Fix typo in priority_zone_criteria docs function docs and format fu…

…nction docs

src/decision/dMCDA.jl

@@ -258,8 +258,10 @@ function rank_sites!(
 end
 
 """
-    retrieve_ranks(S::Matrix, site_ids::Vector, weights::Vector{Float64}, mcda_func::Function)::Matrix{Union{Float64,Int64}}
-    retrieve_ranks(S::Matrix, site_ids::Vector, weights::Vector{Float64}, mcda_func::Type{<:MCDMMethod})::Matrix{Union{Float64,Int64}}
+    retrieve_ranks(S::Matrix, site_ids::Vector, weights::Vector{Float64}, mcda_func::Function)
+        ::Matrix{Union{Float64,Int64}}
+    retrieve_ranks(S::Matrix, site_ids::Vector, weights::Vector{Float64}, mcda_func::
+        Type{<:MCDMMethod})::Matrix{Union{Float64,Int64}}
     retrieve_ranks(site_ids::Vector, scores::Vector, maximize::Bool)::Matrix{Union{Float64,Int64}}
 
 Get location ranks using mcda technique specified in mcda_func, weights and a decision matrix S.
@@ -307,7 +309,8 @@ function retrieve_ranks(
 end
 
 """
-    create_decision_matrix(site_ids, in_conn, out_conn, leftover_space, wave_stress, heat_stress, predec, risk_tol)
+    create_decision_matrix(site_ids, in_conn, out_conn, leftover_space, wave_stress, 
+        heat_stress, predec, risk_tol)
 
 Creates criteria matrix `A`, where each column is a selection criterium and each row is a site.
 Sites are then filtered based on heat and wave stress risk.
@@ -333,7 +336,8 @@ Columns indicate:
 - `wave_stress` : Probability of wave damage.
 - `heat_stress` : Probability of site being affected by heat stress
 - `predec` : List of priority predecessors (sites strongly connected to priority sites).
-- `risk_tol` : Tolerance for wave and heat risk (∈ [0,1]). Sites with heat or wave risk> risk_tol are filtered out.
+- `risk_tol` : Tolerance for wave and heat risk (∈ [0,1]). Sites with heat or wave 
+    risk> risk_tol are filtered out.
 """
 function create_decision_matrix(
     site_ids::Vector{Int64},
@@ -916,18 +920,20 @@ function within_depth_bounds(
 end
 
 """
-    priority_location_criteria(strong_pred::Vector{Int64}, priority_locations::Vector{Int64}, location_ids::Vector{Int64})::Vector{Float64}
+    priority_location_criteria(strong_pred::Vector{Int64}, priority_locations::Vector{Int64}, 
+        location_ids::Vector{Int64})::Vector{Float64}
 
-Calculates the priority location criteria, which prioritises priority locations and locations which are larval sources for these.
+Calculates the priority location criteria, which prioritises priority locations and locations   
+which are larval sources for these.
 
 # Arguments
 - `strong_pred` : Strongest predecessor locations for each location.
 - `priority_locations` : Priority locations  
 - `location_ids` : Full set of location indices (same size as `strong_pred`)
 
 # Returns
-Priority criteria value for each location, the larger the value the better that location contributes to prioritising locations 
-    in the `priority_locations` list.
+Priority criteria value for each location, the larger the value the better that location 
+    contributes to prioritising locations in the `priority_locations` list.
 """
 function priority_location_criteria(
     strong_pred::Vector{Int64},
@@ -938,6 +944,7 @@ function priority_location_criteria(
 
     # Work out which priority predecessors are connected to priority locations
     predprior = strong_pred[in.(location_ids, [priority_locations'])]
+
     # Find index of these locations in location_ids
     pred_idx = dropdims(sum(predprior .== location_ids'; dims = 1); dims = 1) .> 0
 
@@ -946,17 +953,19 @@ function priority_location_criteria(
 end
 
 """
-    priority_zones_criteria(strong_pred::Vector{Int64}, zones::Vector{String}, priority_zones::Vector{String},
-        location_ids::Vector{Int64}; pred_w::Float64=0.2)::Vector{Float64}
+    priority_zones_criteria(strong_pred::Vector{Int64}, zones::Vector{String}, 
+        priority_zones::Vector{String}, location_ids::Vector{Int64}; pred_w::Float64=0.5)::Vector{Float64}
 
-Calculates the priority zone criteria, which prioritises priority GBRMPA zones and zones which are larval sources for these.
+Calculates the priority zone criteria, which prioritises priority GBRMPA zones and zones which 
+are larval sources for these.
 
 # Arguments
 - `strong_pred` : Strongest predecessor locations for each location.
 - `zones` : Zone classification for each location
 - `priority_zones` : Zones to prioritse (in order of priority)
 - `location_ids` : Full set of location indices (same size as `strong_pred`)
-- `pred_w` : Weight designating importance of locations being in the zone vs. larval sources for the zone.
+- `pred_w` : Weight designating importance of locations being in the zone vs. larval sources 
+    for the zone.
 
 # Returns
 Priority zones value for each location, the larger the value the better that location contributes to prioritising 
@@ -970,6 +979,7 @@ function priority_zones_criteria(
     pred_w::Float64 = 0.5,
 )::Vector{Float64}
     n_sites = length(zones)
+
     # Find set of priority zones and scoring system depending on zone priority
     zone_ids = intersect(priority_zones, unique(zones))
     zone_weights = mcda_normalize(collect(length(zone_ids):-1:1))
@@ -979,6 +989,7 @@ function priority_zones_criteria(
     for (k::Int64, z_name::String) in enumerate(zone_ids)
         # Find locations which are strongest predecessors of locations in the zone 
         add_zone_weight = strong_pred[zones .== z_name]
+
         # Positional indices of locations in location_ids
         pred_zone_idx =
             dropdims(sum(add_zone_weight .== location_ids'; dims = 1); dims = 1) .> 0

src/decision/location_selection.jl

@@ -3,7 +3,8 @@ using NamedDims, AxisKeys
 using ADRIA: connectivity_strength, relative_leftover_space, site_k_area
 
 """
-    _location_selection(domain::Domain, sum_cover::AbstractArray, mcda_vars::DMCDA_vars, guided::Int64)::Matrix
+    _location_selection(domain::Domain, sum_cover::AbstractArray, mcda_vars::DMCDA_vars, 
+        guided::Int64)::Matrix
 
 Select locations for a given domain and criteria/weightings/thresholds, using a chosen
 MCDA method.
@@ -57,9 +58,12 @@ function _location_selection(
 end
 
 """
-    rank_locations(domain::Domain, scenarios::DataFrame, sum_cover::NamedDimsArray, area_to_seed::Float64; target_seed_sites=nothing, target_fog_sites=nothing)::NamedDimsArray
-    rank_locations(domain::Domain,scenarios::DataFrame, sum_cover::NamedDimsArray, area_to_seed::Float64, agg_func::Function,
-        iv_type::Union{String,Int64}; target_seed_sites=nothing, target_fog_sites=nothing)::AbstractArray
+    rank_locations(domain::Domain, scenarios::DataFrame, sum_cover::NamedDimsArray, 
+        area_to_seed::Float64; target_seed_sites=nothing, target_fog_sites=nothing)
+        ::NamedDimsArray
+    rank_locations(domain::Domain,scenarios::DataFrame, sum_cover::NamedDimsArray, 
+        area_to_seed::Float64, agg_func::Function,iv_type::Union{String,Int64}; 
+        target_seed_sites=nothing, target_fog_sites=nothing)::AbstractArray
 
 Return location ranks for a given domain and scenarios.
 
@@ -184,8 +188,10 @@ end
 
 """
     ranks_to_frequencies(ranks::NamedDimsArray, n_ranks::Int64)
-    ranks_to_frequencies(ranks::NamedDimsArray{D,T,3,A}; n_ranks=length(ranks.sites), agg_func=x -> dropdims(sum(x; dims=:timesteps); dims=:timesteps),) where {D,T,A}
-    ranks_to_frequencies(ranks::NamedDimsArray{D,T,2,A}; n_ranks=length(ranks.sites), agg_func=nothing) where {D,T,A}
+    ranks_to_frequencies(ranks::NamedDimsArray{D,T,3,A}; n_ranks=length(ranks.sites), 
+        agg_func=x -> dropdims(sum(x; dims=:timesteps); dims=:timesteps),) where {D,T,A}
+    ranks_to_frequencies(ranks::NamedDimsArray{D,T,2,A}; n_ranks=length(ranks.sites), 
+        agg_func=nothing) where {D,T,A}
 
 Returns the frequency with which each location was ranked across scenarios.
 Uses the results from `rank_locations()`.
@@ -243,7 +249,8 @@ end
 
 """
     location_selection_frequencies(ranks::NamedDimsArray; n_iv_locs::Int64=5)
-    location_selection_frequencies(iv_log::NamedDimsArray{D,T,4,A}; dims::Union{Symbol,Vector{Symbol}}=:coral_id) where {D,T,A}
+    location_selection_frequencies(iv_log::NamedDimsArray{D,T,4,A}; dims::Union{Symbol,
+        Vector{Symbol}}=:coral_id) where {D,T,A}
 
 Determines the count of times each location was selected for a specific intervention over a
 set of scenarios.
@@ -281,7 +288,8 @@ end
 _drop_single(x::AbstractMatrix) = dropdims(x; dims = (findall(size(x) .== 1)...,))
 
 """
-    selection_score(ranks::NamedDimsArray{D,T,3,A}; dims::Vector{Symbol}=[:scenarios, :timesteps]) where {D,T,A}
+    selection_score(ranks::NamedDimsArray{D,T,3,A}; dims::Vector{Symbol}=[:scenarios, 
+        :timesteps]) where {D,T,A}
     selection_score(ranks::NamedDimsArray{D,T,2,A}) where {D,T,A}
     selection_score(ranks::NamedDimsArray, dims::Vector{Symbol})
 

src/ecosystem/connectivity.jl

@@ -163,7 +163,8 @@ end
 
 """
     connectivity_strength(TP_base::AbstractArray)::NamedTuple
-    connectivity_strength(area_weighted_TP::AbstractMatrix{Float64}, cover::Vector{Float64}, TP_cache::AbstractMatrix{Float64})::NamedTuple
+    connectivity_strength(area_weighted_TP::AbstractMatrix{Float64}, cover::Vector{Float64}, 
+        TP_cache::AbstractMatrix{Float64})::NamedTuple
 
 Create in/out degree centralities for all nodes, and vector of their strongest predecessors.