Update docstrings

Author: apearsonn

Diff for: docs/src/index.md

@@ -1 +1,15 @@
 # LegendSpecFits.jl
+
+This package provides functionality to fit probability distribution functions to histograms.
+
+This package is part of the [LegendJuliaRegistry](https://github.com/legend-exp/LegendJuliaRegistry), meaning that you can download the package as follows:
+```julia
+import Pkg
+
+# add the LegendJuliaRegistry
+Pkg.Registry.add(Pkg.RegistrySpec(url = "https://github.com/legend-exp/LegendJuliaRegistry"))
+
+# add LegendSpecFits
+Pkg.add("LegendSpecFits")
+```
+

Diff for: src/aoe_ctc.jl

@@ -5,18 +5,37 @@
 """
 ## TO DO: Still needs to be updated once finalized
 
-    ctc_aoe(aoe_all::Array{T}, ecal_all::Array{T}, qdrift_e_all::Array{T}, compton_bands::Array{T}, peak::T, window::T) where T<:Real
+    ctc_aoe(aoe_all::Vector{<:Real}, ecal_all::Vector{<:Unitful.RealOrRealQuantity}, qdrift_e_all::Vector{<:Real}, compton_bands::Vector{<:Unitful.RealOrRealQuantity}, peak::Real = 0.0, window::Tuple{<:Real, <:Real} = (50.0, 8.0), hist_start::Real = -20.0, hist_end::Real = 5.0, bin_width::Real = 0.05; aoe_expression::Union{Symbol, String}="aoe", qdrift_expression::Union{Symbol, String} = "qdrift / e", pseudo_prior::NamedTupleDist = NamedTupleDist(empty = true), pseudo_prior_all::NamedTupleDist = NamedTupleDist(empty = true), pol_order::Int=1)
 
 Correct for the drift time dependence of A/E parameter
 
-# Returns (but may change):
+
+# Arguments 
+    * `aoe_all`: All A/E values
+    * `ecal_all`: All calibrated energies
+    * `qdrift_e_all`: All charge drift energy
+    * `compton_bands`: Compton bands
+    * `peak`: Peak position
+    * `window`: Window size
+    * `hist_start`: Histogram start position
+    * `hist_end`: Histogram end position
+    * `bin_width`: Bin width
+
+# Keywords
+    * `aoe_expression`: A/E expression
+    * `qdrift_expression`: Charge drift expression
+    * `pseudo_prior`: Pseudo prior
+    * `peudo_prior_all`: All pseudo priors
+    * `pol_order`: Polynomial order
+
+# Returns 
     * `peak`: peak position
     * `window`: window size
+    * `func`: function to correct aoe
     * `fct`: correction factor
     * `σ_before`: σ before correction
     * `σ_after`: σ after correction
-    * `func`: function to correct aoe
-    * `func_generic`: generic function to correct aoe
+    * `converged`: Convergence status of the fit
 """
 
 function ctc_aoe(aoe_all::Vector{<:Real}, ecal_all::Vector{<:Unitful.RealOrRealQuantity}, qdrift_e_all::Vector{<:Real}, compton_bands::Vector{<:Unitful.RealOrRealQuantity}, peak::Real = 0.0, window::Tuple{<:Real, <:Real} = (50.0, 8.0), hist_start::Real = -20.0, hist_end::Real = 5.0, bin_width::Real = 0.05; aoe_expression::Union{Symbol, String}="aoe", qdrift_expression::Union{Symbol, String} = "qdrift / e", pseudo_prior::NamedTupleDist = NamedTupleDist(empty = true), pseudo_prior_all::NamedTupleDist = NamedTupleDist(empty = true), pol_order::Int=1) # deleted m_cal since no calibration

Diff for: src/aoe_cut.jl

@@ -4,8 +4,21 @@
 
 Get the survival fraction after a AoE cut value `aoe_cut` for a given `peak` and `window` size from a combined fit to the survived and cut histograms.
 
+# Arguments
+    * `aoe_cut`: A/E cut value
+    * `aoe`: A/E
+    * `e`: Calibrated energies
+    * `peak`: Peak position
+    * `window`: Data window in energy
+    * `bin_width`: Histogram bin widths
+    * `result_before`: Survival fraction before A/E cut
+
+# Keywords
+    * `uncertainty`: Uncertainty of survival fraction 
+    * `fit_func`: Fit function
+
 # Returns
-- `sf`: Survival fraction after the cut
+    * `result.sf`: Survival fraction after the cut
 """
 function get_sf_after_aoe_cut(aoe_cut::Unitful.RealOrRealQuantity, aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, peak::T, window::Vector{T}, bin_width::T, result_before::NamedTuple; uncertainty::Bool=true, fit_func::Symbol=:gamma_def) where T<:Unitful.Energy{<:Real}
     # get energy after cut and create histogram
@@ -25,12 +38,35 @@ export get_sf_after_aoe_cut
             cut_search_interval::Tuple{<:Unitful.RealOrRealQuantity, <:Unitful.RealOrRealQuantity}=(-25.0*unit(first(aoe)), 1.0*unit(first(aoe))), 
             bin_width_window::T=3.0u"keV", max_e_plot::T=3000.0u"keV",  plot_window::Vector{<:T}=[12.0, 50.0]u"keV",
             fixed_position::Bool=true, fit_func::Symbol=:gamma_def, uncertainty::Bool=true) where T<:Unitful.Energy{<:Real}
+            
 Get the AoE cut value for a given `dep` and `window` size while performing a peak fit with fixed position. The AoE cut value is determined by finding the cut value for which the number of counts after the cut is equal to `dep_sf` times the number of counts before the cut.
 The algorhithm utilizes a root search algorithm to find the cut value with a relative tolerance of `rtol`.
+
+# Arguments
+    * `aoe`: A/E
+    * `e`: Calibrated energies
+
+# Keywords
+    * `dep`: DEP energies
+    * `window`: Data window in energy
+    * `dep_sf`: Survival fraction of DEP energies
+    * `rtol`: Relative tolerance
+    * `maxiters`: Maximum iterations
+    * `sigma_high_sided`: Fixed upper limit cut
+    * `cut_search_interval`: Data interval to search for a cut value
+    * `bin_width_window`: Specified window around the peak in which the bin algorithm is applied
+    * `max_e_plot`: Maximum energy plot
+    * `plot_window`: Plot window
+    * `fixed_position`: Fixed position of cut
+    * `fit_func`: Fitted function
+    * `uncertainty`: Uncertainty
+
 # Returns
-- `cut`: AoE cut value
-- `n0`: Number of counts before the cut
-- `nsf`: Number of counts after the cut
+    * `lowcut`: low AoE cut value
+    * `highcut`: high AoE cut value
+    * `n0`: Number of counts before the cut
+    * `nsf`: Number of counts after the cut
+    * `sf`: Survival fraction
 """
 function get_low_aoe_cut(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T},; 
             dep::T=1592.53u"keV", window::Vector{<:T}=[12.0, 10.0]u"keV", dep_sf::Float64=0.9, rtol::Float64=0.001, maxiters::Int=300, sigma_high_sided::Float64=Inf,
@@ -91,9 +127,25 @@ export get_low_aoe_cut
     get_peaks_survival_fractions(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, peaks::Vector{<:T}, peak_names::Vector{Symbol}, windows::Vector{<:Tuple{T, T}}, aoe_cut::Unitful.RealOrRealQuantity,; uncertainty::Bool=true, inverted_mode::Bool=false, bin_width_window::T=2.0u"keV", sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe)), fit_funcs::Vector{Symbol}=fill(:gamma_def, length(peaks))) where T<:Unitful.Energy{<:Real}
 
 Get the survival fraction of a peak after a AoE cut value `aoe_cut` for a given `peak` and `window` size while performing a peak fit with fixed position.
+
+# Arguments
+    * `aoe`: A/E
+    * `e`: Calibrated energies
+    * `peaks`: Data range of several peaks
+    * `peak_names`: Name of the peak
+    * `windows`: Energy data window 
+    * `aoe_cut`: A/E cut value
+
+# Keywords
+    * `uncertainty`: Uncertainty
+    * `inverted_mode`: If set to false: A/E analysis. If True: LQ analysis
+    * `bin_width_window`: Specified window around the peak where the binning algorithm is applied to
+    * `sigma_high_sided`: Fixed upper limit cut
+    * `fit_funcs`: Fitted functions
+
 # Return 
-- `result`: Dict of results for each peak
-- `report`: Dict of reports for each peak
+    * `result`: Dict of results for each peak
+    * `report`: Dict of reports for each peak
 """
 function get_peaks_survival_fractions(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, peaks::Vector{<:T}, peak_names::Vector{Symbol}, windows::Vector{<:Tuple{T, T}}, aoe_cut::Unitful.RealOrRealQuantity,; uncertainty::Bool=true, inverted_mode::Bool=false, bin_width_window::T=2.0u"keV", sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe)), fit_funcs::Vector{Symbol}=fill(:gamma_def, length(peaks))) where T<:Unitful.Energy{<:Real}
     @assert length(peaks) == length(peak_names) == length(windows) "Length of peaks, peak_names and windows must be equal"
@@ -125,16 +177,31 @@ export get_peaks_survival_fractions, get_peaks_surrival_fractions
 
 """
     get_peak_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, peak::T, window::Vector{T}, aoe_cut::Unitful.RealOrRealQuantity,; 
-    uncertainty::Bool=true, inverted_mode::Bool=false, bin_width_window::T=2.0u"keV", sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe)), fit_func::Symbol=:gamma_def) where T<:Unitful.Energy{<:Real}
+    uncertainty::Bool=true, inverted_mode::Bool=false, bin_width_window::T=2.0u"keV", sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe)), fit_func::Symbol=:gamma_def) where T<:Unitful.Energy{<:Real}  
 
 Get the survival fraction of a peak after a AoE cut value `aoe_cut` for a given `peak` and `window` size while performing a peak fit with fixed position.
     
+# Arguments
+    * `aoe`: A/E
+    * `e`: Calibrated energies
+    * `peak`: Peak position
+    * `window`: Data window in energy
+    * `aoe_cut`: A/E cut value
+
+# Keywords
+    * `Uncertainty`: Uncertainty 
+    * `lq_mode`: Inverts the cut logic
+    * `low_e_tail`: Low energy tail
+    * `bin_width_window`: Specified window around the peak to apply the binning algorithm
+    * `sigma_high_sided`: Fixed upper limit cut
+    
 # Returns
-- `peak`: Peak position
-- `n_before`: Number of counts before the cut
-- `n_after`: Number of counts after the cut
-- `sf`: Survival fraction
-- `err`: Uncertainties
+    * `peak`: Peak position
+    * `fit_func`: fitted function
+    * `n_before`: Number of counts before the cut
+    * `n_after`: Number of counts after the cut
+    * `sf`: Survival fraction
+    * `gof`: Goodness of fit 
 """
 function get_peak_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, peak::T, window::Vector{T}, aoe_cut::Unitful.RealOrRealQuantity,; 
     uncertainty::Bool=true, inverted_mode::Bool=false, bin_width_window::T=2.0u"keV", sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe)), fit_func::Symbol=:gamma_def) where T<:Unitful.Energy{<:Real}  
@@ -189,17 +256,29 @@ export get_peak_survival_fraction, get_peak_surrival_fraction
 
 
 """
-    get_continuum_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, center::T, window::T, aoe_cut::Unitful.RealOrRealQuantity,; inverted_mode::Bool=false, sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe))) where T<:Unitful.Energy{<:Real}
+    get_continuum_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, center::T, window::T, aoe_cut::Unitful.RealOrRealQuantity; inverted_mode::Bool=false, sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe))) where T<:Unitful.Energy{<:Real}
+
 Get the survival fraction of a continuum after a AoE cut value `aoe_cut` for a given `center` and `window` size.
 
+# Arguments
+    * `aoe`: A/E
+    * `e`: Calibrated energies
+    * `center`: Center of the fit
+    * `window`: Data window in energy
+    * `aoe_cut`: A/E cut value
+
+# Keywords
+    * `inverted_mode`: Inverted cut logic
+    * `sigma_high_sided`: Fixed upper limit cut 
+    
 # Returns
-- `center`: Center of the continuum
-- `window`: Window size
-- `n_before`: Number of counts before the cut
-- `n_after`: Number of counts after the cut
-- `sf`: Survival fraction
+    * `window`: Window size
+    * `n_before`: Number of counts before the cut
+    * `n_after`: Number of counts after the cut
+    * `sf`: Survival fraction
+
 """
-function get_continuum_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, center::T, window::T, aoe_cut::Unitful.RealOrRealQuantity,; inverted_mode::Bool=false, sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe))) where T<:Unitful.Energy{<:Real}
+function get_continuum_survival_fraction(aoe::Vector{<:Unitful.RealOrRealQuantity}, e::Vector{<:T}, center::T, window::T, aoe_cut::Unitful.RealOrRealQuantity; inverted_mode::Bool=false, sigma_high_sided::Unitful.RealOrRealQuantity=Inf*unit(first(aoe))) where T<:Unitful.Energy{<:Real}
     # scale unit
     e_unit = u"keV"
     # get energy around center

Diff for: src/aoe_filter_optimization.jl

@@ -1,12 +1,24 @@
 
 
 """
-    prepare_sep_peakhist(e::Array{T}, dep::T,; relative_cut::T=0.5, n_bins_cut::Int=500) where T<:Real
+    prepare_sep_peakhist(e::Vector{<:T}; sep::Unitful.Energy{<:Real}=2103.53u"keV", window::Vector{<:Unitful.Energy{<:Real}}=[25.0, 25.0]u"keV", relative_cut::T=0.5, n_bins_cut::Int=-1, fit_func::Symbol=:gamma_def, uncertainty::Bool=true) where T<:Real
 
 Prepare an array of uncalibrated SEP energies for parameter extraction and calibration.
+
+# Arguments
+    * `e`: Uncalibrated energies
+
+# Keywords
+    * `sep`: Single escape peak
+    * `window`: Energy window
+    * `relative_cut`: Relative cut
+    * `n_bins_cut`: Number of cut bins
+    * `fit_func`: fitted function
+    * `uncertainty`: Uncertainty
+:
 # Returns
-- `result`: Result of the initial fit
-- `report`: Report of the initial fit
+    * `result`: Result of the initial fit
+    * `report`: Report of the initial fit
 """
 function prepare_sep_peakhist(e::Vector{<:T}; sep::Unitful.Energy{<:Real}=2103.53u"keV", window::Vector{<:Unitful.Energy{<:Real}}=[25.0, 25.0]u"keV", relative_cut::T=0.5, n_bins_cut::Int=-1, fit_func::Symbol=:gamma_def, uncertainty::Bool=true) where T<:Real
     # get cut window around peak
@@ -27,18 +39,44 @@ function prepare_sep_peakhist(e::Vector{<:T}; sep::Unitful.Energy{<:Real}=2103.5
 end
 
 """
-    fit_sf_wl(dep_sep_data, a_grid_wl_sg, optimization_config)
+    fit_sf_wl(e_dep::Vector{<:Real}, aoe_dep::ArrayOfSimilarArrays{<:Real}, e_sep::Vector{<:Real}, aoe_sep::ArrayOfSimilarArrays{<:Real}, a_grid_wl_sg::StepRangeLen; 
+                    dep::T=1592.53u"keV", dep_window::Vector{<:T}=[12.0, 10.0]u"keV", 
+                    sep::T=2103.53u"keV", sep_window::Vector{<:T}=[25.0, 25.0]u"keV", sep_rel_cut::Real=0.5,
+                    min_aoe_quantile::Real=0.1, max_aoe_quantile::Real=0.99, 
+                    min_aoe_offset::Quantity{<:Real}=0.0u"keV^-1", max_aoe_offset::Quantity{<:Real}=0.05u"keV^-1",
+                    dep_cut_search_fit_func::Symbol=:gamma_def, sep_cut_search_fit_func::Symbol=:gamma_def,
+                    uncertainty::Bool = false) where T<:Unitful.Energy{<:Real}
 
 Fit a A/E filter window length for the SEP data and return the optimal window length and the corresponding survival fraction.
 
 # Arguments
-- `dep_sep_data`: NamedTuple with the DEP and SEP data
-- `a_grid_wl_sg`: range of window lengths to sweep through
-- `optimization_config`: configuration dictionary
+    * `e_dep`: DEP energy
+    * `aoe_dep`: Double escape peak A/E
+    * `e_sep`: SEP energy
+    * `aoe_sep`: Single escape peak A/E
+    * `a_grid_wl_sg`: Range of window lengths to sweep through
+
+# Keywords
+    * `dep`: Double escape peak
+    * `dep_window`: DEP window
+    * `sep`: Single escape peak
+    * `sep_window`: SEP window
+    * `sep_rel_cut`: SEP relative cut
+    * `min_aoe_quantile`: Minimum A/E quantile
+    * `max_aoe_quantile`: Maximum A/E quantile
+    * `min_aoe_offset`: Minimum A/E offset
+    * `max_aoe_offset`: Maximum A/E offset
+    * `dep_cut_search_fit_func`: Symbol describing the function for the DEP cut search
+    * `sep_cut_search_fit_func`: Symbol describing the function for the SEP cut search
+    * `uncertainty`: Uncertainty
+
 
 # Returns
-- `result`: optimal window length and corresponding survival fraction
-- `report`: report with all window lengths and survival fractions
+    * `wl`: Window length
+    * `sf`: Survival fraction
+    * `n_dep`: Number of DEP energies
+    * `n_sep`: Number of SEP energies
+
 """
 function fit_sf_wl(e_dep::Vector{<:Real}, aoe_dep::ArrayOfSimilarArrays{<:Real}, e_sep::Vector{<:Real}, aoe_sep::ArrayOfSimilarArrays{<:Real}, a_grid_wl_sg::StepRangeLen; 
                     dep::T=1592.53u"keV", dep_window::Vector{<:T}=[12.0, 10.0]u"keV", 
@@ -62,7 +100,7 @@ function fit_sf_wl(e_dep::Vector{<:Real}, aoe_dep::ArrayOfSimilarArrays{<:Real},
     sep_sfs = Vector{Quantity}(undef, length(a_grid_wl_sg))
     wls = Vector{eltype(a_grid_wl_sg)}(undef, length(a_grid_wl_sg))
 
-    # for each window lenght, calculate the survival fraction in the SEP
+    # for each window length, calculate the survival fraction in the SEP
     Threads.@threads for i_aoe in eachindex(a_grid_wl_sg)
         # get window length
         wl = a_grid_wl_sg[i_aoe]

Diff for: src/aoe_fit_calibration.jl

@@ -1,17 +1,25 @@
 @. f_aoe_sigma(x, p) = sqrt(p[1]^2 + p[2]^2/x^2)
 f_aoe_mu(x, p) = p[1] .+ p[2].*x
 """
-fit_aoe_corrections(e::Array{<:Unitful.Energy{<:Real}}, μ::Array{<:Real}, σ::Array{<:Real})
+    fit_aoe_corrections(e::Array{<:Unitful.Energy{<:Real}}, μ::Array{<:Real}, σ::Array{<:Real}; aoe_expression::Union{String,Symbol}="a / e", e_expression::Union{String,Symbol}="e")
 
 Fit the corrections for the AoE value of the detector.
+
+# Arguments
+    * `e`: Calibrated energies
+    * `μ`: Mean values
+    * `σ`: Sigma values
+
+# Keywords
+    * `aoe_expression`: A/E expression
+    * `e_expression`: Calibrated energy expression
+
 # Returns
-- `e`: Energy values
-- `μ`: Mean values
-- `σ`: Sigma values
-- `μ_scs`: Fit result for the mean values
-- `f_μ_scs`: Fit function for the mean values
-- `σ_scs`: Fit result for the sigma values
-- `f_σ_scs`: Fit function for the sigma values
+    * `µ_compton`: Mean compton values
+    * `σ_compton`: Sigma compton values
+    * `compton_bands`: Compton bands 
+    * `func`: A/E correction function
+
 """
 function fit_aoe_corrections(e::Array{<:Unitful.Energy{<:Real}}, μ::Array{<:Real}, σ::Array{<:Real}; aoe_expression::Union{String,Symbol}="a / e", e_expression::Union{String,Symbol}="e")
     # fit compton band mus with linear function

Diff for: src/aoe_pseudo_prior.jl

@@ -1,3 +1,20 @@
+"""
+    get_aoe_standard_pseudo_prior(h::Histogram, ps::NamedTuple, fit_func::Symbol; fixed_position::Bool=false)
+
+Gets the A/E of a histogram using a standard pseudo prior. 
+
+# Arguments
+    * `h`: Histogram data
+    * `ps`: Peak statistics 
+    * `fit_func`: Fit function
+
+# Keywords
+    * `fixed_position`: Determines whether the function uses a fixed position or not
+
+# Returns
+    * `pprior_base`: Base pseudo prior
+"""
+
 function get_aoe_standard_pseudo_prior(h::Histogram, ps::NamedTuple, fit_func::Symbol; fixed_position::Bool=false)
     pprior_base = NamedTupleDist(
         μ = ifelse(fixed_position, ConstValueDist(ps.peak_pos), Normal(ps.peak_pos, 0.5*ps.peak_sigma)),
@@ -25,6 +42,23 @@ function get_aoe_standard_pseudo_prior(h::Histogram, ps::NamedTuple, fit_func::S
     end
 end
 
+"""
+    get_aoe_pseudo_prior(h::Histogram, ps::NamedTuple, fit_func::Symbol; pseudo_prior::NamedTupleDist=NamedTupleDist(empty = true), kwargs...)
+
+Gets the A/E of a histogram using a pseudo prior. 
+# Arguments
+    * `h`: Histogram data
+    * `ps`: Peak statistics
+    * `fit_func`: Fit function
+
+# Keywords
+    * `pseudo_prior`: Initial guess for parameters of histogram
+    
+# Returns
+    * `pseudo_prior`: Initial guess for parameters of histogram
+
+"""
+
 function get_aoe_pseudo_prior(h::Histogram, ps::NamedTuple, fit_func::Symbol; pseudo_prior::NamedTupleDist=NamedTupleDist(empty = true), kwargs...)
     standard_pseudo_prior = get_aoe_standard_pseudo_prior(h, ps, fit_func; kwargs...)
     # use standard priors in case of no overwrites given