Add Var.split_by_season

Bug introduced: The documentation show the wrong module name for
Var.split_by_season. It is ClimaAnalysis.Utils.split_by_season instead
of ClimaAnalysis.Var.split_by_season.

NEWS.md

@@ -164,6 +164,45 @@ julia> long_name(integrated_var) # updated long name to reflect the data being i
 "f integrated over lon (-179.5 to 179.5degrees_east) and integrated over lat (-89.5 to 89.5degrees_north)"
 ```
 
+### Split by season
+`OutputVar`s can be split by seasons using `split_by_season(var)` provided that a start date
+can be found in `var.attributes["start_date"]` and time is a dimension in the `OutputVar`.
+The unit of time is expected to be second. The function `split_by_season(var)` returns a
+vector of four `OutputVar`s with each `OutputVar` corresponding to a season. The months of
+the seasons are March to May, June to August, September to November, and December to
+February. The order of the vector is MAM, JJA, SON, and DJF. If there are no dates found for
+a season, then the `OutputVar` for that season will be an empty `OutputVar`.
+
+```@julia split_by_season
+julia> attribs = Dict("start_date" => "2024-1-1");
+
+julia> time = [0., 5_184_000., 13_132_800., 21_081_600.]; # correspond to dates 2024-1-1, 2024-3-1, 2024-6-1, 2024-9-1
+
+julia> dims = OrderedDict(["time" => time]);
+
+julia> dim_attribs = OrderedDict(["time" => Dict("units" => "s")]);
+
+julia> data = [1., 2., 3., 4.];
+
+julia> var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data);
+
+julia> MAM, JJA, SON, DJF = ClimaAnalysis.split_by_season(var);
+
+julia> [MAM.dims["time"], JJA.dims["time"], SON.dims["time"], DJF.dims["time"]]
+4-element Vector{Vector{Float64}}:
+ [5.184e6]
+ [1.31328e7]
+ [2.10816e7]
+ [0.0]
+
+julia> [MAM.data, JJA.data, SON.data, DJF.data]
+4-element Vector{SubArray{Float64, 1, Vector{Float64}, Tuple{Vector{Int64}}, false}}:
+ [2.0]
+ [3.0]
+ [4.0]
+ [1.0]
+```
+
 ## Bug fixes
 
 - Increased the default value for `warp_string` to 72.

docs/src/api.md

@@ -59,6 +59,7 @@ Var.convert_units
 Var.integrate_lonlat
 Var.integrate_lat
 Var.integrate_lon
+Var.split_by_season(var::OutputVar)
 ```
 
 ## Utilities

docs/src/var.md

@@ -100,3 +100,42 @@ julia> integrated_var.data # approximately 4π (the surface area of a sphere)
 julia> long_name(integrated_var) # updated long name to reflect the data being integrated
 "f integrated over lon (-179.5 to 179.5degrees_east) and integrated over lat (-89.5 to 89.5degrees_north)"
 ```
+
+## Split by season
+`OutputVar`s can be split by seasons using `split_by_season(var)` provided that a start date
+can be found in `var.attributes["start_date"]` and time is a dimension in the `OutputVar`.
+The unit of time is expected to be second. The function `split_by_season(var)` returns a
+vector of four `OutputVar`s with each `OutputVar` corresponding to a season. The months of
+the seasons are March to May, June to August, September to November, and December to
+February. The order of the vector is MAM, JJA, SON, and DJF. If there are no dates found for
+a season, then the `OutputVar` for that season will be an empty `OutputVar`.
+
+```@julia split_by_season
+julia> attribs = Dict("start_date" => "2024-1-1");
+
+julia> time = [0., 5_184_000., 13_132_800., 21_081_600.]; # correspond to dates 2024-1-1, 2024-3-1, 2024-6-1, 2024-9-1
+
+julia> dims = OrderedDict(["time" => time]);
+
+julia> dim_attribs = OrderedDict(["time" => Dict("units" => "s")]); # unit is second
+
+julia> data = [1., 2., 3., 4.];
+
+julia> var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data);
+
+julia> MAM, JJA, SON, DJF = ClimaAnalysis.split_by_season(var);
+
+julia> [MAM.dims["time"], JJA.dims["time"], SON.dims["time"], DJF.dims["time"]]
+4-element Vector{Vector{Float64}}:
+ [5.184e6]
+ [1.31328e7]
+ [2.10816e7]
+ [0.0]
+
+julia> [MAM.data, JJA.data, SON.data, DJF.data]
+4-element Vector{SubArray{Float64, 1, Vector{Float64}, Tuple{Vector{Int64}}, false}}:
+ [2.0]
+ [3.0]
+ [4.0]
+ [1.0]
+```

src/Var.jl

@@ -1,5 +1,6 @@
 module Var
 
+import Dates
 import NCDatasets
 import OrderedCollections: OrderedDict
 
@@ -8,7 +9,14 @@ import Statistics: mean
 import NaNStatistics: nanmean
 
 import ..Numerics
-import ..Utils: nearest_index, seconds_to_prettystr, squeeze
+import ..Utils:
+    nearest_index,
+    seconds_to_prettystr,
+    squeeze,
+    split_by_season,
+    time_to_date,
+    date_to_time,
+    _data_at_dim_vals
 
 export OutputVar,
     read_var,
@@ -35,7 +43,8 @@ export OutputVar,
     integrate_lonlat,
     integrate_lon,
     integrate_lat,
-    isempty
+    isempty,
+    split_by_season
 
 """
     Representing an output variable
@@ -901,6 +910,62 @@ function _integrate_over_angle(var::OutputVar, integrate_on, angle_dim_name)
     return integrated_var
 end
 
+
+"""
+    split_by_season(var::OutputVar)
+
+Return a vector of four `OutputVar`s split by season.
+
+The months of the seasons are March to May, June to August, September to November, and
+December to February. The order of the vector is MAM, JJA, SON, and DJF. If there are no
+dates found for a season, then the `OutputVar` for that season will be an empty `OutputVar`.
+
+The function will use the start date in `var.attributes["start_date"]`. The unit of time is
+expected to be second. Also, the interpolations will be inaccurate in time intervals
+outside of their respective season for the returned `OutputVar`s.
+"""
+function split_by_season(var::OutputVar)
+    # Check time exists and unit is second
+    has_time(var) || error("Time is not a dimension in var")
+    dim_units(var, time_name(var)) == "s" ||
+        error("Unit for time is not second")
+
+    # Check start date exists
+    haskey(var.attributes, "start_date") ?
+    start_date = Dates.DateTime(var.attributes["start_date"]) :
+    error("Start date is not found in var")
+
+    season_dates = split_by_season(time_to_date.(start_date, times(var)))
+    season_times =
+        (date_to_time.(start_date, season) for season in season_dates)
+
+    # Split data according to seasons
+    season_data = (
+        collect(
+            _data_at_dim_vals(
+                var.data,
+                times(var),
+                var.dim2index[time_name(var)],
+                season_time,
+            ),
+        ) for season_time in season_times
+    )
+
+    # Construct an OutputVar for each season
+    return map(season_times, season_data) do time, data
+        if isempty(time)
+            dims = empty(var.dims)
+            data = similar(var.data, 0)
+            return OutputVar(dims, data)
+        end
+        ret_dims = deepcopy(var.dims)
+        ret_attribs = deepcopy(var.attributes)
+        ret_dim_attribs = deepcopy(var.dim_attributes)
+        ret_dims[time_name(var)] = time
+        OutputVar(ret_attribs, ret_dims, ret_dim_attribs, data)
+    end
+end
+
 """
     overload_binary_op(op)
 

test/test_Var.jl

@@ -841,3 +841,66 @@ end
         var,
     )
 end
+
+@testset "split_by_season" begin
+    lon = collect(range(-179.5, 179.5, 360))
+    lat = collect(range(-89.5, 89.5, 180))
+    time = [0.0]
+    push!(time, 5_184_000.0) # correspond to 2024-3-1
+    push!(time, 5_184_001.0)
+    push!(time, 13_132_800.0) # correspond to 2024-6-1
+    push!(time, 13_132_802.0)
+    push!(time, 13_132_803.0)
+    data = ones(length(lat), length(time), length(lon))
+    dims = OrderedDict(["lat" => lat, "time" => time, "lon" => lon])
+    attribs = Dict("long_name" => "hi", "start_date" => "2024-1-1")
+    dim_attribs = OrderedDict([
+        "lat" => Dict("units" => "deg"),
+        "time" => Dict("units" => "s"),
+        "lon" => Dict("units" => "deg"),
+    ])
+    var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data)
+
+    MAM, JJA, SON, DJF = ClimaAnalysis.split_by_season(var)
+
+    # Check size of data
+    @test size(MAM.data) == (length(lat), 2, length(lon))
+    @test size(JJA.data) == (length(lat), 3, length(lon))
+    @test size(SON.data) == (0,)
+    @test size(DJF.data) == (length(lat), 1, length(lon))
+
+    # Check times are correct in OutputVars
+    @test MAM.dims["time"] == [5_184_000.0, 5_184_001.0]
+    @test JJA.dims["time"] == [13_132_800.0, 13_132_802.0, 13_132_803.0]
+    @test DJF.dims["time"] == [0.0]
+
+    # Check start date
+    MAM.attributes["start_date"] == "2024-1-1"
+    JJA.attributes["start_date"] == "2024-1-1"
+    DJF.attributes["start_date"] == "2024-1-1"
+
+    # Check empty OutputVar
+    @test isempty(SON) == true
+
+    # Check error handling
+    attribs_no_start_date = Dict("long_name" => "hi")
+    var =
+        ClimaAnalysis.OutputVar(attribs_no_start_date, dims, dim_attribs, data)
+    @test_throws ErrorException ClimaAnalysis.split_by_season(var)
+
+    dim_attribs_no_sec = OrderedDict([
+        "lat" => Dict("units" => "deg"),
+        "time" => Dict("units" => "min"),
+        "lon" => Dict("units" => "deg"),
+    ])
+    var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs_no_sec, data)
+    @test_throws ErrorException ClimaAnalysis.split_by_season(var)
+
+    lon = collect(range(-179.5, 179.5, 360))
+    data = ones(length(lon))
+    dims = OrderedDict(["lon" => lon])
+    attribs = Dict("long_name" => "hi", "start_date" => "2024-1-1")
+    dim_attribs = OrderedDict(["lon" => Dict("units" => "deg")])
+    var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data)
+    @test_throws ErrorException ClimaAnalysis.split_by_season(var)
+end