Update UVDA dark selection procedure

function m_angle_uv, matrix

	a = mean(matrix[10:45, 10:45])

	b = mean(matrix[980:1014, 10:45])

	c = mean(matrix[10:45, 980:1014])

	d = mean(matrix[980:1014, 980:1014])

	return, [a, b, c, d]

end

function m_center_uv, matrix

	a = mean(matrix[454:574, 460:582])

	return, a

end

function correzione_uvda, image, dark

	ima_ndit1 = rebin(image, 1024, 1024, /sample)

	ima_ndit2 = rebin(dark, 1024, 1024, /sample)

	x = [m_angle_uv(ima_ndit2), m_center_uv(ima_ndit2)]

	y = [m_angle_uv(ima_ndit1), m_center_uv(ima_ndit1)]

	r = linfit(x, y, yfit = yfit)

	ima = r[0] + ima_ndit2 * r[1]

	s = size(image)

	ima = rebin(ima, s[1], s[2])

	return, ima

end

function metis_dark_uvda, data, header, cal_pack, error = error, quality_matrix = quality_matrix, history = history

	dark = cal_pack.uv_channel.dark

	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 $

			(flag_use_notfullset or dark[i].fullset) and $

			(dark[i].extrapol eq 'False') then $

			obt_available = [obt_available, dark[i].obt_beg]

	endfor

	; if not, search for extrapolated dark matrices with same parameters

	if ~ isa(obt_available) and flag_use_extrapolated then begin

		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 $

			(dark[i].ndit1 eq ndit1) and (dark[i].ndit2 eq ndit2) and $

			flag_use_notfullset and flag_use_extrapolated  then $

				(flag_use_notfullset or dark[i].fullset) and $

				(dark[i].extrapol eq 'True') then $

				obt_available = [obt_available, dark[i].obt_beg]

		endfor

	endif

	; if not, exit with error code

	if ~ isa(obt_available) then begin

		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))

	dark_nobin = dark_image

	dark_image = rebin(dark_image, header.naxis1, header.naxis2) * nbin

	dark_image = dark_image * ndit1 * ndit2

	; apply normalization if required

	if flag_normalize_dark and masking.contains('disabled', /fold) then $

		dark_image = correzione_uvda(data, dark_image)

	mask = where(data eq 0., count)

	data = data - dark_image