add more documentation for functions and clean out old/commented code

[hematthi]

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

Lines 1 to 99 in 98d5a94

	function filter_line_list(df::DataFrame, inst::IT ; λmin::Real = default_λmin, λmax::Real = default_λmax ) where { IT<:HARPSN.AnyHARPSN }
	df |> @filter(λmin <= _.lambda <= λmax) |>
	# @filter( _.lambda < 6000.0 ) |> # Avoid tellurics at redder wavelengths
	# @filter( _.lambda >6157 || _.lambda < 6155 ) |> # Avoid "line" w/ large variability
	DataFrame
	end
	""" read_telluric_ranges( filename )
	Return DataFrame (keys lambda_lo and lambda_hi) with wavelength ranges to be avoided as containing tellurics
	based on provided CSV file. Assumes path is included in filename.
	"""
	function read_telluric_ranges(fn::String)
	@assert occursin(r"\.csv$",fn)
	#println("Trying to read from >",fn,"<.")
	@assert isfile(fn)
	df = CSV.read(fn, DataFrame)
	@assert size(df,1) >= 1
	@assert hasproperty(df,:lambda_lo)
	@assert hasproperty(df,:lambda_hi)
	return df
	end
	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
	#=
	#old neid function
	function make_good_orders_pixels_df(neid_data::DT ; orders::A4 = orders_to_use_default(first(neid_data).inst) ) 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},
	T4 <: Integer, A4<:AbstractArray{T4} }
	@warn "Deprecated use make_good_orders_pixels_df(inst) instead."
	df = DataFrame(order=Int[],pixels=UnitRange[])
	for ord in orders
	pixel_array = EchelleInstruments.calc_complement_index_ranges(get_pixel_range(NEID2D(),ord),EchelleInstruments.NEID.bad_col_ranges(NEID2D(),ord))
	order_array = repeat([ord],length(pixel_array))
	df_tmp = DataFrame(:order=>order_array,:pixels=>pixel_array)
	append!(df,df_tmp)
	end
	return df
	end
	function make_good_orders_pixels_df(inst::IT ; orders::A4 = orders_to_use_default(inst) ) where {
	IT<:NEID.AnyNEID, T4 <: Integer, A4<:AbstractArray{T4} }
	df = DataFrame(order=Int[],pixels=UnitRange[])
	for ord in orders
	pixel_array = EchelleInstruments.calc_complement_index_ranges(get_pixel_range(NEID2D(),ord),EchelleInstruments.NEID.bad_col_ranges(NEID2D(),ord))
	order_array = repeat([ord],length(pixel_array))
	df_tmp = DataFrame(:order=>order_array,:pixels=>pixel_array)
	append!(df,df_tmp)
	end
	return df
	end
	=#
	#=
	function make_clean_line_list_from_bad_columns(line_list::DataFrame, neid_data::DT ;
	Δv_to_avoid_tellurics::Real = 0.0, v_center_to_avoid_tellurics::Real = 0.0
	) 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} }
	if size(df_bad_col_λs,1) < 1 return line_list end
	line_list_to_search_for_tellurics = copy(line_list)
	line_list_to_search_for_tellurics.lambda_lo = line_list_to_search_for_tellurics.lambda./calc_doppler_factor(Δv_to_avoid_tellurics).*calc_doppler_factor(v_center_to_avoid_tellurics)
	line_list_to_search_for_tellurics.lambda_hi = line_list_to_search_for_tellurics.lambda.*calc_doppler_factor(Δv_to_avoid_tellurics).*calc_doppler_factor(v_center_to_avoid_tellurics)
	line_list_no_tellurics_df = line_list_to_search_for_tellurics |> # @filter(λmin <= _.lambda <= λmax) |>
	@filter( !RvSpectMLBase.is_in_wavelength_range_list(_.lambda, order=_.order, list=df_bad_col_λs ) ) |>
	DataFrame
	end
	=#
	#old neid functions

[hematthi]

Same for harps:

EchelleInstruments.jl/src/harps/util.jl

Lines 1 to 100 in 98d5a94

	function filter_line_list(df::DataFrame, inst::IT ; λmin::Real = default_λmin, λmax::Real = default_λmax ) where { IT<:HARPS.AnyHARPS }
	df |> @filter(λmin <= _.lambda <= λmax) |>
	# @filter( _.lambda < 6000.0 ) |> # Avoid tellurics at redder wavelengths
	# @filter( _.lambda >6157 || _.lambda < 6155 ) |> # Avoid "line" w/ large variability
	DataFrame
	end
	""" read_telluric_ranges( filename )
	Return DataFrame (keys lambda_lo and lambda_hi) with wavelength ranges to be avoided as containing tellurics
	based on provided CSV file. Assumes path is included in filename.
	"""
	function read_telluric_ranges(fn::String)
	@assert occursin(r"\.csv$",fn)
	#println("Trying to read from >",fn,"<.")
	@assert isfile(fn)
	df = CSV.read(fn, DataFrame)
	@assert size(df,1) >= 1
	@assert hasproperty(df,:lambda_lo)
	@assert hasproperty(df,:lambda_hi)
	return df
	end
	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
	#=
	#old neid functions
	function make_good_orders_pixels_df(neid_data::DT ; orders::A4 = orders_to_use_default(first(neid_data).inst) ) 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},
	T4 <: Integer, A4<:AbstractArray{T4} }
	@warn "Deprecated use make_good_orders_pixels_df(inst) instead."
	df = DataFrame(order=Int[],pixels=UnitRange[])
	for ord in orders
	pixel_array = EchelleInstruments.calc_complement_index_ranges(get_pixel_range(NEID2D(),ord),EchelleInstruments.NEID.bad_col_ranges(NEID2D(),ord))
	order_array = repeat([ord],length(pixel_array))
	df_tmp = DataFrame(:order=>order_array,:pixels=>pixel_array)
	append!(df,df_tmp)
	end
	return df
	end
	function make_good_orders_pixels_df(inst::IT ; orders::A4 = orders_to_use_default(inst) ) where {
	IT<:NEID.AnyNEID, T4 <: Integer, A4<:AbstractArray{T4} }
	df = DataFrame(order=Int[],pixels=UnitRange[])
	for ord in orders
	pixel_array = EchelleInstruments.calc_complement_index_ranges(get_pixel_range(NEID2D(),ord),EchelleInstruments.NEID.bad_col_ranges(NEID2D(),ord))
	order_array = repeat([ord],length(pixel_array))
	df_tmp = DataFrame(:order=>order_array,:pixels=>pixel_array)
	append!(df,df_tmp)
	end
	return df
	end
	=#
	#=
	function make_clean_line_list_from_bad_columns(line_list::DataFrame, neid_data::DT ;
	Δv_to_avoid_tellurics::Real = 0.0, v_center_to_avoid_tellurics::Real = 0.0
	) 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} }
	if size(df_bad_col_λs,1) < 1 return line_list end
	line_list_to_search_for_tellurics = copy(line_list)
	line_list_to_search_for_tellurics.lambda_lo = line_list_to_search_for_tellurics.lambda./calc_doppler_factor(Δv_to_avoid_tellurics).*calc_doppler_factor(v_center_to_avoid_tellurics)
	line_list_to_search_for_tellurics.lambda_hi = line_list_to_search_for_tellurics.lambda.*calc_doppler_factor(Δv_to_avoid_tellurics).*calc_doppler_factor(v_center_to_avoid_tellurics)
	line_list_no_tellurics_df = line_list_to_search_for_tellurics |> # @filter(λmin <= _.lambda <= λmax) |>
	@filter( !RvSpectMLBase.is_in_wavelength_range_list(_.lambda, order=_.order, list=df_bad_col_λs ) ) |>
	DataFrame
	end
	=#
	#old neid functions