line_list argument not used in function

[hematthi]

EchelleInstruments.jl/src/harps/util.jl

Lines 25 to 50 in 98d5a94

	function make_λ_list_for_bad_columns(line_list::DataFrame, harps_data::DT ) where {
	T1<:Real, A1<:AbstractArray{T1}, T2<:Real, A2<:AbstractArray{T2}, T3<:Real, A3<:AbstractArray{T3},
	IT<:HARPS.AnyHARPS, ST<:Spectra2DBasic{T1,T2,T3,A1,A2,A3,IT}, DT<:AbstractArray{ST,1} }
	order_list = 1:size(first(harps_data).λ,2)
	df_bad_col_λs = DataFrame(:order=>Int[], :lambda_lo=>Float64[], :lambda_hi=>Float64[])
	for order in order_list
	λ_extrema = NaNMath.extrema(first(harps_data).λ[:,order])
	if isnan(first(λ_extrema)) || isnan(last(λ_extrema)) continue end
	for bcr in bad_col_ranges(HARPS2D(), order)
	pixlo = first(bcr)
	pixhi = pixlo + 1
	if pixhi > size(first(harps_data).λ,1)
	pixhi -= 1
	pixlo -= 1
	end
	doppler_factors = map(obsid-> haskey(harps_data[obsid].metadata,:doppler_factor) ? harps_data[obsid].metadata[:doppler_factor] : 1 , 1:length(harps_data))
	Δλ_pixel = (first(harps_data).λ[pixhi,order] - first(harps_data).λ[pixlo,order]) * doppler_factors[1]
	(λ_lo, λ_hi) = mapreduce(obsid->extrema(harps_data[obsid].λ[bcr,order]) .* doppler_factors[obsid],
	(a,b) -> (min(a[1],b[1]), max(a[2],b[2])), 1:length(harps_data) )
	λ_lo -= Δλ_pixel/2
	λ_hi += Δλ_pixel/2
	push!(df_bad_col_λs, Dict(:order=>order, :lambda_lo=>λ_lo, :lambda_hi=>λ_hi) )
	end
	end
	return df_bad_col_λs
	end

[hematthi]

Same for harps-n:

EchelleInstruments.jl/src/harps-n/util.jl

Lines 24 to 49 in 98d5a94

	function make_λ_list_for_bad_columns(line_list::DataFrame, harpsn_data::DT ) where {
	T1<:Real, A1<:AbstractArray{T1}, T2<:Real, A2<:AbstractArray{T2}, T3<:Real, A3<:AbstractArray{T3},
	IT<:HARPSN.AnyHARPSN, ST<:Spectra2DBasic{T1,T2,T3,A1,A2,A3,IT}, DT<:AbstractArray{ST,1} }
	order_list = 1:size(first(harpsn_data).λ,2)
	df_bad_col_λs = DataFrame(:order=>Int[], :lambda_lo=>Float64[], :lambda_hi=>Float64[])
	for order in order_list
	λ_extrema = NaNMath.extrema(first(harpsn_data).λ[:,order])
	if isnan(first(λ_extrema)) || isnan(last(λ_extrema)) continue end
	for bcr in bad_col_ranges(HARPSN2D(), order)
	pixlo = first(bcr)
	pixhi = pixlo + 1
	if pixhi > size(first(harpsn_data).λ,1)
	pixhi -= 1
	pixlo -= 1
	end
	doppler_factors = map(obsid-> haskey(harpsn_data[obsid].metadata,:doppler_factor) ? harpsn_data[obsid].metadata[:doppler_factor] : 1 , 1:length(harpsn_data))
	Δλ_pixel = (first(harpsn_data).λ[pixhi,order] - first(harpsn_data).λ[pixlo,order]) * doppler_factors[1]
	(λ_lo, λ_hi) = mapreduce(obsid->extrema(harpsn_data[obsid].λ[bcr,order]) .* doppler_factors[obsid],
	(a,b) -> (min(a[1],b[1]), max(a[2],b[2])), 1:length(harpsn_data) )
	λ_lo -= Δλ_pixel/2
	λ_hi += Δλ_pixel/2
	push!(df_bad_col_λs, Dict(:order=>order, :lambda_lo=>λ_lo, :lambda_hi=>λ_hi) )
	end
	end
	return df_bad_col_λs
	end

[hematthi]

Same for neid:

EchelleInstruments.jl/src/neid/util.jl

Lines 23 to 48 in 98d5a94

	function make_λ_list_for_bad_columns(line_list::DataFrame, neid_data::DT ) where {
	T1<:Real, A1<:AbstractArray{T1}, T2<:Real, A2<:AbstractArray{T2}, T3<:Real, A3<:AbstractArray{T3},
	IT<:NEID.AnyNEID, ST<:Spectra2DBasic{T1,T2,T3,A1,A2,A3,IT}, DT<:AbstractArray{ST,1} }
	order_list = 1:size(first(neid_data).λ,2)
	df_bad_col_λs = DataFrame(:order=>Int[], :lambda_lo=>Float64[], :lambda_hi=>Float64[])
	for order in order_list
	λ_extrema = NaNMath.extrema(first(neid_data).λ[:,order])
	if isnan(first(λ_extrema)) || isnan(last(λ_extrema)) continue end
	for bcr in bad_col_ranges(NEID2D(), order)
	pixlo = first(bcr)
	pixhi = pixlo + 1
	if pixhi > size(first(neid_data).λ,1)
	pixhi -= 1
	pixlo -= 1
	end
	doppler_factors = map(obsid-> haskey(neid_data[obsid].metadata,:doppler_factor) ? neid_data[obsid].metadata[:doppler_factor] : 1 , 1:length(neid_data))
	Δλ_pixel = (first(neid_data).λ[pixhi,order] - first(neid_data).λ[pixlo,order]) * doppler_factors[1]
	(λ_lo, λ_hi) = mapreduce(obsid->extrema(neid_data[obsid].λ[bcr,order]) .* doppler_factors[obsid],
	(a,b) -> (min(a[1],b[1]), max(a[2],b[2])), 1:length(neid_data) )
	λ_lo -= Δλ_pixel/2
	λ_hi += Δλ_pixel/2
	push!(df_bad_col_λs, Dict(:order=>order, :lambda_lo=>λ_lo, :lambda_hi=>λ_hi) )
	end
	end
	return df_bad_col_λs
	end