Merge 273b8c9 into 718da93

docs/src/UserGuide/group.md

@@ -50,22 +50,22 @@ nothing # hide
 ````julia
 function weighted_seasons(ds)
     # calculate weights 
-    tempo = dims(ds, :Ti)
+    tempo = dims(ds, :time)
     month_length = YAXArray((tempo,), daysinmonth.(tempo))
-    g_tempo = groupby(month_length, Ti => seasons(; start=December))
-    sum_days = sum.(g_tempo, dims=:Ti)
+    g_tempo = groupby(month_length, Dim{:time} => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:time)
     weights = map(./, g_tempo, sum_days)
     # unweighted seasons
-    g_ds = groupby(ds, Ti => seasons(; start=December))
-    mean_g = mean.(g_ds, dims=:Ti)
-    mean_g = dropdims.(mean_g, dims=:Ti)
+    g_ds = groupby(ds, Dim{:time} => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:time)
+    mean_g = dropdims.(mean_g, dims=:time)
     # weighted seasons
     g_dsW = broadcast_dims.(*, weights, g_ds)
-    weighted_g = sum.(g_dsW, dims = :Ti);
-    weighted_g = dropdims.(weighted_g, dims=:Ti)
+    weighted_g = sum.(g_dsW, dims = :time);
+    weighted_g = dropdims.(weighted_g, dims=:time)
     # differences
     diff_g = map(.-, weighted_g, mean_g)
-    seasons_g = lookup(mean_g, :Ti)
+    seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
 end
 ````
@@ -74,29 +74,29 @@ end
 Now, we continue with the `groupby` operations as usual
 
 ````@ansi compareXarray
-g_ds = groupby(ds, Ti => seasons(; start=December))
+g_ds = groupby(ds, Dim{:time} => seasons(; start=December))
 ````
 
 And the mean per season is calculated as follows
 
 ````@ansi compareXarray
-mean_g = mean.(g_ds, dims=:Ti)
+mean_g = mean.(g_ds, dims=:time)
 ````
 
 ### dropdims
 
 Note that now the time dimension has length one, we can use `dropdims` to remove it
 
 ````@ansi compareXarray
-mean_g = dropdims.(mean_g, dims=:Ti)
+mean_g = dropdims.(mean_g, dims=:time)
 ````
 
 ### seasons
 
 Due to the `groupby` function we will obtain new grouping names, in this case in the time dimension:
 
 ````@example compareXarray
-seasons_g = lookup(mean_g, :Ti)
+seasons_g = lookup(mean_g, :time)
 ````
 
 Next, we will weight this grouping by days/month in each group.
@@ -106,20 +106,20 @@ Next, we will weight this grouping by days/month in each group.
 Create a `YAXArray` for the month length
 
 ````@example compareXarray
-tempo = dims(ds, :Ti)
+tempo = dims(ds, :time)
 month_length = YAXArray((tempo,), daysinmonth.(tempo))
 ````
 
 Now group it by season 
 
 ````@ansi compareXarray  
-g_tempo = groupby(month_length, Ti => seasons(; start=December))
+g_tempo = groupby(month_length, Dim{:time} => seasons(; start=December))
 ````
 
 Get the number of days per season
 
 ````@ansi compareXarray  
-sum_days = sum.(g_tempo, dims=:Ti)
+sum_days = sum.(g_tempo, dims=:time)
 ````
 
 ### weights
@@ -146,8 +146,8 @@ g_dsW = broadcast_dims.(*, weights, g_ds)
 apply a `sum` over the time dimension and drop it
 
 ````@ansi compareXarray
-weighted_g = sum.(g_dsW, dims = :Ti);
-weighted_g = dropdims.(weighted_g, dims=:Ti)
+weighted_g = sum.(g_dsW, dims = :time);
+weighted_g = dropdims.(weighted_g, dims=:time)
 ````
 
 Calculate the differences
@@ -181,9 +181,9 @@ with_theme(theme_ggplot2()) do
     fig = Figure(; size = (850,500))
     axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
     for (j, s) in enumerate(seasons_g)
-        hm_o = heatmap!(axs[1,j], mean_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
-        hm_w = heatmap!(axs[2,j], weighted_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
-        hm_d = heatmap!(axs[3,j], diff_g[Ti=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
+        hm_o = heatmap!(axs[1,j], mean_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_w = heatmap!(axs[2,j], weighted_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_d = heatmap!(axs[3,j], diff_g[time=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
             colormap=:diverging_bwr_20_95_c54_n256)
     end
     Colorbar(fig[1:2,5], hm_o, label=cb_label)

src/Cubes/Cubes.jl

@@ -480,14 +480,7 @@ function Base.getindex(a::YAXArray, args::DD.Dimension...; kwargs...)
     for (k,v) in kwargsdict
         d = getAxis(k,a)
         if d !== nothing
-            if d isa DD.Ti
-                if v isa UnitRange{Int}
-                    v = Date(first(v))..Date(last(v),12,31)
-                end
-                d2[:Ti] = v
-            else
-                d2[DD.name(d)] = v
-            end
+            d2[DD.name(d)] = v
         else
             d2[k] = v
         end

src/DatasetAPI/Datasets.jl

@@ -285,7 +285,7 @@ function toaxis(dimname, g, offs, len)
         catch
             ar[:]
         end
-        DD.Ti(tsteps[offs+1:end])
+        DD.rebuild(DD.name2dim(axname), tsteps[offs+1:end])
     elseif haskey(aratts, "_ARRAYVALUES")
         vals = identity.(aratts["_ARRAYVALUES"])
         DD.rebuild(DD.name2dim(axname),(vals))

test/Datasets/datasets.jl

@@ -24,7 +24,7 @@ using Dates
         YAXArray(axlist2, data[3], props[3]),
     )
     ds = Dataset(avar=c1, something=c2, smaller=c3)
-    # previous version will throw this error: `KeyError: key :Ti not found`
+    # previous version will throw this error: `KeyError: key :time not found`
     f = "./temp.zarr"
     @test_nowarn savedataset(ds; path=f)
     rm(f, recursive=true, force=true)
@@ -215,11 +215,11 @@ end
             ds = open_dataset("test.mock")
             @test size(ds.Var1) == (10, 5, 2)
             @test size(ds.Var2) == (10, 5)
-            @test all(in(keys(ds.axes)), (:Ti, :d2, :d3))
+            @test all(in(keys(ds.axes)), (:time, :d2, :d3))
             ar = Cube(ds)
             @test ar isa YAXArray
             @test size(ar) == (10, 5, 2, 2)
-            @test DD.name.(ar.axes) == (:Ti, :d2, :d3, :Variable)
+            @test DD.name.(ar.axes) == (:time, :d2, :d3, :Variable)
             @test DD.lookup(ar.axes[4]) == ["Var1", "Var3"]
         end
         @testset "Dataset creation" begin