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include "ms.h"
# T_FIT_GAUSS5 -- Fit the GAUSS5 model.
#
# This task selects the database, the sample lines to be modeled, the
# model fitting algorithm, whether to track models from one sample line
# to the next or model them independently.
procedure t_fit_gauss5 ()
char image[SZ_FNAME] # Image
int lines[3, MAX_RANGES] # Sample lines to be modeled
bool track # Track model solution
int start # Starting line for modeling
int naverage # Number of image lines to average
real lower # Starting point of profile
real upper # Ending point of profile
int i, nsamples, sample_start, sample, line, improved
int len_line, len_profile, nspectra, nparams
pointer ms, im
pointer sp, data, model, profiles, ranges, samples
int get_sample_line(), get_sample_lines()
int g5_fit1(), g5_fit2()
int clgeti(), clgranges(), btoi()
bool clgetb()
real clgetr()
pointer msmap(), immap()
include "fitgauss5.com"
begin
# Access the database and the image.
call clgstr ("image", image, SZ_FNAME)
ms = msmap (image, READ_WRITE, 0)
im = immap (image, READ_ONLY, 0)
# Get the task parameters.
i = clgranges ("lines", 1, MS_LEN(ms, 2), lines, MAX_RANGES)
i = clgranges ("spectra", 1, MS_NSPECTRA(ms), spectra, MAX_RANGES)
track = clgetb ("track")
start = clgeti ("start")
naverage = clgeti ("naverage")
lower = clgetr ("lower")
upper = clgetr ("upper")
factor = clgetr ("factor")
# Algorithm 1 fits the parameters selected in the parameters array
# simultaneously. Algorithm 2 does not require the user to specify
# the parameters.
algorithm = clgeti ("algorithm")
if (algorithm == 1) {
parameters[I0_INDEX] = btoi (clgetb ("fit_i0"))
parameters[X0_INDEX] = btoi (clgetb ("fit_x0"))
parameters[S0_INDEX] = btoi (clgetb ("fit_s0"))
parameters[S1_INDEX] = btoi (clgetb ("fit_s1"))
parameters[S2_INDEX] = btoi (clgetb ("fit_s2"))
}
# Select whether to smooth the shape parameters after fitting.
# If smoothing is desired get the spline smoothing parameters.
smooth[S0_INDEX] = btoi (clgetb ("smooth_s0"))
smooth[S1_INDEX] = btoi (clgetb ("smooth_s1"))
smooth[S2_INDEX] = btoi (clgetb ("smooth_s2"))
if ((smooth[S0_INDEX] == YES) || (smooth[S1_INDEX] == YES) ||
(smooth[S2_INDEX] == YES)) {
call ms_set_smooth (1., real(MS_LEN(ms, 1)), MS_NSPECTRA(ms))
}
call g5_set_verbose (clgetb ("verbose"))
call g5_prnt1 (image, naverage, track, start)
# Set the various array dimensions and allocate memory.
len_line = MS_LEN(ms, 1)
len_profile = nint (upper - lower + 2)
nspectra = MS_NSPECTRA(ms)
nparams = MS_NGAUSS5
call smark (sp)
call salloc (samples, MS_NSAMPLES(ms), TY_INT)
call salloc (data, len_line, TY_REAL)
call salloc (model, len_line, TY_REAL)
call salloc (profiles, len_profile * nspectra * nparams, TY_REAL)
call salloc (ranges, nspectra * LEN_RANGES, TY_REAL)
# Convert from image lines to sample lines.
nsamples = get_sample_lines (ms, lines, Memi[samples])
sample_start = get_sample_line (ms, start)
# Initialize forward tracking. If tracking get the initial parameters,
# model profiles and model line from the starting line.
if (track) {
call msggauss5 (ms, sample_start)
call mod_gauss5 (ms, lower, Memr[profiles], Memr[ranges],
len_profile, nspectra)
call set_model (ms, Memr[model], Memr[profiles], Memr[ranges],
len_line, len_profile, nspectra)
}
# Track forward from the starting line to the specified sample lines.
do i = 1, nsamples {
sample = Memi[samples + i - 1]
if (sample < sample_start)
next
line = LINE(ms, sample)
# Get the image data line.
call msgimage (im, line, naverage, Memr[data])
# If not tracking get the initial parameters, model profiles, and
# model line for the current line. Otherwise record the starting
# parameters.
if (!track) {
call msggauss5 (ms, sample)
call mod_gauss5 (ms, lower, Memr[profiles], Memr[ranges],
len_profile, nspectra)
call set_model (ms, Memr[model], Memr[profiles], Memr[ranges],
len_line, len_profile, nspectra)
} else
call mspgauss5 (ms, sample)
call g5_prnt2 (line, Memr[data], len_line)
# Do the model fitting using the selected algorithm.
switch (algorithm) {
case 1:
improved = g5_fit1 (ms, Memr[data], Memr[model], Memr[profiles],
Memr[ranges], lower, len_profile)
case 2:
improved = g5_fit2 (ms, Memr[data], Memr[model], Memr[profiles],
Memr[ranges], lower, len_profile)
}
# If the new model parameters have improved the fit record them in
# the database.
if (improved == YES)
call mspgauss5 (ms, sample)
}
# Initialize backward tracking. If tracking get the initial parameters,
# model profiles and model line from the starting line.
if (track) {
call msggauss5 (ms, sample_start)
call mod_gauss5 (ms, lower, Memr[profiles], Memr[ranges],
len_profile, nspectra)
call set_model (ms, Memr[model], Memr[profiles], Memr[ranges],
len_line, len_profile, nspectra)
}
# Track backward from the starting line to the specified sample lines.
do i = nsamples, 1, -1 {
sample = Memi[samples + i - 1]
if (sample >= sample_start)
next
line = LINE(ms, sample)
# Get the image data line.
call msgimage (im, line, naverage, Memr[data])
# If not tracking get the initial parameters, model profiles, and
# model line for the current line. Else record the starting
# parameters.
if (!track) {
call msggauss5 (ms, sample)
call mod_gauss5 (ms, lower, Memr[profiles], Memr[ranges],
len_profile, nspectra)
call set_model (ms, Memr[model], Memr[profiles], Memr[ranges],
len_line, len_profile, nspectra)
} else
call mspgauss5 (ms, sample)
call g5_prnt2 (line, Memr[data], len_line)
# Do the model fitting using the selected algorithm.
switch (algorithm) {
case 1:
improved = g5_fit1 (ms, Memr[data], Memr[model], Memr[profiles],
Memr[ranges], lower, len_profile)
case 2:
improved = g5_fit2 (ms, Memr[data], Memr[model], Memr[profiles],
Memr[ranges], lower, len_profile)
}
# If the new model parameters have improved the fit record them in
# the database.
if (improved == YES)
call mspgauss5 (ms, sample)
}
# Finish up.
if ((smooth[S0_INDEX] == YES) || (smooth[S1_INDEX] == YES) ||
(smooth[S2_INDEX] == YES)) {
call ms_free_smooth ()
}
call imunmap (im)
call history (ms, "Fit model")
call msunmap (ms)
call sfree (sp)
end
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