diff --git a/docs/src/assets/imgs/criteria_spatial_plots.png b/docs/src/assets/imgs/criteria_spatial_plots.png new file mode 100644 index 000000000..bc3f8c6ed Binary files /dev/null and b/docs/src/assets/imgs/criteria_spatial_plots.png differ diff --git a/docs/src/usage/analysis.md b/docs/src/usage/analysis.md index 286935d8b..cab9c9a10 100644 --- a/docs/src/usage/analysis.md +++ b/docs/src/usage/analysis.md @@ -186,6 +186,35 @@ save("ranks_plot.png", rank_fig) ![Rank frequency plots for multiple ranks](/ADRIA.jl/dev/assets/imgs/ranks_plot.png?raw=true "Rank frequency plot 3 ranks") +## Intervention location selection - plot criteria maps + +```julia + +dom = ADRIA.load_domain("path to domain", "45") +scens = ADRIA.sample_guided(dom, 8) + +mcda_funcs = ADRIA.decision.mcda_methods() + +scens = ADRIA.sample_guided(dom, 2^5) +rs = ADRIA.run_scenarios(dom, scens, "45") + +# Remove any risk filtering +scens[1, ["deployed_coral_risk_tol"]] .= [1.0] + +# Create decision matrices for first scenario, get aggregate score using the +# first MCDA method +decision_dict = ADRIA.decision.decision_matrices(rs, scens[1, :], mcda_funcs[1]) + +# Plot maps of seeding criteria and aggreagte selection score +fig_criteria = hs = ADRIA.viz.map( + rs, decision_dict[:seed_matrix], decision_dict[:seed_scores] +) +save("criteria_maps.png", fig_criteria) +``` + +![Spatial maps of location selection criteria](/ADRIA.jl/dev/assets/imgs/criteria_spatial_plots.png?raw=true "Spatial maps of location selection criteria") + + ### PAWN sensitivity (heatmap overview) The PAWN sensitivity analysis method is a moment-independent approach to Global Sensitivity diff --git a/ext/AvizExt/viz/location_selection.jl b/ext/AvizExt/viz/location_selection.jl index d450a907f..b3207e573 100644 --- a/ext/AvizExt/viz/location_selection.jl +++ b/ext/AvizExt/viz/location_selection.jl @@ -20,6 +20,8 @@ Plot a spatial map of location selection frequencies. - `opts` : Aviz options - `colorbar_label`, label for colorbar. Defaults to "Relative Cover". - `color_map`, preferred colormap for plotting heatmaps. + - `colorbar_limits` : Upper and lower limits displayed on colorbar, + (default is (0.0, maximum(data))) - `axis_opts` : Additional options to pass to adjust Axis attributes See: https://docs.makie.org/v0.19/api/index.html#Axis - `fig_opts` : Additional options to pass to adjust Figure creation @@ -29,7 +31,7 @@ Plot a spatial map of location selection frequencies. Figure """ function ADRIA.viz.ranks_to_frequencies!( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, rs::ResultSet, frequencies::NamedDimsArray, rank_ids::Vector{Int64}; @@ -86,7 +88,7 @@ function ADRIA.viz.ranks_to_frequencies!( return g end function ADRIA.viz.ranks_to_frequencies!( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, rs::ResultSet, frequencies::NamedDimsArray, rank_id::Int64; @@ -110,10 +112,9 @@ end function ADRIA.viz.ranks_to_frequencies( rs::ResultSet, frequencies::NamedDimsArray, - rank_ids::Union{Int64,Vector{Int64}}; + rank_ids::Union{Int64, Vector{Int64}}; opts::Dict=Dict(), fig_opts::Dict=Dict(), axis_opts::Dict=Dict()) - f = Figure(; fig_opts...) g = f[1, 1] = GridLayout() ADRIA.viz.ranks_to_frequencies!( @@ -141,7 +142,7 @@ Retrieve set of colormaps for plotting overlayed colormaps. Maps for each key in rank_groups to a unique colormap. """ function _default_colormap( - rank_groups::Dict{Symbol,BitVector}, alpha_vals::Dict{Symbol,Float64} + rank_groups::Dict{Symbol, BitVector}, alpha_vals::Dict{Symbol, Float64} ) rank_colors = colors(rank_groups, alpha_vals) rank_ids = keys(rank_groups) diff --git a/ext/AvizExt/viz/scenarios.jl b/ext/AvizExt/viz/scenarios.jl index 347a92c95..96934cebf 100644 --- a/ext/AvizExt/viz/scenarios.jl +++ b/ext/AvizExt/viz/scenarios.jl @@ -56,13 +56,13 @@ function ADRIA.viz.scenarios( ) end function ADRIA.viz.scenarios!( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, rs::ResultSet, outcomes::NamedDimsArray; opts::Dict=Dict(:by_RCP => false), axis_opts::Dict=Dict(), series_opts::Dict=Dict(), -)::Union{GridLayout,GridPosition} +)::Union{GridLayout, GridPosition} opts[:histogram] = get(opts, :histogram, false) return ADRIA.viz.scenarios!( @@ -91,13 +91,13 @@ function ADRIA.viz.scenarios( return f end function ADRIA.viz.scenarios!( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, scenarios::DataFrame, outcomes::NamedDimsArray; opts::Dict=Dict(), axis_opts::Dict=Dict(), series_opts::Dict=Dict(), -)::Union{GridLayout,GridPosition} +)::Union{GridLayout, GridPosition} # Ensure last year is always shown in x-axis xtick_vals = get(axis_opts, :xticks, _time_labels(timesteps(outcomes))) xtick_rot = get(axis_opts, :xticklabelrotation, 2 / π) @@ -121,14 +121,14 @@ function ADRIA.viz.scenarios!( ) end function ADRIA.viz.scenarios!( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, ax::Axis, outcomes::NamedDimsArray, - scen_groups::Dict{Symbol,BitVector}; + scen_groups::Dict{Symbol, BitVector}; opts::Dict=Dict(), axis_opts::Dict=Dict(), series_opts::Dict=Dict(), -)::Union{GridLayout,GridPosition} +)::Union{GridLayout, GridPosition} if get(opts, :summarize, true) scenarios_confint!(ax, outcomes, scen_groups) else @@ -149,7 +149,7 @@ function ADRIA.viz.scenarios!( end function _confints( - outcomes::NamedDimsArray, scen_groups::Dict{Symbol,BitVector} + outcomes::NamedDimsArray, scen_groups::Dict{Symbol, BitVector} )::Array{Float64} groups::Vector{Symbol} = _sort_keys(scen_groups, outcomes) n_timesteps::Int64 = size(outcomes, 1) @@ -171,10 +171,9 @@ function scenarios_confint!( ax::Axis, confints::AbstractArray, ordered_groups::Vector{Symbol}, - _colors::Dict{Symbol,Union{Symbol,RGBA{Float32}}}; - x_vals::Union{Vector{Int64},Vector{Float64}}=collect(1:size(confints, 1)), + _colors::Dict{Symbol, Union{Symbol, RGBA{Float32}}}; + x_vals::Union{Vector{Int64}, Vector{Float64}}=collect(1:size(confints, 1)), )::Nothing - for idx in eachindex(ordered_groups) band_color = (_colors[ordered_groups[idx]], 0.4) y_lower, y_upper = confints[:, idx, 1], confints[:, idx, 3] @@ -187,9 +186,9 @@ function scenarios_confint!( return nothing end function scenarios_confint!( - ax::Axis, outcomes::NamedDimsArray, scen_groups::Dict{Symbol,BitVector} + ax::Axis, outcomes::NamedDimsArray, scen_groups::Dict{Symbol, BitVector} )::Nothing - _colors::Dict{Symbol,Union{Symbol,RGBA{Float32}}} = colors(scen_groups) + _colors::Dict{Symbol, Union{Symbol, RGBA{Float32}}} = colors(scen_groups) ordered_groups = _sort_keys(scen_groups, outcomes) confints = _confints(outcomes, scen_groups) return scenarios_confint!( @@ -204,13 +203,13 @@ end function scenarios_series!( ax::Axis, outcomes::NamedDimsArray, - scen_groups::Dict{Symbol,BitVector}; + scen_groups::Dict{Symbol, BitVector}; series_opts::Dict=Dict(), - x_vals::Union{Vector{Int64},Vector{Float64}}=collect(1:size(outcomes, 1)), + x_vals::Union{Vector{Int64}, Vector{Float64}}=collect(1:size(outcomes, 1)), sort_by=:size, )::Nothing - _colors::Dict{Symbol,Union{Symbol,RGBA{Float32}}} = colors(scen_groups) - _alphas::Dict{Symbol,Float64} = alphas(scen_groups) + _colors::Dict{Symbol, Union{Symbol, RGBA{Float32}}} = colors(scen_groups) + _alphas::Dict{Symbol, Float64} = alphas(scen_groups) for group in _sort_keys(scen_groups, outcomes; by=sort_by) color = (_colors[group], _alphas[group]) @@ -222,9 +221,9 @@ function scenarios_series!( end function scenarios_hist( - g::Union{GridLayout,GridPosition}, + g::Union{GridLayout, GridPosition}, outcomes::NamedDimsArray, - scen_groups::Dict{<:Any,BitVector}, + scen_groups::Dict{<:Any, BitVector}, )::Nothing scen_dist = dropdims(mean(outcomes; dims=:timesteps); dims=:timesteps) ax_hist = Axis(g[1, 2]; width=100) @@ -248,9 +247,9 @@ function scenarios_hist( end function _render_legend( - g::Union{GridLayout,GridPosition}, - scen_groups::Dict{<:Any,BitVector}, - legend_position::Tuple{Int64,Int64}, + g::Union{GridLayout, GridPosition}, + scen_groups::Dict{<:Any, BitVector}, + legend_position::Tuple{Int64, Int64}, )::Nothing group_names::Vector{Symbol} = sort(collect(keys(scen_groups))) _colors = colors(scen_groups) @@ -274,7 +273,7 @@ Sort types by variance in reverse order. - :counterfactual """ function _sort_keys( - scenario_types::Dict{Symbol,BitVector}, + scenario_types::Dict{Symbol, BitVector}, outcomes::AbstractArray; by=:variance, )::Vector{Symbol} diff --git a/ext/AvizExt/viz/spatial.jl b/ext/AvizExt/viz/spatial.jl index 64e0cfaa2..0440bfd53 100644 --- a/ext/AvizExt/viz/spatial.jl +++ b/ext/AvizExt/viz/spatial.jl @@ -20,6 +20,8 @@ Create a spatial choropleth figure. - `centroids` : Vector{Tuple}, of lon and lats - `show_colorbar` : Whether to show a colorbar (true) or not (false) - `colorbar_label` : Label to use for color bar +- `colorbar_limits` : Upper and lower limits displayed on colorbar, + (default is (0.0, maximum(data))) - `color_map` : Type of colormap to use, See: https://docs.makie.org/stable/documentation/colors/#colormaps - `legend_params` : Legend parameters @@ -34,6 +36,7 @@ function create_map!( centroids::Vector, show_colorbar::Bool=true, colorbar_label::String="", + colorbar_limits::Tuple{Float64, Float64}=(0.0, maximum(data)), color_map::Union{Symbol, Vector{Symbol}, RGBA{Float32}, Vector{RGBA{Float32}}}=:grayC, legend_params::Union{Tuple, Nothing}=nothing, axis_opts::Dict=Dict(), @@ -58,17 +61,12 @@ function create_map!( spatial.yticklabelpad = 50 spatial.ytickalign = 10 - max_val = @lift(maximum($data)) - - # Plot geodata polygons using data as internal color - color_range = (0.0, max_val[]) poly!( spatial, geodata; color=data, colormap=color_map, - colorrange=color_range, strokecolor=(:black, 0.05), strokewidth=1.0, ) @@ -76,10 +74,10 @@ function create_map!( if show_colorbar Colorbar( f[1, 2]; - colorrange=color_range, colormap=color_map, label=colorbar_label, height=Relative(0.65), + limits=colorbar_limits, ) end @@ -126,17 +124,32 @@ function create_map!( end """ - ADRIA.viz.map(rs::Union{Domain,ResultSet}; opts=Dict(by_RCP => false), fig_opts=Dict(), axis_opts=Dict(), series_opts=Dict()) - ADRIA.viz.map(rs::ResultSet, y::NamedDimsArray; opts=Dict(by_RCP => false), fig_opts=Dict(), axis_opts=Dict(), series_opts=Dict()) - ADRIA.viz.map!(f::Union{GridLayout,GridPosition}, rs::ADRIA.ResultSet, y::NamedDimsArray; opts=Dict(by_RCP => false), axis_opts=Dict(), series_opts=Dict()) + ADRIA.viz.map(rs::Union{Domain,ResultSet}; opts=Dict(by_RCP => false), fig_opts=Dict(), + axis_opts=Dict(), series_opts=Dict()) + ADRIA.viz.map(rs::ResultSet, y::NamedDimsArray; opts=Dict(by_RCP => false), fig_opts=Dict(), + axis_opts=Dict(), series_opts=Dict()) + ADRIA.viz.map(rs::ResultSet, S::NamedDimsArray, scores::Vector{Float64}; + criteria::Vector{Symbol} = S.criteria, opts::Dict = Dict(), axis_opts::Dict = Dict(), + fig_opts::Dict = Dict()) + ADRIA.viz.map!(f::Union{GridLayout,GridPosition}, rs::ADRIA.ResultSet, y::NamedDimsArray; + opts=Dict(by_RCP => false), axis_opts=Dict(), series_opts=Dict()) + ADRIA.viz.map!(g::Union{GridLayout,GridPosition},rs::ResultSet, S::NamedDimsArray, + scores::Vector{Float64}; criteria::Vector{Symbol} = S.criteria, opts::Dict = Dict(), + axis_opts::Dict = Dict(), fig_opts::Dict = Dict()) Plot spatial choropleth of outcomes. # Arguments - `rs` : ResultSet - `y` : results of scenario metric +- `S` : A normalised decision matrix calculated using decison.decision_matrices +- `scores` : Aggregated criteria scores. +- `criteria` : Names of criteria to be plotted, if not specified all criteria in + S will be plotted. - `opts` : Aviz options - `colorbar_label`, label for colorbar. Defaults to "Relative Cover" + -`colorbar_limits`, min and max values to be shown on the colorbar. + Defaults to (0.0,maximum(y)). - `color_map`, preferred colormap for plotting heatmaps - `axis_opts` : Additional options to pass to adjust Axis attributes See: https://docs.makie.org/v0.19/api/index.html#Axis @@ -196,7 +209,7 @@ function ADRIA.viz.map!( legend_params = get(opts, :legend_params, nothing) show_colorbar = get(opts, :show_colorbar, true) color_map = get(opts, :color_map, :grayC) - + colorbar_limits = get(opts, :colorbar_limits, (0.0, maximum(y))) return create_map!( g, geodata, @@ -205,11 +218,82 @@ function ADRIA.viz.map!( ADRIA.centroids(rs), show_colorbar, c_label, + colorbar_limits, color_map, legend_params, axis_opts, ) end +function ADRIA.viz.map( + rs::ResultSet, + S::NamedDimsArray, + scores::Vector{Float64}; + criteria::Vector{Symbol}=S.criteria, + opts::Dict=Dict(), + axis_opts::Dict=Dict(), + fig_opts::Dict=Dict(), +) + f = Figure(; fig_opts...) + g = f[1, 1] = GridLayout() + ADRIA.viz.map!( + g, rs, S, scores; criteria=criteria, opts=opts, axis_opts=axis_opts + ) + return f +end +function ADRIA.viz.map!( + g::Union{GridLayout, GridPosition}, + rs::ResultSet, + S::NamedDimsArray, + scores::Vector{Float64}; + criteria::Vector{Symbol}=S.criteria, + opts::Dict=Dict(), + axis_opts::Dict=Dict(), +) + if length(rs.site_data.site_id) != size(S, 1) + error("Only unfiltered decision matrices can be plotted.") + end + + opts[:color_map] = get(opts, :color_map, :viridis) + opts[:colorbar_limits] = get(opts, :colorbar_limits, (0.0, 1.0)) + + m_spec = model_spec(rs) + criteria_names::Vector{String} = m_spec[ + dropdims( + any( + reshape(criteria, 1, length(criteria)) .== m_spec[:, "fieldname"]; dims=2 + ); + dims=2, + ), "name"] + n_criteria::Int64 = length(criteria) + n_rows, n_cols = _calc_gridsize(n_criteria + 1) + step::Int64 = 1 + + for row in 1:n_rows, col in 1:n_cols + if step > length(criteria_names) + ADRIA.viz.map!( + g[row, col], + rs, + vec(scores); + opts=opts, + axis_opts=Dict(:title => "Aggregate criteria score"; axis_opts...), + ) + break + end + axis_opts_temp = Dict(:title => criteria_names[step]; axis_opts...) + ADRIA.viz.map!( + g[row, col], + rs, + vec(S(criteria[step])); + opts=opts, + axis_opts=axis_opts_temp, + ) + + step += 1 + end + + # Clear empty figures + return trim!(g) +end """ make_geojson_copy(ds::Union{ResultSet,Domain})::String diff --git a/src/ADRIA.jl b/src/ADRIA.jl index a2bdeff3e..6027708a4 100644 --- a/src/ADRIA.jl +++ b/src/ADRIA.jl @@ -63,11 +63,6 @@ include("decision/CriteriaWeights.jl") include("Domain.jl") include("io/inputs.jl") -# Note: The decision module is imported after CriteriaWeights as CriteriaWeights is needed in -# Domain.jl but Domain.jl is needed in dMCDA.jl. A restructure is needed so that Domain.jl is -# not required in dMCDA.jl. -include("decision/dMCDA.jl") - include("interventions/Interventions.jl") include("interventions/seeding.jl") include("interventions/fogging.jl") @@ -75,6 +70,13 @@ include("interventions/fogging.jl") include("io/ResultSet.jl") include("io/result_io.jl") include("io/result_post_processing.jl") + +# Note: The decision module is imported after CriteriaWeights as CriteriaWeights is needed in +# Domain.jl but Domain.jl is needed in dMCDA.jl. A restructure is needed so that Domain.jl is +# not required in dMCDA.jl. It is also imported after ResultSet as ResultSet is needed in +# `decision_matrices`, but the decision module is also needed in sampling. +include("decision/dMCDA.jl") + include("io/sampling.jl") include("metrics/metrics.jl") include("metrics/performance.jl") @@ -107,12 +109,89 @@ const COMPAT_DPKG = ["0.3.1", "0.4.0-rc", "0.4.0"] # This adds ~30 seconds to package load times if ccall(:jl_generating_output, Cint, ()) == 1 - Base.precompile(Tuple{typeof(load_domain),String}) # time: 19.120537 - Base.precompile(Tuple{typeof(load_domain),String,String}) - Base.precompile(Tuple{typeof(setup_result_store!),Domain,DataFrame}) # time: 4.6720815 - Base.precompile(Tuple{typeof(combine_results),Vector{String}}) # time: 4.0178256 - Base.precompile(Tuple{typeof(growthODE),Matrix{Float64},Matrix{Float64},NamedTuple{(:r, :k, :mb, :comp, :sm_comp, :small_massives, :small, :mid, :large, :acr_5_11, :acr_6_12, :rec, :sigma, :M_sm, :sXr, :X_mb, :cover),Tuple{Matrix{Float64},Vector{Float64},Matrix{Float64},Float64,Matrix{Float64},SVector{3,Int64},SVector{6,Int64},SVector{19,Int64},SVector{4,Int64},SVector{2,Int64},SVector{2,Int64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Vector{Float64}}},Float64}) # time: 1.4354926 - Base.precompile(Tuple{typeof(combine_results),ResultSet{String,Vector{Any},Vector{Any},Vector{Float64},NamedDimsArray{(:timesteps, :sites, :intervention, :scenarios),Float32,4,ZArray{Float32,4,Zarr.BloscCompressor,DirectoryStore}},NamedDimsArray{(:timesteps, :coral_id, :sites, :scenarios),Float32,4,ZArray{Float32,4,Zarr.BloscCompressor,DirectoryStore}},NamedDimsArray{(:timesteps, :sites, :scenarios),Float32,3,ZArray{Float32,3,Zarr.BloscCompressor,DirectoryStore}},Dict{String,AbstractArray},DataFrame}}) # time: 0.9439985 + Base.precompile(Tuple{typeof(load_domain), String}) # time: 19.120537 + Base.precompile(Tuple{typeof(load_domain), String, String}) + Base.precompile(Tuple{typeof(setup_result_store!), Domain, DataFrame}) # time: 4.6720815 + Base.precompile(Tuple{typeof(combine_results), Vector{String}}) # time: 4.0178256 + Base.precompile( + Tuple{ + typeof(growthODE), + Matrix{Float64}, + Matrix{Float64}, + NamedTuple{ + ( + :r, + :k, + :mb, + :comp, + :sm_comp, + :small_massives, + :small, + :mid, + :large, + :acr_5_11, + :acr_6_12, + :rec, + :sigma, + :M_sm, + :sXr, + :X_mb, + :cover, + ), + Tuple{ + Matrix{Float64}, + Vector{Float64}, + Matrix{Float64}, + Float64, + Matrix{Float64}, + SVector{3, Int64}, + SVector{6, Int64}, + SVector{19, Int64}, + SVector{4, Int64}, + SVector{2, Int64}, + SVector{2, Int64}, + Matrix{Float64}, + Matrix{Float64}, + Matrix{Float64}, + Matrix{Float64}, + Matrix{Float64}, + Vector{Float64}, + }, + }, + Float64, + }, + ) # time: 1.4354926 + Base.precompile( + Tuple{ + typeof(combine_results), + ResultSet{ + String, + Vector{Any}, + Vector{Any}, + Vector{Float64}, + NamedDimsArray{ + (:timesteps, :sites, :intervention, :scenarios), + Float32, + 4, + ZArray{Float32, 4, Zarr.BloscCompressor, DirectoryStore}, + }, + NamedDimsArray{ + (:timesteps, :coral_id, :sites, :scenarios), + Float32, + 4, + ZArray{Float32, 4, Zarr.BloscCompressor, DirectoryStore}, + }, + NamedDimsArray{ + (:timesteps, :sites, :scenarios), + Float32, + 3, + ZArray{Float32, 3, Zarr.BloscCompressor, DirectoryStore}, + }, + Dict{String, AbstractArray}, + DataFrame, + }, + }, + ) # time: 0.9439985 Base.precompile( Tuple{ typeof(decision.rank_sites!), @@ -135,9 +214,36 @@ if ccall(:jl_generating_output, Cint, ()) == 1 Int64, }, ) # time: 0.3170264 - Base.precompile(Tuple{typeof(scenario_attributes),String,String,Vector{String},String,EnvLayer{String,Vector{Int64}},SimConstants,Vector{String},Vector{Float64},Vector{Float64},Vector{Tuple{Float64,Float64}}}) # time: 0.2140636 - Base.precompile(Tuple{typeof(model_spec),Model}) # time: 0.1997914 - Base.precompile(Tuple{typeof(bleaching_mortality!),Matrix{Float64},Matrix{Float64},Vector{Float64},Int64,Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Float64}) # time: 0.1940948 + Base.precompile( + Tuple{ + typeof(scenario_attributes), + String, + String, + Vector{String}, + String, + EnvLayer{String, Vector{Int64}}, + SimConstants, + Vector{String}, + Vector{Float64}, + Vector{Float64}, + Vector{Tuple{Float64, Float64}}, + }, + ) # time: 0.2140636 + Base.precompile(Tuple{typeof(model_spec), Model}) # time: 0.1997914 + Base.precompile( + Tuple{ + typeof(bleaching_mortality!), + Matrix{Float64}, + Matrix{Float64}, + Vector{Float64}, + Int64, + Vector{Float64}, + Vector{Float64}, + Vector{Float64}, + Vector{Float64}, + Float64, + }, + ) # time: 0.1940948 Base.precompile( Tuple{ typeof(decision.create_decision_matrix), @@ -153,21 +259,53 @@ if ccall(:jl_generating_output, Cint, ()) == 1 Matrix{Float64}, Matrix{Float64}, Float64, - }, + } ) # time: 0.1929096 - Base.precompile(Tuple{typeof(scenario_attributes),String,String,Vector{String},String,EnvLayer{String,Vector{Any}},Dict{String,Any},Vector{Any},Vector{Float64},Vector{Float64},Vector{Any}}) # time: 0.1755622 - Base.precompile(Tuple{typeof(proportional_adjustment!),Matrix{Float64},Vector{Float64}}) # time: 0.1680073 + Base.precompile( + Tuple{ + typeof(scenario_attributes), + String, + String, + Vector{String}, + String, + EnvLayer{String, Vector{Any}}, + Dict{String, Any}, + Vector{Any}, + Vector{Float64}, + Vector{Float64}, + Vector{Any}, + }, + ) # time: 0.1755622 + Base.precompile( + Tuple{typeof(proportional_adjustment!), Matrix{Float64}, Vector{Float64}} + ) # time: 0.1680073 Base.precompile(Tuple{typeof(_remove_workers)}) # time: 0.1593244 Base.precompile(Tuple{typeof(_setup_workers)}) # time: 0.1571776 - Base.precompile(Tuple{typeof(switch_RCPs!),Domain,String}) # time: 0.1284853 - Base.precompile(Tuple{typeof(component_params),DataFrame,Type{CriteriaWeights}}) # time: 0.1223987 - Base.precompile(Tuple{Type{Domain},String,String,String,Vector{Int64},String,String,String,String,String,String,String}) # time: 0.1113899 - Base.precompile(Tuple{typeof(setup_cache),Domain}) # time: 0.1060752 - Base.precompile(EnvLayer, (String, String, String, String, String, String, String, String, Any)) + Base.precompile(Tuple{typeof(switch_RCPs!), Domain, String}) # time: 0.1284853 + Base.precompile(Tuple{typeof(component_params), DataFrame, Type{CriteriaWeights}}) # time: 0.1223987 + Base.precompile( + Tuple{ + Type{Domain}, + String, + String, + String, + Vector{Int64}, + String, + String, + String, + String, + String, + String, + String, + }, + ) # time: 0.1113899 + Base.precompile(Tuple{typeof(setup_cache), Domain}) # time: 0.1060752 + Base.precompile( + EnvLayer, (String, String, String, String, String, String, String, String, Any) + ) Base.precompile(load_results, (String,)) end - # @setup_workload begin # # Putting some things in `setup` can reduce the size of the # # precompile file and potentially make loading faster. diff --git a/src/analysis/analysis.jl b/src/analysis/analysis.jl index 63bb76a57..7ddedcee3 100644 --- a/src/analysis/analysis.jl +++ b/src/analysis/analysis.jl @@ -12,7 +12,9 @@ using Statistics, DataFrames Normalize a matrix on a per-column basis (∈ [0, 1]). """ -function col_normalize(data::AbstractMatrix{T})::AbstractMatrix{T} where {T<:Union{Missing,Real}} +function col_normalize( + data::AbstractMatrix{T} +)::AbstractMatrix{T} where {T <: Union{Missing, Real}} d = copy(data) Threads.@threads for ax in axes(d, 2) @inbounds d[:, ax] .= normalize!(d[:, ax]) @@ -20,7 +22,9 @@ function col_normalize(data::AbstractMatrix{T})::AbstractMatrix{T} where {T<:Uni return d end -function col_normalize(data::AbstractVector{T})::AbstractVector{T} where {T<:Union{Missing,Real}} +function col_normalize( + data::AbstractVector{T} +)::AbstractVector{T} where {T <: Union{Missing, Real}} return normalize(data) end @@ -29,7 +33,9 @@ end Normalize a matrix or vector (∈ [0, 1]). """ -function normalize(data::AbstractArray{T})::AbstractArray{T} where {T<:Union{Missing,Real}} +function normalize( + data::AbstractArray{T} +)::AbstractArray{T} where {T <: Union{Missing, Real}} d = copy(data) normalize!(d) @@ -41,7 +47,9 @@ end Normalize a matrix or vector (∈ [0, 1]) in place. """ -function normalize!(data::AbstractArray{T})::AbstractArray{T} where {T<:Union{Missing,Real}} +function normalize!( + data::AbstractArray{T} +)::AbstractArray{T} where {T <: Union{Missing, Real}} if count(!ismissing, data) == 0 data .= zeros(size(data)...) return data @@ -54,7 +62,7 @@ function normalize!(data::AbstractArray{T})::AbstractArray{T} where {T<:Union{Mi return data end - data .= (data .- mi) ./ (ma - mi) + return data .= (data .- mi) ./ (ma - mi) end """ @@ -68,13 +76,13 @@ S-1 := 0.9 - 0.8 etc """ function discretize_outcomes(y; S=20) - steps = 0.0:(1/S):1.0 + steps = 0.0:(1 / S):1.0 y_s_hat = col_normalize(y) y_disc = zeros(size(y)...) for i in axes(steps, 1)[2:end] Threads.@threads for j in size(y_s_hat, 2) - y_disc[steps[i-1]. 0, dims=[:timesteps, :scenarios]), dims=:timesteps)) - end - - return interv_freq +function intervention_frequency( + rs::ResultSet, scen_indices::NamedTuple, log_type::Symbol +)::NamedDimsArray + log_type ∈ [:seed, :shade, :fog] || ArgumentError("Unsupported log") + + # Get requested log + interv_log = getfield(rs, Symbol("$(log_type)_log")) + rcps = collect(Symbol.(keys(scen_indices))) + n_locs = n_locations(rs) + + interv_freq = NamedDimsArray( + zeros(n_locs, length(rcps)); locations = rs.site_ids, rcps = rcps + ) + for rcp in rcps + # Select scenarios satisfying condition and tally selection for each location + logged_data = dropdims( + sum(interv_log[scenarios = scen_indices[rcp]]; dims = :coral_id); + dims = :coral_id, + ) + interv_freq(; rcps = rcp) .= vec( + dropdims( + sum(logged_data .> 0; dims = [:timesteps, :scenarios]); dims = :timesteps + ), + ) + end + + return interv_freq +end + +""" + decision_matrices(rs::ResultSet, criteria_weights::DataFrameRow; + loc_coral_cover = rs.site_max_coral_cover::Vector{Float64}, + area_to_seed::Float64 = 962.11) + +Calculates a decision matrix for a specified intervention, using a scenario specification + and Domain alone. These can be visualised spatially using `viz.decision_matrices`. + +# Arguments +- `rs` : ADRIA ResultSet +- `criteria_weights` : A row of a scenario dataframe, containing intervention criteria weights. +- `int_type` : Intervention type (e.g. :seed or :fog) +- `loc_coral_cover` : Relative coral cover to site k area (dims: nspecies*nsites), default + is max cover over scenarios in rs. +- `area_to_seed` : Area of corals seeded in the scenario considered + +# Returns +Selection score +""" +function decision_matrices( + rs::ResultSet, + criteria_weights::DataFrameRow; + loc_coral_cover = rs.site_max_coral_cover::Vector{Float64}, + area_to_seed::Float64 = 962.11, + RCP::String = "45", +) + site_ids = collect(1:length(rs.site_data.site_id)) + leftover_space = relative_leftover_space(loc_coral_cover) .* site_k_area(rs) + + wave_stress = ADRIA.decision.summary_stat_env( + rs.wave_stats[RCP](; stat = "mean"), (:wave_scenario) + ) + heat_stress = ADRIA.decision.summary_stat_env( + rs.dhw_stats[RCP](; stat = "mean"), (:dhw_scenario) + ) + TP_data = rs.connectivity_data[RCP] + connectivity_data = connectivity_strength( + TP_data .* site_k_area(rs), vec(loc_coral_cover), TP_data + ) + strong_pred = connectivity_data.strongest_predecessor + + predec = ADRIA.decision.priority_predecessor_criteria( + strong_pred, vec(rs.sim_constants["priority_sites"]), length(site_ids) + ) + zones_crit = ADRIA.decision.zones_criteria( + vec(rs.sim_constants["priority_zones"]), rs.site_data.zone_type, strong_pred, + site_ids, + ) + + A, filtered_sites = ADRIA.decision.create_decision_matrix( + site_ids, + connectivity_data.in_conn, + connectivity_data.out_conn, + leftover_space[site_ids], + wave_stress[site_ids], + heat_stress[site_ids], + rs.site_data.depth_med[site_ids], + predec, + zones_crit, + criteria_weights.deployed_coral_risk_tol, + ) + + S, wse = ADRIA.decision.create_seed_matrix( + A, + criteria_weights.seed_in_connectivity, + criteria_weights.seed_out_connectivity, + criteria_weights.seed_wave_stress, + criteria_weights.seed_heat_stress, + criteria_weights.seed_priority, + criteria_weights.seed_zone, + criteria_weights.coral_cover_low, + criteria_weights.seed_depth, + ) + + SE = NamedDimsArray( + S[:, 2:end]; + locations = rs.site_data.site_id, + criteria = [ + :in_connectivity, + :out_connectivity, + :wave_stress, + :heat_stress, + :priority_predecessor, + :zones, + :leftover_space, + :depth, + ], + ) + + S, wsh = ADRIA.decision.create_fog_matrix( + A, + site_k_area(rs)[site_ids][filtered_sites], + criteria_weights.fog_connectivity, + criteria_weights.fog_wave_stress, + criteria_weights.fog_heat_stress, + criteria_weights.fog_priority, + criteria_weights.fog_zone, + criteria_weights.coral_cover_high, + ) + SH = NamedDimsArray( + S[:, 2:end]; + locations = rs.site_data.site_id, + criteria = [ + :in_connectivity, + :out_connectivity, + :wave_stress, + :heat_stress, + :priority_predecessor, + :zones, + :coral_area, + ], + ) + + return SE, wse, SH, wsh end diff --git a/src/analysis/scenario.jl b/src/analysis/scenario.jl index 944a711d5..b5950a6a6 100644 --- a/src/analysis/scenario.jl +++ b/src/analysis/scenario.jl @@ -1,18 +1,18 @@ const SCENARIO_TYPES = [:counterfactual, :unguided, :guided] -function scenario_clusters(clusters::BitVector)::Dict{Symbol,BitVector} +function scenario_clusters(clusters::BitVector)::Dict{Symbol, BitVector} return Dict(:target => clusters, :non_target => .!clusters) end -function scenario_clusters(clusters::Vector{Int64})::Dict{Symbol,BitVector} +function scenario_clusters(clusters::Vector{Int64})::Dict{Symbol, BitVector} return Dict(Symbol("Cluster_$(c)") => clusters .== c for c in unique(clusters)) end -function scenario_rcps(scenarios::DataFrame)::Dict{Symbol,BitVector} +function scenario_rcps(scenarios::DataFrame)::Dict{Symbol, BitVector} rcps::Vector{Symbol} = Symbol.(:RCP, Int64.(scenarios[:, :RCP])) return Dict(rcp => rcps .== rcp for rcp in unique(rcps)) end -function scenario_types(scenarios::DataFrame)::Dict{Symbol,BitVector} +function scenario_types(scenarios::DataFrame)::Dict{Symbol, BitVector} return Dict( type => eval(type)(scenarios) for type in SCENARIO_TYPES if count(eval(type)(scenarios)) != 0 diff --git a/src/decision/CriteriaWeights.jl b/src/decision/CriteriaWeights.jl index cae5c68e3..9804dff69 100644 --- a/src/decision/CriteriaWeights.jl +++ b/src/decision/CriteriaWeights.jl @@ -1,66 +1,66 @@ -Base.@kwdef struct CriteriaWeights{U2,U3,DO} <: EcoModel - seed_wave_stress::U2 = Factor( +Base.@kwdef struct CriteriaWeights{U2, U3, DO} <: EcoModel + seed_in_connectivity::U2 = Factor( 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:seed, :weight), - name="Seed Wave Stress", - description="Importance of avoiding wave stress when seeding. Higher values places more weight on areas with low wave stress.", + name="Incoming Connectivity (Seed)", + description="Higher values give preference to locations with high incoming connectivity (i.e., receives larvae from other sites) for enhanced coral deployments.", ) - seed_heat_stress::U2 = Factor( + seed_out_connectivity::U2 = Factor( 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:seed, :weight), - name="Seed Heat Stress", - description="Importance of avoiding heat stress when seeding. Higher values places more weight on areas with low heat stress.", + name="Outgoing Connectivity (Seed)", + description="Higher values give preference to locations with high outgoing connectivity (i.e., provides larvae to other sites) for enhanced coral deployments.", ) - fog_wave_stress::U2 = Factor( + seed_wave_stress::U2 = Factor( 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:fog, :weight), - name="Shade Wave Stress", - description="Importance of avoiding wave stress when fogging. Higher values places more weight on areas with low wave stress.", + criteria_keywords=(:seed, :weight), + name="Seed Wave Stress", + description="Importance of avoiding wave stress when seeding. Higher values places more weight on areas with low wave stress.", ) - fog_heat_stress::U2 = Factor( + seed_heat_stress::U2 = Factor( 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:fog, :weight), - name="Shade Heat Stress", - description="Importance of avoiding heat stress when fogging. Higher values places more weight on areas with low heat stress.", + criteria_keywords=(:seed, :weight), + name="Seed Heat Stress", + description="Importance of avoiding heat stress when seeding. Higher values places more weight on areas with low heat stress.", ) - fog_connectivity::U2 = Factor( - 0.0; + seed_priority::U2 = Factor( + 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:fog, :weight), - name="Shade Connectivity", - description="Higher values give preference to locations with high connectivity for shading deployments.", + criteria_keywords=(:seed, :weight), + name="Predecessor Priority (Seed)", + description="Importance of seeding sites that provide larvae to priority reefs.", ) - seed_in_connectivity::U2 = Factor( - 1.0; + seed_zone::U2 = Factor( + 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:seed, :weight), - name="Incoming Connectivity (Seed)", - description="Higher values give preference to locations with high incoming connectivity (i.e., receives larvae from other sites) for enhanced coral deployments.", + name="Zone Predecessor (Seed)", + description="Importance of seeding sites that provide larvae to priority (target) zones.", ) - seed_out_connectivity::U2 = Factor( - 1.0; + seed_coral_cover_low::U2 = Factor( + 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:seed, :weight), - name="Outgoing Connectivity (Seed)", - description="Higher values give preference to locations with high outgoing connectivity (i.e., provides larvae to other sites) for enhanced coral deployments.", + name="Low Coral Cover", + description="Higher values give greater preference to sites with low coral cover for seeding deployments." ) seed_depth::U2 = Factor( 1.0; @@ -71,32 +71,41 @@ Base.@kwdef struct CriteriaWeights{U2,U3,DO} <: EcoModel name="Depth (Seed)", description="Higher values give preference to deeper locations for enhanced coral deployments.", ) - coral_cover_low::U2 = Factor( + fog_in_connectivity::U2 = Factor( 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:seed, :weight), - name="Low Coral Cover", - description="Higher values give greater preference to sites with low coral cover for seeding deployments.", + criteria_keywords=(:fog, :weight), + name="Outgoing Connectivity (Fog)", + description="Higher values give preference to locations with high incoming connectivity for foggining deployments.", ) - coral_cover_high::U2 = Factor( + fog_out_connectivity::U2 = Factor( 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:fog, :weight), - name="High Coral Cover", - description="Higher values give preference to sites with high coral cover for shading deployments.", + name="Incoming Connectivity (Fog)", + description="Higher values give preference to locations with high outgoing connectivity for fogging deployments." ) - seed_priority::U2 = Factor( + fog_wave_stress::U2 = Factor( 1.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:seed, :weight), - name="Predecessor Priority (Seed)", - description="Importance of seeding sites that provide larvae to priority reefs.", + criteria_keywords=(:fog, :weight), + name="Shade Wave Stress", + description="Importance of avoiding wave stress when fogging. Higher values places more weight on areas with low wave stress.", + ) + fog_heat_stress::U2 = Factor( + 1.0; + ptype="continuous", + dist=Uniform, + dist_params=(0.0, 1.0), + criteria_keywords=(:fog, :weight), + name="Shade Heat Stress", + description="Importance of avoiding heat stress when fogging. Higher values places more weight on areas with low heat stress.", ) fog_priority::U2 = Factor( 0.0; @@ -107,23 +116,23 @@ Base.@kwdef struct CriteriaWeights{U2,U3,DO} <: EcoModel name="Predecessor Priority (Shade)", description="Importance of shading sites that provide larvae to priority reefs.", ) - seed_zone::U2 = Factor( + fog_zone::U2 = Factor( 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), - criteria_keywords=(:seed, :weight), - name="Zone Predecessor (Seed)", - description="Importance of seeding sites that provide larvae to priority (target) zones.", + criteria_keywords=(:fog, :weight), + name="Zone Predecessor (Shade)", + description="Importance of shading sites that provide larvae to priority (target) zones.", ) - fog_zone::U2 = Factor( + fog_coral_cover_high::U2 = Factor( 0.0; ptype="continuous", dist=Uniform, dist_params=(0.0, 1.0), criteria_keywords=(:fog, :weight), - name="Zone Predecessor (Shade)", - description="Importance of shading sites that provide larvae to priority (target) zones.", + name="High Coral Cover", + description="Higher values give preference to sites with high coral cover for shading deployments." ) deployed_coral_risk_tol::U3 = Factor( 1.0; @@ -165,11 +174,12 @@ Retrieve relevant criteria by associated keywords. """ function criteria_params( crit::DataFrame, - criteria_keywords::Union{Tuple{Symbol,Symbol},Tuple{Symbol,Symbol,Symbol}}, + criteria_keywords::Union{Tuple{Symbol, Symbol}, Tuple{Symbol, Symbol, Symbol}, String}, )::DataFrame sel_crit = vec( all( - hcat([in.(c_k, crit.criteria_keywords) for c_k in criteria_keywords]...); dims=2 + hcat([in.(c_k, crit.criteria_keywords) for c_k in criteria_keywords]...); + dims=2, ); ) return crit[sel_crit, :] diff --git a/src/decision/dMCDA.jl b/src/decision/dMCDA.jl index 1105ed721..61e34267c 100644 --- a/src/decision/dMCDA.jl +++ b/src/decision/dMCDA.jl @@ -27,7 +27,8 @@ struct DMCDA_vars # {V, I, F, M} where V <: Vector area_to_seed #::V wt_in_conn_seed # ::F wt_out_conn_seed # ::F - wt_conn_fog # ::F + wt_in_conn_fog # ::F + wt_out_conn_fog # ::F wt_waves_seed # ::F wt_waves_fog # ::F wt_heat_seed # ::F @@ -74,15 +75,15 @@ end """ DMCDA_vars(domain::Domain, criteria::NamedDimsArray, - site_ids::AbstractArray, leftover_space::AbstractArray, area_to_seed::Float64, - waves::AbstractArray, dhws::AbstractArray)::DMCDA_vars + site_ids::AbstractArray, leftover_space::AbstractArray, area_to_seed::Float64, + waves::AbstractArray, dhws::AbstractArray)::DMCDA_vars DMCDA_vars(domain::Domain, criteria::NamedDimsArray, site_ids::AbstractArray, - leftover_space::AbstractArray, area_to_seed::Float64)::DMCDA_vars + leftover_space::AbstractArray, area_to_seed::Float64)::DMCDA_vars DMCDA_vars(domain::Domain, criteria::DataFrameRow, site_ids::AbstractArray, - leftover_space::AbstractArray, area_to_seed::Float64)::DMCDA_vars + leftover_space::AbstractArray, area_to_seed::Float64)::DMCDA_vars DMCDA_vars(domain::Domain, criteria::DataFrameRow, site_ids::AbstractArray, - leftover_space::AbstractArray, area_to_seed::Float64, - waves::AbstractArray, dhw::AbstractArray)::DMCDA_vars + leftover_space::AbstractArray, area_to_seed::Float64, + waves::AbstractArray, dhw::AbstractArray)::DMCDA_vars Constuctors for DMCDA variables. """ @@ -118,14 +119,15 @@ function DMCDA_vars( area_to_seed, criteria("seed_in_connectivity"), criteria("seed_out_connectivity"), - criteria("fog_connectivity"), + criteria("fog_in_connectivity"), + criteria("fog_out_connectivity"), criteria("seed_wave_stress"), criteria("fog_wave_stress"), criteria("seed_heat_stress"), criteria("fog_heat_stress"), criteria("seed_depth"), - criteria("coral_cover_high"), - criteria("coral_cover_low"), + criteria("fog_coral_cover_high"), + criteria("seed_coral_cover_low"), criteria("seed_priority"), criteria("fog_priority"), criteria("seed_zone"), @@ -161,7 +163,7 @@ function DMCDA_vars( waves::AbstractArray, dhw::AbstractArray, )::DMCDA_vars - criteria_vec::NamedDimsArray = NamedDimsArray(collect(criteria); rows = names(criteria)) + criteria_vec::NamedDimsArray = NamedDimsArray(collect(criteria); rows=names(criteria)) return DMCDA_vars( domain, criteria_vec, site_ids, leftover_space, area_to_seed, waves, dhw ) @@ -173,7 +175,7 @@ function DMCDA_vars( leftover_space::AbstractArray, area_to_seed::Float64, )::DMCDA_vars - criteria_vec::NamedDimsArray = NamedDimsArray(collect(criteria); rows = names(criteria)) + criteria_vec::NamedDimsArray = NamedDimsArray(collect(criteria); rows=names(criteria)) return DMCDA_vars(domain, criteria_vec, site_ids, leftover_space, area_to_seed) end @@ -192,7 +194,7 @@ end Normalize a Matrix (SE/SH) for MCDA. """ function mcda_normalize(x::Matrix)::Matrix - return x ./ sqrt.(sum(x .^ 2; dims = 1)) + return x ./ sqrt.(sum(x .^ 2; dims=1)) end """ @@ -201,7 +203,17 @@ end Normalize weights for a set of scenarios (wse/wsh) for MCDA. """ function mcda_normalize(x::DataFrame)::DataFrame - return x ./ sum(Matrix(x); dims = 2) + return x ./ sum(Matrix(x); dims=2) +end + +""" +align_rankings!(rankings::Array, s_order::Matrix, col::Int64)::Nothing + +Filter all criteria which are zero for all sites from the decision matrix x. +""" +function _filter_preferred(x::Union{Matrix, NamedDimsArray})::BitVector + # weights in order of: in_conn, out_conn, wave, heat, predecessors, low cover + return vec(.!all(x .== 0; dims=1)) end """ @@ -240,7 +252,7 @@ function rank_sites!( mcda_func::Union{Function, Type{<:MCDMMethod}}, rank_col)::Tuple{Vector{Int64}, Matrix{Union{Float64, Int64}}} # Filter out all non-preferred sites - selector = vec(.!all(S[:, 2:end] .== 0; dims = 1)) + selector = vec(.!all(S[:, 2:end] .== 0; dims=1)) # weights in order of: in_conn, out_conn, wave, heat, predecessors, low cover weights = weights[selector] @@ -258,8 +270,10 @@ 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. @@ -302,12 +316,13 @@ function retrieve_ranks( scores::Vector, maximize::Bool, )::Matrix{Union{Float64, Int64}} - s_order::Vector{Int64} = sortperm(scores; rev = maximize) + s_order::Vector{Int64} = sortperm(scores; rev=maximize) return Union{Float64, Int64}[Int64.(site_ids[s_order]) scores[s_order]] 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. @@ -382,7 +397,7 @@ function create_decision_matrix( rule = (A[:, 4] .<= risk_tol) .& (A[:, 5] .> risk_tol) A[rule, 5] .= NaN - filtered = vec(.!any(isnan.(A); dims = 2)) + filtered = vec(.!any(isnan.(A); dims=2)) # Remove rows with NaNs A = A[filtered, :] @@ -438,7 +453,8 @@ function create_seed_matrix( wt_predec_seed::T, wt_predec_zones_seed::T, wt_low_cover::T, - wt_depth_seed::T + wt_depth_seed::T; + filter_space::T=0.0 )::Tuple{Matrix{Float64}, Vector{Float64}} where {T <: Float64} # Define seeding decision matrix, based on copy of A SE = copy(A) @@ -460,20 +476,21 @@ function create_seed_matrix( SE[SE[:, 8] .<= 0.0, 8] .= NaN # Filter out sites with no space # Filter out identified locations - SE = SE[vec(.!any(isnan.(SE); dims = 2)), :] + SE = SE[vec(.!any(isnan.(SE); dims=2)), :] return SE, wse end """ - create_fog_matrix(A, wt_conn_fog , wt_waves_fog, wt_heat_fog, wt_predec_fog, wt_hi_cover) + create_fog_matrix(A, wt_in_conn_fog , wt_out_conn_fog, wt_waves_fog, wt_heat_fog, wt_predec_fog, wt_hi_cover) Create shading specific decision matrix and apply weightings. # Arguments - `A` : Criteria matrix - `k_area`: Carrying capacity (m²) for coral. -- `wt_conn_fog` : Shading connectivity weight +- `wt_in_conn_fog` : Shading connectivity weight +- `wt_out_conn_fog` : Shading connectivity weight - `wt_waves_fog` : Wave stress weight - `wt_heat_fog` : Heat stress weight - `wt_predec_zones_fog` : Priority zones weight for fogging @@ -490,18 +507,20 @@ Tuple (SH, wsh) 5. Damage risk (higher values = less risk) 6. Priority predecessors relating to coral real estate relative to max capacity 7. Available space -- `wsh` : 5-element vector of criteria weights - 1. fog connectivity - 2. wave - 3. heat - 4. fog predecessors (weights importance of sites highly connected to priority sites for fogging) - 4. fog zones (weights importance of sites highly connected to or within priority zones) - 5. high cover (weights importance of sites with high cover of coral to fog) +- `wsh` : 7-element vector of criteria weights + 1. incoming fog connectivity + 2. incoming fog connectivity + 3. wave + 4. heat + 5. fog predecessors (weights importance of sites highly connected to priority sites for fogging) + 6. fog zones (weights importance of sites highly connected to or within priority zones) + 7. high cover (weights importance of sites with high cover of coral to fog) """ function create_fog_matrix( A::Matrix{Float64}, k_area::Vector{T}, - wt_conn_fog::T, + wt_in_conn_fog::T, + wt_out_conn_fog::T, wt_waves_fog::T, wt_heat_fog::T, wt_predec_fog::T, @@ -511,8 +530,8 @@ function create_fog_matrix( # Define weights vector wsh = [ - wt_conn_fog, - wt_conn_fog, + wt_in_conn_fog, + wt_out_conn_fog, wt_waves_fog, wt_heat_fog, wt_predec_fog, @@ -552,10 +571,10 @@ end # Returns Tuple : - - `pref_seed_locs` : Vector, Indices of preferred seeding locations - - `pref_fog_locs` : Vector, Indices of preferred shading locations - - `rankings` : Matrix[n_sites ⋅ 3] where columns are site_id, seeding_rank, shading_rank - Values of 0 indicate sites that were not considered +- `pref_seed_locs` : Vector, Indices of preferred seeding locations +- `pref_fog_locs` : Vector, Indices of preferred shading locations +- `rankings` : Matrix[n_sites ⋅ 3] where columns are site_id, seeding_rank, shading_rank +Values of 0 indicate sites that were not considered """ function guided_site_selection( d_vars::DMCDA_vars, @@ -568,8 +587,10 @@ function guided_site_selection( in_conn::Vector{Float64}, out_conn::Vector{Float64}, strong_pred::Vector{Int64}; - methods_mcda = mcda_methods() -)::Tuple{Vector{T}, Vector{T}, Matrix{T}} where { + methods_mcda=mcda_methods() +)::Tuple{ + Vector{T}, Vector{T}, Matrix{T} +} where { T <: Int64, IA <: AbstractArray{<:Int64}, IB <: AbstractArray{<:Int64}, B <: Bool } site_ids = copy(d_vars.site_ids) @@ -585,7 +606,9 @@ function guided_site_selection( end n_iv_locs::Int64 = d_vars.n_site_int - priority_sites::Array{Int64} = d_vars.priority_sites[in.(d_vars.priority_sites, [site_ids])] + priority_sites::Array{Int64} = d_vars.priority_sites[in.( + d_vars.priority_sites, [site_ids] + )] priority_zones::Array{String} = d_vars.priority_zones zones = d_vars.zones[site_ids] @@ -593,33 +616,9 @@ function guided_site_selection( # site_id, seeding rank, shading rank rankings = Int64[site_ids zeros(Int64, n_sites) zeros(Int64, n_sites)] - # work out which priority predecessors are connected to priority sites - predec::Matrix{Float64} = zeros(n_sites, 3) - predec[:, 1:2] .= strong_pred - predprior = predec[in.(predec[:, 1], [priority_sites']), 2] - predprior = Int64[x for x in predprior if !isnan(x)] - - predec[predprior, 3] .= 1.0 - - # for zones, find sites which are zones and strongest predecessors of sites in zones - zone_ids = intersect(priority_zones, unique(zones)) - zone_weights = mcda_normalize(collect(length(zone_ids):-1:1)) - zone_preds = zeros(n_sites) - zone_sites = zeros(n_sites) - - for (k::Int64, z_name::String) in enumerate(zone_ids) - # find sites which are strongest predecessors of sites in the zone - zone_preds_temp::Vector{Int64} = strong_pred[zones .== z_name] - for s::Int64 in unique(zone_preds_temp) - # for each predecessor site, add zone_weights * (no. of zone sites the site is a strongest predecessor for) - zone_preds[site_ids .== s] .= zone_preds[site_ids .== s] .+ (zone_weights[k] .* sum(zone_preds_temp .== s)) - end - # add zone_weights for sites in the zone (whether a strongest predecessor of a zone or not) - zone_sites[zones .== z_name] .= zone_weights[k] - end - - # add weights for strongest predecessors and zones to get zone criteria - zones_criteria = zone_preds .+ zone_sites + # calculate priority predecessor and zones criteria + predec = priority_predecessor_criteria(strong_pred, priority_sites, n_sites) + zones_crit = zones_criteria(priority_zones, zones, strong_pred, site_ids) mcda_func = methods_mcda[alg_ind] leftover_space = vec(d_vars.leftover_space) @@ -633,7 +632,7 @@ function guided_site_selection( d_vars.heat_stress_prob[site_ids], d_vars.site_depth[site_ids], predec, - zones_criteria, + zones_crit, d_vars.risk_tol, ) if isempty(A) @@ -668,7 +667,8 @@ function guided_site_selection( SH, wsh = create_fog_matrix( A, d_vars.k_area[site_ids][filtered_sites], - d_vars.wt_conn_fog, + d_vars.wt_in_conn_fog, + d_vars.wt_out_conn_fog, d_vars.wt_waves_fog, d_vars.wt_heat_fog, d_vars.wt_predec_fog, @@ -713,6 +713,49 @@ function guided_site_selection( return pref_seed_locs, pref_fog_locs, rankings end +function priority_predecessor_criteria( + strong_pred::Vector{Int64}, priority_sites::Vector{<:Union{Int64, Any}}, n_sites::Int64 +) + predec::Matrix{Float64} = zeros(n_sites, 3) + predec[:, 1:2] .= strong_pred + predprior = predec[in.(predec[:, 1], [priority_sites']), 2] + predprior = Int64[x for x in predprior if !isnan(x)] + + predec[predprior, 3] .= 1.0 + + return predec +end + +function zones_criteria( + priority_zones::Vector{<:Union{String, Any}}, + zones::Vector{String}, + strong_pred::Vector{Int64}, + site_ids::Vector{Int64}, +) + n_sites = length(site_ids) + # for zones, find sites which are zones and strongest predecessors of sites in zones + zone_ids = intersect(priority_zones, unique(zones)) + zone_weights = mcda_normalize(collect(length(zone_ids):-1:1)) + zone_preds = zeros(n_sites) + zone_sites = zeros(n_sites) + + for (k::Int64, z_name::String) in enumerate(zone_ids) + # find sites which are strongest predecessors of sites in the zone + zone_preds_temp::Vector{Int64} = strong_pred[zones .== z_name] + for s::Int64 in unique(zone_preds_temp) + # for each predecessor site, add zone_weights * (no. of zone sites the site is a strongest predecessor for) + zone_preds[site_ids .== s] .= + zone_preds[site_ids .== s] .+ + (zone_weights[k] .* sum(zone_preds_temp .== s)) + end + # add zone_weights for sites in the zone (whether a strongest predecessor of a zone or not) + zone_sites[zones .== z_name] .= zone_weights[k] + end + + # add weights for strongest predecessors and zones to get zone criteria + return zone_preds .+ zone_sites +end + """ constrain_reef_cluster(reefs::Union{Vector{String}, Vector{Float64}}, s_order::Matrix{Union{Float64, Int64}}, rankings::Matrix{Int64}, @@ -732,7 +775,7 @@ end Tuple : - `pref_locs` : Vector, Indices of preferred intervention locations - `rankings` : Matrix[n_sites ⋅ 3] where columns are site_id, seeding_rank, shading_rank - Values of 0 indicate sites that were not considered +Values of 0 indicate sites that were not considered """ function constrain_reef_cluster( reefs::Union{Vector{String}, Vector{Float64}}, @@ -753,7 +796,9 @@ function constrain_reef_cluster( local num_locs::Int64 for _ in 1:max_iters # If enough space for seeding corals, keep n_site_int, else expand as needed - num_locs = max(findfirst(>=(area_to_seed), cumsum(available_space[loc_ordered_ids])), n_iv_locs) + num_locs = max( + findfirst(>=(area_to_seed), cumsum(available_space[loc_ordered_ids])), n_iv_locs + ) pref_locs = loc_ordered_ids[1:num_locs] @@ -766,7 +811,7 @@ function constrain_reef_cluster( pref_reefs = reefs[pref_locs] # Reefs that selected locations sit within # Number of times a reef appears within each location - reef_occurances = vec(sum(pref_reefs .== unique_reefs; dims = 1)) + reef_occurances = vec(sum(pref_reefs .== unique_reefs; dims=1)) # If more than n_reefs locations in a reef, swap out the worst locations reefs_swap = unique_reefs[(reef_occurances .> max_members)] @@ -779,9 +824,11 @@ function constrain_reef_cluster( # Find locations in reefs which need replacement, and find the ids of lowest # ranked locations in this set - locs_to_replace = vcat([ - pref_locs[pref_reefs .== reef][replace_start:end] for reef in reefs_swap - ]...) + locs_to_replace = vcat( + [ + pref_locs[pref_reefs .== reef][replace_start:end] for reef in reefs_swap + ]..., + ) # Acceptable reefs to switch out for reef_switch_ids = unique_reefs[(reef_occurances .+ 1) .<= max_members] @@ -797,10 +844,13 @@ function constrain_reef_cluster( # Indices of the subset of locations which can be added which also sit within an # allowed reef - add_locs_ind = findall(dropdims(any( - reshape(reefs[alternate_loc_ids], 1, length(reefs[alternate_loc_ids])) - .== - reef_switch_ids; dims = 1); dims = 1)) + add_locs_ind = findall( + dropdims( + any( + reshape(reefs[alternate_loc_ids], 1, length(reefs[alternate_loc_ids])) + .== + reef_switch_ids; dims=1); dims=1), + ) # New preferred location set locs_to_add_inds = add_locs_ind[1:length(locs_to_replace)] @@ -864,13 +914,15 @@ function unguided_site_selection( if seed_years pref_seed_locs = zeros(Int64, n_site_int) - pref_seed_locs[1:s_n_site_int] .= StatsBase.sample(candidate_sites, s_n_site_int; replace = false) + pref_seed_locs[1:s_n_site_int] .= StatsBase.sample( + candidate_sites, s_n_site_int; replace=false + ) end if fog_years pref_fog_locs = zeros(Int64, n_site_int) pref_fog_locs[1:s_n_site_int] .= StatsBase.sample( - candidate_sites, s_n_site_int; replace = false + candidate_sites, s_n_site_int; replace=false ) end @@ -890,14 +942,16 @@ Calculates mean over specified dimensions plus half the standard deviation. # Returns Weighted combination of mean and standard deviation of the projected environmental conditions (e.g., DHWs, wave stress, etc): - (μ * w) + (σ * (1 - w)) +(μ * w) + (σ * (1 - w)) """ function summary_stat_env( env_layer::AbstractArray, dims::Union{Int64, Symbol, Tuple{Symbol, Symbol}}; - w = 0.5, + w=0.5, )::Vector{Float64} - return vec((mean(env_layer; dims = dims) .* w) .+ (std(env_layer; dims = dims) .* (1.0 - w))) + return vec( + (mean(env_layer; dims=dims) .* w) .+ (std(env_layer; dims=dims) .* (1.0 - w)) + ) end """ diff --git a/src/decision/location_selection.jl b/src/decision/location_selection.jl index 83835a879..e352b6367 100644 --- a/src/decision/location_selection.jl +++ b/src/decision/location_selection.jl @@ -1,9 +1,13 @@ using NamedDims, AxisKeys -using ADRIA: connectivity_strength, relative_leftover_space, site_k_area +using ADRIA: ResultSet, CriteriaWeights, connectivity_strength, relative_leftover_space, + site_k_area, + model_spec, criteria_params, + component_params """ - _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. @@ -34,7 +38,7 @@ function _location_selection( in_conn, out_conn, strong_pred = connectivity_strength( domain.TP_data .* site_k_area(domain), vec(sum_cover), - similar(domain.TP_data) # tmp cache matrix + similar(domain.TP_data), # tmp cache matrix ) # Perform location selection for seeding and shading. @@ -50,16 +54,19 @@ function _location_selection( rankingsin, in_conn[site_ids], out_conn[site_ids], - strong_pred[site_ids] + strong_pred[site_ids], ) return ranks[:, 2:3] 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. @@ -67,10 +74,10 @@ Return location ranks for a given domain and scenarios. - `domain` : The geospatial domain locations were selected from - `scenarios` : Scenario specification - `sum_cover` : Matrix[n_scenarios ⋅ n_sites] containing the total relative coral cover at each - location, for each scenario +location, for each scenario - `area_to_seed` : Area of coral to be seeded at each time step in km² - `agg_func` : Aggregation function to apply, e.g `ranks_to_frequencies` or - `ranks_to_location_order` +`ranks_to_location_order` - `iv_type` : ID of intervention (1 = seeding, 2 = fogging) - `target_seed_sites` : list of candidate locations for seeding (indices) - `target_fog_sites` : list of candidate location to fog (indices) @@ -90,7 +97,7 @@ function rank_locations( k_area_locs = site_k_area(domain) ranks_store = NamedDimsArray( - zeros(n_locs, 2, nrow(scenarios)), + zeros(n_locs, 2, nrow(scenarios)); sites=1:n_locs, intervention=["seed", "fog"], scenarios=1:nrow(scenarios), @@ -122,11 +129,10 @@ function rank_locations( depth_criteria = within_depth_bounds( domain.site_data.depth_med, scen.depth_min .+ scen.depth_offset, - scen.depth_min + scen.depth_min, ) depth_priority = findall(depth_criteria) - considered_sites = target_site_ids[findall(in(depth_priority), target_site_ids)] mcda_vars_temp = DMCDA_vars( domain, @@ -136,7 +142,7 @@ function rank_locations( area_to_seed, summary_stat_env(wave_scens[:, :, target_wave_scens], (:timesteps, :scenarios)), summary_stat_env(dhw_scens[:, :, target_dhw_scens], (:timesteps, :scenarios)) - ) + ) ranks_store(; scenarios=scen_idx, sites=considered_sites) .= _location_selection( domain, @@ -147,7 +153,7 @@ function rank_locations( end # Set filtered locations as n_locs+1 for consistency with time dependent ranks - ranks_store[ranks_store.==0.0] .= length(domain.site_ids)+1 + ranks_store[ranks_store .== 0.0] .= length(domain.site_ids) + 1 return ranks_store end @@ -180,14 +186,15 @@ function rank_locations( iv_id = iv_type end - return agg_func(ranks(intervention=iv_id)) + return agg_func(ranks(; intervention=iv_id)) 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()`. @@ -221,10 +228,10 @@ function ranks_to_frequencies(ranks::NamedDimsArray, n_ranks::Int64)::NamedDimsA return rank_frequencies end function ranks_to_frequencies( - ranks::NamedDimsArray{D,T,3,A}; + ranks::NamedDimsArray{D, T, 3, A}; n_ranks::Int64=length(ranks.sites), agg_func=nothing, -)::NamedDimsArray where {D,T,A} +)::NamedDimsArray where {D, T, A} if !isnothing(agg_func) return agg_func(ranks_to_frequencies(ranks, n_ranks)) end @@ -232,10 +239,10 @@ function ranks_to_frequencies( return ranks_to_frequencies(ranks, n_ranks) end function ranks_to_frequencies( - ranks::NamedDimsArray{D,T,2,A}; + ranks::NamedDimsArray{D, T, 2, A}; n_ranks::Int64=length(ranks.sites), agg_func=nothing, -)::NamedDimsArray where {D,T,A} +)::NamedDimsArray where {D, T, A} if !isnothing(agg_func) return agg_func(ranks_to_frequencies(ranks, n_ranks)) end @@ -245,7 +252,8 @@ 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. @@ -269,9 +277,9 @@ function location_selection_frequencies( return loc_count end function location_selection_frequencies( - iv_log::NamedDimsArray{D,T,4,A}; - dims::Union{Symbol,Vector{Symbol}}=:coral_id, -)::NamedDimsArray where {D,T,A} + iv_log::NamedDimsArray{D, T, 4, A}; + dims::Union{Symbol, Vector{Symbol}}=:coral_id, +)::NamedDimsArray where {D, T, A} loc_count = dropdims( sum(dropdims(sum(iv_log; dims=dims); dims=dims) .> 0; dims=:scenarios); dims=:scenarios, @@ -279,12 +287,12 @@ function location_selection_frequencies( return loc_count end - """Drop single dimensions.""" -_drop_single(x::AbstractMatrix) = dropdims(x, dims=(findall(size(x) .== 1)...,)) +_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}) @@ -301,22 +309,148 @@ The score reflects the location ranking and frequency of attaining a high rank. Selection score """ function selection_score( - ranks::NamedDimsArray{D,T,3,A}; + ranks::NamedDimsArray{D, T, 3, A}; dims::Vector{Symbol}=[:scenarios, :timesteps], -)::NamedDimsArray where {D,T,A} +)::NamedDimsArray where {D, T, A} return _drop_single(selection_score(ranks, dims)) end function selection_score( - ranks::NamedDimsArray{D,T,2,A}; + ranks::NamedDimsArray{D, T, 2, A}; dims::Vector{Symbol}=[:scenarios], -)::NamedDimsArray where {D,T,A} +)::NamedDimsArray where {D, T, A} return selection_score(ranks, dims) end function selection_score( ranks::NamedDimsArray, dims::Vector{Symbol}, )::NamedDimsArray - lowest_rank = maximum(ranks) # 1 is best rank, n_sites + 1 is worst rank + lowest_rank = maximum(ranks) # 1 is best rank, n_sites + 1 is worst rank selection_score = dropdims(sum(lowest_rank .- ranks; dims=dims); dims=dims[1]) return selection_score ./ ((lowest_rank - 1) * prod([size(ranks, d) for d in dims])) end + +""" + decision_matrices(rs::ResultSet, criteria_row::DataFrameRow; + loc_coral_cover = rs.site_max_coral_cover::Vector{Float64}, RCP::String = "45") + +Calculates a decision matrix for a specified intervention, using a scenario specification +and Domain alone. These can be visualised spatially using `viz.decision_matrices`. + +# Arguments +- `rs` : ADRIA ResultSet +- `criteria_row` : A row of a scenario dataframe, containing intervention criteria weights. +- `loc_coral_cover` : Relative coral cover to site k area (dims: nspecies*nsites), default +is max cover over scenarios in rs. +- `RCP` : RCP scenario to use (waves and dhws), default is "45". + +# Returns +Selection score +""" +function decision_matrices( + rs::ResultSet, + criteria_row::DataFrameRow, + mcda_func::Function; + loc_coral_cover=rs.site_max_coral_cover::Vector{Float64}, + RCP::String="45", + env_aggregation::Function=summary_stat_env, +)::Dict + site_ids = collect(1:length(rs.site_data.site_id)) + site_names = rs.site_data.site_id + n_locs = length(site_ids) + leftover_space = relative_leftover_space(loc_coral_cover) .* site_k_area(rs) + + wave_stress = env_aggregation( + rs.wave_stats[RCP](; stat="mean"), (:wave_scenario) + ) + heat_stress = env_aggregation( + rs.dhw_stats[RCP](; stat="mean"), (:dhw_scenario) + ) + + conn_data = rs.connectivity_data[RCP] # connectivity matrix + connectivity_data = connectivity_strength( + conn_data .* site_k_area(rs), loc_coral_cover, conn_data + ) + # Strongest larval source location for each location + strong_pred = connectivity_data.strongest_predecessor + + # Criteria for strongest larval sources to priority locations + priority_source_criteria = priority_predecessor_criteria( + strong_pred, vec(rs.sim_constants["priority_sites"]), length(site_ids) + ) + # Criteria for strongest larval sources to/members of priority zones + priority_zones_criteria = zones_criteria( + vec(rs.sim_constants["priority_zones"]), rs.site_data.zone_type, strong_pred, + site_ids, + ) + + spec = model_spec(rs) + crit = component_params(spec, CriteriaWeights) + weights_seed_crit = criteria_params(crit, "(:seed, :weight)") + weights_fog_crit = criteria_params(crit, "(:fog, :weight)") + + A, filtered_sites = create_decision_matrix( + site_ids, + connectivity_data.in_conn, + connectivity_data.out_conn, + leftover_space[site_ids], + wave_stress[site_ids], + heat_stress[site_ids], + rs.site_data.depth_med[site_ids], + priority_source_criteria, + priority_zones_criteria, + criteria_row.deployed_coral_risk_tol, + ) + + S, wse = create_seed_matrix( + A, + criteria_row.seed_in_connectivity, + criteria_row.seed_out_connectivity, + criteria_row.seed_wave_stress, + criteria_row.seed_heat_stress, + criteria_row.seed_priority, + criteria_row.seed_zone, + criteria_row.seed_coral_cover_low, + criteria_row.seed_depth; + filter_space=-1.0, + ) + # Remove any criteria which are zeros for all sites + selector = _filter_preferred(S) + SE = NamedDimsArray( + zeros(n_locs, length(wse[selector[2:end]])); + locations=site_names, + criteria=weights_seed_crit.fieldname[selector[2:end]], + ) + + # Only add entries for locations which were not filtered (the filtered locations will have zeros) + SE(site_names[Int.(S[:, 1])]) .= + mcda_normalize(wse[selector[2:end]])' .* mcda_normalize(S[:, selector][:, 2:end]) + + S, wsh = create_fog_matrix( + A, + site_k_area(rs)[site_ids][filtered_sites], + criteria_row.fog_in_connectivity, + criteria_row.fog_out_connectivity, + criteria_row.fog_wave_stress, + criteria_row.fog_heat_stress, + criteria_row.fog_priority, + criteria_row.fog_zone, + criteria_row.fog_coral_cover_high, + ) + selector = _filter_preferred(S) + SH = NamedDimsArray( + zeros(n_locs, length(wsh[selector[2:end]])); + locations=site_names, + criteria=weights_fog_crit.fieldname[selector[2:end]], + ) + SH(site_names[Int.(S[:, 1])]) .= + mcda_normalize(wsh[selector[2:end]])' .* mcda_normalize(S[:, selector][:, 2:end]) + + # Create decision info struct + decision_dict = Dict( + :seed_matrix => SE, + :seed_scores => vec(mcda_func(Matrix(SE))), + :fog_matrix => SH, + :fog_scores => vec(mcda_func(Matrix(SH))), + ) + return decision_dict +end diff --git a/src/ecosystem/connectivity.jl b/src/ecosystem/connectivity.jl index a838fa176..b8abde1e4 100644 --- a/src/ecosystem/connectivity.jl +++ b/src/ecosystem/connectivity.jl @@ -1,13 +1,13 @@ """ -site_connectivity(file_loc::String, unique_site_ids::Vector{String}; - con_cutoff::Float64=1e-6, agg_func::Function=mean, swap::Bool=false)::NamedTuple + site_connectivity(file_loc::String, unique_site_ids::Vector{String}; + con_cutoff::Float64=1e-6, agg_func::Function=mean, swap::Bool=false)::NamedTuple Create transitional probability matrix indicating connectivity between sites, level of centrality, and the strongest predecessor for each site. NOTE: Transposes transitional probability matrix if `swap == true` - If multiple files are read in, this assumes all file rows/cols - follow the same order as the first file read in. + If multiple files are read in, this assumes all file rows/cols + follow the same order as the first file read in. # Examples ```julia @@ -17,10 +17,10 @@ NOTE: Transposes transitional probability matrix if `swap == true` # Arguments - `file_loc` : Path to data file (or datasets) to load - If a folder, searches subfolders as well + If a folder, searches subfolders as well - `unique_site_ids` : Unique site ids in their expected order - `con_cutoff` : Percent thresholds of max for weak connections in - network (defined by user or defaults in `SimConstants`) + network (defined by user or defaults in `SimConstants`) - `agg_func` : Summary statistic to take (defaults to `mean`) - `swap` : Whether to transpose data (defaults to `false`) @@ -141,7 +141,7 @@ function site_connectivity( end function site_connectivity( file_loc::String, - unique_site_ids::Vector{Union{Missing,String}}; + unique_site_ids::Vector{Union{Missing, String}}; con_cutoff::Float64=1e-6, agg_func::Function=mean, swap::Bool=false, @@ -179,7 +179,9 @@ NamedTuple: - `out_conn` : sites ranked by outgoing connectivity - `strongest_predecessor` : strongest predecessor for each site """ -function connectivity_strength(TP_base::AbstractMatrix{Float64})::NamedTuple +function connectivity_strength( + TP_base::AbstractMatrix{<:Union{Float32, Float64}} +)::NamedTuple g = SimpleDiGraph(TP_base) # Measure centrality based on number of incoming connections @@ -210,8 +212,8 @@ function connectivity_strength(TP_base::AbstractMatrix{Float64})::NamedTuple end function connectivity_strength( area_weighted_TP::AbstractMatrix{Float64}, - cover::Vector{<:Union{Float32,Float64}}, - TP_cache::AbstractMatrix{Float64}, + cover::Vector{<:Union{Float32, Float64}}, + TP_cache::AbstractMatrix{<:Union{Float32, Float64}}, )::NamedTuple # Accounts for cases where there is no coral cover diff --git a/src/io/result_io.jl b/src/io/result_io.jl index 9f296223d..1f0c0654e 100644 --- a/src/io/result_io.jl +++ b/src/io/result_io.jl @@ -1,17 +1,17 @@ -const COMPRESSOR = Zarr.BloscCompressor(; cname = "zstd", clevel = 2, shuffle = true) +const COMPRESSOR = Zarr.BloscCompressor(; cname="zstd", clevel=2, shuffle=true) function get_geometry(df::DataFrame) - if columnindex(df, :geometry) > 0 - return df.geometry - elseif columnindex(df, :geom) > 0 - return df.geom - end + if columnindex(df, :geometry) > 0 + return df.geometry + elseif columnindex(df, :geom) > 0 + return df.geom + end return error("No geometry data found") end """ - centroids(df::DataFrame) + centroids(df::DataFrame) Extract and return long/lat from a GeoDataFrame. @@ -30,7 +30,7 @@ function centroids(ds::Union{Domain, ResultSet})::Vector{Tuple{Float64, Float64} end """ - summarize_env_data(data_cube::AbstractArray) + summarize_env_data(data_cube::AbstractArray) Summarize environmental data layers (mean and standard deviation). @@ -40,13 +40,13 @@ Matrix{Float64, 2}, of mean and standard deviation for each environmental scenar function summarize_env_data(data::AbstractArray)::Array{Float64} # TODO: Update once stats_store::Array{Float64} = zeros(2, size(data, 3), size(data, 2)) - stats_store[1, :, :] .= dropdims(mean(data; dims = 1); dims = 1)' - stats_store[2, :, :] .= dropdims(std(data; dims = 1); dims = 1)' + stats_store[1, :, :] .= dropdims(mean(data; dims=1); dims=1)' + stats_store[2, :, :] .= dropdims(std(data; dims=1); dims=1)' return stats_store end """ - store_env_summary(data_cube::AbstractArray, type::String, file_loc::String, compressor::Zarr.Compressor) + store_env_summary(data_cube::AbstractArray, type::String, file_loc::String, compressor::Zarr.Compressor) Retrieve summary statistics matrices from DataFrames of dhws and waves. Produce summary statistics (mean/std) for given data cube saved to a Zarr data store. @@ -76,19 +76,19 @@ function store_env_summary( stats_store = zcreate( Float32, (2, size(stats, 2), size(stats, 3))...; - fill_value = nothing, fill_as_missing = false, - path = joinpath(file_loc, rcp), - attrs = Dict( + fill_value=nothing, fill_as_missing=false, + path=joinpath(file_loc, rcp), + attrs=Dict( :structure => ("stat", type, "locations"), :stats => ["mean", "std"], :scenarios => string.(1:size(stats, 2)), :locations => string.(1:size(stats, 3)), :rcp => rcp), - compressor = compressor) + compressor=compressor) - stats_store[:, :, :] .= stats + stats_store[:, :, :] .= stats - return stats_store + return stats_store end """ @@ -111,19 +111,19 @@ function store_conn( conn_data::NamedDimsArray, file_loc::String, rcp::String, - compressor::Zarr.Compressor + compressor::Zarr.Compressor, )::ZArray conn_store = zcreate( Float32, size(conn_data)...; - fill_value = nothing, fill_as_missing = false, - path = joinpath(file_loc, rcp), - attrs = Dict( + fill_value=nothing, fill_as_missing=false, + path=joinpath(file_loc, rcp), + attrs=Dict( :structure => ("Source", "Receiving"), :Source => conn_data.Source, :Receiving => conn_data.Receiving, :rcp => rcp), - compressor = compressor) + compressor=compressor) conn_store[:, :] .= Matrix(conn_data) return conn_store @@ -168,7 +168,7 @@ function scenario_attributes( :site_centroids => centroids, ) - return attrs + return attrs end function scenario_attributes(domain::Domain, param_df::DataFrame) return scenario_attributes(domain.name, domain.RCP, names(param_df), @@ -179,7 +179,7 @@ function scenario_attributes(domain::Domain, param_df::DataFrame) end """ - setup_logs(z_store, unique_sites, n_scens, tf, n_sites) + setup_logs(z_store, unique_sites, n_scens, tf, n_sites) - `z_store` : ZArray - `unique_sites` : Unique site ids @@ -190,9 +190,9 @@ end Note: This setup relies on hardcoded values for number of species represented and seeded. """ function setup_logs(z_store, unique_sites, n_scens, tf, n_sites) - # Set up logs for site ranks, seed/fog log - zgroup(z_store, LOG_GRP) - log_fn::String = joinpath(z_store.folder, LOG_GRP) + # Set up logs for site ranks, seed/fog log + zgroup(z_store, LOG_GRP) + log_fn::String = joinpath(z_store.folder, LOG_GRP) # Store ranked sites rank_dims::Tuple{Int64, Int64, Int64, Int64} = (tf, n_sites, 2, n_scens) # sites, site id and rank, no. scenarios @@ -209,12 +209,12 @@ function setup_logs(z_store, unique_sites, n_scens, tf, n_sites) ranks = zcreate( Float32, rank_dims...; - name = "rankings", - fill_value = nothing, - fill_as_missing = false, - path = log_fn, - chunks = (rank_dims[1:3]..., 1), - attrs = attrs, + name="rankings", + fill_value=nothing, + fill_as_missing=false, + path=log_fn, + chunks=(rank_dims[1:3]..., 1), + attrs=attrs, ) attrs = Dict( @@ -224,51 +224,51 @@ function setup_logs(z_store, unique_sites, n_scens, tf, n_sites) seed_log = zcreate( Float32, seed_dims...; - name = "seed", - fill_value = nothing, - fill_as_missing = false, - path = log_fn, - chunks = (seed_dims[1:3]..., 1), - attrs = attrs, + name="seed", + fill_value=nothing, + fill_as_missing=false, + path=log_fn, + chunks=(seed_dims[1:3]..., 1), + attrs=attrs, ) attrs = Dict( :structure => ("timesteps", "sites", "scenarios"), - :unique_site_ids => unique_sites + :unique_site_ids => unique_sites, ) fog_log = zcreate( Float32, fog_dims...; - name = "fog", - fill_value = nothing, - fill_as_missing = false, - path = log_fn, - chunks = (fog_dims[1:2]..., 1), - attrs = attrs, + name="fog", + fill_value=nothing, + fill_as_missing=false, + path=log_fn, + chunks=(fog_dims[1:2]..., 1), + attrs=attrs, ) shade_log = zcreate( Float32, fog_dims...; - name = "shade", - fill_value = nothing, - fill_as_missing = false, - path = log_fn, - chunks = (fog_dims[1:2]..., 1), - attrs = attrs, + name="shade", + fill_value=nothing, + fill_as_missing=false, + path=log_fn, + chunks=(fog_dims[1:2]..., 1), + attrs=attrs, ) - # TODO: Could log bleaching mortality - # attrs = Dict( - # :structure => ("timesteps", "sites", "scenarios"), - # :unique_site_ids => unique_sites, - # ) - # bleach_log = zcreate(Float32, fog_dims...; name="bleaching_mortality", fill_value=nothing, fill_as_missing=false, path=log_fn, chunks=(fog_dims[1:2]..., 1), attrs=attrs) + # TODO: Could log bleaching mortality + # attrs = Dict( + # :structure => ("timesteps", "sites", "scenarios"), + # :unique_site_ids => unique_sites, + # ) + # bleach_log = zcreate(Float32, fog_dims...; name="bleaching_mortality", fill_value=nothing, fill_as_missing=false, path=log_fn, chunks=(fog_dims[1:2]..., 1), attrs=attrs) - # Log for coral DHW thresholds - attrs = Dict( - :structure => ("timesteps", "species", "sites", "scenarios"), - :unique_site_ids => unique_sites, - ) + # Log for coral DHW thresholds + attrs = Dict( + :structure => ("timesteps", "species", "sites", "scenarios"), + :unique_site_ids => unique_sites, + ) # 36 is the number of species/groups represented local coral_dhw_log @@ -279,12 +279,12 @@ function setup_logs(z_store, unique_sites, n_scens, tf, n_sites) 36, n_sites, n_scens; - name = "coral_dhw_log", - fill_value = nothing, - fill_as_missing = false, - path = log_fn, - chunks = (tf, 36, n_sites, 1), - attrs = attrs, + name="coral_dhw_log", + fill_value=nothing, + fill_as_missing=false, + path=log_fn, + chunks=(tf, 36, n_sites, 1), + attrs=attrs, ) else coral_dhw_log = zcreate( @@ -293,20 +293,20 @@ function setup_logs(z_store, unique_sites, n_scens, tf, n_sites) 36, 1, n_scens; - name = "coral_dhw_log", - fill_value = 0.0, - fill_as_missing = false, - path = log_fn, - chunks = (tf, 36, 1, 1), - attrs = attrs, + name="coral_dhw_log", + fill_value=0.0, + fill_as_missing=false, + path=log_fn, + chunks=(tf, 36, 1, 1), + attrs=attrs, ) end - return ranks, seed_log, fog_log, shade_log, coral_dhw_log + return ranks, seed_log, fog_log, shade_log, coral_dhw_log end """ - setup_result_store!(domain::Domain, param_df::DataFrame) + setup_result_store!(domain::Domain, param_df::DataFrame) Sets up an on-disk result store. @@ -351,11 +351,11 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple string(now()), "T" => "_", ":" => "_", "." => "_" ) - # Collect defined RCPs - rcps = string.(unique(scen_spec, "RCP")[!, "RCP"]) - log_location::String = _result_location(domain, rcps) + # Collect defined RCPs + rcps = string.(unique(scen_spec, "RCP")[!, "RCP"]) + log_location::String = _result_location(domain, rcps) - z_store = DirectoryStore(log_location) + z_store = DirectoryStore(log_location) # Store copy of inputs input_loc::String = joinpath(z_store.folder, INPUTS) @@ -366,28 +366,28 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple mkdir(joinpath(log_location, "site_data")) geo_fn = joinpath(log_location, "site_data", basename(attrs[:name]) * ".gpkg") try - GDF.write(geo_fn, domain.site_data; driver = "geojson") + GDF.write(geo_fn, domain.site_data; driver="geojson") catch err if !isa(err, ArgumentError) rethrow(err) end - GDF.write(geo_fn, domain.site_data; geom_columns = (:geom,), driver = "geojson") + GDF.write(geo_fn, domain.site_data; geom_columns=(:geom,), driver="geojson") end - # Store copy of model specification as CSV - mkdir(joinpath(log_location, "model_spec")) - model_spec(domain, joinpath(log_location, "model_spec", "model_spec.csv")) + # Store copy of model specification as CSV + mkdir(joinpath(log_location, "model_spec")) + model_spec(domain, joinpath(log_location, "model_spec", "model_spec.csv")) # Create store for scenario spec inputs = zcreate( Float64, input_dims...; - fill_value = -9999.0, - fill_as_missing = false, - path = input_loc, - chunks = input_dims, - attrs = attrs, + fill_value=-9999.0, + fill_as_missing=false, + path=input_loc, + chunks=input_dims, + attrs=attrs, ) # Store table of factor values @@ -395,23 +395,23 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple tf, n_sites, _ = size(domain.dhw_scens) - # Set up stores for each metric - function dim_lengths(metric_structure) - dl = [] - for d in metric_structure - if d == "timesteps" - append!(dl, tf) - elseif d == "species" - append!(dl, domain.coral_growth.n_species) - elseif d == "sites" - append!(dl, n_sites) - elseif d == "scenarios" - append!(dl, nrow(scen_spec)) - end - end - - return (dl...,) - end + # Set up stores for each metric + function dim_lengths(metric_structure) + dl = [] + for d in metric_structure + if d == "timesteps" + append!(dl, tf) + elseif d == "species" + append!(dl, domain.coral_growth.n_species) + elseif d == "sites" + append!(dl, n_sites) + elseif d == "scenarios" + append!(dl, nrow(scen_spec)) + end + end + + return (dl...,) + end met_names = [:relative_cover, :relative_shelter_volume, :absolute_shelter_volume, :relative_juveniles, :juvenile_indicator, :coral_evenness, @@ -426,11 +426,11 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple # Create stores for each metric stores = [ zcreate(Float32, result_dims...; - fill_value = nothing, fill_as_missing = false, - path = joinpath(z_store.folder, RESULTS, string(m_name)), - chunks = (result_dims[1:(end - 1)]..., 1), - attrs = dim_struct, - compressor = COMPRESSOR) + fill_value=nothing, fill_as_missing=false, + path=joinpath(z_store.folder, RESULTS, string(m_name)), + chunks=(result_dims[1:(end - 1)]..., 1), + attrs=dim_struct, + compressor=COMPRESSOR) for m_name in met_names ] @@ -438,13 +438,13 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple push!( stores, zcreate(Float32, (result_dims[1], 6, result_dims[3])...; - fill_value = nothing, fill_as_missing = false, - path = joinpath(z_store.folder, RESULTS, "relative_taxa_cover"), - chunks = ((result_dims[1], 6)..., 1), - attrs = Dict( + fill_value=nothing, fill_as_missing=false, + path=joinpath(z_store.folder, RESULTS, "relative_taxa_cover"), + chunks=((result_dims[1], 6)..., 1), + attrs=Dict( :structure => string.(ADRIA.metrics.relative_taxa_cover.dims) ), - compressor = COMPRESSOR)) + compressor=COMPRESSOR)) push!(met_names, :relative_taxa_cover) # dhw and wave zarrays @@ -481,7 +481,7 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple domain.TP_data, joinpath(z_store.folder, "connectivity"), rcp, - COMPRESSOR + COMPRESSOR, ), ) @@ -519,7 +519,7 @@ function setup_result_store!(domain::Domain, scen_spec::DataFrame)::Tuple end """ - _recreate_stats_from_store(zarr_store_path::String)::Dict{String, AbstractArray} + _recreate_stats_from_store(zarr_store_path::String)::Dict{String, AbstractArray} Recreate data structure holding RCP summary statistics from Zarr store. """ @@ -529,7 +529,7 @@ function _recreate_stats_from_store(zarr_store_path::String)::Dict{String, Abstr stat_d = Dict{String, AbstractArray}() for (i, sd) in enumerate(rcp_stat_dirs) - store = zopen(sd; fill_as_missing = false) + store = zopen(sd; fill_as_missing=false) dim_names = Symbol.(store.attrs["structure"]) stats = string.(store.attrs["stats"]) @@ -537,13 +537,13 @@ function _recreate_stats_from_store(zarr_store_path::String)::Dict{String, Abstr loc_ids = string.(store.attrs["locations"]) stat_set = NamedDimsArray( store[:, :, :]; - zip(dim_names, [stats, scenario_ids, loc_ids])... + zip(dim_names, [stats, scenario_ids, loc_ids])..., ) - stat_d[rcp_dirs[i]] = stat_set - end + stat_d[rcp_dirs[i]] = stat_set + end - return stat_d + return stat_d end """ @@ -557,14 +557,14 @@ function _recreate_conn_from_store(zarr_store_path::String)::Dict{String, Abstra conn_d = Dict{String, AbstractArray}() for (i, sd) in enumerate(rcp_stat_dirs) - store = zopen(sd; fill_as_missing = false) + store = zopen(sd; fill_as_missing=false) dim_names = Symbol.(store.attrs["structure"]) source_ids = string.(store.attrs["Source"]) recieving_ids = string.(store.attrs["Receiving"]) conn_set = NamedDimsArray( store[:, :]; - zip(dim_names, [source_ids, recieving_ids])... + zip(dim_names, [source_ids, recieving_ids])..., ) conn_d[rcp_dirs[i]] = conn_set @@ -574,18 +574,18 @@ function _recreate_conn_from_store(zarr_store_path::String)::Dict{String, Abstra end """ - load_results(result_loc::String)::ResultSet - load_results(domain::Domain)::ResultSet + load_results(result_loc::String)::ResultSet + load_results(domain::Domain)::ResultSet Create interface to a given Zarr result set. """ function load_results(result_loc::String)::ResultSet - !isdir(result_loc) ? error("Not a directory: $(result_loc)") : nothing + !isdir(result_loc) ? error("Not a directory: $(result_loc)") : nothing # Read in results local raw_set try - raw_set = zopen(joinpath(result_loc, RESULTS); fill_as_missing = false) + raw_set = zopen(joinpath(result_loc, RESULTS); fill_as_missing=false) catch err if !occursin("ArgumentError", sprint(showerror, err)) rethrow(err) @@ -593,8 +593,8 @@ function load_results(result_loc::String)::ResultSet end # Read in logs - log_set = zopen(joinpath(result_loc, LOG_GRP); fill_as_missing = false) - input_set = zopen(joinpath(result_loc, INPUTS); fill_as_missing = false) + log_set = zopen(joinpath(result_loc, LOG_GRP); fill_as_missing=false) + input_set = zopen(joinpath(result_loc, INPUTS); fill_as_missing=false) dhw_stat_set = _recreate_stats_from_store(joinpath(result_loc, ENV_STATS, "dhw")) wave_stat_set = _recreate_stats_from_store(joinpath(result_loc, ENV_STATS, "wave")) @@ -605,21 +605,21 @@ function load_results(result_loc::String)::ResultSet result_loc = result_loc[1:(end - 1)] end - # Spatial data - site_data = GDF.read(joinpath(result_loc, SITE_DATA, input_set.attrs["name"] * ".gpkg")) - sort!(site_data, [Symbol(input_set.attrs["unique_site_id_col"])]) + # Spatial data + site_data = GDF.read(joinpath(result_loc, SITE_DATA, input_set.attrs["name"] * ".gpkg")) + sort!(site_data, [Symbol(input_set.attrs["unique_site_id_col"])]) # Model specification model_spec = CSV.read( - joinpath(result_loc, MODEL_SPEC, "model_spec.csv"), DataFrame; comment = "#" + joinpath(result_loc, MODEL_SPEC, "model_spec.csv"), DataFrame; comment="#" ) - # Standardize fieldnames to Symbol - # TODO: Match all other column data types with original model spec - model_spec.fieldname .= Symbol.(model_spec.fieldname) + # Standardize fieldnames to Symbol + # TODO: Match all other column data types with original model spec + model_spec.fieldname .= Symbol.(model_spec.fieldname) - r_vers_id = input_set.attrs["ADRIA_VERSION"] - t_vers_id = "v" * string(PkgVersion.Version(@__MODULE__)) + r_vers_id = input_set.attrs["ADRIA_VERSION"] + t_vers_id = "v" * string(PkgVersion.Version(@__MODULE__)) if r_vers_id != t_vers_id msg = """Results were produced with a different version of ADRIA ($(r_vers_id)). @@ -630,12 +630,12 @@ function load_results(result_loc::String)::ResultSet stores results relative to available area. """ - @warn msg - end + @warn msg + end - # The inputs used - input_cols::Array{String} = input_set.attrs["columns"] - inputs_used::DataFrame = DataFrame(input_set[:, :], input_cols) + # The inputs used + input_cols::Array{String} = input_set.attrs["columns"] + inputs_used::DataFrame = DataFrame(input_set[:, :], input_cols) # Details of the environmental data layer used for the sims env_layer_md::EnvLayer = EnvLayer( @@ -651,23 +651,23 @@ function load_results(result_loc::String)::ResultSet ) outcomes = Dict{Symbol, NamedDimsArray}() - subdirs = filter(isdir, readdir(joinpath(result_loc, RESULTS); join = true)) + subdirs = filter(isdir, readdir(joinpath(result_loc, RESULTS); join=true)) for sd in subdirs if !(occursin(LOG_GRP, sd)) && !(occursin(INPUTS, sd)) - res = zopen(sd; fill_as_missing = false) + res = zopen(sd; fill_as_missing=false) sz = size(res) - # Construct dimension names and metadata - st = [] - for (i, s) in enumerate(res.attrs["structure"]) - if s == "timesteps" - push!(st, input_set.attrs["timeframe"]) - elseif s == "sites" - push!(st, res.attrs["unique_site_ids"]) - else - push!(st, 1:sz[i]) - end - end + # Construct dimension names and metadata + st = [] + for (i, s) in enumerate(res.attrs["structure"]) + if s == "timesteps" + push!(st, input_set.attrs["timeframe"]) + elseif s == "sites" + push!(st, res.attrs["unique_site_ids"]) + else + push!(st, 1:sz[i]) + end + end try outcomes[Symbol(basename(sd))] = NamedDimsArray( @@ -706,11 +706,11 @@ function load_results(result_loc::String)::ResultSet ) end function load_results(domain::Domain)::ResultSet - return load_results(result_location(domain)) + return load_results(result_location(domain)) end """ - result_location(d::Domain)::String + result_location(d::Domain)::String Generate path to the data store of results for the given Domain. """ @@ -721,7 +721,7 @@ function result_location(d::Domain)::String end """ - _result_location(d::Domain, rcps::Vector{String})::String + _result_location(d::Domain, rcps::Vector{String})::String Generate path to the data store of results for the given Domain and RCPs names. """ diff --git a/test/data/Test_domain/test_scenarios.csv b/test/data/Test_domain/test_scenarios.csv index d87242e3c..503fa990c 100644 --- a/test/data/Test_domain/test_scenarios.csv +++ b/test/data/Test_domain/test_scenarios.csv @@ -1,6 +1,33 @@ -guided,seed_TA,seed_CA,fogging,SRM,a_adapt,n_adapt,seed_years,shade_years,seed_freq,shade_freq,seed_year_start,shade_year_start,wave_stress,heat_stress,shade_connectivity,seed_connectivity,coral_cover_high,coral_cover_low,seed_priority,shade_priority,deployed_coral_risk_tol,depth_min,depth_offset,tabular_acropora_6_2_mean_colony_diameter_m,abhorescent_acropora_5_1_wavemort90,large_massives_4_6_growth_rate,corymbose_non_acropora_6_4_mb_rate,corymbose_non_acropora_1_4_growth_rate,corymbose_non_acropora_3_4_growth_rate,tabular_acropora_4_2_fecundity,corymbose_acropora_4_3_growth_rate,corymbose_non_acropora_2_4_growth_rate,small_massives_2_5_fecundity,corymbose_non_acropora_6_4_fecundity,small_massives_6_5_growth_rate,tabular_acropora_4_2_growth_rate,abhorescent_acropora_3_1_mean_colony_diameter_m,abhorescent_acropora_4_1_fecundity,small_massives_2_5_mb_rate,small_massives_3_5_wavemort90,small_massives_1_5_growth_rate,abhorescent_acropora_5_1_fecundity,large_massives_5_6_basecov,corymbose_acropora_5_3_growth_rate,corymbose_acropora_6_3_fecundity,corymbose_non_acropora_5_4_wavemort90,small_massives_5_5_basecov,corymbose_acropora_2_3_growth_rate,tabular_acropora_2_2_mb_rate,tabular_acropora_3_2_bleach_resist,small_massives_3_5_fecundity,abhorescent_acropora_2_1_bleach_resist,corymbose_acropora_5_3_basecov,abhorescent_acropora_6_1_bleach_resist,small_massives_2_5_wavemort90,large_massives_4_6_fecundity,corymbose_non_acropora_6_4_mean_colony_diameter_m,large_massives_1_6_bleach_resist,tabular_acropora_5_2_wavemort90,small_massives_4_5_basecov,abhorescent_acropora_6_1_basecov,corymbose_acropora_4_3_fecundity,small_massives_2_5_basecov,small_massives_5_5_growth_rate,corymbose_non_acropora_4_4_bleach_resist,corymbose_non_acropora_5_4_basecov,corymbose_acropora_5_3_mean_colony_diameter_m,tabular_acropora_6_2_wavemort90,abhorescent_acropora_6_1_wavemort90,corymbose_acropora_6_3_bleach_resist,small_massives_2_5_mean_colony_diameter_m,small_massives_4_5_mean_colony_diameter_m,tabular_acropora_6_2_bleach_resist,small_massives_3_5_mean_colony_diameter_m,large_massives_3_6_fecundity,abhorescent_acropora_1_1_growth_rate,abhorescent_acropora_1_1_fecundity,tabular_acropora_1_2_fecundity,large_massives_6_6_growth_rate,abhorescent_acropora_5_1_basecov,abhorescent_acropora_3_1_growth_rate,abhorescent_acropora_1_1_wavemort90,abhorescent_acropora_3_1_basecov,tabular_acropora_3_2_mean_colony_diameter_m,corymbose_non_acropora_4_4_wavemort90,corymbose_non_acropora_1_4_fecundity,abhorescent_acropora_1_1_basecov,abhorescent_acropora_5_1_bleach_resist,abhorescent_acropora_2_1_wavemort90,corymbose_acropora_1_3_mean_colony_diameter_m,corymbose_non_acropora_3_4_wavemort90,abhorescent_acropora_1_1_mean_colony_diameter_m,tabular_acropora_5_2_mean_colony_diameter_m,abhorescent_acropora_3_1_mb_rate,corymbose_acropora_1_3_fecundity,corymbose_non_acropora_2_4_mean_colony_diameter_m,abhorescent_acropora_2_1_basecov,abhorescent_acropora_4_1_mb_rate,tabular_acropora_2_2_bleach_resist,abhorescent_acropora_2_1_fecundity,large_massives_2_6_mb_rate,corymbose_non_acropora_6_4_growth_rate,large_massives_5_6_growth_rate,corymbose_acropora_3_3_fecundity,large_massives_5_6_bleach_resist,tabular_acropora_4_2_basecov,tabular_acropora_6_2_growth_rate,corymbose_non_acropora_3_4_basecov,large_massives_3_6_wavemort90,tabular_acropora_4_2_bleach_resist,abhorescent_acropora_3_1_wavemort90,abhorescent_acropora_4_1_growth_rate,small_massives_5_5_mean_colony_diameter_m,corymbose_acropora_2_3_fecundity,abhorescent_acropora_4_1_mean_colony_diameter_m,large_massives_1_6_wavemort90,corymbose_acropora_1_3_mb_rate,large_massives_1_6_mb_rate,corymbose_acropora_1_3_wavemort90,corymbose_non_acropora_6_4_bleach_resist,large_massives_4_6_basecov,large_massives_3_6_bleach_resist,corymbose_acropora_2_3_bleach_resist,abhorescent_acropora_3_1_fecundity,small_massives_6_5_mean_colony_diameter_m,small_massives_3_5_basecov,large_massives_2_6_growth_rate,corymbose_acropora_2_3_mean_colony_diameter_m,small_massives_3_5_growth_rate,tabular_acropora_3_2_wavemort90,abhorescent_acropora_6_1_mean_colony_diameter_m,corymbose_acropora_2_3_mb_rate,corymbose_non_acropora_3_4_bleach_resist,corymbose_non_acropora_1_4_mean_colony_diameter_m,tabular_acropora_2_2_wavemort90,abhorescent_acropora_4_1_bleach_resist,small_massives_1_5_mean_colony_diameter_m,corymbose_non_acropora_4_4_basecov,corymbose_acropora_3_3_basecov,corymbose_acropora_1_3_bleach_resist,corymbose_acropora_2_3_wavemort90,large_massives_3_6_mean_colony_diameter_m,corymbose_non_acropora_5_4_fecundity,corymbose_acropora_6_3_growth_rate,corymbose_acropora_3_3_mean_colony_diameter_m,tabular_acropora_2_2_basecov,small_massives_3_5_bleach_resist,tabular_acropora_1_2_mean_colony_diameter_m,abhorescent_acropora_5_1_mean_colony_diameter_m,corymbose_non_acropora_2_4_mb_rate,abhorescent_acropora_1_1_bleach_resist,corymbose_non_acropora_1_4_mb_rate,large_massives_4_6_mean_colony_diameter_m,corymbose_acropora_6_3_wavemort90,tabular_acropora_5_2_fecu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\ No newline at end of file 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diff --git a/test/data/example_scenarios.csv b/test/data/example_scenarios.csv index 492e32fac..503fa990c 100644 --- a/test/data/example_scenarios.csv +++ b/test/data/example_scenarios.csv @@ -1,33 +1,33 @@ -dhw_scenario,wave_scenario,cyclone_mortality_scenario,guided,N_seed_TA,N_seed_CA,N_seed_SM,fogging,SRM,a_adapt,seed_years,shade_years,fog_years,plan_horizon,seed_freq,shade_freq,fog_freq,seed_year_start,shade_year_start,fog_year_start,seed_wave_stress,seed_heat_stress,fog_wave_stress,fog_heat_stress,fog_connectivity,seed_in_connectivity,seed_out_connectivity,seed_depth,coral_cover_low,coral_cover_high,seed_priority,fog_priority,seed_zone,fog_zone,deployed_coral_risk_tol,depth_min,depth_offset,heritability,abhorescent_acropora_1_1_mean_colony_diameter_m,abhorescent_acropora_1_1_growth_rate,abhorescent_acropora_1_1_fecundity,abhorescent_acropora_1_1_mb_rate,abhorescent_acropora_1_1_dist_mean,abhorescent_acropora_1_1_dist_std,abhorescent_acropora_1_2_mean_colony_diameter_m,abhorescent_acropora_1_2_growth_rate,abhorescent_acropora_1_2_fecundity,abhorescent_acropora_1_2_mb_rate,abhorescent_acropora_1_2_dist_mean,abhorescent_acropora_1_2_dist_std,abhorescent_acropora_1_3_mean_colony_diameter_m,abhorescent_acropora_1_3_growth_rate,abhorescent_acropora_1_3_fecundity,abhorescent_acropora_1_3_mb_rate,abhorescent_acropora_1_3_dist_mean,abhorescent_acropora_1_3_dist_std,abhorescent_acropora_1_4_mean_colony_diameter_m,abhorescent_acropora_1_4_growth_rate,abhorescent_acropora_1_4_fecundity,abhorescent_acropora_1_4_mb_rate,abhorescent_acropora_1_4_dist_mean,abhorescent_acropora_1_4_dist_std,abhorescent_acropora_1_5_mean_colony_diameter_m,abhorescent_acropora_1_5_growth_rate,abhorescent_acropora_1_5_fecundity,abhorescent_acropora_1_5_mb_rate,abhorescent_acropora_1_5_dist_mean,abhorescent_acropora_1_5_dist_std,abhorescent_acropora_1_6_mean_colony_diameter_m,abhorescent_acropora_1_6_growth_rate,abhorescent_acropora_1_6_fecundity,abhorescent_acropora_1_6_mb_rate,abhorescent_acropora_1_6_dist_mean,abhorescent_acropora_1_6_dist_std,tabular_acropora_2_1_mean_colony_diameter_m,tabular_acropora_2_1_growth_rate,tabular_acropora_2_1_fecundity,tabular_acropora_2_1_mb_rate,tabular_acropora_2_1_dist_mean,tabular_acropora_2_1_dist_std,tabular_acropora_2_2_mean_colony_diameter_m,tabular_acropora_2_2_growth_rate,tabular_acropora_2_2_fecundity,tabular_acropora_2_2_mb_rate,tabular_acropora_2_2_dist_mean,tabular_acropora_2_2_dist_std,tabular_acropora_2_3_mean_colony_diameter_m,tabular_acropora_2_3_growth_rate,tabular_acropora_2_3_fecundity,tabular_acropora_2_3_mb_rate,tabular_acropora_2_3_dist_mean,tabular_acropora_2_3_dist_std,tabular_acropora_2_4_mean_colony_diameter_m,tabular_acropora_2_4_growth_rate,tabular_acropora_2_4_fecundity,tabular_acropora_2_4_mb_rate,tabular_acropora_2_4_dist_mean,tabular_acropora_2_4_dist_std,tabular_acropora_2_5_mean_colony_diameter_m,tabular_acropora_2_5_growth_rate,tabular_acropora_2_5_fecundity,tabular_acropora_2_5_mb_rate,tabular_acropora_2_5_dist_mean,tabular_acropora_2_5_dist_std,tabular_acropora_2_6_mean_colony_diameter_m,tabular_acropora_2_6_growth_rate,tabular_acropora_2_6_fecundity,tabular_acropora_2_6_mb_rate,tabular_acropora_2_6_dist_mean,tabular_acropora_2_6_dist_std,corymbose_acropora_3_1_mean_colony_diameter_m,corymbose_acropora_3_1_growth_rate,corymbose_acropora_3_1_fecundity,corymbose_acropora_3_1_mb_rate,corymbose_acropora_3_1_dist_mean,corymbose_acropora_3_1_dist_std,corymbose_acropora_3_2_mean_colony_diameter_m,corymbose_acropora_3_2_growth_rate,corymbose_acropora_3_2_fecundity,corymbose_acropora_3_2_mb_rate,corymbose_acropora_3_2_dist_mean,corymbose_acropora_3_2_dist_std,corymbose_acropora_3_3_mean_colony_diameter_m,corymbose_acropora_3_3_growth_rate,corymbose_acropora_3_3_fecundity,corymbose_acropora_3_3_mb_rate,corymbose_acropora_3_3_dist_mean,corymbose_acropora_3_3_dist_std,corymbose_acropora_3_4_mean_colony_diameter_m,corymbose_acropora_3_4_growth_rate,corymbose_acropora_3_4_fecundity,corymbose_acropora_3_4_mb_rate,corymbose_acropora_3_4_dist_mean,corymbose_acropora_3_4_dist_std,corymbose_acropora_3_5_mean_colony_diameter_m,corymbose_acropora_3_5_growth_rate,corymbose_acropora_3_5_fecundity,corymbose_acropora_3_5_mb_rate,corymbose_acropora_3_5_dist_mean,corymbose_acropora_3_5_dist_std,corymbose_acropora_3_6_mean_colony_diameter_m,corymbose_acropora_3_6_growth_rate,corymbose_acropora_3_6_fecundity,corymbose_acropora_3_6_mb_rate,corymbose_acropora_3_6_dist_mean,corymbose_acropora_3_6_dist_std,corymbose_non_acropora_4_1_mean_colony_diameter_m,corymbose_non_acropora_4_1_growth_rate,corymbose_non_acropora_4_1_fecundity,corymbose_non_acropora_4_1_mb_rate,corymbose_non_acropora_4_1_dist_mean,corymbose_non_acropora_4_1_dist_std,corymbose_non_acropora_4_2_mean_colony_diameter_m,corymbose_non_acropora_4_2_growth_rate,corymbose_non_acropora_4_2_fecundity,corymbose_non_acropora_4_2_mb_rate,corymbose_non_acropora_4_2_dist_mean,corymbose_non_acropora_4_2_dist_std,corymbose_non_acropora_4_3_mean_colony_diameter_m,corymbose_non_acropora_4_3_growth_rate,corymbose_non_acropora_4_3_fecundity,corymbose_non_acropora_4_3_mb_rate,corymbose_non_acropora_4_3_dist_mean,corymbose_non_acropora_4_3_dist_std,corymb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