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maxent_post_vectorized_prior.pro
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;use # of samples as weighting
;removed exp_n as a passed in argument
;uses local prior for bin_files
; Version 3.0 - use new procedure that calculates prior probability using box grids (pdf_2d)
; the grid passed in for local prior probability should span the same area as the probability images
; Version history
; Version 2.0 - uses the procedure combine_prob.pro
PRO maxent_post_vectorized_prior, xdim, ydim, in_dir, out_dir, def_bin_file, local_pdf
;read values from bin file.
;format is class name, min bin val, max bin val, npoints, total plots, bin mean
noerror = 0
if (!error_state.code eq 0) then noerror = 1
bin_data = read_csv(def_bin_file)
if (!error_state.code ne 0) then print, !error_state.code, !error_state.msg
if noerror then message, /reset
nClass = (size(bin_data.(0)))[1]
mBioVals = dblarr(nClass)
pTotal = dblarr(nClass)
for i=0, nClass-1 do begin
mBioVals[i] = (bin_data.(5))[i]
endfor
bin_npoints = ulong(bin_data.(3))
total_points = total(bin_npoints)
n_segments = ydim ; switch back to reading 1 line at a time so that local probability can be easily assigned
pdf_size = size(local_pdf)
;--------------------------------------------
journal, out_dir+'/maxent_post_n3.log'
in_files=strarr(nClass)
for n=0, nClass-1 do begin
; in_files[n] = in_dir + '/' + strtrim(string(Ranges[n]),2) + '-' + strtrim(string(Ranges[n+1]),2) + '.bil'
in_files[n]=in_dir + '/' + (bin_data.(0))[n] + '.bil'
print, in_files[n]
endfor
;obtain input file size and segment information
in_file_info = file_info(in_files[0])
in_file_size = in_file_info.size
n_pixels = in_file_size / n_segments / 4ULL ;4 for floating point data type
;divide input file into n_segments for processing
maxent_prob = dblarr(n_pixels,nClass)
prior_prob = dblarr(n_pixels,nClass)
out_expect_val = fltarr(n_pixels)
out_error_val = fltarr(n_pixels)
tmp_flt_line = fltarr(n_pixels)
tmp_byt_line = bytarr(n_pixels)
classP = dblarr(xdim,nClass)
PNormal = dblarr(xdim,nClass)
stdBio = dblarr(xdim)
meanBio = dblarr(xdim)
inLuns = intarr(nClass)
classWeights = dblarr(nClass)
outLuns = intarr(2)
;open files for reading
for n=0, nClass-1 do begin
openr, 10+n, in_files[n]
inLuns[n] = 10+n
classWeights[n] = float(bin_npoints[n]) / float(total_points)
endfor
;openr, zoneLun, zone_image_file, /get_lun
print, 'class weights:', classWeights
;calculate total probabilities
print, 'calculating total probabilities....'
PTotal[*] = 0.
for j=0ULL, n_segments-1 do begin
for n=0, nClass-1 do begin
tmpLun = inLuns[n]
readu, tmpLun, tmp_flt_line
index = where(tmp_Flt_Line gt 0, count)
if (count gt 0) then pTotal[n] += total(tmp_flt_line[index])
endfor
endfor
print, pTotal
;reset file pointers
for n=0, nClass-1 do begin
tmpLun = inLuns[n]
point_lun, tmpLun, 0
endfor
openw, 1, out_dir + '/maxent_value_n3_weight4.flt'
outLuns[0] = 1
openw, 2, out_dir + '/maxent_error_n3_weight4.flt'
outLuns[1] = 2
print, 'creating maps....'
;set up local probabilities
pdf_sum = total(local_pdf, 1)
local_pdf_norm = double(local_pdf)
index = where(pdf_sum gt 0, count)
for i = 0, nClass-1 do begin
local_pdf_norm[i,index] /= double(pdf_sum[index])
endfor
scale_i = ulong(xdim / pdf_size[2])
for j=0UL, n_segments-1 do begin
if (j mod 10 eq 0) then print, j
maxent_prob[*] = 0
prior_prob[*] = 0
out_expect_val[*] = 0
out_error_val[*] = 0
;read maxent probabilities and normalize
;print, 'reading data segment...'
for n=0, nClass-1 do begin
tmpLun = inLuns[n]
readu, tmpLun, tmp_flt_line
;set masked areas to 0
tmp_flt_line = tmp_flt_line > 0
;normalize
maxent_prob[*,n] = tmp_flt_line / PTotal[n]
endfor
;create arrays for prior probabilities and mean values
;print, 'creating prior arrays...'
prob_j = ulong(float(j)/float(n_segments) * float(pdf_size[3]))
for i=0, pdf_size[2]-1 do begin
max_i = ((i+1)*scale_i-1) < (xdim-1)
prior_prob[i*scale_i : max_i, *] = rebin(reform(local_pdf_norm[*,i,prob_j],1,nClass),(max_i-i*scale_i+1), nClass)
endfor
;readu, zoneLun, tmp_byt_line
;for iZone=0, nZones-1 do begin
; tmp_index = where(tmp_byt_line eq iZone, count)
; if (count gt 0) then prior_prob[tmp_index,*] = rebin(reform(loc_prob(*,iZone),1,nClass),count,nClass)
;endfor
;Calculate for one line of data
combine_prob, maxent_prob, prior_prob, mBioVals, out_expect_val, out_error_val
writeu, outLuns[0], out_expect_val
writeu, outLuns[1], out_error_val
endfor
for n=0, (nClass-1) do begin
tmpLun = inLuns[n]
free_lun, tmpLun
endfor
for n=0, 1 do begin
tmpLun = outLuns[n]
free_lun, tmpLun
endfor
end