C~Scape Result Interface

docs/src/usage/results.md

@@ -31,10 +31,66 @@ data_dir
         results.nc
         scenarios.csv
 ```
-In order to reduce the duplication of geospatial and conectivity data, the data directory
-and results directory can be supplied seperately to avoid having copies for each resuilt set
+
+In order to reduce the duplication of geospatial and connectivity data, the data directory
+and results directory can be supplied separately to avoid having copies for each result set
 analysed.
 
 ```julia
 rs = ADRIA.load_domain(RMEResultSet, "<path to data dir>", "<path to results dir>")
 ```
+
+## Loading C~scape Results
+
+Results from C~scape can be loaded with the `load_results` function.
+
+```julia
+# Assumes NetCDFs are contained in result subdirectory (see example directory tree below)
+rs = ADRIA.load_results(CScapeResultSet, "<path to data dir>")
+
+# Retrieves NetCDFs from separate directory
+rs = ADRIA.load_results(CScapeResultSet, "<path to data dir>", "<path to result directory>")
+
+# Manually pass in a list of files to load as results
+rs = ADRIA.load_results(CScapeResultSet, "<path to data dir>", ["netcdf_fn1", "netcdf_fn2", ...])
+```
+
+The expected directory structure is:
+
+```bash
+data_dir
+│   ScenarioID.csv
+│
+├───connectivity
+│       connectivity.csv
+│
+├───site_data
+│       geospatial_data.gpkg
+│
+├───initial_cover
+│       initial_cover.csv
+│
+└───results (optional)
+        NetCDF_Scn_140001.nc
+        NetCDF_Scn_140002.nc
+        NetCDF_Scn_140003.nc
+        ...
+```
+
+### Loading Variables using the interface
+
+All variables except from relative cover are not loaded automatically. The outcomes
+dictionary contained in the result structure is where all outputs are stored.
+
+```julia
+julia> rs.outcomes[:relative_cover]
+```
+
+To analyse a variable contained in the NetCDFs, use the function
+
+```julia
+julia> var = ADRIA.load_variable!(rs, :var_name)
+
+# The variable will also be stored in the outcomes dictionary.
+julia> rs.outcomes[:var_name]
+```

src/ADRIA.jl

@@ -69,6 +69,7 @@ include("io/ResultSet.jl")
 include("spatial/spatial.jl")
 include("io/result_io.jl")
 include("io/rme_result_io.jl")
+include("io/cscape_result_io.jl")
 include("io/result_post_processing.jl")
 include("io/sampling.jl")
 include("metrics/metrics.jl")
@@ -98,6 +99,7 @@ export RMEDomain
 export ReefModDomain
 
 export RMEResultSet
+export CScapeResultSet
 # metric helper methods
 # export dims, ndims
 

src/analysis/scenario.jl

@@ -21,23 +21,41 @@ end
 
 function counterfactual(scenarios::DataFrame)::BitVector
     no_seed = _no_seed(scenarios)
-    no_fog = scenarios.fogging .== 0
-    no_SRM = scenarios.SRM .== 0
+    has_fog_col = "fogging" in names(scenarios)
+    no_fog = has_fog_col ? scenarios.fogging .== 0 : fill(true, size(scenarios, 1))
+    has_SRM_col = "SRM" in names(scenarios)
+    no_SRM = has_SRM_col ? scenarios.SRM .== 0 : fill(true, size(scenarios, 1))
+
     return no_seed .& no_fog .& no_SRM
 end
 
 function unguided(scenarios::DataFrame)::BitVector
+    has_guided_col = "guided" in names(scenarios)
+    # If the results set does not have a guided type column default to unguided
+    is_unguided = has_guided_col ? scenarios.guided .== 0 : fill(true, size(scenarios, 1))
+
+    has_fog_col = "fogging" in names(scenarios)
+    has_fog = has_fog_col ? scenarios.fogging .> 0 : fill(true, size(scenarios, 1))
+
+    has_SRM_col = "SRM" in names(scenarios)
+    has_SRM = has_SRM_col ? scenarios.SRM .> 0 : fill(true, size(scenarios, 1))
+
     has_seed = .!_no_seed(scenarios)
-    has_shade = (scenarios.fogging .> 0) .| (scenarios.SRM .> 0)
-    return (scenarios.guided .== 0) .& (has_seed .| has_shade)
+    has_shade = (has_fog) .| (has_SRM)
+
+    return is_unguided .& (has_seed .| has_shade)
 end
 
 function guided(scenarios::DataFrame)::BitVector
     return .!(counterfactual(scenarios) .| unguided(scenarios))
 end
 
 function _no_seed(scenarios::DataFrame)::BitVector
-    return (scenarios.N_seed_TA .== 0) .&
-           (scenarios.N_seed_CA .== 0) .&
-           (scenarios.N_seed_SM .== 0)
+    seed_cols::Vector{String} = filter(
+        x -> contains(x, "N_seed_"), names(scenarios)
+    )
+
+    return BitVector(
+        all(collect(scen_row) .== 0) for scen_row in eachrow(scenarios[:, seed_cols])
+    )
 end

src/ecosystem/corals/Corals.jl

@@ -220,7 +220,7 @@ function coral_spec()::NamedTuple
     params.coral_id = String[join(x, "_") for x in zip(tn, params.taxa_id, params.class_id)]
 
     # Ecological parameters
-    # To be more consistent with parameters in ReefMod, C~Scape and RRAP
+    # To be more consistent with parameters in ReefMod, C~scape and RRAP
     # interventions, we express coral abundance as colony numbers in different
     # size classes and growth rates as linear extension (in cm per year).
     colony_area_mean_cm², mean_colony_diameter_m = colony_areas()