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pdl::fit::gaussian(3) [redhat man page]

Gaussian(3)						User Contributed Perl Documentation					       Gaussian(3)

NAME

       PDL::Fit::Gaussian - routines for fitting gaussians

DESCRIPTION

       This module contains some custom gaussian fitting routines.  These were developed in collaboration with Alison Offer, they do a reasonably
       robust job and are quite useful.

       Gaussian fitting is something I do a lot of, so I figured it was worth putting in my special code.

       Note this code is also used in the Karma package.

SYNOPSIS

	       use PDL::Fit::Gaussian;
	       ($cen, $pk, $fwhm, $back, $err, $fit) = fitgauss1d($x, $data);
	       ($pk, $fwhm, $back, $err, $fit) = fitgauss1dr($r, $data);

FUNCTIONS

       fitgauss1d

       Fit 1D Gassian to data piddle

       ($cen, $pk, $fwhm2, $back, $err, $fit) = fitgauss1d($x, $data);

       ($cen, $pk, $fwhm2, $back, $err, $fit) = fitgauss1d($x, $data);

       xval(n); data(n); [o]xcentre();[o]peak_ht(); [o]fwhm(); [o]background();int [o]err(); [o]datafit(n); [t]sig(n);	[t]xtmp(n); [t]ytmp(n);
       [t]yytmp(n); [t]rtmp(n);

       Fit's a 1D Gaussian robustly free parameters are the centre, peak height, FWHM. The background is NOT fit, because I find this is generally
       unreliable, rather a median is determined in the 'outer' 10% of pixels (i.e. those at the start/end of the data piddle). The initial esti-
       mate of the FWHM is the length of the piddle/3, so it might fail if the piddle is too long. (This is non-robust anyway). Most data does
       just fine and this is a good default gaussian fitter.

       SEE ALSO: fitgauss1dr() for fitting radial gaussians

       fitgauss1dr

       Fit 1D Gassian to radial data piddle

       ($pk, $fwhm2, $back, $err, $fit) = fitgauss1dr($r, $data);

       ($pk, $fwhm2, $back, $err, $fit) = fitgauss1dr($r, $data);

       xval(n); data(n); [o]peak_ht(); [o]fwhm(); [o]background();int [o]err(); [o]datafit(n); [t]sig(n);  [t]xtmp(n); [t]ytmp(n); [t]yytmp(n);
       [t]rtmp(n);

       Fit's a 1D radial Gaussian robustly free parameters are the peak height, FWHM. Centre is assumed to be X=0 (i.e. start of piddle).  The
       background is NOT fit, because I find this is generally unreliable, rather a median is determined in the 'outer' 10% of pixels (i.e. those
       at the end of the data piddle). The initial estimate of the FWHM is the length of the piddle/3, so it might fail if the piddle is too long.
       (This is non-robust anyway). Most data does just fine and this is a good default gaussian fitter.

       SEE ALSO: fitgauss1d() to fit centre as well.

BUGS

       May not converge for weird data, still pretty good!

AUTHOR

       This file copyright (C) 1999, Karl Glazebrook (kgb@aaoepp.aao.gov.au), Gaussian fitting code by Alison Offer (aro@aaocbn.aao.gov.au).  All
       rights reserved. There is no warranty. You are allowed to redistribute this software / documentation under certain conditions. For details,
       see the file COPYING in the PDL distribution. If this file is separated from the PDL distribution, the copyright notice should be included
       in the file.

perl v5.8.0							    2003-01-29							       Gaussian(3)

Check Out this Related Man Page

Polynomial(3)						User Contributed Perl Documentation					     Polynomial(3)

NAME

       PDL::Fit::Polynomial - routines for fitting with polynomials

DESCRIPTION

       This module contains routines for doing simple polynomial fits to data

SYNOPSIS

	   $yfit = fitpoly1d $data;

FUNCTIONS

       fitpoly1d

       Fit 1D polynomials to data using min chi^2 (least squares)

	Usage: ($yfit, [$coeffs]) = fitpoly1d [$xdata], $data, $order, [Options...]

       Signature: (x(n); y(n); [o]yfit(n); [o]coeffs(order))

       Uses a standard matrix inversion method to do a least squares/min chi^2 polynomial fit to data. Order=2 is a linear fit (two parameters).

       Returns the fitted data and optionally the coefficients.

       One can thread over extra dimensions to do multiple fits (except the order can not be threaded over - i.e. it must be one fixed scalar num-
       ber like "4").

       The data is normalised internally to avoid overflows (using the mean of the abs value) which are common in large polynomial series but the
       returned fit, coeffs are in unnormalised units.

	 $yfit = fitpoly1d $data,2; # Least-squares line fit
	 ($yfit, $coeffs) = fitpoly1d $x, $y, 4; # Fit a cubic

	 $fitimage = fitpoly1d $image,2  # Fit a quadratic to each row of an image

	 $myfit = fitpoly1d $line, 2, {Weights => $w}; # Weighted fit

	 Options:
	    Weights    Weights to use in fit, e.g. 1/$sigma**2 (default=1)

BUGS

       May not work too well for data with large dynamic range.

SEE ALSO

       "polyfit" in PDL::Slatec

AUTHOR

       This file copyright (C) 1999, Karl Glazebrook (kgb@aaoepp.aao.gov.au).  All rights reserved. There is no warranty. You are allowed to
       redistribute this software documentation under certain conditions. For details, see the file COPYING in the PDL distribution. If this file
       is separated from the PDL distribution, the copyright notice should be included in the file.

perl v5.8.0							    2001-07-13							     Polynomial(3)
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