improved in the framework of changing resolution

Jean-Philippe Bernard
1 parent bbfe2364
Showing 8 changed files with 120 additions and 187 deletions Show diff stats
Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio
LabTools/IRAP/JPB/phangs_isrf_pipeline.pro
LabTools/IRAP/JPB/phangs_make_co_images.pro
LabTools/IRAP/JPB/phangs_make_jwst_images.pro
LabTools/IRAP/JPB/phangs_smooth_muse_isrf.pro
LabTools/IRAP/JPB/srun/make_phangs_smooth_isrf.pro
src/idl/dustem_plugin_phangs_stellar_continuum.pro
src/idl/dustem_read_cb19_stellar_templates.pro
-<mxfile host="Electron" modified="2024-03-25T08:10:38.187Z" agent="5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/17.2.1 Chrome/96.0.4664.174 Electron/16.1.0 Safari/537.36" etag="lI2TwhACyrzwwAsilAsv" version="17.2.1" type="device" pages="6"><diagram id="IKMg62xr7mvPSjzlxw8D" name="Dustem_wrap_flow">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</diagram><diagram id="-igyU8uB8o-wUyM342J-" name="plugin_managment">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</diagram><diagram id="VhkHcouieJL9I0J5Rexb" name="Phangs_fiting_SEDs">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</diagram><diagram id="ls9p14zF9e6sMFj8i8uX" name="Phangs_pluggins">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</diagram><diagram id="UJ_gMtkV_-GboZ4knRsY" name="dustem_make_sed_table">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</diagram><diagram id="DRomuT0JwROQc1QwEnjN" name="PHANGS_ISRF_schematics">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</diagram></mxfile>
 \ No newline at end of file
+<mxfile host="Electron" modified="2024-03-29T11:52:11.566Z" agent="5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/17.2.1 Chrome/96.0.4664.174 Electron/16.1.0 Safari/537.36" etag="QMVbU--GVvV7yJHgRuVw" version="17.2.1" type="device" pages="6"><diagram id="IKMg62xr7mvPSjzlxw8D" name="Dustem_wrap_flow">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</diagram><diagram id="-igyU8uB8o-wUyM342J-" name="plugin_managment">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</diagram><diagram id="VhkHcouieJL9I0J5Rexb" name="Phangs_fiting_SEDs">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</diagram><diagram id="ls9p14zF9e6sMFj8i8uX" name="Phangs_pluggins">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</diagram><diagram id="UJ_gMtkV_-GboZ4knRsY" name="dustem_make_sed_table">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</diagram><diagram id="DRomuT0JwROQc1QwEnjN" name="PHANGS_ISRF_schematics">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</diagram></mxfile>
 \ No newline at end of file
@@ -52,7 +52,13 @@ phangs_brute_force_fit_with_isrf_grid,source_name=&#39;ngc0628&#39;,/include_herschel,/n
  
 phangs_brute_force_fit_with_isrf_grid,source_name='ngc0628',/include_herschel,/normalize,/fit_G0,/from_restore
  
+resolution_filter='SPIRE3'
+phangs_make_jwst_images,source_name='ngc0628',/save,/show,/nostop,resolution_filter=resolution_filter
+
+
+
 phangs_smooth_muse_isrf,'ngc0628',reso_filter='SPIRE3',/save
+phangs_smooth_muse_isrf,'ngc0628',reso_filter='SPIRE1',/save
  
 ;=== NGC3351
 ;Note: faire un chmod a+rwx * apres transfert sur alma1
-PRO phangs_make_co_images,source_name=source_name,save=save,show_images=show_images,nostop=nostop,help=help
+PRO phangs_make_co_images,source_name=source_name,save=save,show_images=show_images,nostop=nostop,help=help,resolution_filter=resolution_filter
  
 ;+
 ; NAME:
@@ -13,6 +13,7 @@ PRO phangs_make_co_images,source_name=source_name,save=save,show_images=show_ima
 ;    None
 ; OPTIONAL INPUT PARAMETERS:
 ;    source_name            : source name (default = 'ngc0628')
+;    resolution_filter      : if set makes images at this resolution (default none)
 ; OUTPUTS:
 ;    None
 ; OPTIONAL OUTPUT PARAMETERS:
@@ -44,6 +45,7 @@ IF keyword_set(help) THEN BEGIN
 ENDIF
  
 dustem_define_la_common
+dustem_init
  
 use_source_name='ngc0628'
 IF keyword_set(source_name) THEN use_source_name=source_name
@@ -66,7 +68,19 @@ d=readfits(file,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 WCO=project2(h,d,href,/silent) ELSE WCO=d
+reso_str=''
+IF keyword_set(resolution_filter) THEN BEGIN
+	reso_str='_'+resolution_filter
+	data_reso=sxpar(h,'BMAJ')
+	final_reso=dustem_filter2reso(resolution_filter)
+  d=degrade_res(d,h,data_reso,final_reso,hout)
+  h=hout
+ENDIF
+IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN BEGIN
+	WCO=project2(h,d,href,/silent)
+ENDIF ELSE BEGIN
+	WCO=d
+ENDELSE
 fact=4.e20/0.65/1.e21
 NHCO=la_mul(WCO,fact)   ;NH from CO in 1e21 H/cm2
 tit=source_name+' '+'NHCO [1e21 H/cm2]'
@@ -79,7 +93,7 @@ IF keyword_set(show_images) THEN BEGIN
 ENDIF
  
 IF keyword_set(save) THEN BEGIN
-	save_file=save_data_dir+use_source_name+'_CO_images.sav'
+	save_file=save_data_dir+use_source_name+'_CO_images'+reso_str+'.sav'
 	save,NHCO,href,file=save_file
 	message,'Saved '+save_file,/continue
 ENDIF
-PRO phangs_make_jwst_images,source_name=source_name,save=save,show_images=show_images,nostop=nostop,help=help
+PRO phangs_make_jwst_images,source_name=source_name,save=save,show_images=show_images,nostop=nostop,help=help,resolution_filter=resolution_filter
  
 ;+
 ; NAME:
@@ -13,6 +13,7 @@ PRO phangs_make_jwst_images,source_name=source_name,save=save,show_images=show_i
 ;    None
 ; OPTIONAL INPUT PARAMETERS:
 ;    source_name            : source name (default = 'ngc0628')
+;    resolution_filter      : if set makes images at this resolution (default none)
 ; OUTPUTS:
 ;    None
 ; OPTIONAL OUTPUT PARAMETERS:
@@ -25,7 +26,9 @@ PRO phangs_make_jwst_images,source_name=source_name,save=save,show_images=show_i
 ; COMMON BLOCKS:
 ;    None
 ; SIDE EFFECTS:
-;    A file is written
+;    Files written:
+;    _ref_header.sav
+;    _jwst_images.sav
 ; RESTRICTIONS:
 ;    None
 ; PROCEDURE:
@@ -47,6 +50,7 @@ use_source_name=&#39;ngc0628&#39;
 IF keyword_set(source_name) THEN use_source_name=source_name
  
 pdp_define_la_common
+dustem_init
 ;window,0
 obp=[1.1,0,1.15,1]
 win=0L
@@ -68,182 +72,77 @@ IF keyword_set(show_images) THEN BEGIN
 	image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
 ENDIF
  
+reso_str=''
+IF keyword_set(resolution_filter) THEN BEGIN
+	reso_str='_'+resolution_filter
+	data_reso=dustem_filter2reso(dustem_filter_names2filters('F300M'))
+	final_reso=dustem_filter2reso(resolution_filter)
+  dd=degrade_res(d,href,data_reso,final_reso,hout)
+  href=hout
+ENDIF
+
+IF keyword_set(show_images) THEN BEGIN
+	window,win & win=win+1
+	image_cont20,dd,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
+ENDIF
+
 ;=== save reference header
 IF keyword_set(save) THEN BEGIN
-	save_file=save_data_dir+use_source_name+'_ref_header.sav'
+	save_file=save_data_dir+use_source_name+'_ref_header'+reso_str+'.sav'
 	save,href,file=save_file
 	message,'Saved '+save_file,/continue
 ENDIF
  
-stop
-
-
 Nx=sxpar(href,'NAXIS1')
 Ny=sxpar(href,'NAXIS2')
  
 filters_names=['F200W','F300M','F335M','F360M','F0770W','F1000W','F1130W','F2100W']
+filters_names2=['F200W','F300M','F335M','F360M','F770W','F1000W','F1130W','F2100W']   ;This is to be used for file names
+instrus=dustem_filter2instru(dustem_filter_names2filters(filters_names))
 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+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-
-;stop
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.2,5],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	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
-ENDIF
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.2,5],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.3,20],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-IF not keyword_set(nostop) THEN stop
+;========== Make JWST images
+FOR i=0L,Nfilters-1 DO BEGIN
+	file=data_dir+use_source_name+'_'+strlowcase(instrus[i])+'_lv3_'+strlowcase(filters_names2[i])+'_i2d_anchor_atgauss1.fits'
+	st_info=file_info(file)
+	IF st_info.exists NE 1 THEN BEGIN
+		message,'requested file '+file+' does not exist',/info
+		stop
+	ENDIF
+	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 keyword_set(resolution_filter) THEN BEGIN
+		data_reso=dustem_filter2reso(dustem_filter_names2filters(filters_names[i]))
+		final_reso=dustem_filter2reso(resolution_filter)
+  	d=degrade_res(d,h,data_reso,final_reso,hout)
+    h=hout
+	ENDIF
+	IF sxpar(h,'NAXIS1') NE Nx OR sxpar(h,'NAXIS2') NE Ny THEN BEGIN
+		d=project2(h,d,href,/silent)
+	ENDIF
+	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
+	IF keyword_set(show_images) THEN BEGIN
+		window,win & win=win+1
+		image_cont20,d,href,/square,imrange=[-0.1,5],image_color_table='jpbloadct',/silent,tit=tit
+		IF not keyword_set(nostop) THEN stop
+	ENDIF
+ENDFOR
  
-file=data_dir+use_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
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,d,href,/square,imrange=[-0.5,10],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
-IF not keyword_set(nostop) THEN stop
+stop
  
-;Invent variances (will have to do better)
-;jwst_images[*,*,1,*]=(jwst_images[*,*,0,*]*5./100.)^2   ;assumed intensity variance
+;===== Invent variances (will have to do better)
 perc_error=5./100.
 jwst_images[*,*,1,*]=la_power(la_mul(jwst_images[*,*,0,*],perc_error),2)   ;assumed intensity variance
 ;=== check for null variances
@@ -253,28 +152,27 @@ IF count NE 0 THEN BEGIN
 	stop
 ENDIF
  
-;WCO map:
-file=NH_data_dir+use_source_name+'_12m+7m+tp_co21_broad_mom0.fits'
-;file='/Volumes/PILOT_FLIGHT1/PHANGS-JWST/'+use_source_name+'_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]'
-IF keyword_set(show_images) THEN BEGIN
-	window,win & win=win+1
-	image_cont20,NHCO,href,/square,imrange=[-0.5,10],image_color_table='jpbloadct',/silent,tit=tit
-	IF not keyword_set(nostop) THEN stop
-ENDIF
+; ;===== WCO map:
+; file=NH_data_dir+use_source_name+'_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]'
+; IF keyword_set(show_images) THEN BEGIN
+; 	window,win & win=win+1
+; 	image_cont20,NHCO,href,/square,imrange=[-0.5,10],image_color_table='jpbloadct',/silent,tit=tit
+; 	IF not keyword_set(nostop) THEN stop
+; ENDIF
  
 IF keyword_set(save) THEN BEGIN
-	save_file=save_data_dir+use_source_name+'_jwst_images.sav'
-	save,jwst_images,filters,href,NHCO,file=save_file
+	save_file=save_data_dir+use_source_name+'_jwst_images'+reso_str+'.sav'
+	save,jwst_images,filters,href,file=save_file
 	message,'Saved '+save_file,/continue
 ENDIF
  
@@ -38,6 +38,12 @@ vor_sizes=2.*sqrt(1.*vor_num*sxpar(href,&#39;CDELT2&#39;)^2/!pi)   ;FWHM in deg
 print,minmax(vor_sizes)*60.^2
  
 ;stop
+;==== remove negative values in ISRFs
+;ind=where(ISRFs LE 0,count)
+;IF count NE 0 THEN BEGIN
+;	ISRFs[ind]=0.
+;ENDIF
+
 ;==== Make the ISRF cube
 message,'Making the ISRF cube',/info
 Nx=sxpar(href,'NAXIS1')
@@ -18,6 +18,8 @@ bidon=1
  
 phangs_smooth_muse_isrf,'ngc0628',reso_filter='SPIRE3',/save
  
+phangs_smooth_muse_isrf,'ngc0628',reso_filter='PACS3',/save
+
 t1=systime(/sec)
  
 message,'It took '+strtrim((t1-t0)/60./60.,2)+' hrs',/info
@@ -181,7 +181,6 @@ output[*,0]=output[*,0]*paramvalues[0]
 ;output[*,1]=0.
 ;output[*,2]=0. 
  
-
 ;==== Caution:
 ;output is Flambda in ergs/s/cm2/AA
 ;at this point, output is unredenned 
@@ -133,7 +133,12 @@ FOR i=0L,Ntemplates-1 DO BEGIN
   u2=abs(toto-fix(toto))
   ;print,round(toto),toto,fix(toto)
   ;print,1.-u1,1.-u2
-  templates[i]=ptr_new((*ssps_used[round(toto)])*(1.-u1) + (*ssps_used[fix(toto)])*(1.-u2))
+  vec=(*ssps_used[round(toto)])*(1.-u1) + (*ssps_used[fix(toto)])*(1.-u2)
+  ind=where(vec LT 0,count)
+  IF count NE 0 THEN BEGIN
+  	stop
+  ENDIF
+  templates[i]=ptr_new(vec)
 	;stop
 ENDFOR
  
@@ -153,9 +158,12 @@ template_wav=wavs*angstroem2mic   ;mic
 ;IF do_interpol THEN BEGIN
 	;stop
 	lambir=dustem_get_wavelengths()
+	ind_extrapol=where(lambir GT max(template_wav) OR lambir LT min(template_wav),count_extrapol)
 	FOR i=0L,Ntemplates-1 DO BEGIN
 		;ind=where(lambir LE max(template_wav) AND lambir GE min(template_wav),count)
-		templates[i]=ptr_new(interpol(*templates[i],template_wav,lambir))
+		vec=interpol(*templates[i],template_wav,lambir)
+		vec[ind_extrapol]=0.   ;set extrapolated values to 0 (otherwise would be slightly negative)
+		templates[i]=ptr_new(vec)
 	ENDFOR
 	template_wav=lambir
 ;ENDIF