fit_phangs_ngc0628_nir_continuum.pro
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PRO fit_phangs_ngc0628_nir_continuum,from_restore=from_restore,brute_force=brute_force, $
extract_seds=extract_seds,nostop=nostop,muse_stuff=muse_stuff,muse_data=muse_data,astrosat_data=astrosat_data, $
show_plots=show_plots,grid_brute_force=grid_brute_force
;fit_phangs_ngc0628_nir_continuum,/nostop
;fit_phangs_ngc0628_nir_continuum,/muse_stuff
;fit_phangs_ngc0628_nir_continuum,/muse_data ;do the Muse data "filter" save file
;fit_phangs_ngc0628_nir_continuum,/astrosat_data ;do the Astrosat data save file
;fit_phangs_ngc0628_nir_continuum,/extract_seds
;fit_phangs_ngc0628_nir_continuum,/from_restore,/show_plots ;fit seds of some voronoi bins
;fit_phangs_ngc0628_nir_continuum,/from_restore,/brute_force
;fit_phangs_ngc0628_nir_continuum,/from_restore,/grid_brute_force
;fit_phangs_ngc0628_nir_continuum,/muse_data
pdp_define_la_common
window,0
obp=[1.1,0,1.15,1]
source_name='ngc0628'
;data_dir='/Volumes/PILOT_FLIGHT1/PHANGS-JWST/DR1/'
data_dir=!phangs_data_dir+'/ISRF/WORK/'
grids_data_dir=!phangs_data_dir+'/ISRF/Grids/'
;stop
IF keyword_set(from_restore) THEN goto,from_restore
IF keyword_set(extract_seds) THEN goto,extract_seds
IF keyword_set(muse_stuff) THEN goto,muse_stuff
;IF keyword_set(extract_muse) THEN goto,NH_map
IF keyword_set(muse_data) THEN goto,muse_data
IF keyword_set(astrosat_data) THEN goto,astrosat_data
jwst_stuff:
;========== get JWST reference header
file=data_dir+source_name+'_nircam_lv3_f300m_i2d_anchor_atgauss1.fits'
d=mrdfits(file,1,h)
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
print,sxpar(h,'CDELT1')
href=cd2astro_header(h)
sxaddpar,href,'EQUINOX',2000.
image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
;stop
Nx=sxpar(href,'NAXIS1')
Ny=sxpar(href,'NAXIS2')
filters_names=['F200W','F300M','F335M','F360M','F0770W','F1000W','F1130W','F2100W']
filters=dustem_filter_names2filters(filters_names)
Nfilters=n_elements(filters)
jwst_images=fltarr(Nx,Ny,2,Nfilters)
IF not keyword_set(nostop) THEN stop
;========== JWST stuff
i=0L
file=data_dir+source_name+'_nircam_lv3_f200w_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
help,d
;D DOUBLE = Array[7289, 9476]
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
;stop
file=data_dir+source_name+'_nircam_lv3_f300m_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
help,d
;D DOUBLE = Array[3515, 4576]
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_nircam_lv3_f335m_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_nircam_lv3_f360m_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.2,5],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_miri_lv3_f770w_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.2,10],image_color_table='jpbloadct',/silent,tit=tit,off_bar=obp
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_miri_lv3_f1000w_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.2,5],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_miri_lv3_f1130w_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.3,20],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
file=data_dir+source_name+'_miri_lv3_f2100w_i2d_anchor_atgauss1.fits'
d=mrdfits(file,0,h0)
d=mrdfits(file,1,h)
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN d=project2(h,d,href,/silent)
jwst_images[*,*,0,i]=d & i=i+1
print,sxpar(h,'EXTNAME')
filter_name=sxpar(h0,'FILTER')
print,filter_name
tit=source_name+' '+filter_name
image_cont20,d,href,/square,imrange=[-0.5,10],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
;WCO map:
file='/Volumes/PILOT_FLIGHT1/PHANGS-JWST/ngc0628_12m+7m+tp_co21_broad_mom0.fits'
d=readfits(file,h)
;sxaddpar,h,'CTYPE1','RA---TAN'
;sxaddpar,h,'CTYPE2','RA---TAN'
sxaddpar,h,'EQUINOX',2000.
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN WCO=project2(h,d,href,/silent) ELSE WCO=d
fact=4.e20/1.e21
NHCO=la_mul(WCO,fact) ;NH from CO in 1e21 H/cm2
tit=source_name+' '+'NHCO [1e21 H/cm2]'
image_cont20,NHCO,href,/square,imrange=[-0.5,10],image_color_table='jpbloadct',/silent,tit=tit
IF not keyword_set(nostop) THEN stop
;Invent variances (will have to do better)
;jwst_images[*,*,1,*]=(jwst_images[*,*,0,*]*5./100.)^2 ;assumed intensity variance
perc_error=5./100.
jwst_images[*,*,1,*]=la_power(la_mul(jwst_images[*,*,0,*],perc_error),2) ;assumed intensity variance
;=== check for null variances
ind=where(jwst_images[*,*,1,*] EQ 0.,count)
IF count NE 0 THEN BEGIN
message,'There are null variances ...',/continue
stop
ENDIF
save,jwst_images,filters,href,NHCO,file=data_dir+'ngc0628_jwst_images.sav'
MUSE_data:
;=== this is just to get href
restore,data_dir+'ngc0628_jwst_images.sav',/verb
muse_data_dir='/Volumes/PILOT_FLIGHT1/PHANGS_MUSE/DR2p2/coopt/filterimages/NGC0628/'
;muse_filters=['COUSIN','DUPONT']
;muse_filters=['SDSS2','SDSS3','SDSS4']
muse_filters=dustem_filter_names2filters(['sdss_g','sdss_r','sdss_i'])
Nmuse_filter=n_elements(muse_filters)
muse_images=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'),2,Nmuse_filter)
files=muse_data_dir+'NGC0628_'+['SDSS_gcopt','SDSS_rcopt','SDSS_icopt']+'.fits'
;imrange=[-0.5,200]/2000.
imrange=[-1.e-4,0.001]
imrange=[-1.e-2,1.]
muse_undefined=0.
FOR i=0L,Nmuse_filter-1 DO BEGIN
file=files[i]
;toto=mrdfits(file,0,hh)
d=mrdfits(file,1,h) ;here undefined values are at 0.
hh=cd2astro_header(h)
dv=mrdfits(file,2,hv) ;is this looks like it's a variance
mask=mrdfits(file,3,hm)
ind=where(mask EQ 1,count) ;this looks like a mask
IF count NE 0 THEN BEGIN
d[ind]=la_undef()
dv[ind]=la_undef()
ENDIF
print,la_min(d),la_max(d)
; -7.37902 17186.9
factor=dustem_muse_filter_conversion_factor(muse_filters[i],pivot_wav=pivot_wav) ;goes from Muse filter units to muJy/pix
omega_pix=(sxpar(hh,'CDELT2')/!radeg)^2 ;simeq 9e-13 sr
;use_factor=factor/omega_pix*1.e-12 ;simeq 500
;use_factor=factor/omega_pix*1.e-20 ;simeq 500
use_factor=(1./factor)/omega_pix*1.e-12 ;simeq 500
;jpb's estimate:
cmic=2.99792458e14
use_factor_jp=1.e-7*1.e4*pivot_wav*(pivot_wav/1.e4)/cmic/omega_pix
print,factor,omega_pix,use_factor,use_factor_jp
use_factor=use_factor_jp
;stop
; 492.255 9.4017732e-13 5.2357685e-06
d=la_mul(d,use_factor) ;Now supposidely in MJy/sr (but in what unit convention ??)
dv=la_mul(dv,use_factor^2) ;Now supposidely in (MJy/sr)^2
print,la_min(d),la_max(d)
;-3.8634841e-05 0.089986754
tit=source_name+' '+muse_filters[i]
image_cont20,d,h,/square,imrange=imrange,image_color_table='jpbloadct',/silent,tit=tit,off_bar_pos=obp,axis_color_table=1
;stop
IF sxpar(h,'NAXIS1') NE sxpar(href,'NAXIS1') OR sxpar(h,'NAXIS2') NE sxpar(href,'NAXIS2') THEN BEGIN
d=project2(h,d,href,/silent,save_it='/tmp/save_project2.sav')
dv=project2(h,dv,href,/silent,restore_it='/tmp/save_project2.sav')
ENDIF
muse_images[*,*,0,i]=d
muse_images[*,*,1,i]=dv
;print,filter_name
image_cont20,d,href,/square,imrange=imrange,image_color_table='jpbloadct',/silent,tit=tit,off_bar_pos=obp,axis_color_table=1
ENDFOR
;Invent variances (will have to do better)
;CAUTION: some variances were 0 because some fluxes are 0, had to add abs_variance
;perc_error=5./100.
;abs_variance=(1.e-12)^2
;muse_images[*,*,1,*]=la_add(la_power(la_mul(muse_images[*,*,0,*],perc_error),2),abs_variance) ;assumed intensity variance
;=== check for null variances
ind=where(muse_images[*,*,1,*] EQ 0.,count)
IF count NE 0 THEN BEGIN
message,'There are null variances ...',/continue
stop
toto=muse_images[*,*,1,*]
toto[ind]=la_undef()
muse_images[*,*,1,*]=toto
ENDIF
save,muse_images,muse_filters,href,file=data_dir+'ngc0628_muse_filters_data.sav'
message,'Saved '+data_dir+'ngc0628_muse_filters_data.sav',/info
stop
astrosat_data:
;BUNIT = 'Angstrom-1 cm-2 erg s-1'
;in fact Angstrom-1 cm-2 erg s-1 PER PIXEL ?!
restore,data_dir+'ngc0628_jwst_images.sav',/verb
astrosat_data_dir='/Volumes/PILOT_FLIGHT1/PHANGS_ASTROSAT/v1p0/release/'
;muse_filters=['COUSIN','DUPONT']
astrosat_filters=dustem_filter_names2filters(['F148W','F154W','F169M','F172M','N219M'])
Nastrosat_filter=n_elements(astrosat_filters)
astrosat_images=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'),2,Nastrosat_filter)
files=astrosat_data_dir+'NGC0628_'+['F148','F154','F169','F172','N219']+'_bkg_subtracted_mw_corrected.fits'
;NGC0628_F148_bkg_subtracted_mw_corrected.fits NGC0628_F169_bkg_subtracted_mw_corrected.fits NGC0628_N219_bkg_subtracted_mw_corrected.fits
;NGC0628_F154_bkg_subtracted_mw_corrected.fits NGC0628_F172_bkg_subtracted_mw_corrected.fits
file_save_astrosat=data_dir+'ngc0628_astrosat_data.sav'
wavs_mic=dustem_filter2wav(astrosat_filters)
cmic=3.e14 ;mic/sec
facts=wavs_mic/cmic*(wavs_mic*1.e4)/4./!pi*1.e-7*1.e4*1.e20 ;from Flambda in ergs/cm2/s/AA to MJy/sr
imrange=[-0.5,2]*1e-17 ;erg/s/cm2/AA
imrange=[-0.5,1] ;MJy/sr
FOR i=0L,Nastrosat_filter-1 DO BEGIN
file=files[i]
d=mrdfits(file,0,h)
steradians=(sxpar(h,'CDELT2')/!radeg)^2 ;pixel in sr
fact=facts[i]*4.*!pi/steradians
ind=where(finite(d) NE 1,count)
IF count NE 0 THEN d[ind]=la_undef()
d=la_mul(d,fact)
image_cont20,d,h,/square,imrange=imrange,image_color_table='jpbloadct',/silent,tit=tit
IF sxpar(h,'NAXIS1') NE sxpar(href,'NAXIS1') OR sxpar(h,'NAXIS2') NE sxpar(href,'NAXIS2') THEN d=project2(h,d,href,/silent)
tit=source_name+' '+astrosat_filters[i]
image_cont20,d,href,/square,imrange=imrange,image_color_table='jpbloadct',/silent,tit=tit
astrosat_images[*,*,0,i]=d
stop
ENDFOR
;Invent variances (will have to do better)
perc_error=5./100.
astrosat_images[*,*,1,*]=la_power(la_mul(astrosat_images[*,*,0,*],perc_error),2) ;assumed intensity variance
;=== check for null variances
ind=where(astrosat_images[*,*,1,*] EQ 0.,count)
IF count NE 0 THEN BEGIN
message,'There are null variances ...',/continue
stop
ENDIF
save,astrosat_images,astrosat_filters,href,file=file_save_astrosat
message,'Saved '+file_save_astrosat,/info
stop
muse_stuff:
restore,data_dir+'ngc0628_jwst_images.sav',/verb
;========== MUSE stuff
;Muse stellar bins definition
;st_templates=read_muse_templates_info(age_values=age_values,metalicity_values=metalicity_values,Nbins=Nbins,Nage=Nage,NZ=NZ)
;print,Nage,NZ,Nbins
; 13 6 78
;st_templates=read_muse_templates_info(age_values=age_values,metalicity_values=metalicity_values,/young,Nbins=Nbins,Nage=Nage,NZ=NZ,bins=bins)
;print,Nage,NZ,Nbins
; 17 7 119
;print,la_min(bins),la_max(bins)
; 0 67
st_templates=read_muse_templates_info(age_values=age_values,metalicity_values=metalicity_values,Nbins=Nbins,Nage=Nage,NZ=NZ,bins=bins)
print,Nage,NZ,Nbins
; 13 6 78
print,la_min(bins),la_max(bins)
; 0 77
;Muse weights
;st_muse_weights=read_muse_phangs_weights(object='NGC0628',/young,bin_numbers=bin_numbers)
;print,n_elements(bin_numbers)
; 68
st_muse_weights=read_muse_phangs_weights(object='NGC0628',bin_numbers=bin_numbers)
print,n_elements(bin_numbers)
; 78
voronoi_id=read_muse_phangs_voronoi_bins('NGC0628',header_in=href,snr_bin=snr_bin,snr_flux=snr_flux,flux=muse_flux) ;This is the voronoi bin map
image_cont20,voronoi_id,href,/square,/silent,image_color_table='jpbloadct',title='Voronoi Bins'
save,st_templates,st_muse_weights,voronoi_id,age_values,metalicity_values,bins,href,file=data_dir+'ngc0628_muse_images.sav'
message,'saved '+data_dir+'ngc0628_muse_images.sav',/info
stop
extract_seds:
restore,data_dir+'ngc0628_jwst_images.sav',/verb
restore,data_dir+'ngc0628_muse_images.sav',/verb
restore,data_dir+'ngc0628_astrosat_data.sav',/verb
restore,data_dir+'ngc0628_muse_filters_data.sav',/verb
stop
;=== extract and save observed seds in Muse Voronoi bins
extract_all_muse_phangs_seds,source_name,voronoi_id,jwst_images,filters,indices=all_seds_indices,file=data_dir+'ngc0628_jwst_seds_muse_pixels.sav',counts=counts
restore,data_dir+'ngc0628_jwst_seds_muse_pixels.sav',/verb ;just to get all_seds_indices
extract_all_muse_phangs_seds,source_name,voronoi_id,muse_images,muse_filters,use_these_indices=all_seds_indices,file=data_dir+'ngc0628_muse_seds_muse_pixels.sav',counts=counts
extract_all_muse_phangs_seds,source_name,voronoi_id,astrosat_images,astrosat_filters,use_these_indices=all_seds_indices,file=data_dir+'ngc0628_astrosat_seds_muse_pixels.sav',counts=counts
;==== The following is not really useful, can be done through SED aggregation later
;==== However, not equivalent to the above, because of undefined pixels in some of the images, and the way this is handled by sed extractor
stop
use_filters=[astrosat_filters,muse_filters,filters]
use_Nfilters=n_elements(use_filters)
use_images=fltarr([sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'),2,use_Nfilters])
use_images[*,*,*,0:n_elements(filters)-1]=jwst_images
use_images[*,*,*,n_elements(filters):n_elements(filters)+n_elements(muse_filters)-1]=muse_images
use_images[*,*,*,n_elements(filters)+n_elements(muse_filters):n_elements(filters)+n_elements(muse_filters)+n_elements(astrosat_filters)-1]=astrosat_images
extract_all_muse_phangs_seds,source_name,voronoi_id,use_images,use_filters,indices=all_seds_indices,file=data_dir+'ngc0628_all_seds_muse_pixels.sav',counts=counts
stop
NH_map:
restore,data_dir+'ngc0628_jwst_images.sav',/verb
restore,data_dir+'ngc0628_muse_images.sav',/verb
restore,data_dir+'ngc0628_jwst_seds_muse_pixels.sav',/verb
Nvor=max(voronoi_id)
ebv=st_muse_weights.reddening
Rv=3.1
Av_o_NH=1./2. ;2 mag per 1e21 H/cm2
;Do an Av map
NH_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2')) ;in 1e21 H/cm2
FOR vid=0LL,Nvor-1 DO BEGIN
IF vid mod 100 EQ 0 THEN print,strtrim(vid/Nvor*100.,2)+' %'
;ind=where(voronoi_id EQ vid)
Nh_map[*all_seds_indices[vid]]=Rv*ebv[vid]/Av_o_NH
ENDFOR
save,NH_map,file=data_dir+'ngc0628_muse_NH.sav'
from_restore:
dustem_init,show_plots=show_plots
restore,data_dir+'ngc0628_jwst_images.sav',/verb
;% RESTORE: Restored variable: JWST_IMAGES.
;% RESTORE: Restored variable: FILTERS.
;% RESTORE: Restored variable: HREF.
;% RESTORE: Restored variable: NHCO.
restore,data_dir+'ngc0628_muse_images.sav',/verb
;% RESTORE: Restored variable: ST_TEMPLATES.
;% RESTORE: Restored variable: ST_MUSE_WEIGHTS.
;% RESTORE: Restored variable: VORONOI_ID.
;% RESTORE: Restored variable: AGE_VALUES.
;% RESTORE: Restored variable: METALICITY_VALUES.
;% RESTORE: Restored variable: BINS.
;% RESTORE: Restored variable: HREF.
restore,data_dir+'ngc0628_muse_NH.sav',/verb
;% RESTORE: Restored variable: NH_MAP.
restore,data_dir+'ngc0628_astrosat_data.sav',/verb
;% RESTORE: Restored variable: ASTROSAT_IMAGES.
;% RESTORE: Restored variable: ASTROSAT_FILTERS.
;% RESTORE: Restored variable: HREF.
restore,data_dir+'ngc0628_jwst_seds_muse_pixels.sav',/verb
;% RESTORE: Restored variable: ALL_SEDS.
;% RESTORE: Restored variable: ALL_SEDS_INDICES.
jwst_seds=all_seds
jwst_filters=(*jwst_seds[0]).filter
restore,data_dir+'ngc0628_muse_seds_muse_pixels.sav',/verb ;contains the Muse SEDs in voronoi bins
;% RESTORE: Restored variable: ALL_SEDS.
;% RESTORE: Restored variable: ALL_SEDS_INDICES.
muse_seds=all_seds
muse_filters=(*muse_seds[0]).filter
restore,data_dir+'ngc0628_astrosat_seds_muse_pixels.sav',/verb
;% RESTORE: Restored variable: ALL_SEDS.
;% RESTORE: Restored variable: ALL_SEDS_INDICES.
astrosat_seds=all_seds
astrosat_filters=(*astrosat_seds[0]).filter
Nvor=max(voronoi_id)
all_filters=[jwst_filters,astrosat_filters,muse_filters]
Nfilters=n_elements(all_filters)
all_seds=fltarr(Nfilters,Nvor)
;===== Compute seds (pointer)
one_sed=(*jwst_seds[0])
all_filters=[jwst_filters,astrosat_filters,muse_filters]
Nfilters=n_elements(all_filters)
seds_ptr=ptrarr(Nvor)
nperc=5./100.
FOR ii=0L,Nvor-1 DO BEGIN
seds_ptr[ii]=ptr_new([*astrosat_seds[ii],*muse_seds[ii],*jwst_seds[ii]])
;=== check for 0 variances
ind=where((*seds_ptr[ii]).sigmaII EQ 0,count)
IF count NE 0 THEN BEGIN
this_sed=*seds_ptr[ii]
Ivalues=((*seds_ptr[ii]).stokesI)[ind]
sigmaii_values=(Ivalues*nperc)^2
this_sed[ind].sigmaii=sigmaii_values
seds_ptr[ii]=ptr_new(this_sed)
ENDIF
ENDFOR
;stop
;FOR i=0L,Nvor-1 DO BEGIN
; (*muse_seds[i]).stokesI=((*muse_seds[i]).stokesI)/242315.*1.e8
;ENDFOR
FOR i=0L,Nvor-1 DO all_seds[*,i]=[(*astrosat_seds[i]).stokesI,((*muse_seds[i]).stokesI),(*jwst_seds[i]).stokesI]
;look for maximum astrosat value
ind=where(all_seds[2,*] EQ max(all_seds[2,*]))
print,ind
; 39665
print,all_seds[*,ind]
; 2.35202 2.75261 3.46727 3.68084 1.62485 1054.94 927.261 404.267 10.5454 -32768.0 -32768.0 -32768.0 -32768.0 -32768.0 -32768.0 -32768.0
ind=where(all_seds[2,*] GT 3.*la_mean(all_seds[2,*]))
print,ind[100]
;4699
print,all_seds[*,ind[100]]
; 0.0954950 0.126592 0.144323 0.120900 0.114967 68.6855 52.6373 38.7998 1.15606 0.638490 0.606459 0.529654 2.30200 0.782402 2.51761 1.86948
ind=where(all_seds[2,*]/all_seds[10,*] GT 1.)
print,ind[0]
;12306
print,all_seds[*,ind[0]]
; 0.323970 0.441198 0.591049 0.494629 0.182164 67.2941 50.9925 33.8923 0.967236 0.531406 0.498163 0.374723 1.39314 0.683776 1.90688 1.85371
print,ind[3]
print,all_seds[*,ind[3]]
; 12631
; 0.532320 0.654246 0.788243 0.643166 0.604815 85.0280 61.7483 41.8740 1.29023 0.754277 0.714327 0.569998 1.49024 1.07281 2.24375 1.84148
print,ind[4]
print,all_seds[*,ind[4]]
; 12640
; 0.421350 0.600934 0.639170 0.736419 0.392632 102.730 74.2435 50.7555 1.26155 0.704108 0.633040 0.518208 1.23460 0.844332 1.82023 2.09209
ind=where(all_seds[2,*]/all_seds[10,*] GT 0.6 and all_seds[2,*]/all_seds[10,*] LT 1.)
print,ind[0]
print,all_seds[*,ind[0]]
; 5960
; 0.0603282 0.103469 0.260063 0.171956 0.0811385 53.2903 43.1425 29.1814 0.689211 0.402946 0.368217 0.300235 1.35131 0.478370 1.73637 1.34104
print,ind[20]
print,all_seds[*,ind[20]]
; 13439
; 0.625768 0.544996 0.378714 0.838214 0.591592 92.5699 65.3618 41.9704 1.00230 0.566440 0.614708 0.448454 2.74491 1.15966 3.66431 2.25899
;stop
IF keyword_set(brute_force) THEN goto,brute_force
IF keyword_set(grid_brute_force) THEN goto,grid_brute_force
;toto=dustem_read_emiles_stellar_templates(age_values,metalicity_values,template_wav=template_wav,Mstars=Mstars,info_only=info_only,info_st=info_st)
;stop
;==== SFH ?? (Not sure what this is)
;file='/Volumes/PILOT_FLIGHT1/PHANGS_MUSE/DR2p2/coopt/MUSEDAP/fiducial/NGC0628-0.92asec_table_SFH.fits'
;toto=mrdfits(file,0,h) ;This is ?
;st_sfh=mrdfits(file,1,h) ;This is ?
;help,st_sfh,/str
;** Structure <478ef448>, 10 tags, length=72, data length=68, refs=1:
; ID LONG 0
; BIN_ID LONG 47068
; X DOUBLE 121.80000
; Y DOUBLE 0.60000000
; FLUX DOUBLE 24.835750
; SNR DOUBLE 6.8757743
; XBIN DOUBLE 121.99930
; YBIN DOUBLE 2.9014085
; SNRBIN DOUBLE 103.86098
; NSPAX LONG 284
;print,minmax(st_sfh.id)
; 0 1425
;print,minmax(st_sfh.bin_id)
; 47028 47068
;vid=2000L ;selected voronoi ID
;vid=3000L ;selected voronoi ID
;vid=10000L
;vid=15000L ;selected voronoi ID
;vid=20000L ;selected voronoi ID
;vid=25000L
;vid=30000L ;selected voronoi ID
vid=35000L ;selected voronoi ID
use_NHmap=NHCO ;used NH map in 1.e21 (from CO)
;galaxy_distance=10. ;MPc
;use_model='DBP90' ;Example with default keywords uses the DBP90 model
;use_model='MC10' ;Example with default keywords uses the DBP90 model
use_model='DL07' ;Example with default keywords uses the DBP90 model
use_polarization=0 ; initialize Dustemwrap in no polarization mode
use_window=2 ; default graphics window number to use for plotting the results
use_verbose=0
use_Nitermax=5 ; maximum number of iterations for the fit
dustem_define_la_common
;parameters to fit
pd = [ $
'(*!dustem_params).G0', $ ;G0
'(*!dustem_params).grains(0).mdust_o_mh',$ ;PAH0 mass fraction
'(*!dustem_params).grains(1).mdust_o_mh',$ ;PAH1 mass fraction
'dustem_plugin_phangs_stellar_continuum_1'] ;stellar continuum amplitude
iv = [1.6, [2.2e-3, 2.2e-3],5.e-5]
llims=[1.e-2,[1.e-4,1.e-4],1.e-10]
;This allows fitting E(B-V)
pd = [ $
'(*!dustem_params).G0', $ ;G0
'(*!dustem_params).grains(0).mdust_o_mh',$ ;PAH0 mass fraction
'(*!dustem_params).grains(1).mdust_o_mh',$ ;PAH1 mass fraction
'dustem_plugin_phangs_stellar_continuum_1', $ ;stellar continuum amplitude
'dustem_plugin_phangs_stellar_continuum_2'] ;E(B-V)
iv = [1.6, [2.2e-3, 2.2e-3],5.e-5,0.01]
llims=[1.e-2,[1.e-4,1.e-4],1.e-10,0.]
Npar=n_elements(pd)
ulimed=replicate(0,Npar)
llimed=replicate(1,Npar)
pd_to_update=['dustem_plugin_phangs_stellar_continuum_1','(*!dustem_params).grains(0).mdust_o_mh']
;pd_filter_names=['NIRCAM11','MIRI2']
pd_filter_names=['SDSS4','MIRI2']
;== INITIALISE DUSTEM
dustem_init,model=use_model,polarization=use_polarization,show_plots=show_plots
;stop
!dustem_nocatch=1
!dustem_verbose=use_verbose
IF keyword_set(noobj) THEN !dustem_noobj=1
!EXCEPT=2 ; for debugging
;=== INFORMATION TO MAKE THE PLOT
;yr=[1.00e-2,10.0] ; y-axis limits
yr=[1.00e-3,100.0] ; y-axis limits
;xr=[0.5,1.00e2] ; x-axis limits
xr=[0.1,1.00e2] ; x-axis limits
tit='FIT INTENSITY EXAMPLE' ; plot title
ytit=textoidl('I_\nu (MJy/sr) for N_H=10^{20} H/cm^2') ; y-axis title
xtit=textoidl('\lambda (\mum)') ; x-axis title
;voronoi bins for test. Last series are randomly picked
vids=[12640,5960,13439,12631,12306,4699,[2000,3000,10000,15000,20000,25000,30000L,35000,400000,450000]]
Nvids=n_elements(vids)
one_sed=(*jwst_seds[0])[0]
Nfilters=n_elements(astrosat_filters)+n_elements(muse_filters)+n_elements(filters)
seds=replicate(one_sed,Nfilters)
FOR ii=0L,Nvids-1 DO BEGIN
vid=vids[ii]
;===== get weights for the given voronoi bins
weights=phangs_binid2weights(st_muse_weights,vid,st_templates,age_values,metalicity_values,reddening=reddening)
sed=*seds_ptr[vid]
;===== fixed parameters
;fpd=phangs_stellar_continuum_plugin_weight2params(weights,parameter_values=fiv,redenning=reddening,/force_include_reddening)
fpd=phangs_stellar_continuum_plugin_weight2params(weights,parameter_values=fiv)
NH_value=la_mul(la_mean(use_NHmap[*all_seds_indices[vid]]),10.) ;NH value in 1.e20 H/cm2
sed.stokesI=sed.stokesI/NH_value ;normalize SED to 1.e20 H/cm2
sed.sigmaII=sed.sigmaII/NH_value^2 ;normalize SED to 1.e20 H/cm2
;== SET THE OBSERVATIONAL STRUCTURE
dustem_set_data,m_fit=sed,m_show=sed
;get a first guess at parameters (must be done after dustem_set_data)
new_iv=dustem_first_guess(pd,iv,sed,pd_to_update,pd_filter_names,fpd=fpd,fiv=fiv,pol=pol)
;== SET INITIAL VALUES AND LIMITS OF THE PARAMETERS THAT WILL BE
;== ADJUSTED DURING THE FIT
dustem_init_params,use_model,pd,new_iv,fpd=fpd,fiv=fiv,ulimed=ulimed,llimed=llimed,ulims=ulims,llims=llims
tit='Voronoi '+strtrim(vid,2)
;=== RUN THE FIT
;stop
t1=systime(0,/sec)
res=dustem_mpfit_data(tol=tol,Nitermax=use_Nitermax,gtol=gtol $
,/xlog,/ylog,xr=xr,yr=yr,xtit=xtit,ytit=ytit,title=tit $
,legend_xpos=legend_xpos,legend_ypos=legend_ypos $
,errors=errors,chi2=chi2,rchi2=rchi2,show_plot=show_plots)
t2=systime(0,/sec)
IF not keyword_set(nostop) THEN stop
ENDFOR
message,'========== Finished individual fitting',/continue
stop
;==== Brute force
brute_force:
;stop
;==== This is a test to brute-force fit PAH abudance and G0 Using the Mathis ISRF
Nvor=max(voronoi_id)
;ebv=st_muse_weights.reddening
;Rv=3.1
;Av_o_NH=1./2. ;2 mag per 1e21 H/cm2
;;Do an Av map
;NH_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2')) ;in 1e21 H/cm2
;FOR vid=0LL,Nvor-1 DO BEGIN
; IF vid mod 100 EQ 0 THEN print,strtrim(vid/Nvor*100.,2)+' %'
; ind=where(voronoi_id EQ vid)
; Nh_map[ind]=Rv*ebv[vid]/Av_o_NH
;ENDFOR
;stop
;dir=!dustem_wrap_soft_dir+'/Grids/'
;==== This is to use the Mathis Field
table_name=grids_data_dir+'DBP90_JWST_G0_YPAH_YVSG_4Phangs.fits'
;table_name=dir+'TEST_DBP90_JWST_G0_YPAH_YVSG_4Phangs.fits'
;Nvor=20000 ;for test
GOs=fltarr(Nvor)
Ypahs=fltarr(Nvor)
YVSGs=fltarr(Nvor)
facts=fltarr(Nvor)
chi2s=fltarr(Nvor)
rchi2s=fltarr(Nvor)
dGOs=fltarr(Nvor)
dYpahs=fltarr(Nvor)
dYVSGs=fltarr(Nvor)
use_NHmap=NHCO ;used NH map in 1.e21 (from CO)
;use_NHmap=NH_map ;used NH map in 1.e21 (from MUSE)
;==== This is all the filters in a given SED
all_filters=(*seds_ptr[0]).filter
;==== filters used for brute force fit
use_filters_names=['F360M','F0770W','F1000W','F1130W','F2100W']
use_filters=dustem_filter_names2filters(use_filters_names)
use_Nfilters=n_elements(use_filters)
ind_filters=[-1]
FOR i=0L,use_Nfilters-1 DO BEGIN
indd=where(all_filters EQ use_filters[i],count)
IF count NE 0 THEN ind_filters=[ind_filters,indd]
ENDFOR
ind_filters=ind_filters[1:*]
;show_sed=1
show_sed=0
FOR vid=0LL,Nvor-1 DO BEGIN
IF vid mod 10 EQ 0 THEN BEGIN
message,'Doing sed '+strtrim(vid,2)+' '+strtrim(1.*vid/Nvor*100.,2)+' %',/continue
ENDIF
;sed=all_seds[ind_filters,vid]
sed=*seds_ptr[vid]
;=== restrict sed to requested filters
sed=sed[ind_filters]
;=== normalize to NH=1.e20 H/cm2
NH_value=la_mul(la_mean(use_NHmap[*all_seds_indices[vid]]),10.) ;NH value in 1.e20 H/cm2
sed.stokesI=la_div(sed.stokesI,NH_value)
sed.sigmaII=la_div(sed.sigmaII,NH_value^2)
;index=where(voronoi_id EQ vid,count)
;sed=dustem_sed_extractor(jwst_images,index,filters,/total_intensity_only)
;stop
params=dustem_brute_force_fit(sed,table_name,use_filters,fact=fact,chi2=chi2,/normalize,rchi2=rchi2,show_sed=show_sed $
,params_hit=params_hit,params_uncertainties=params_uncertainties,params_min=params_min,params_max=params_max)
GOs[vid]=params[0]
Ypahs[vid]=params[1]/fact ;extenssive quantities must be devided by normalization factor
Yvsgs[vid]=params[2]/fact
facts[vid]=fact
chi2s[vid]=chi2
rchi2s[vid]=rchi2
dGOs[vid]=params_uncertainties[0]
dYpahs[vid]=params_uncertainties[1]/fact ;extenssive quantities must be devided by normalization factor
dYvsgs[vid]=params_uncertainties[2]/fact
print,params[0],params[1],params[2],fact,chi2
ENDFOR
stop
;save,GOs,Ypahs,Yvsgs,file=dir+source_name+'DBP90_JWST_G0_YPAH_YVSG.fits'
save,GOs,Ypahs,Yvsgs,file=dir+source_name+'TEST_DBP90_JWST_G0_YPAH_YVSG.fits'
;stop
;=== make maps of parameters
chi2_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
rchi2_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
GO_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
Ypah_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
YVSG_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
dGO_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
dYpah_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
dYVSG_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
fact_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))
FOR vid=0LL,Nvor-1 DO BEGIN
IF vid mod 100 EQ 0 THEN print,(1.*vid)/Nvor*100.
;ind=where(voronoi_id EQ vid)
GO_map[*all_seds_indices[vid]]=GOs[vid]
Ypah_map[*all_seds_indices[vid]]=Ypahs[vid]
YVSG_map[*all_seds_indices[vid]]=Yvsgs[vid]
dGO_map[*all_seds_indices[vid]]=dGOs[vid]
dYpah_map[*all_seds_indices[vid]]=dYpahs[vid]
dYVSG_map[*all_seds_indices[vid]]=dYvsgs[vid]
chi2_map[*all_seds_indices[vid]]=chi2s[vid]
rchi2_map[*all_seds_indices[vid]]=rchi2s[vid]
fact_map[*all_seds_indices[vid]]=facts[vid]
ENDFOR
;stop
jwst_4_coutours=fltarr((size(jwst_images))[1],(size(jwst_images))[2],2)
jwst_4_coutours[*,*,0]=jwst_images[*,*,0,5]
jwst_4_coutours[*,*,1]=jwst_images[*,*,0,5]
levs=create_contour_st(jwst_4_coutours,lev1=[1,5])
levs.(0).color=100
levs.(1).color=200
levs.(1).thick=2
obp=[1.1,0.,1.15,1]
image_cont20,jwst_4_coutours[*,*,0],href,/square,imrange=[-1.,10],image_color_table='jpbloadct',/silent,title='',off_bar=obp,axis_color_table=1,levels=levs
window,0,xsize=800,ysize=1000
aa=alog10(GO_map)
wset,0 & image_cont20,GO_map,href,/square,imrange=[-1.,100],image_color_table='jpbloadct',/silent,title='G0',off_bar=obp,axis_color_table=1
wset,0 & image_cont20,aa,href,/square,imrange=[0.5,2.5],image_color_table='jpbloadct',/silent,title='G0',off_bar=obp,axis_color_table=1,levels=levs
window,1,xsize=800,ysize=1000
wset,1 & image_cont20,Ypah_map,href,/square,imrange=[-0.005,0.5],image_color_table='jpbloadct',/silent,title='Ypah',off_bar=obp,axis_color_table=1,levels=levs
window,2,xsize=800,ysize=1000
wset,2 & image_cont20,Yvsg_map,href,/square,imrange=[-0.005,0.5],image_color_table='jpbloadct',/silent,title='Yvsg',off_bar=obp,axis_color_table=1,levels=levs
window,3,xsize=800,ysize=1000
wset,3 & image_cont20,alog10(chi2_map),href,/square,imrange=[2,6],image_color_table='jpbloadct',/silent,title='chi2',off_bar=obp,axis_color_table=1,levels=levs
window,4,xsize=800,ysize=1000
image_cont20,alog10(fact_map),href,/square,imrange=[-1,5],image_color_table='jpbloadct',/silent,title='fact',off_bar=obp,axis_color_table=1,levels=levs
stop
wset,1 & image_cont20,Ypah_map,href,/square,imrange=[-0.0001,0.005],image_color_table='jpbloadct',/silent,title='Ypah',off_bar=obp,axis_color_table=1
wset,1 & image_cont20,Ypah_map,href,/square,imrange=[-0.0001,0.1],image_color_table='jpbloadct',/silent,title='Ypah',off_bar=obp,axis_color_table=1
wset,2 & image_cont20,Yvsg_map,href,/square,imrange=[-0.001,0.015],image_color_table='jpbloadct',/silent,title='Yvsg',off_bar=obp,axis_color_table=1
window,5
!p.multi=[0,3,2]
histGO=histogram(GOs,locations=GOv,Nbins=30)
cgplot,GOv,histGO,psym=10,/ylog,xtit='G0',yrange=[1,1.e5]
histYpah=histogram(Ypahs,locations=Ypahsv,Nbins=10)
cgplot,Ypahsv,histYpah,psym=10,/ylog,xtit='Ypah'
histYvsg=histogram(Yvsgs,locations=Yvsgsv,Nbins=10)
cgplot,Yvsgsv,histYvsg,psym=10,/ylog,xtit='Yvsg'
histchi2=histogram(chi2s,locations=chisv,Nbins=10)
cgplot,chisv,histchi2,psym=10,/ylog,xtit='Chi2'
histfacts=histogram(facts,locations=factsv,Nbins=10)
cgplot,factsv,histfacts,psym=10,/ylog,xtit='facts'
save,GO_map,Ypah_map,Yvsg_map,href,chi2_map,fact_map,GOs,Ypahs,Yvsgs,chi2s,facts,file=data_dir+'ngc0628_brute_force_fit_results.sav'
restore,data_dir+'ngc0628_brute_force_fit_results.sav',/verb
Nid=la_max(voronoi_id)
ids=fltarr(Nid)
snrs=fltarr(Nid)
counts=fltarr(Nid)
FOR i=0L,Nid-1 DO BEGIN
ind=where(voronoi_id EQ i,count)
ids[i]=i
snrs[i]=snr[ind[0]]
counts[i]=count
ENDFOR
cgplot,ids,snrs
cgplot,ids,counts
cgplot,counts,snrs
ind1=where(snr EQ max(snr))
ind=where(voronoi_id EQ voronoi_id[ind1],count)
print,count
ind=where(voronoi_id EQ 0,count)
file='/Volumes/PILOT_FLIGHT1/PHANGS_MUSE/DR2p2/coopt/MUSEDAP/fiducial/NGC0628-0.92asec_ppxf_SFH.fits'
st=mrdfits(file,1,h)
help,st,/str
stop
;==== Brute force fit using ISRF classes
grid_brute_force:
;stop
Nvor=max(voronoi_id)
GOs=fltarr(Nvor)+la_undef()
Ypahs=fltarr(Nvor)+la_undef()
YVSGs=fltarr(Nvor)+la_undef()
facts=fltarr(Nvor)+la_undef()
chi2s=fltarr(Nvor)+la_undef()
rchi2s=fltarr(Nvor)+la_undef()
dGOs=fltarr(Nvor)+la_undef()
dYpahs=fltarr(Nvor)+la_undef()
dYVSGs=fltarr(Nvor)+la_undef()
use_NHmap=NHCO ;used NH map in 1.e21 (from CO)
;use_NHmap=NH_map ;used NH map in 1.e21 (from MUSE)
;==== This is all the filters in a given SED
all_filters=(*seds_ptr[0]).filter
;==== filters used for brute force fit
use_filters_names=['F360M','F0770W','F1000W','F1130W','F2100W']
use_filters=dustem_filter_names2filters(use_filters_names)
use_Nfilters=n_elements(use_filters)
ind_filters=[-1]
FOR i=0L,use_Nfilters-1 DO BEGIN
indd=where(all_filters EQ use_filters[i],count)
IF count NE 0 THEN ind_filters=[ind_filters,indd]
ENDFOR
ind_filters=ind_filters[1:*]
;show_sed=1
show_sed=0
model='DBP90'
;dir_grids=!dustem_wrap_soft_dir+'/Grids/'
file=data_dir+'/ngc0682_isrf_classes_one_ratio.sav'
restore,file,/verb
;% RESTORE: Restored variable: CLASSES.
;% RESTORE: Restored variable: VOR_CLASS.
;% RESTORE: Restored variable: MAP_CLASSES.
ind=where(vor_class NE 0)
class_min=min(vor_class[ind])
class_max=max(vor_class[ind])
;For test
class_min=14
class_max=15
FOR isrf_class=class_min,class_max DO BEGIN
;==== This is to use the grid for this ISRF class
isrf_class_str='_isrfclass'+strtrim(isrf_class,2)
table_name=grids_data_dir+model+'_MuseISRF_JWST_G0_YPAH_YVSG_4Phangs'+isrf_class_str+'.fits'
;select Voronoi bins with that ISRF class
ind_class=where(vor_class EQ isrf_class,Nvor_class)
IF Nvor_class NE 0 THEN BEGIN
FOR vvid=0LL,Nvor_class-1 DO BEGIN
IF vvid mod 100 EQ 0 THEN BEGIN
message,'Doing sed '+strtrim(vvid,2)+' '+strtrim(1.*vvid/Nvor_class*100.,2)+' %',/continue
ENDIF
vid=ind_class[vvid]
sed=*seds_ptr[vid]
;=== restrict sed to requested filters
sed=sed[ind_filters]
;=== normalize to NH=1.e20 H/cm2
NH_value=la_mul(la_mean(use_NHmap[*all_seds_indices[vid]]),10.) ;NH value in 1.e20 H/cm2
sed.stokesI=la_div(sed.stokesI,NH_value)
sed.sigmaII=la_div(sed.sigmaII,NH_value^2)
params=dustem_brute_force_fit(sed,table_name,use_filters,fact=fact,chi2=chi2,/normalize,rchi2=rchi2,show_sed=show_sed $
,params_hit=params_hit,params_uncertainties=params_uncertainties,params_min=params_min,params_max=params_max)
GOs[vid]=params[0]
Ypahs[vid]=params[1]/fact ;extenssive quantities must be devided by normalization factor
Yvsgs[vid]=params[2]/fact
facts[vid]=fact
chi2s[vid]=chi2
rchi2s[vid]=rchi2
dGOs[vid]=params_uncertainties[0]
dYpahs[vid]=params_uncertainties[1]/fact ;extenssive quantities must be devided by normalization factor
dYvsgs[vid]=params_uncertainties[2]/fact
print,params[0],params[1],params[2],fact,chi2
ENDFOR
ENDIF ELSE BEGIN
message,'No Voronoi bins found with ISRF class '+strtrim(isrf_class,2),/continue
ENDELSE
ENDFOR
;stop
;save,GOs,Ypahs,Yvsgs,file=dir+source_name+'DBP90_JWST_G0_YPAH_YVSG.fits'
save,GOs,Ypahs,Yvsgs,file=data_dir+source_name+'_DBP90_JWST_G0_YPAH_YVSG_ISRFclasses.fits'
;Nvor=max(voronoi_id)
;stop
;=== make maps of parameters
chi2_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
rchi2_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
GO_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
Ypah_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
YVSG_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
dGO_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
dYpah_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
dYVSG_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
fact_map=fltarr(sxpar(href,'NAXIS1'),sxpar(href,'NAXIS2'))+la_undef()
FOR vid=0LL,Nvor-1 DO BEGIN
IF vid mod 100 EQ 0 THEN print,(1.*vid)/Nvor*100.
;ind=where(voronoi_id EQ vid)
GO_map[*all_seds_indices[vid]]=GOs[vid]
Ypah_map[*all_seds_indices[vid]]=Ypahs[vid]
YVSG_map[*all_seds_indices[vid]]=Yvsgs[vid]
dGO_map[*all_seds_indices[vid]]=dGOs[vid]
dYpah_map[*all_seds_indices[vid]]=dYpahs[vid]
dYVSG_map[*all_seds_indices[vid]]=dYvsgs[vid]
chi2_map[*all_seds_indices[vid]]=chi2s[vid]
rchi2_map[*all_seds_indices[vid]]=rchi2s[vid]
fact_map[*all_seds_indices[vid]]=facts[vid]
ENDFOR
;FOR vid=0LL,Nvor-1 DO GO_map[*all_seds_indices[vid]]=GOs[vid]
;FOR vid=0LL,Nvor-1 DO Ypah_map[*all_seds_indices[vid]]=Ypahs[vid]
;FOR vid=0LL,Nvor-1 DO YVSG_map[*all_seds_indices[vid]]=Yvsgs[vid]
;FOR vid=0LL,Nvor-1 DO chi2_map[*all_seds_indices[vid]]=chi2s[vid]
stop
image_cont20,GO_map,Href,/square,imrange=[0.01,100],image_color_table='jpbloadct',/silent
image_cont20,Ypah_map,Href,/square,imrange=[1,100]*1.e-3,image_color_table='jpbloadct',/silent
image_cont20,YVSG_map,Href,/square,imrange=[1,100]*1.e-3,image_color_table='jpbloadct',/silent
image_cont20,chi2_map,Href,/square,imrange=[0.01,100]*1.e4,image_color_table='jpbloadct',/silent
;stop
jwst_4_coutours=fltarr((size(jwst_images))[1],(size(jwst_images))[2],2)
jwst_4_coutours[*,*,0]=jwst_images[*,*,0,5]
jwst_4_coutours[*,*,1]=jwst_images[*,*,0,5]
levs=create_contour_st(jwst_4_coutours,lev1=[1,5])
levs.(0).color=100
levs.(1).color=200
levs.(1).thick=2
obp=[1.1,0.,1.15,1]
image_cont20,jwst_4_coutours[*,*,0],href,/square,imrange=[-1.,10],image_color_table='jpbloadct',/silent,title='',off_bar=obp,axis_color_table=1,levels=levs
window,0,xsize=800,ysize=1000
aa=alog10(GO_map)
wset,0 & image_cont20,GO_map,href,/square,imrange=[-1.,100],image_color_table='jpbloadct',/silent,title='G0',off_bar=obp,axis_color_table=1
wset,0 & image_cont20,aa,href,/square,imrange=[0.5,2.5],image_color_table='jpbloadct',/silent,title='G0',off_bar=obp,axis_color_table=1,levels=levs
window,1,xsize=800,ysize=1000
wset,1 & image_cont20,Ypah_map,href,/square,imrange=[-0.005,0.5],image_color_table='jpbloadct',/silent,title='Ypah',off_bar=obp,axis_color_table=1,levels=levs
window,2,xsize=800,ysize=1000
wset,2 & image_cont20,Yvsg_map,href,/square,imrange=[-0.005,0.5],image_color_table='jpbloadct',/silent,title='Yvsg',off_bar=obp,axis_color_table=1,levels=levs
window,3,xsize=800,ysize=1000
wset,3 & image_cont20,alog10(chi2_map),href,/square,imrange=[2,6],image_color_table='jpbloadct',/silent,title='chi2',off_bar=obp,axis_color_table=1,levels=levs
window,4,xsize=800,ysize=1000
image_cont20,alog10(fact_map),href,/square,imrange=[-1,5],image_color_table='jpbloadct',/silent,title='fact',off_bar=obp,axis_color_table=1,levels=levs
stop
END