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no more Ti #479

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merged 14 commits into from
Dec 16, 2024
Merged

no more Ti #479

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c55e8d7
no more Ti
lazarusA Dec 12, 2024
f845879
go back to :time
lazarusA Dec 12, 2024
1ac2dff
no Ti check
lazarusA Dec 13, 2024
273b8c9
use Dim{:time}
lazarusA Dec 13, 2024
7c99072
adds dims
lazarusA Dec 15, 2024
2adb67c
add YAX
lazarusA Dec 15, 2024
b19dad3
adds dim Variables
lazarusA Dec 15, 2024
1d9f0b5
export it
lazarusA Dec 15, 2024
8dd7fd7
fix tests
lazarusA Dec 15, 2024
115440b
fix docs
lazarusA Dec 15, 2024
dc0af09
fix msc
lazarusA Dec 15, 2024
6326181
fixes doc strings
lazarusA Dec 15, 2024
d195581
no Rasters
lazarusA Dec 15, 2024
856eaf0
more docs
lazarusA Dec 16, 2024
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28 changes: 14 additions & 14 deletions docs/src/UserGuide/group.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
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)
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
````
Expand All @@ -80,23 +80,23 @@ g_ds = groupby(ds, Ti => 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.
Expand All @@ -106,7 +106,7 @@ 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))
````

Expand All @@ -119,7 +119,7 @@ g_tempo = groupby(month_length, Ti => 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
Expand All @@ -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
Expand Down
2 changes: 1 addition & 1 deletion src/Cubes/Cubes.jl
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Expand Up @@ -484,7 +484,7 @@ function Base.getindex(a::YAXArray, args::DD.Dimension...; kwargs...)
if v isa UnitRange{Int}
v = Date(first(v))..Date(last(v),12,31)
end
d2[:Ti] = v
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d2[:time] = v
else
d2[DD.name(d)] = v
end
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2 changes: 1 addition & 1 deletion src/DatasetAPI/Datasets.jl
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Expand Up @@ -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])
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Should we make the axis that we get here a subtype of Ti?

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no

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I agree we should not do this, since it would only work with eval anyway.

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We could use the @dim macro from DimensionalData to define this subtyping and as far as I can see this is not using eval for that.

The point of making it a subtype is, that in the analysis packages we can use Ti to select a time series no matter what the time axis is named in a dataset.

elseif haskey(aratts, "_ARRAYVALUES")
vals = identity.(aratts["_ARRAYVALUES"])
DD.rebuild(DD.name2dim(axname),(vals))
Expand Down
6 changes: 3 additions & 3 deletions test/Datasets/datasets.jl
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Expand Up @@ -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)
Expand Down Expand Up @@ -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
Expand Down
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