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# XT_STAT -- Compute statistics from a sample.
#
# The sample array will be sorted.
define NMIN 10 # Minimum number of pixels for mode calculation
define ZSTEP 0.01 # Step size for search for mode
define ZBIN 0.1 # Bin size for mode.
procedure xt_stats (sample, nsample, frac, mean, sigma, median, mode)
short sample[nsample] #I Sample
int nsample #I Number of sample pixels
real frac #I Fraction of data to use
real mean, sigma, median, mode #O Statistics
int i, j, k, nmax
real z1, z2, zstep, zbin
bool fp_equalr()
begin
# Sort the sample.
call asrts (sample, sample, nsample)
# Set fraction to use.
i = max (1, 1 + nsample * (1. - frac) / 2.)
j = min (nsample, 1 + nsample * (1. + frac) / 2.)
z1 = sample[i]
z2 = sample[j]
# Compute the mean and sigma.
call aavgs (sample[i], j-i+1, mean, sigma)
# Compute the median.
median = sample[nsample/2]
z1 = median - 2 * sigma
if (z1 < sample[1])
i = 1
else {
k = i
do i = k, 2, -1 {
if (sample[i] <= z1)
break
}
}
z1 = sample[i]
z2 = median + 2 * sigma
if (z2 > sample[nsample])
i = nsample
else {
k = j
do j = k, nsample-1 {
if (sample[j] >= z1)
break
}
}
z2 = sample[j]
# Compute the mode.
if (nsample < NMIN)
mode = median
else if (fp_equalr (z1, z2))
mode = z1
else {
zstep = ZSTEP * sigma
zbin = ZBIN * sigma
zstep = max (1., zstep)
zbin = max (1., zbin)
z1 = z1 - zstep
k = i
nmax = 0
repeat {
z1 = z1 + zstep
z2 = z1 + zbin
for (; i < j && sample[i] < z1; i=i+1)
;
for (; k < j && sample[k] < z2; k=k+1)
;
if (k - i > nmax) {
nmax = k - i
mode = sample[(i+k)/2]
}
} until (k >= j)
}
end
procedure xt_stati (sample, nsample, frac, mean, sigma, median, mode)
int sample[nsample] #I Sample
int nsample #I Number of sample pixels
real frac #I Fraction of data to use
real mean, sigma, median, mode #O Statistics
int i, j, k, nmax
real z1, z2, zstep, zbin
bool fp_equalr()
begin
# Sort the sample.
call asrti (sample, sample, nsample)
# Set fraction to use.
i = max (1, 1 + nsample * (1. - frac) / 2.)
j = min (nsample, 1 + nsample * (1. + frac) / 2.)
z1 = sample[i]
z2 = sample[j]
# Compute the mean and sigma.
call aavgi (sample[i], j-i+1, mean, sigma)
# Compute the median.
median = sample[nsample/2]
z1 = median - 2 * sigma
if (z1 < sample[1])
i = 1
else {
k = i
do i = k, 2, -1 {
if (sample[i] <= z1)
break
}
}
z1 = sample[i]
z2 = median + 2 * sigma
if (z2 > sample[nsample])
i = nsample
else {
k = j
do j = k, nsample-1 {
if (sample[j] >= z1)
break
}
}
z2 = sample[j]
# Compute the mode.
if (nsample < NMIN)
mode = median
else if (fp_equalr (z1, z2))
mode = z1
else {
zstep = ZSTEP * sigma
zbin = ZBIN * sigma
zstep = max (1., zstep)
zbin = max (1., zbin)
z1 = z1 - zstep
k = i
nmax = 0
repeat {
z1 = z1 + zstep
z2 = z1 + zbin
for (; i < j && sample[i] < z1; i=i+1)
;
for (; k < j && sample[k] < z2; k=k+1)
;
if (k - i > nmax) {
nmax = k - i
mode = sample[(i+k)/2]
}
} until (k >= j)
}
end
procedure xt_statr (sample, nsample, frac, mean, sigma, median, mode)
real sample[nsample] #I Sample
int nsample #I Number of sample pixels
real frac #I Fraction of data to use
real mean, sigma, median, mode #O Statistics
int i, j, k, nmax
real z1, z2, zstep, zbin
bool fp_equalr()
begin
# Sort the sample.
call asrtr (sample, sample, nsample)
# Set fraction to use.
i = max (1, 1 + nsample * (1. - frac) / 2.)
j = min (nsample, 1 + nsample * (1. + frac) / 2.)
z1 = sample[i]
z2 = sample[j]
# Compute the mean and sigma.
call aavgr (sample[i], j-i+1, mean, sigma)
# Compute the median.
median = sample[nsample/2]
z1 = median - 2 * sigma
if (z1 < sample[1])
i = 1
else {
k = i
do i = k, 2, -1 {
if (sample[i] <= z1)
break
}
}
z1 = sample[i]
z2 = median + 2 * sigma
if (z2 > sample[nsample])
i = nsample
else {
k = j
do j = k, nsample-1 {
if (sample[j] >= z1)
break
}
}
z2 = sample[j]
# Compute the mode.
if (nsample < NMIN)
mode = median
else if (fp_equalr (z1, z2))
mode = z1
else {
zstep = ZSTEP * sigma
zbin = ZBIN * sigma
z1 = z1 - zstep
k = i
nmax = 0
repeat {
z1 = z1 + zstep
z2 = z1 + zbin
for (; i < j && sample[i] < z1; i=i+1)
;
for (; k < j && sample[k] < z2; k=k+1)
;
if (k - i > nmax) {
nmax = k - i
mode = sample[(i+k)/2]
}
} until (k >= j)
}
end
procedure xt_statd (sample, nsample, frac, mean, sigma, median, mode)
double sample[nsample] #I Sample
int nsample #I Number of sample pixels
real frac #I Fraction of data to use
double mean, sigma, median, mode #O Statistics
int i, j, k, nmax
double z1, z2, zstep, zbin
bool fp_equald()
begin
# Sort the sample.
call asrtd (sample, sample, nsample)
# Set fraction to use.
i = max (1, 1 + nsample * (1. - frac) / 2.)
j = min (nsample, 1 + nsample * (1. + frac) / 2.)
z1 = sample[i]
z2 = sample[j]
# Compute the mean and sigma.
call aavgd (sample[i], j-i+1, mean, sigma)
# Compute the median.
median = sample[nsample/2]
z1 = median - 2 * sigma
if (z1 < sample[1])
i = 1
else {
k = i
do i = k, 2, -1 {
if (sample[i] <= z1)
break
}
}
z1 = sample[i]
z2 = median + 2 * sigma
if (z2 > sample[nsample])
i = nsample
else {
k = j
do j = k, nsample-1 {
if (sample[j] >= z1)
break
}
}
z2 = sample[j]
# Compute the mode.
if (nsample < NMIN)
mode = median
else if (fp_equald (z1, z2))
mode = z1
else {
zstep = ZSTEP * sigma
zbin = ZBIN * sigma
z1 = z1 - zstep
k = i
nmax = 0
repeat {
z1 = z1 + zstep
z2 = z1 + zbin
for (; i < j && sample[i] < z1; i=i+1)
;
for (; k < j && sample[k] < z2; k=k+1)
;
if (k - i > nmax) {
nmax = k - i
mode = sample[(i+k)/2]
}
} until (k >= j)
}
end
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