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Make use of planning horizon in sims #340

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May 5, 2023
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Make use of planning horizon in sims #340

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62 changes: 28 additions & 34 deletions src/scenario.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -337,7 +337,7 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
Yfog = SparseArray(spzeros(tf, n_sites))
Yseed = SparseArray(zeros(tf, 2, n_sites)) # 2 = the two enhanced coral types

# Intervention strategy: 0 is random, > 0 is guided
# Intervention strategy: < 0 is no intervention, 0 is random location selection, > 0 is guided
is_guided = param_set("guided") > 0

# Years at which to reassess seeding site selection
Expand All @@ -347,7 +347,7 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
seed_start_year = max(seed_start_year, 2)
if param_set("seed_freq") > 0
max_consider = min(seed_start_year + seed_years - 1, tf)
seed_decision_years[seed_start_year:Int(param_set("seed_freq")):max_consider] .= true
seed_decision_years[seed_start_year:Int64(param_set("seed_freq")):max_consider] .= true
else
# Start at year 2 or the given specified seed start year
seed_decision_years[seed_start_year] = true
Expand All @@ -356,7 +356,7 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
shade_start_year = max(shade_start_year, 2)
if param_set("shade_freq") > 0
max_consider = min(shade_start_year + shade_years - 1, tf)
shade_decision_years[shade_start_year:Int(param_set("shade_freq")):max_consider] .= true
shade_decision_years[shade_start_year:Int64(param_set("shade_freq")):max_consider] .= true
else
# Start at year 2 or the given specified shade start year
shade_decision_years[shade_start_year] = true
Expand Down Expand Up @@ -456,14 +456,12 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
growth::ODEProblem = ODEProblem{true}(growthODE, ode_u, tspan, p)
tmp::Matrix{Float64} = zeros(size(Y_cover[1, :, :])) # temporary array to hold intermediate covers

env_horizon = zeros(Int64(param_set("plan_horizon") + 1), n_sites) # temporary cache for planning horizon

# basal_area_per_settler is the area in m^2 of a size class one coral
basal_area_per_settler = corals.colony_area_cm2[corals.class_id.==1] ./ 100 .^ 2
# debug_log = zeros(tf, n_sites)
# rec_log = zeros(tf, 6, n_sites)
# juv_log = zeros(tf, 12, n_sites)
# juves = [1, 2, 7, 8, 13, 14, 19, 20, 25, 26, 31, 32]
@inbounds for tstep::Int64 in 2:tf
p_step = tstep - 1
p_step::Int64 = tstep - 1
Y_pstep[:, :] .= Y_cover[p_step, :, :]

sf .= stressed_fecundity(tstep, a_adapt, n_adapt, dhw_scen[p_step, :],
Expand All @@ -481,19 +479,30 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
p.rec .= settler_cover(fec_scope, sf, TP_data, leftover_space_prop,
sim_params.max_settler_density, sim_params.max_larval_density, basal_area_per_settler)

# rec_log[tstep, :, :] .= p.rec

in_shade_years = (shade_start_year <= tstep) && (tstep <= (shade_start_year + shade_years - 1))
in_seed_years = ((seed_start_year <= tstep) && (tstep <= (seed_start_year + seed_years - 1)))

@views dhw_t .= dhw_scen[tstep, :] # subset of DHW for given timestep

# Apply regional cooling effect before selecting locations to seed
if (srm > 0.0) && in_shade_years
Yshade[tstep, :] .= srm

# Apply reduction in DHW due to SRM
dhw_t .= max.(0.0, dhw_t .- srm)
end

if is_guided && (in_seed_years || in_shade_years)
# Update dMCDA values
mcda_vars.heat_stress_prob .= dhw_t
mcda_vars.dam_prob .= sum(Sw_t[tstep, :, :], dims=1)'
end
horizon = tstep:tstep+Int64(param_set("plan_horizon"))

if is_guided && in_seed_years
env_horizon .= dhw_scen[horizon, :]
env_horizon[1, :] .= dhw_t
mcda_vars.heat_stress_prob .= vec((mean(env_horizon, dims=1) .+ std(env_horizon, dims=1)) .* 0.5)

mcda_vars.dam_prob .= vec(sum(dropdims((mean(Sw_t[horizon, :, :], dims=1) .+ std(Sw_t[horizon, :, :], dims=1)) .* 0.5, dims=1), dims=1))
end
if is_guided && (in_seed_years || in_shade_years)
mcda_vars.sum_cover .= site_coral_cover
(prefseedsites, prefshadesites, rankings) = guided_site_selection(mcda_vars, MCDA_approach,
seed_decision_years[tstep], shade_decision_years[tstep],
Expand All @@ -502,7 +511,7 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
# Log site ranks
# First col only holds site index ids so skip (with 2:end)
site_ranks[tstep, rankings[:, 1], :] = rankings[:, 2:end]
elseif seed_corals && in_seed_years
elseif seed_corals && (in_seed_years || in_shade_years)
# Unguided deployment, seed/shade corals anywhere, so long as available space > 0
prefseedsites, prefshadesites = unguided_site_selection(prefseedsites, prefshadesites,
seed_decision_years[tstep], shade_decision_years[tstep],
Expand All @@ -514,21 +523,10 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,

has_shade_sites::Bool = !all(prefshadesites .== 0)
has_seed_sites::Bool = !all(prefseedsites .== 0)
if (srm > 0.0) && in_shade_years
Yshade[tstep, :] .= srm

# Apply reduction in DHW due to SRM
dhw_t .= max.(0.0, dhw_t .- srm)
end

if (fogging > 0.0) && in_shade_years && (has_seed_sites || has_shade_sites)
if has_seed_sites
# Always fog seeded locations if they are selected
site_locs::Vector{Int64} = prefseedsites
elseif has_shade_sites
# Use locations selected for fogging otherwise
site_locs = prefshadesites
end
# Fog selected locations
if (fogging > 0.0) && in_shade_years && has_shade_sites
site_locs = prefshadesites

dhw_t[site_locs] .= dhw_t[site_locs] .* (1.0 .- fogging)
Yfog[tstep, site_locs] .= fogging
Expand Down Expand Up @@ -570,11 +568,7 @@ function run_model(domain::Domain, param_set::NamedDimsArray, corals::DataFrame,
alg_hints=[:nonstiff], abstol=1e-6, reltol=1e-4) # , adaptive=false, dt=1.0
# Using the last step from ODE above, proportionally adjust site coral cover
# if any are above the maximum possible (i.e., the site `k` value)
# debug_log[tstep, :] = sum(sol.u[end] .* absolute_k_area ./ total_site_area, dims=1)
# juv_log[tstep, :, :] = (sol.u[end].*absolute_k_area./total_site_area)[juves, :]

@views Y_cover[tstep, :, :] .= sol.u[end] .* absolute_k_area ./ total_site_area
# proportional_adjustment!(Y_cover[tstep, :, :], cover_tmp, max_cover)
@views Y_cover[tstep, :, :] .= clamp.(sol.u[end] .* absolute_k_area ./ total_site_area, 0.0, 1.0)
end

# Avoid placing importance on sites that were not considered
Expand Down