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n2gaussfit stsdas.analysis.fitting



n2gaussfit -- Fit a 2-dimensional elliptical Gaussian function to image data.


ngaussfit input output


This task is a simple tool that can be used to fit a 2-dimensional elliptical Gaussian to one or more image sections. Chi-square fitting is performed by either the downhill simplex minimization algorithm ("amoeba") or the Levenberg-Marquardt algorithm.

Multiple image sections may be processed simultaneously using wildcard characters in the file name template. Fit results will be written to a STSDAS table. The table format is described below. If the table does not exist, it will be created. Otherwise, the fit results will be appended as the last row in the table. Any table previously created by other tasks in this package is suitable as output.

The functional form fitted to the data is an elliptical Gaussian represented by seven parameters: ampl, xcent, ycent, fwhm, ellip, theta and a (the background value). Amplitudes are specified above a constant background. If other background trends are present in the data, they must be removed prior to running this task. Positions and fwhm are given in pixel units, and are relative to any possible image section specification given in input. To force a "round" Gaussian fit, ellip must be set to zero, and vellip to no. ellip is defined as (1 - b/a), where a is the major axis length and b is the minor axis length. Position angles are defined in degrees, counterclockwise (towards +Y) from the +X direction.

The fitting algorithms need initial guesses for the function coefficients. Guesses can be specified through task parameters, or from a previously generated table. A parameter set (pset) is used for function coefficients at startup. See help for the tgausspars pset.

You can specify which coefficients are to be varied and which are to be held fixed during the fitting process. This information is entered in the tgausspars pset, and is always read by the task, regardless of the source from which initial guesses are taken.

Error information, needed for properly computing chi-square, can be input in two ways. If the pixel data is subject to counting statistics, Poisson error bars can be assigned to each pixel. Or a constant error bar, the same for all pixels in the image, can be set. See help page for the errorpars pset.

The STSDAS output table contains the information described below. Each particular fit will result in a new line being appended to the table.

label           Contents
======          ========

file		Name of the original file for which the fit was performed.
time		Date and time original fit was performed.
function	Fitted function, 'twodgauss'.
ncoeff		7
unit		'*'
npoints		Number of data points used in the fit.
xmin, xmax	Not used.
chisq		Chi-square of fit
rms		Root mean square of fit.
coeff1		First coefficient (background).
err1		First coefficient error.
coeff2		Second coefficient (amplitude).
err2		Second coefficient error.

See the prfit task; it shows the table contents in a more user-friendly format. See also the funcform help page; it shows the actual function used in computing two-D gaussian values.


input [file name template]
List of images to be fitted.
output [file name]
Name of the STSDAS table to which fitting information will be written. If an empty string is used, no output is written to the table.
(rt = no) [boolean]
Read the initial coefficient guesses from a table?
(tablein = "") [string]
File name of the table from which to read the initial coefficient guesses.
(row) [integer, min=1]
Table row from which to read initial coefficient guesses. If more than one image is being fitted, the same values will be used for all images.
(tgausspars = "") [string]
The name of the pset containing the function-dependent Gaussian parameters.
(errorpars = "") [string]
The name of the file containing the error-related parameters (pset).
(controlpars = "") [string]
The name of the file containing the algorithm control parameters (pset).
(verbose = yes) [boolean]
Display results at the terminal?
(averbose = no) [boolean]
Display information about each amoeba iteration?


1. Fit a Gaussian to a section of the image test and store the fit results in the file testfit.db in the user's home directory:

  fi> n2gaussfit test[100:500,256:300] home$testfit.db


The iterative "amoeba" algorithm may misbehave in cases when ellip is very near zero. In this case, the position angle becomes ill-defined, and the algorithm can enter an endless loop. Do the fit in steps, keeping ellip fixed while varying the position angle, and vice-versa. Also, avoid very large (>500) values for maxit.


This task was written by I.Busko


i2gaussfit, errorpars, controlpars, funcform

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