Update UVDA dark selection procedure

metis_dark_uvda.pro

-function metis_dark_uvda, data, header, cal_pack, history = history
+function metis_dark_uvda, data, header, cal_pack, error = error, quality_matrix = quality_matrix, history = history
 
 	dark = cal_pack.uv_channel.dark
 
-	dit = header.dit/1000.D0
+	dit = header.dit
+	nbin = header.nbin 
 	ndit1 = header.ndit1
 	ndit2 = header.ndit2
-	nbin = sqrt(header.nbin)
-	tsensor = header.tsensor
 	obj_cnt = header.obj_cnt
 	obt_beg = header.obt_beg
+	masking = header.masking
+	tsensor = header.tsensor
 	
 	; WARN - temporary patch to handle local l1 fits files
-	; if tsensor eq 0. then tsensor = -25.
-
-	for i = 0, n_elements(dark) - 1 do begin
-		if (dark[i].dit eq dit) and (dark[i].ndit1 eq ndit1) and (dark[i].ndit2 eq ndit2) and (abs(dark[i].tsensor - tsensor) lt 5.) and (abs(obt_beg - dark[i].obt_beg) lt (3600. * 24. * 10.)) then begin
-			if obj_cnt eq 1 then begin
-				dark_file = dark[i].file_name.cnt_1
-				dark_image = float(readfits(cal_pack.path + dark_file, /silent))
-				dark_image = rebin(dark_image, header.naxis1, header.naxis2) * nbin^2
-
-				mask = where(data eq 0., count)
-				data = data - dark_image * ndit1 * ndit2
-				data[mask] = 0.
-
-				goto, jump
-			endif else begin
-				dark_file = dark[i].file_name.cnt_2
-				dark_image = float(readfits(cal_pack.path + dark_file, /silent))
-				dark_image = rebin(dark_image, header.naxis1, header.naxis2) * nbin^2
-
-				mask = where(data eq 0., count)
-				data = data - dark_image * ndit1 * ndit2
-				data[mask] = 0.
+	if tsensor eq 0. then tsensor = -25.
 
-				goto, jump
-			endelse
-		endif
-	endfor
+	flag_use_extrapolated = 1
+	flag_use_notfullset = 1
+	flag_normalize_dark = 0
 
 	obt_available = !null
+
+	; check if one or more dark matrices with same dit, ndit1, ndit2, and tsensor exist
 	for i = 0, n_elements(dark) - 1 do begin
-		if (dark[i].dit eq dit) and (abs(dark[i].tsensor - tsensor) lt 5.) then obt_available = [obt_available, dark[i].obt_beg]
+		if $
+			(dark[i].dit eq dit) and $
+			(abs(dark[i].tsensor - tsensor) lt 5) and $
+			(dark[i].ndit1 eq ndit1) and (dark[i].ndit2 eq ndit2) and $
+			flag_use_notfullset and flag_use_extrapolated  then $
+			obt_available = [obt_available, dark[i].obt_beg]
 	endfor
 
-	if not isa(obt_available) then begin
+	; if not, exit with error code
+	if ~ isa(obt_available) then begin
 		journal, 'Error 12: UV applicable dark file not found.'
 		journal
 		exit, status = 12
 	endif
 
+	; if yes, chose the temporally closest
 	delta_obt = min(abs(obt_beg - obt_available), j)
 	i = where(dark.obt_beg eq obt_available[j])
 
-	if obj_cnt eq 1 then begin
-		if (ndit1 eq dark[i].ndit1) and (ndit2 eq dark[i].ndit2) then begin
-			dark_file = dark[i].file_name.cnt_1
-			dark_image = float(readfits(cal_pack.path + dark_file, /silent))
-		endif else begin
-			q = ndit2/dark[i].ndit2
-			dark_file = dark[i].file_name.cnt_1 + ' - ' + dark[i].file_name.cnt_2
-			dark_1 = float(readfits(cal_pack.path + dark[i].file_name.cnt_1, /silent))
-			dark_2 = float(readfits(cal_pack.path + dark[i].file_name.cnt_2, /silent))
-			dark_image = (dark_1 + (q - 1) * dark_2)/q
-		endelse
-	endif else begin
-		dark_file = dark[i].file_name.cnt_2
+	transient = where(dark[i].filename.cnt_1 ne '', n)
+	
+	; select the dark matrix corresponding to the correct transient phase
+	if obj_cnt le n then $
+		dark_file = dark[i].filename.cnt_1[obj_cnt - 1] else $
+		dark_file = dark[i].filename.cnt_2
+
+	; apply normalization if required
+	if flag_normalize_dark and masking.contains('disabled', /fold) then $
+		dark_image = correzione_uvda(data, cal_pack.path + dark_file) else $
 		dark_image = float(readfits(cal_pack.path + dark_file, /silent))
-	endelse
 
-	dark_image = rebin(dark_image, header.naxis1, header.naxis2) * nbin^2
+	dark_nobin = dark_image
+
+	; rebin the dark and take into account the exposure time correction of l1 images
+	dark_image = rebin(dark_image, header.naxis1, header.naxis2) * nbin
+	dark_image = dark_image * ndit1 * ndit2
 
 	mask = where(data eq 0., count)
-	data = data - dark_image * ndit1 * ndit2
-	data[mask] = 0.
+	data = data - dark_image
+	
+	; set values in masked areas equal to zero
+	if count gt 0 then data[mask] = 0.
+
+	; read the dark error matrix
+	dark_error_file = dark[i].error
+	if file_test(dark_error_file) then $
+		dark_error = float(readfits(cal_pack.path + dark_error_file, /silent)) else $
+		dark_error = fltarr(1024, 1024)
+
+	; rebin it and take into account the exposure time correction
+	dark_error = dark_image * sqrt(rebin((dark_error/dark_nobin)^2, header.naxis1, header.naxis2)/nbin)
+	
+	; calculate the total uncertainty
+	if isa(error) then error += (data/cal_pack.uv_channel.radiometry.iaps_gain.value * cal_pack.uv_channel.radiometry.f_noise.value + 2. * dark_error^2)/data^2
 
 	jump:
+	s = where(data le 0., count)
+	quality_matrix[s] = 0
 	
 	if ~ isa(history) then history = !null
 	history = [history, 'Dark-current correction: ', '  ' + dark_file]
 
 	journal, 'Dark-current correction:'
-	journal, '  dit = ' + string(dit, format = '(F0)') + ' s'
+	journal, '  dit = ' + string(dit, format = '(I0)') + ' ms'
 	journal, '  ndit1 = ' + string(ndit1, format = '(I0)')
 	journal, '  ndit2 = ' + string(ndit2, format = '(I0)')
 	journal, '  obj_cnt = ' + string(obj_cnt, format = '(I0)')
 	journal, '  tsensor = ' + string(tsensor, format = '(F0)') + ' degC'
 	journal, '  dark file = ' + dark_file
+	journal, '  extrapolated dark = ' + dark[i].extrapol.tolower()
 
 	return, data
 end