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rotfind stsdas.analysis.dither


NAME · USAGE · PARAMETERS · DESCRIPTION · EXAMPLES · BUGS · REFERENCES
SEE_ALSO

NAME

rotfind -- Finds rotation angle between two single-group images by examining a set of cross-correlation images which span a range of angles.

USAGE

rotfind input output

PARAMETERS

input [string]
Root name for the cross-correlation image family.
output [file name]
Output ASCII file with results.
(xcenter, ycenter = INDEF) [real]
Position of search box in image. If INDEF, the search box is centered in the image.
(boxsize = INDEF) [real]
Size of box where to search for peak. If INDEF, the full image is scanned (thus xcenter,ycenter are not used).
(fwhm = 7.) [real]
FWHM of the Gaussian used to fit cross-correlation peaks.
(ellip = 0.05) [real]
Ellipticity (1 - b/a) of the Gaussian used to fit cross-correlation peaks.
(pa = 45.) [real]
Position angle (in degress) of the Gaussian used to fit cross-correlation peaks. Position angle increases counterclockwise from the +x direction.
(fitbox = 7) [int]
Size of box where the 2-D Gaussian fit takes place.
(kellip = yes) [bool]
Keep ellipticity fixed during fit ?
(kpa = yes) [bool]
Keep position angle fixed during fit ?
(intable = "") [file name]
Optional table with angle vs. peak pairs.
(tempdir = "tmp$") [path name]
Path to directory where temporary files are kept.
(interactive = yes) [bool]
Interactive mode ?
(verbose = yes) [boolean]
Print out log?

DESCRIPTION

Rotfind examines a set of cross-correlation images, each of which was produced by crossdriz to determine the rotation angle between the two original images input into crossdriz.

The set of images produced by crossdriz has one image for each of a series of test angles. The cross-correlation image whose test angle most closely approximates the true rotation angle between the original images should have the highest cross-correlation peak. Rotfind determines the correct angle by fitting a polynomial function of rotation angle to the observed peaks, and then interpolating to find the maximum. The default polynomial is a parabola, since all continuously differentiable functions behave like a quadratic near a maximum. After finding the peak rotation angle, rotfind returns to the test images closest in rotation and interpolates intepolates the shift in x and y between the two images (note: usually the dependence of shift on rotation angle is extremely small).

The polynomial fitting is performed by the nfit1d task in the stsdas.analysis.fitting package. This task can be invoked in interactive mode and its interactive resources used to perform the fit. We recommend that a new user - and perhaps all users - perform these fits in interactive mode. The :help command will show the various options of nfit1d. Among these are changing the order of the polynomial, and the range of the fit. When changing the order of the polynomial, the user may find it helpful to change the default starting value of the higher order term from zero, to avoid being caught in a local extremum.

As the user may determine slightly different rotation angles from the different groups of a WFPC2 image, the task also supplies the derivative of the estimated shift with rotation angle, so that if the user decides to employ a sligtly different rotation angle in subsequent analysis, he/she can modify the shift appropirately. In practice, this derivative is often negligible when applied to the typically small errors in rotation angle.

The search and fit for the cross-correlation peaks on the individual images is performed as in shiftfind, and the associated parameters are the same as in shiftfind. Users should see the shiftfind help file for more information.

EXAMPLES

Here we run rotfind on a series of 12 crosscorrelation images produced by crossdriz. These images v2x1_01 ... v2x1_12 are successively read in and a peak found. The user the is allowed to examine the fit and in this case approves. An output best fit is found.

di> rotfind v2x1* g2vX.rot
Cross-corr image template/list (v2x1*.hhh): 
Output file name (g4i.rot): g2vX.rot
Making file list...
Making table with rough peak values...
Updating peak values with Gaussian fit...

table file:  tmp$dither4646g

Original file:  V2x1g2_01.hhh
Time:           Thu 09:01:29 13-Nov-97
Row:            1
Function:  twodgaus       chi-square: 4.03E9        rms: 61430.21
Units:     Auto           npts:   49
Ampl. =  8.379E6          X     =    374.2          FWHM  =    18.05
Back. =       0.          Y     =    343.3          Ellip =     0.05
                                                    Ang.  =      45.

Original file:  V2x1g2_02.hhh
Time:           Thu 09:01:29 13-Nov-97
Row:            2
Function:  twodgaus       chi-square: 4.33E9        rms: 63676.75
Units:     Auto           npts:   49
Ampl. =  8.534E6          X     =    374.1          FWHM  =    17.11
Back. =       0.          Y     =    343.4          Ellip =     0.05
                                                    Ang.  =      45.

Original file:  V2x1g2_03.hhh
Time:           Thu 09:01:30 13-Nov-97
Row:            3
Function:  twodgaus       chi-square: 5.83E9        rms: 73905.38
Units:     Auto           npts:   49
Ampl. =  8.689E6          X     =     374.          FWHM  =    16.16
Back. =       0.          Y     =    343.5          Ellip =     0.05
                                                    Ang.  =      45.
.
.
.
.  Original file:  V2x1g2_11.hhh 
Time:           Thu 09:01:33 13-Nov-97
Row:            11
Function:  twodgaus       chi-square: 4.67E9        rms: 66161.11
Units:     Auto           npts:   49
Ampl. =  8.669E6          X     =    373.2          FWHM  =    16.38
Back. =       0.          Y     =    344.1          Ellip =     0.05
                                                    Ang.  =      45.

Original file:  V2x1g2_12.hhh
Time:           Thu 09:01:34 13-Nov-97
Row:            12
Function:  twodgaus       chi-square: 3.83E9        rms: 59917.18
Units:     Auto           npts:   49
Ampl. =  8.526E6          X     =    373.1          FWHM  =    17.21
Back. =       0.          Y     =    344.1          Ellip =     0.05
                                                    Ang.  =      45.


Final solution:  Xshift   = -22.560116084 	Error = 0.067416587844515
                 Yshift   = 7.232406536 	Error = 0.098611152042179
                 Rotation = 2.390201 		Error = 0.003070099
                  dx/dr   = -0.71599999999989 	dy/dr  = 0.66400000000044

BUGS

The 2-D Gaussian peak fitting routine can be quite sensitive to first guess values for the Gaussian parameters. If one does not get reasonable results using the task's default values, the user should experiment with the boxsize, kellip and kpa controls to restrict the peak search to a smaller region and to allow more flexibility in the fit routine.

REFERENCES

This task was written by I. Busko in collabortion with A. Fruchter. More information can be found in A. S. Fruchter, R. N. Hook, I. C. Busko, and M. Mutchler, 1997, "A Package for the Reduction of Dithered Undersampled Images", in "The 1997 HST Calibration Workshop", S. Casertano et al., ed. (Baltimore: Space Telescope Science Institute), in press. See http://www.stsci.edu/meetings/cal97.

SEE ALSO

crossdriz, shiftfind, n2gaussfit, nfit1d


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