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include <math.h>
include "../lib/find.h"
# Set up the gaussian fitting structure.
# AP_EGPARAMS -- Calculate the parameters of the elliptical Gaussian needed
# to compute the kernel.
procedure ap_egparams (sigma, ratio, theta, nsigma, a, b, c, f, nx, ny)
real sigma # sigma of Gaussian in x
real ratio # Ratio of half-width in y to x
real theta # position angle of Gaussian
real nsigma # limit of convolution
real a, b, c, f # ellipse parameters
int nx, ny # dimensions of the kernel
real sx2, sy2, cost, sint, discrim
bool fp_equalr ()
begin
# Define some temporary variables.
sx2 = sigma ** 2
sy2 = (ratio * sigma) ** 2
cost = cos (DEGTORAD (theta))
sint = sin (DEGTORAD (theta))
# Compute the ellipse parameters.
if (fp_equalr (ratio, 0.0)) {
if (fp_equalr (theta, 0.0) || fp_equalr (theta, 180.)) {
a = 1. / sx2
b = 0.0
c = 0.0
} else if (fp_equalr (theta, 90.0)) {
a = 0.0
b = 0.0
c = 1. / sx2
} else
call error (0, "AP_EGPARAMS: Cannot make 1D Gaussian.")
f = nsigma ** 2 / 2.
nx = 2 * int (max (sigma * nsigma * abs (cost), RMIN)) + 1
ny = 2 * int (max (sigma * nsigma * abs (sint), RMIN)) + 1
} else {
a = cost ** 2 / sx2 + sint ** 2 / sy2
b = 2. * (1.0 / sx2 - 1.0 / sy2) * cost * sint
c = sint ** 2 / sx2 + cost ** 2 / sy2
discrim = b ** 2 - 4. * a * c
f = nsigma ** 2 / 2.
nx = 2 * int (max (sqrt (-8. * c * f / discrim), RMIN)) + 1
ny = 2 * int (max (sqrt (-8. * a * f / discrim), RMIN)) + 1
}
end
# AP_EGKERNEL -- Compute the elliptical Gaussian kernel.
real procedure ap_egkernel (gkernel, ngkernel, dkernel, skip, nx, ny, gsums, a,
b, c, f)
real gkernel[nx,ny] # output Gaussian amplitude kernel
real ngkernel[nx,ny] # output normalized Gaussian amplitude kernel
real dkernel[nx,ny] # output Gaussian sky kernel
int skip[nx,ny] # output skip subraster
int nx, ny # input dimensions of the kernel
real gsums[ARB] # output array of gsums
real a, b, c, f # ellipse parameters
int i, j, x0, y0, x, y
real npts, rjsq, rsq, relerr,ef
begin
# Initialize.
x0 = nx / 2 + 1
y0 = ny / 2 + 1
gsums[GAUSS_SUMG] = 0.0
gsums[GAUSS_SUMGSQ] = 0.0
npts = 0.0
# Compute the kernel and principal sums.
do j = 1, ny {
y = j - y0
rjsq = y ** 2
do i = 1, nx {
x = i - x0
rsq = sqrt (x ** 2 + rjsq)
ef = 0.5 * (a * x ** 2 + c * y ** 2 + b * x * y)
gkernel[i,j] = exp (-1.0 * ef)
if (ef <= f || rsq <= RMIN) {
#gkernel[i,j] = exp (-ef)
ngkernel[i,j] = gkernel[i,j]
dkernel[i,j] = 1.0
gsums[GAUSS_SUMG] = gsums[GAUSS_SUMG] + gkernel[i,j]
gsums[GAUSS_SUMGSQ] = gsums[GAUSS_SUMGSQ] +
gkernel[i,j] ** 2
skip[i,j] = NO
npts = npts + 1.0
} else {
#gkernel[i,j] = 0.0
ngkernel[i,j] = 0.0
dkernel[i,j] = 0.0
skip[i,j] = YES
}
}
}
# Store the remaining sums.
gsums[GAUSS_PIXELS] = npts
gsums[GAUSS_DENOM] = gsums[GAUSS_SUMGSQ] - gsums[GAUSS_SUMG] ** 2 /
npts
gsums[GAUSS_SGOP] = gsums[GAUSS_SUMG] / npts
# Normalize the amplitude kernel.
do j = 1, ny {
do i = 1, nx {
if (skip[i,j] == NO)
ngkernel[i,j] = (gkernel[i,j] - gsums[GAUSS_SGOP]) /
gsums[GAUSS_DENOM]
}
}
# Normalize the sky kernel
do j = 1, ny {
do i = 1, nx {
if (skip[i,j] == NO)
dkernel[i,j] = dkernel[i,j] / npts
}
}
relerr = 1.0 / gsums[GAUSS_DENOM]
return (sqrt (relerr))
end
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