections
(pclip = -0.5) pclip: Percentile clipping parameter
(grow = 0.) Radius (pixels) for neighbor rejection
(mode = ql)
-----------------------
and here are the inputs used to create test2.fits
------------------------
PACKAGE = immatch
TASK = imcombine
input = @com List of images to combine
output = test2 List of output images
(headers= ) List of header files (optional)
(bpmasks= ) List of bad pixel masks (optional)
(rejmask= ) List of rejection masks (optional)
(nrejmas= ) List of number rejected masks (optional)
(expmask= ) List of exposure masks (optional)
(sigmas = ) List of sigma images (optional)
(logfile= STDOUT) Log file
(combine= median) Type of combine operation
(reject = minmax) Type of rejection
(project= no) Project highest dimension of input images?
(outtype= real) Output image pixel datatype
(outlimi= ) Output limits (x1 x2 y1 y2 ...)
(offsets= none) Input image offsets
(masktyp= none) Mask type
(maskval= 0) Mask value
(blank = 0.) Value if there are no pixels
(scale = none) Image scaling
(zero = none) Image zero point offset
(weight = none) Image weights
(statsec= ) Image section for computing statistics
(expname= ) Image header exposure time keyword
(lthresh= INDEF) Lower threshold
(hthresh= INDEF) Upper threshold
(nlow = 5) minmax: Number of low pixels to reject
(nhigh = 5) minmax: Number of high pixels to reject
(nkeep = 1) Minimum to keep (pos) or maximum to reject (neg)
(mclip = yes) Use median in sigma clipping algorithms?
(lsigma = 3.) Lower sigma clipping factor
(hsigma = 3.) Upper sigma clipping factor
(rdnoise= 0.) ccdclip: CCD readout noise (electrons)
(gain = 1.) ccdclip: CCD gain (electrons/DN)
(snoise = 0.) ccdclip: Sensitivity noise (fraction)
(sigscal= 0.1) Tolerance for sigma clipping scaling corrections
(pclip = -0.5) pclip: Percentile clipping parameter
(grow = 0.) Radius (pixels) for neighbor rejection
(mode = ql)
------------------
OUTPUT for test1.fits
------------------
Jun 5 19:45: IMCOMBINE
combine = median, scale = none, zero = none, weight = none
reject = minmax, nlow = 30, nhigh = 30
blank = 0.
Images
p525-gvp526.df_imsubf.fits_imshft.fits
p527-gvp526.df_imsubf.fits_imshft.fits
p530-gvp529.df_imsubf.fits_imshft.fits
p541-gvp540.df_imsubf.fits_imshft.fits
p542-gvp543.df_imsubf.fits_imshft.fits
p544-gvp543.df_imsubf.fits_imshft.fits
p547-gvp546.df_imsubf.fits_imshft.fits
p548-gvp549.df_imsubf.fits_imshft.fits
p550-gvp549.df_imsubf.fits_imshft.fits
p551-gvp552.df_imsubf.fits_imshft.fits
p553-gvp552.df_imsubf.fits_imshft.fits
p554-gvp555.df_imsubf.fits_imshft.fits
p556-gvp555.df_imsubf.fits_imshft.fits
p558-gvp557.df_imsubf.fits_imshft.fits
p559-gvp560.df_imsubf.fits_imshft.fits
p561-gvp560.df_imsubf.fits_imshft.fits
p562-gvp563.df_imsubf.fits_imshft.fits
p564-gvp563.df_imsubf.fits_imshft.fits
p565-gvp566.df_imsubf.fits_imshft.fits
p567-gvp566.df_imsubf.fits_imshft.fits
p568-gvp569.df_imsubf.fits_imshft.fits
p570-gvp569.df_imsubf.fits_imshft.fits
p571-gvp572.df_imsubf.fits_imshft.fits
p573-gvp572.df_imsubf.fits_imshft.fits
p578-gvp577.df_imsubf.fits_imshft.fits
p626-gvp625.df_imsubf.fits_imshft.fits
p627-gvp628.df_imsubf.fits_imshft.fits
p629-gvp628.df_imsubf.fits_imshft.fits
p630-gvp631.df_imsubf.fits_imshft.fits
p632-gvp631.df_imsubf.fits_imshft.fits
p633-gvp634.df_imsubf.fits_imshft.fits
p643-gvp642.df_imsubf.fits_imshft.fits
p644-gvp645.df_imsubf.fits_imshft.fits
p647-gvp648.df_imsubf.fits_imshft.fits
p649-gvp648.df_imsubf.fits_imshft.fits
p650-gvp651.df_imsubf.fits_imshft.fits
p652-gvp651.df_imsubf.fits_imshft.fits
p653-gvp654.df_imsubf.fits_imshft.fits
p655-gvp654.df_imsubf.fits_imshft.fits
p656-gvp657.df_imsubf.fits_imshft.fits
p658-gvp657.df_imsubf.fits_imshft.fits
p660-gvp659.df_imsubf.fits_imshft.fits
p661-gvp662.df_imsubf.fits_imshft.fits
p663-gvp662.df_imsubf.fits_imshft.fits
p664-gvp665.df_imsubf.fits_imshft.fits
p666-gvp665.df_imsubf.fits_imshft.fits
p667-gvp668.df_imsubf.fits_imshft.fits
p669-gvp668.df_imsubf.fits_imshft.fits
p670-gvp671.df_imsubf.fits_imshft.fits
p672-gvp671.df_imsubf.fits_imshft.fits
p673-gvp674.df_imsubf.fits_imshft.fits
p675-gvp674.df_imsubf.fits_imshft.fits
p677-gvp676.df_imsubf.fits_imshft.fits
p678-gvp679.df_imsubf.fits_imshft.fits
p680-gvp679.df_imsubf.fits_imshft.fits
p681-gvp682.df_imsubf.fits_imshft.fits
p683-gvp682.df_imsubf.fits_imshft.fits
p684-gvp685.df_imsubf.fits_imshft.fits
p686-gvp685.df_imsubf.fits_imshft.fits
p687-gvp688.df_imsubf.fits_imshft.fits
p689-gvp688.df_imsubf.fits_imshft.fits
p690-gvp691.df_imsubf.fits_imshft.fits
p692-gvp691.df_imsubf.fits_imshft.fits
p694-gvp693.df_imsubf.fits_imshft.fits
p695-gvp696.df_imsubf.fits_imshft.fits
p697-gvp696.df_imsubf.fits_imshft.fits
p698-gvp699.df_imsubf.fits_imshft.fits
p700-gvp699.df_imsubf.fits_imshft.fits
p701-gvp702.df_imsubf.fits_imshft.fits
p703-gvp702.df_imsubf.fits_imshft.fits
p704-gvp705.df_imsubf.fits_imshft.fits
p706-gvp705.df_imsubf.fits_imshft.fits
p707-gvp708.df_imsubf.fits_imshft.fits
p709-gvp708.df_imsubf.fits_imshft.fits
p710-gvp711.df_imsubf.fits_imshft.fits
p728-gvp727.df_imsubf.fits_imshft.fits
p729-gvp730.df_imsubf.fits_imshft.fits
p731-gvp730.df_imsubf.fits_imshft.fits
p732-gvp733.df_imsubf.fits_imshft.fits
p734-gvp733.df_imsubf.fits_imshft.fits
p735-gvp736.df_imsubf.fits_imshft.fits
p737-gvp736.df_imsubf.fits_imshft.fits
p738-gvp739.df_imsubf.fits_imshft.fits
p740-gvp739.df_imsubf.fits_imshft.fits
p741-gvp742.df_imsubf.fits_imshft.fits
p743-gvp742.df_imsubf.fits_imshft.fits
Output image = test1, ncombine = 86
-----------------------------
Output for test2.fits
-----------------------------
Jun 5 19:48: IMCOMBINE
combine = median, scale = none, zero = none, weight = none
reject = minmax, nlow = 5, nhigh = 5
blank = 0.
Images
p525-gvp526.df_imsubf.fits_imshft.fits
p527-gvp526.df_imsubf.fits_imshft.fits
p530-gvp529.df_imsubf.fits_imshft.fits
p541-gvp540.df_imsubf.fits_imshft.fits
p542-gvp543.df_imsubf.fits_imshft.fits
p544-gvp543.df_imsubf.fits_imshft.fits
p547-gvp546.df_imsubf.fits_imshft.fits
p548-gvp549.df_imsubf.fits_imshft.fits
p550-gvp549.df_imsubf.fits_imshft.fits
p551-gvp552.df_imsubf.fits_imshft.fits
p553-gvp552.df_imsubf.fits_imshft.fits
p554-gvp555.df_imsubf.fits_imshft.fits
p556-gvp555.df_imsubf.fits_imshft.fits
p558-gvp557.df_imsubf.fits_imshft.fits
p559-gvp560.df_imsubf.fits_imshft.fits
p561-gvp560.df_imsubf.fits_imshft.fits
p562-gvp563.df_imsubf.fits_imshft.fits
p564-gvp563.df_imsubf.fits_imshft.fits
p565-gvp566.df_imsubf.fits_imshft.fits
p567-gvp566.df_imsubf.fits_imshft.fits
p568-gvp569.df_imsubf.fits_imshft.fits
p570-gvp569.df_imsubf.fits_imshft.fits
p571-gvp572.df_imsubf.fits_imshft.fits
p573-gvp572.df_imsubf.fits_imshft.fits
p578-gvp577.df_imsubf.fits_imshft.fits
p626-gvp625.df_imsubf.fits_imshft.fits
p627-gvp628.df_imsubf.fits_imshft.fits
p629-gvp628.df_imsubf.fits_imshft.fits
p630-gvp631.df_imsubf.fits_imshft.fits
p632-gvp631.df_imsubf.fits_imshft.fits
p633-gvp634.df_imsubf.fits_imshft.fits
p643-gvp642.df_imsubf.fits_imshft.fits
p644-gvp645.df_imsubf.fits_imshft.fits
p647-gvp648.df_imsubf.fits_imshft.fits
p649-gvp648.df_imsubf.fits_imshft.fits
p650-gvp651.df_imsubf.fits_imshft.fits
p652-gvp651.df_imsubf.fits_imshft.fits
p653-gvp654.df_imsubf.fits_imshft.fits
p655-gvp654.df_imsubf.fits_imshft.fits
p656-gvp657.df_imsubf.fits_imshft.fits
p658-gvp657.df_imsubf.fits_imshft.fits
p660-gvp659.df_imsubf.fits_imshft.fits
p661-gvp662.df_imsubf.fits_imshft.fits
p663-gvp662.df_imsubf.fits_imshft.fits
p664-gvp665.df_imsubf.fits_imshft.fits
p666-gvp665.df_imsubf.fits_imshft.fits
p667-gvp668.df_imsubf.fits_imshft.fits
p669-gvp668.df_imsubf.fits_imshft.fits
p670-gvp671.df_imsubf.fits_imshft.fits
p672-gvp671.df_imsubf.fits_imshft.fits
p673-gvp674.df_imsubf.fits_imshft.fits
p675-gvp674.df_imsubf.fits_imshft.fits
p677-gvp676.df_imsubf.fits_imshft.fits
p678-gvp679.df_imsubf.fits_imshft.fits
p680-gvp679.df_imsubf.fits_imshft.fits
p681-gvp682.df_imsubf.fits_imshft.fits
p683-gvp682.df_imsubf.fits_imshft.fits
p684-gvp685.df_imsubf.fits_imshft.fits
p686-gvp685.df_imsubf.fits_imshft.fits
p687-gvp688.df_imsubf.fits_imshft.fits
p689-gvp688.df_imsubf.fits_imshft.fits
p690-gvp691.df_imsubf.fits_imshft.fits
p692-gvp691.df_imsubf.fits_imshft.fits
p694-gvp693.df_imsubf.fits_imshft.fits
p695-gvp696.df_imsubf.fits_imshft.fits
p697-gvp696.df_imsubf.fits_imshft.fits
p698-gvp699.df_imsubf.fits_imshft.fits
p700-gvp699.df_imsubf.fits_imshft.fits
p701-gvp702.df_imsubf.fits_imshft.fits
p703-gvp702.df_imsubf.fits_imshft.fits
p704-gvp705.df_imsubf.fits_imshft.fits
p706-gvp705.df_imsubf.fits_imshft.fits
p707-gvp708.df_imsubf.fits_imshft.fits
p709-gvp708.df_imsubf.fits_imshft.fits
p710-gvp711.df_imsubf.fits_imshft.fits
p728-gvp727.df_imsubf.fits_imshft.fits
p729-gvp730.df_imsubf.fits_imshft.fits
p731-gvp730.df_imsubf.fits_imshft.fits
p732-gvp733.df_imsubf.fits_imshft.fits
p734-gvp733.df_imsubf.fits_imshft.fits
p735-gvp736.df_imsubf.fits_imshft.fits
p737-gvp736.df_imsubf.fits_imshft.fits
p738-gvp739.df_imsubf.fits_imshft.fits
p740-gvp739.df_imsubf.fits_imshft.fits
p741-gvp742.df_imsubf.fits_imshft.fits
p743-gvp742.df_imsubf.fits_imshft.fits
Output image = test2, ncombine = 86
I was expecting that by running Imcombine with my parameter (below), the nlow and nhigh pixels would be excluded prior to combining. In other words for test1 the pixels of the 30 frames with the lowest values would be excluded as well as the pixels of the 30 frames with the highest values before combining (median). So I thought that the S/N of the second image should be higher than that of the first. It was therefore my expectation that the resultant image test2-test1 would not be null. I also tried to change "nkeep" because I'm using a badpix map but this didn't help. I also tried to use the task Combine but this also didn't help. I also used a different IRAF installation and this didn't help.
I am obviously doing something wrong. What should my input parameters really be in order to use the minmax rejection algorithm correctly?
----------------------------------------------
Here are the inputs used to create test1.fits
-----------------------------------------------
PACKAGE = immatch
TASK = imcombine
input = @com List of images to combine
output = test1 List of output images
(headers= ) List of header files (optional)
(bpmasks= ) List of bad pixel masks (optional)
(rejmask= ) List of rejection masks (optional)
(nrejmas= ) List of number rejected masks (optional)
(expmask= ) List of exposure masks (optional)
(sigmas = ) List of sigma images (optional)
(logfile= STDOUT) Log file
(combine= median) Type of combine operation
(reject = minmax) Type of rejection
(project= no) Project highest dimension of input images?
(outtype= real) Output image pixel datatype
(outlimi= ) Output limits (x1 x2 y1 y2 ...)
(offsets= none) Input image offsets
(masktyp= none) Mask type
(maskval= 0) Mask value
(blank = 0.) Value if there are no pixels
(scale = none) Image scaling
(zero = none) Image zero point offset
(weight = none) Image weights
(statsec= ) Image section for computing statistics
(expname= ) Image header exposure time keyword
(lthresh= INDEF) Lower threshold
(hthresh= INDEF) Upper threshold
(nlow = 30) minmax: Number of low pixels to reject
(nhigh = 30) minmax: Number of high pixels to reject
(nkeep = 1) Minimum to keep (pos) or maximum to reject (neg)
(mclip = yes) Use median in sigma clipping algorithms?
(lsigma = 3.) Lower sigma clipping factor
(hsigma = 3.) Upper sigma clipping factor
(rdnoise= 0.) ccdclip: CCD readout noise (electrons)
(gain = 1.) ccdclip: CCD gain (electrons/DN)
(snoise = 0.) ccdclip: Sensitivity noise (fraction)
(sigscal= 0.1) Tolerance for sigma clipping scaling corrections
(pclip = -0.5) pclip: Percentile clipping parameter
(grow = 0.) Radius (pixels) for neighbor rejection
(mode = ql)
-----------------------
and here are the inputs used to create test2.fits
------------------------
PACKAGE = immatch
TASK = imcombine
input = @com List of images to combine
output = test2 List of output images
(headers= ) List of header files (optional)
(bpmasks= ) List of bad pixel masks (optional)
(rejmask= ) List of rejection masks (optional)
(nrejmas= ) List of number rejected masks (optional)
(expmask= ) List of exposure masks (optional)
(sigmas = ) List of sigma images (optional)
(logfile= STDOUT) Log file
(combine= median) Type of combine operation
(reject = minmax) Type of rejection
(project= no) Project highest dimension of input images?
(outtype= real) Output image pixel datatype
(outlimi= ) Output limits (x1 x2 y1 y2 ...)
(offsets= none) Input image offsets
(masktyp= none) Mask type
(maskval= 0) Mask value
(blank = 0.) Value if there are no pixels
(scale = none) Image scaling
(zero = none) Image zero point offset
(weight = none) Image weights
(statsec= ) Image section for computing statistics
(expname= ) Image header exposure time keyword
(lthresh= INDEF) Lower threshold
(hthresh= INDEF) Upper threshold
(nlow = 5) minmax: Number of low pixels to reject
(nhigh = 5) minmax: Number of high pixels to reject
(nkeep = 1) Minimum to keep (pos) or maximum to reject (neg)
(mclip = yes) Use median in sigma clipping algorithms?
(lsigma = 3.) Lower sigma clipping factor
(hsigma = 3.) Upper sigma clipping factor
(rdnoise= 0.) ccdclip: CCD readout noise (electrons)
(gain = 1.) ccdclip: CCD gain (electrons/DN)
(snoise = 0.) ccdclip: Sensitivity noise (fraction)
(sigscal= 0.1) Tolerance for sigma clipping scaling corrections
(pclip = -0.5) pclip: Percentile clipping parameter
(grow = 0.) Radius (pixels) for neighbor rejection
(mode = ql)
------------------
OUTPUT for test1.fits
------------------
Jun 5 19:45: IMCOMBINE
combine = median, scale = none, zero = none, weight = none
reject = minmax, nlow = 30, nhigh = 30
blank = 0.
Images
p525-gvp526.df_imsubf.fits_imshft.fits
p527-gvp526.df_imsubf.fits_imshft.fits
p530-gvp529.df_imsubf.fits_imshft.fits
p541-gvp540.df_imsubf.fits_imshft.fits
p542-gvp543.df_imsubf.fits_imshft.fits
p544-gvp543.df_imsubf.fits_imshft.fits
p547-gvp546.df_imsubf.fits_imshft.fits
p548-gvp549.df_imsubf.fits_imshft.fits
p550-gvp549.df_imsubf.fits_imshft.fits
p551-gvp552.df_imsubf.fits_imshft.fits
p553-gvp552.df_imsubf.fits_imshft.fits
p554-gvp555.df_imsubf.fits_imshft.fits
p556-gvp555.df_imsubf.fits_imshft.fits
p558-gvp557.df_imsubf.fits_imshft.fits
p559-gvp560.df_imsubf.fits_imshft.fits
p561-gvp560.df_imsubf.fits_imshft.fits
p562-gvp563.df_imsubf.fits_imshft.fits
p564-gvp563.df_imsubf.fits_imshft.fits
p565-gvp566.df_imsubf.fits_imshft.fits
p567-gvp566.df_imsubf.fits_imshft.fits
p568-gvp569.df_imsubf.fits_imshft.fits
p570-gvp569.df_imsubf.fits_imshft.fits
p571-gvp572.df_imsubf.fits_imshft.fits
p573-gvp572.df_imsubf.fits_imshft.fits
p578-gvp577.df_imsubf.fits_imshft.fits
p626-gvp625.df_imsubf.fits_imshft.fits
p627-gvp628.df_imsubf.fits_imshft.fits
p629-gvp628.df_imsubf.fits_imshft.fits
p630-gvp631.df_imsubf.fits_imshft.fits
p632-gvp631.df_imsubf.fits_imshft.fits
p633-gvp634.df_imsubf.fits_imshft.fits
p643-gvp642.df_imsubf.fits_imshft.fits
p644-gvp645.df_imsubf.fits_imshft.fits
p647-gvp648.df_imsubf.fits_imshft.fits
p649-gvp648.df_imsubf.fits_imshft.fits
p650-gvp651.df_imsubf.fits_imshft.fits
p652-gvp651.df_imsubf.fits_imshft.fits
p653-gvp654.df_imsubf.fits_imshft.fits
p655-gvp654.df_imsubf.fits_imshft.fits
p656-gvp657.df_imsubf.fits_imshft.fits
p658-gvp657.df_imsubf.fits_imshft.fits
p660-gvp659.df_imsubf.fits_imshft.fits
p661-gvp662.df_imsubf.fits_imshft.fits
p663-gvp662.df_imsubf.fits_imshft.fits
p664-gvp665.df_imsubf.fits_imshft.fits
p666-gvp665.df_imsubf.fits_imshft.fits
p667-gvp668.df_imsubf.fits_imshft.fits
p669-gvp668.df_imsubf.fits_imshft.fits
p670-gvp671.df_imsubf.fits_imshft.fits
p672-gvp671.df_imsubf.fits_imshft.fits
p673-gvp674.df_imsubf.fits_imshft.fits
p675-gvp674.df_imsubf.fits_imshft.fits
p677-gvp676.df_imsubf.fits_imshft.fits
p678-gvp679.df_imsubf.fits_imshft.fits
p680-gvp679.df_imsubf.fits_imshft.fits
p681-gvp682.df_imsubf.fits_imshft.fits
p683-gvp682.df_imsubf.fits_imshft.fits
p684-gvp685.df_imsubf.fits_imshft.fits
p686-gvp685.df_imsubf.fits_imshft.fits
p687-gvp688.df_imsubf.fits_imshft.fits
p689-gvp688.df_imsubf.fits_imshft.fits
p690-gvp691.df_imsubf.fits_imshft.fits
p692-gvp691.df_imsubf.fits_imshft.fits
p694-gvp693.df_imsubf.fits_imshft.fits
p695-gvp696.df_imsubf.fits_imshft.fits
p697-gvp696.df_imsubf.fits_imshft.fits
p698-gvp699.df_imsubf.fits_imshft.fits
p700-gvp699.df_imsubf.fits_imshft.fits
p701-gvp702.df_imsubf.fits_imshft.fits
p703-gvp702.df_imsubf.fits_imshft.fits
p704-gvp705.df_imsubf.fits_imshft.fits
p706-gvp705.df_imsubf.fits_imshft.fits
p707-gvp708.df_imsubf.fits_imshft.fits
p709-gvp708.df_imsubf.fits_imshft.fits
p710-gvp711.df_imsubf.fits_imshft.fits
p728-gvp727.df_imsubf.fits_imshft.fits
p729-gvp730.df_imsubf.fits_imshft.fits
p731-gvp730.df_imsubf.fits_imshft.fits
p732-gvp733.df_imsubf.fits_imshft.fits
p734-gvp733.df_imsubf.fits_imshft.fits
p735-gvp736.df_imsubf.fits_imshft.fits
p737-gvp736.df_imsubf.fits_imshft.fits
p738-gvp739.df_imsubf.fits_imshft.fits
p740-gvp739.df_imsubf.fits_imshft.fits
p741-gvp742.df_imsubf.fits_imshft.fits
p743-gvp742.df_imsubf.fits_imshft.fits
Output image = test1, ncombine = 86
-----------------------------
Output for test2.fits
-----------------------------
Jun 5 19:48: IMCOMBINE
combine = median, scale = none, zero = none, weight = none
reject = minmax, nlow = 5, nhigh = 5
blank = 0.
Images
p525-gvp526.df_imsubf.fits_imshft.fits
p527-gvp526.df_imsubf.fits_imshft.fits
p530-gvp529.df_imsubf.fits_imshft.fits
p541-gvp540.df_imsubf.fits_imshft.fits
p542-gvp543.df_imsubf.fits_imshft.fits
p544-gvp543.df_imsubf.fits_imshft.fits
p547-gvp546.df_imsubf.fits_imshft.fits
p548-gvp549.df_imsubf.fits_imshft.fits
p550-gvp549.df_imsubf.fits_imshft.fits
p551-gvp552.df_imsubf.fits_imshft.fits
p553-gvp552.df_imsubf.fits_imshft.fits
p554-gvp555.df_imsubf.fits_imshft.fits
p556-gvp555.df_imsubf.fits_imshft.fits
p558-gvp557.df_imsubf.fits_imshft.fits
p559-gvp560.df_imsubf.fits_imshft.fits
p561-gvp560.df_imsubf.fits_imshft.fits
p562-gvp563.df_imsubf.fits_imshft.fits
p564-gvp563.df_imsubf.fits_imshft.fits
p565-gvp566.df_imsubf.fits_imshft.fits
p567-gvp566.df_imsubf.fits_imshft.fits
p568-gvp569.df_imsubf.fits_imshft.fits
p570-gvp569.df_imsubf.fits_imshft.fits
p571-gvp572.df_imsubf.fits_imshft.fits
p573-gvp572.df_imsubf.fits_imshft.fits
p578-gvp577.df_imsubf.fits_imshft.fits
p626-gvp625.df_imsubf.fits_imshft.fits
p627-gvp628.df_imsubf.fits_imshft.fits
p629-gvp628.df_imsubf.fits_imshft.fits
p630-gvp631.df_imsubf.fits_imshft.fits
p632-gvp631.df_imsubf.fits_imshft.fits
p633-gvp634.df_imsubf.fits_imshft.fits
p643-gvp642.df_imsubf.fits_imshft.fits
p644-gvp645.df_imsubf.fits_imshft.fits
p647-gvp648.df_imsubf.fits_imshft.fits
p649-gvp648.df_imsubf.fits_imshft.fits
p650-gvp651.df_imsubf.fits_imshft.fits
p652-gvp651.df_imsubf.fits_imshft.fits
p653-gvp654.df_imsubf.fits_imshft.fits
p655-gvp654.df_imsubf.fits_imshft.fits
p656-gvp657.df_imsubf.fits_imshft.fits
p658-gvp657.df_imsubf.fits_imshft.fits
p660-gvp659.df_imsubf.fits_imshft.fits
p661-gvp662.df_imsubf.fits_imshft.fits
p663-gvp662.df_imsubf.fits_imshft.fits
p664-gvp665.df_imsubf.fits_imshft.fits
p666-gvp665.df_imsubf.fits_imshft.fits
p667-gvp668.df_imsubf.fits_imshft.fits
p669-gvp668.df_imsubf.fits_imshft.fits
p670-gvp671.df_imsubf.fits_imshft.fits
p672-gvp671.df_imsubf.fits_imshft.fits
p673-gvp674.df_imsubf.fits_imshft.fits
p675-gvp674.df_imsubf.fits_imshft.fits
p677-gvp676.df_imsubf.fits_imshft.fits
p678-gvp679.df_imsubf.fits_imshft.fits
p680-gvp679.df_imsubf.fits_imshft.fits
p681-gvp682.df_imsubf.fits_imshft.fits
p683-gvp682.df_imsubf.fits_imshft.fits
p684-gvp685.df_imsubf.fits_imshft.fits
p686-gvp685.df_imsubf.fits_imshft.fits
p687-gvp688.df_imsubf.fits_imshft.fits
p689-gvp688.df_imsubf.fits_imshft.fits
p690-gvp691.df_imsubf.fits_imshft.fits
p692-gvp691.df_imsubf.fits_imshft.fits
p694-gvp693.df_imsubf.fits_imshft.fits
p695-gvp696.df_imsubf.fits_imshft.fits
p697-gvp696.df_imsubf.fits_imshft.fits
p698-gvp699.df_imsubf.fits_imshft.fits
p700-gvp699.df_imsubf.fits_imshft.fits
p701-gvp702.df_imsubf.fits_imshft.fits
p703-gvp702.df_imsubf.fits_imshft.fits
p704-gvp705.df_imsubf.fits_imshft.fits
p706-gvp705.df_imsubf.fits_imshft.fits
p707-gvp708.df_imsubf.fits_imshft.fits
p709-gvp708.df_imsubf.fits_imshft.fits
p710-gvp711.df_imsubf.fits_imshft.fits
p728-gvp727.df_imsubf.fits_imshft.fits
p729-gvp730.df_imsubf.fits_imshft.fits
p731-gvp730.df_imsubf.fits_imshft.fits
p732-gvp733.df_imsubf.fits_imshft.fits
p734-gvp733.df_imsubf.fits_imshft.fits
p735-gvp736.df_imsubf.fits_imshft.fits
p737-gvp736.df_imsubf.fits_imshft.fits
p738-gvp739.df_imsubf.fits_imshft.fits
p740-gvp739.df_imsubf.fits_imshft.fits
p741-gvp742.df_imsubf.fits_imshft.fits
p743-gvp742.df_imsubf.fits_imshft.fits
Output image = test2, ncombine = 86
Francisco Valdes wrote on Jun 05, 2008
Hello,
This is occurring because you are using "combine=median". If you use "average" then it will do what you expect. The purpose of minmax is so that you use an average which is more efficient (in the statistical sense) than median with good, unbiased outlier rejection. Because minmax and median are both basically sorting the pixel values and you reject the same number of high and low then the median value will be the same with and without the minmax rejection.
Yours,
Frank Valdes
This is occurring because you are using "combine=median". If you use "average" then it will do what you expect. The purpose of minmax is so that you use an average which is more efficient (in the statistical sense) than median with good, unbiased outlier rejection. Because minmax and median are both basically sorting the pixel values and you reject the same number of high and low then the median value will be the same with and without the minmax rejection.
Yours,
Frank Valdes
Last post on Jun 05, 2008