diff --git a/previews/PR479/404.html b/previews/PR479/404.html index ba775fd8..6d3ef179 100644 --- a/previews/PR479/404.html +++ b/previews/PR479/404.html @@ -9,7 +9,7 @@ - + @@ -19,7 +19,7 @@
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/cache.html b/previews/PR479/UserGuide/cache.html index 84387d9d..b406b808 100644 --- a/previews/PR479/UserGuide/cache.html +++ b/previews/PR479/UserGuide/cache.html @@ -9,9 +9,9 @@ - + - + @@ -26,7 +26,7 @@ cachesize = 500 #MB cache(ds,maxsize = cachesize)

The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays

julia
yax = ds.avariable
 cache(yax,maxsize = 1000)
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/chunk.html b/previews/PR479/UserGuide/chunk.html index 97b16607..361a93c8 100644 --- a/previews/PR479/UserGuide/chunk.html +++ b/previews/PR479/UserGuide/chunk.html @@ -9,9 +9,9 @@ - + - + @@ -119,7 +119,7 @@ Variables: x, y, z

Suggestions on how to improve or add to these examples is welcome.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/combine.html b/previews/PR479/UserGuide/combine.html index de0819cc..40e468ef 100644 --- a/previews/PR479/UserGuide/combine.html +++ b/previews/PR479/UserGuide/combine.html @@ -9,11 +9,11 @@ - + - + - + @@ -26,9 +26,8 @@ first_half = YAXArray((YAX.time(1:6),), rand(6)) second_half = YAXArray((YAX.time(7:12),), rand(6)) -whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
-│ 12-element YAXArray{Float64,1} │
-├────────────────────────────────┴──────────────────────────────── dims ┐
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
   ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
 ├───────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -41,9 +40,8 @@
 precipitation = YAXArray((YAX.time(1:6),), rand(6))
 cubes = [temperature,precipitation]
 var_axis = Variables(["temp", "prep"])
-combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
-│ 6×2 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────── dims ┐
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
   ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
   → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
 ├─────────────────────────────────────────────────────── metadata ┤
@@ -51,7 +49,7 @@
 ├────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
 └─────────────────────────────────────────────────────────────────┘
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/compute.html b/previews/PR479/UserGuide/compute.html index 50495e4b..69381fa8 100644 --- a/previews/PR479/UserGuide/compute.html +++ b/previews/PR479/UserGuide/compute.html @@ -9,11 +9,11 @@ - + - + - + @@ -32,9 +32,8 @@ ) data = rand(30, 10, 15) properties = Dict(:origin => "user guide") -a = YAXArray(axlist, data, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -43,9 +42,8 @@
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.062032476785460866
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.053923887690016103
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -57,9 +55,8 @@
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
-end
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -69,18 +66,16 @@
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

This keeps all dimensions unchanged. Note, that here we can not access neighboring elements. In this case, we can use mapslices or mapCube instead. Each element of the array is processed individually.

The code runs very fast, because map applies the function lazily. Actual computation will be performed only on demand, e.g. when elements were explicitly requested or further computations were performed.

mapslices ​

Reduce the time dimension by calculating the average value of all points in time:

julia
import Statistics: mean
-mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
-│ 10×15 YAXArray{Union{Missing, Float64},2} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 1.17 KB
-└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
-│ 30-element YAXArray{Union{Missing, Float64},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
┌ 30-element YAXArray{Union{Missing, Float64}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -90,26 +85,23 @@
 using YAXArrays: YAXArrays as YAX
 using Dates

Define function in space and time

julia
f(lo, la, t) = (lo + la + Dates.dayofyear(t))
f (generic function with 1 method)

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
-end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
╭──────────────────────────────╮
-│ 15-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 120.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
╭──────────────────────────────╮
-│ 10-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
-time_yax = YAXArray(YAX.time(tspan))
╭─────────────────────────────╮
-│ 30-element YAXArray{Date,1} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -120,9 +112,8 @@
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
@@ -151,9 +142,8 @@
            outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
@@ -194,9 +184,8 @@
 data = rand(1:5, 5, 4, 3, 2)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3×2 YAXArray{Int64,4} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -223,9 +212,8 @@
 outdims_two = OutDims("Time"; properties=properties_two)
 outdims_flat = OutDims(;) # it will get the default `layer` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
     indims = indims_one,
-    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
╭───────────────────────────────────────────╮
-│ 5×4×3×2 YAXArray{Union{Missing, Int64},4} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -236,9 +224,8 @@
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
-yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
-│ 4×3×2 YAXArray{Int64,3} │
-├─────────────────────────┴───────────────────────────────────────── dims ┐
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
   ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
@@ -271,9 +258,8 @@
 data = rand(3.0:5.0, 5, 4, 3)
 
 axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
-yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
╭───────────────────────────╮
-│ 4×3×7 YAXArray{Float64,3} │
-├───────────────────────────┴───────────────────────── dims ┐
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
   ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
@@ -288,9 +274,8 @@
     lon(1:4), lat(1:3),)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3 YAXArray{Float64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -310,9 +295,8 @@
     return nothing
 end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
     indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
-    outdims = OutDims("Time"))
╭───────────────────────────────────────────╮
-│ 5×4×3 YAXArray{Union{Missing, Float64},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -331,9 +315,8 @@
     lat(range(1, 5, length=15)),
 )
 data = rand(30, 10, 15)
-raster_arr = YAXArray(axlist, data)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -347,9 +330,8 @@
     10 <= lon < 15 && 1 <= lat < 5 && return "C"
     return "D"
 end
-regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
╭──────────────────────────╮
-│ 10×15 DimArray{String,2} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
@@ -363,9 +345,8 @@
   7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
- 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
-│ 10×15 DimArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -394,9 +375,8 @@
         # aggregate values of points in the current region at the current date
         xout[region_pos] = sum(view(xin, points))
     end
-end
╭──────────────────────────────────────────╮
-│ 4×30 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -433,7 +413,7 @@
 mapCube(mymean, a, indims=InDims("time"), outdims=OutDims())

In the last example, mapCube was used to map the mymean function. mapslices is a convenient function that can replace mapCube, where you can omit defining an extra function with the output argument as an input (e.g. mymean). It is possible to simply use mapslice

julia
mapslices(mean ∘ skipmissing, a, dims="time")

It is also possible to distribute easily the workload on a cluster, with little modification to the code. To do so, we use the ClusterManagers package.

julia
using Distributed
 using ClusterManagers
 addprocs(SlurmManager(10))
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/convert.html b/previews/PR479/UserGuide/convert.html index cbaba2b1..95538123 100644 --- a/previews/PR479/UserGuide/convert.html +++ b/previews/PR479/UserGuide/convert.html @@ -9,11 +9,11 @@ - + - + - + @@ -24,9 +24,8 @@
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Convert YAXArrays ​

This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.

WARNING

YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.

Convert Base.Array ​

Convert Base.Array to YAXArray:

julia
using YAXArrays
 
 m = rand(5,10)
-a = YAXArray(m)
╭──────────────────────────╮
-│ 5×10 YAXArray{Float64,2} │
-├──────────────────────────┴──────────────────────────────────── dims ┐
+a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -47,18 +46,16 @@
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
-a = yaxconvert(YAXArray, dim_arr)
╭─────────────────────────╮
-│ 5×6 YAXArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────── loaded in memory ┤
   data size: 240.0 bytes
-└──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
-│ 5×6 DimArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
@@ -70,7 +67,7 @@
  3     0.492817   0.718667    0.0702532   0.926096   0.225542   0.100622
  4     0.268675   0.0566881   0.916686    0.973332   0.744521   0.052264
  5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625

INFO

At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/create.html b/previews/PR479/UserGuide/create.html index 1e5584aa..8f2a83fd 100644 --- a/previews/PR479/UserGuide/create.html +++ b/previews/PR479/UserGuide/create.html @@ -9,11 +9,11 @@ - + - + - + @@ -24,9 +24,8 @@
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Create YAXArrays and Datasets ​

This section describes how to create arrays and datasets by filling values directly.

Create a YAXArray ​

We can create a new YAXArray by filling the values directly:

julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
-│ 10×20×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────── dims ┐
+a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -43,9 +42,8 @@
 )
 data2 = rand(30, 10, 15)
 properties = Dict(:origin => "user guide")
-a2 = YAXArray(axlist, data2, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -71,7 +69,7 @@
 a2, a3
 
 Properties: Dict(:origin => "user guide")
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/faq.html b/previews/PR479/UserGuide/faq.html index 86400016..782d56ad 100644 --- a/previews/PR479/UserGuide/faq.html +++ b/previews/PR479/UserGuide/faq.html @@ -9,11 +9,11 @@ - + - + - + @@ -22,9 +22,8 @@
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Frequently Asked Questions (FAQ) ​

The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.

Extract the axes names from a Cube ​

julia
using YAXArrays
-using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
-│ 10×10×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -39,27 +38,24 @@
 ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

INFO

Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.

rebuild ​

As an example let's consider the following

julia
using YAXArrays
 using DimensionalData
 
-c = YAXArray(ones(Int, 10,10))
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────── dims ┐
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────┘

then creating a new c with the same structure (axes) but different values is done by

julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────┘

then creating a new c with the same structure (axes) but different values is done by

julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘

note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -77,9 +73,8 @@
   7
   8
   9
- 10

to collect data from a cube works exactly the same as doing it from an array

julia
julia> c[:, :, 1]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+ 10

to collect data from a cube works exactly the same as doing it from an array

julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -99,9 +94,8 @@
 ds1 = YAXArray(axlist, data1)
 
 data2 = rand(20, 10, 15)
-ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
@@ -114,9 +108,8 @@
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. `a .. b`.

Define the time span of the YAXArray

julia
t = Date("2020-01-01"):Month(1):Date("2022-12-31")
Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")

create YAXArray axes

julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
 → Lat  1:10,
-↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
-│ 10×10×36 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -124,9 +117,8 @@
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 28.12 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
-│ 10×10×12 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
┌ 10×10×12 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -134,18 +126,16 @@
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

julia
ytime2 = y[Time=At(Date("2021-05-01"))]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────── dims ┐
   ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
-│ 10×10×8 YAXArray{Int64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
┌ 10×10×8 YAXArray{Int64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -153,9 +143,8 @@
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 6.25 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
-│ 5×6×36 YAXArray{Int64,3} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
┌ 5×6×36 YAXArray{Int64, 3} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -221,7 +210,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -232,7 +221,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes

If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes

If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -255,9 +244,8 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes

How do I apply map algebra? ​

Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  longitudes

How do I apply map algebra? ​

Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -265,9 +253,8 @@
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -277,9 +264,8 @@
   data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

To add some complexity, we will multiply each value for π and then divided for the sum of each time step. We will use the ds1 cube for this purpose.

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
-       end
╭──────────────────────────────────────────────╮
-│ 10×15×20 YAXArray{Union{Missing, Float64},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
   ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
@@ -287,9 +273,8 @@
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function? ​

The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.

Here we will use the ds1 Cube defined previously and we create a mask for data classification.

Cube containing a mask with classes 1, 2 and 3.

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
-│ 10×15 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function? ​

The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.

Here we will use the ds1 Cube defined previously and we create a mask for data classification.

Cube containing a mask with classes 1, 2 and 3.

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -306,17 +291,15 @@
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
-first(c_tbl, 5)

In this line we calculate the Mean for each class

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
-│ 3-element YAXArray{Union{Missing, Float64},1} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+first(c_tbl, 5)

In this line we calculate the Mean for each class

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 24.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘

We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
-│ 3×20 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
   → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -334,15 +317,15 @@
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
   Variables: 
-  c
+  a
 
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
-  a
+  c
 
   Additional Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -365,7 +348,7 @@
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
@@ -377,16 +360,15 @@
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1

and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others

julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];

and voilà

julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock3

and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others

julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];

and voilà

julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
 
 Variables: 
 Stock1, Stock2, Stock3

now they are printed together, showing that is exactly the same axis structure for all variables.

Create a YAXArray with unions containing Strings ​

julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
-yax_string = YAXArray(test_x)
╭──────────────────────────────────────╮
-│ 2×2 YAXArray{Union{Int64, String},2} │
-├──────────────────────────────────────┴──────────────────────── dims ┐
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -394,9 +376,8 @@
 ├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
-yax_bool = YAXArray(test_bool)
╭─────────────────────╮
-│ 3×3 YAXArray{Any,2} │
-├─────────────────────┴───────────────────────────────────────── dims ┐
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -404,7 +385,7 @@
 ├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 172.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

WARNING

Note that although their creation is allowed, it is not possible to save these types into Zarr or NetCDF.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/group.html b/previews/PR479/UserGuide/group.html index 844d8a69..6dc3742f 100644 --- a/previews/PR479/UserGuide/group.html +++ b/previews/PR479/UserGuide/group.html @@ -9,11 +9,11 @@ - + - + - + @@ -28,12 +28,40 @@ using Dates using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
 filename = Downloads.download(url_path, "rasm.nc")
-ds_o = Cube(filename)

WARNING

The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642

julia
axs = dims(ds_o) # get the dimensions
-data = ds_o.data[:,:,:] # read the data
-_FillValue = ds_o.properties["_FillValue"]
-data = replace(data, _FillValue => NaN)
-# create new YAXArray
-ds = YAXArray(axs, data)

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

WARNING

The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642

julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
     # calculate weights 
     tempo = dims(ds, :time)
     month_length = YAXArray((tempo,), daysinmonth.(tempo))
@@ -52,8 +80,7 @@
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end

Now, we continue with the groupby operations as usual

julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
╭──────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+end

Now, we continue with the groupby operations as usual

julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -65,8 +92,7 @@
  :Dec_Jan_Feb  275×205×9 YAXArray
  :Mar_Apr_May  275×205×9 YAXArray
  :Jun_Jul_Aug  275×205×9 YAXArray
- :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  275×205×9 YAXArray

Note how we are referencing the `time` dimension via `YAX.time`. This approach is used to avoid name clashes with `time` (`Time`) from `Base` (`Dates`). For convenience, we have defined the `Dimensions` `time` and `Time` in `YAXArrays.jl`, which are only accessible when explicitly called.

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -76,8 +102,7 @@
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -93,16 +118,14 @@
  :Mar_Apr_May
  :Jun_Jul_Aug
  :Sep_Oct_Nov

Next, we will weight this grouping by days/month in each group.

GroupBy: weight ​

Create a YAXArray for the month length

julia
tempo = dims(ds, :time)
-month_length = YAXArray((tempo,), daysinmonth.(tempo))
╭──────────────────────────────╮
-│ 36-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 288.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
╭────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Int64,0},1} │
+└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -114,8 +137,7 @@
  :Dec_Jan_Feb  9-element YAXArray
  :Mar_Apr_May  9-element YAXArray
  :Jun_Jul_Aug  9-element YAXArray
- :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Int64, 1, Vector{Int64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -125,8 +147,7 @@
  :Dec_Jan_Feb  [270]
  :Mar_Apr_May  [276]
  :Jun_Jul_Aug  [276]
- :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} groupby │
+ :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -136,9 +157,8 @@
  :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
  :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
  :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
- :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
╭───────────────────────────────╮
-│ 4-element DimArray{Float64,1} │
-├───────────────────────────────┴──────────────────────────────────────── dims ┐
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, Any} with 1 entry:
@@ -147,8 +167,7 @@
  :Dec_Jan_Feb  1.0
  :Mar_Apr_May  1.0
  :Jun_Jul_Aug  1.0
- :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -160,8 +179,7 @@
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
  :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :time);
 
-julia> weighted_g = dropdims.(weighted_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -171,8 +189,7 @@
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -212,7 +229,7 @@
     rowgap!(fig.layout, 5)
     fig
 end

which shows a good agreement with the results first published by Joe Hamman.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/read.html b/previews/PR479/UserGuide/read.html index c455293e..b7604066 100644 --- a/previews/PR479/UserGuide/read.html +++ b/previews/PR479/UserGuide/read.html @@ -9,11 +9,11 @@ - + - + - + @@ -39,9 +39,8 @@ Variables: tas -Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)

We can set path to a URL, a local directory, or in this case to a cloud object storage path.

A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tas
╭────────────────────────────────────╮
-│ 384×192×251288 YAXArray{Float32,3} │
-├────────────────────────────────────┴─────────────────────────────────── dims ┐
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)

We can set path to a URL, a local directory, or in this case to a cloud object storage path.

A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
   ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
@@ -73,9 +72,8 @@
 Variables: 
 tos
 
-Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -118,9 +116,8 @@
 Variables: 
 Blue, Green, Red
 
-Properties: Dict{String, Any}("projection" => "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")

Load data into memory ​

For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.

readcubedata ​

julia
readcubedata(ds.tos)
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("projection" => "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")

Load data into memory ​

For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.

readcubedata ​

julia
readcubedata(ds.tos)
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -139,7 +136,7 @@
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Note how the loading status changes from loaded lazily to loaded in memory.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/select.html b/previews/PR479/UserGuide/select.html index c06397ea..a80650f4 100644 --- a/previews/PR479/UserGuide/select.html +++ b/previews/PR479/UserGuide/select.html @@ -9,11 +9,11 @@ - + - + - + @@ -35,9 +35,8 @@ Variables: tos -Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

julia
tos = ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -55,9 +54,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -75,9 +73,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘

Select elements ​

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Select elements ​

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -93,9 +90,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -113,9 +109,8 @@
   data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
-tos[time = At(time1)]
╭─────────────────────────────────────────────╮
-│ 180×170 YAXArray{Union{Missing, Float32},2} │
-├─────────────────────────────────────────────┴───────────────── dims ┐
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────────────────┴ metadata ┐
@@ -132,9 +127,8 @@
   "original_name"  => "sosstsst"
 ├───────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 119.53 KB
-└────────────────────────────────────────────────────────────────────────────────┘

Select ranges ​

Here we subset an interval of a dimension using positional integer indexing.

julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└────────────────────────────────────────────────────────────────────────────────┘

Select ranges ​

Here we subset an interval of a dimension using positional integer indexing.

julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -152,9 +146,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -172,9 +165,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Read more about the At selector in the package DimensionalData. Get values within a tolerances:

julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
-│ 10×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Read more about the At selector in the package DimensionalData. Get values within a tolerances:

julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -192,9 +184,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 159.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals ​

Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:

julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals ​

Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:

julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -212,9 +203,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘

This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -232,9 +222,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -252,9 +241,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -272,9 +260,8 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -292,30 +279,25 @@
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension ​

Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:

julia
collect(tos.lat)
170-element Vector{Float64}:
- -79.5
- -78.5
- -77.5
- -76.5
- -75.5
- -74.5
- -73.5
- -72.5
- -71.5
- -70.5
-   ⋮
-  81.5
-  82.5
-  83.5
-  84.5
-  85.5
-  86.5
-  87.5
-  88.5
-  89.5

These values are defined as lookups in the package DimensionalData:

julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+└──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension ​

Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:

julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  89.5   89.5

These values are defined as lookups in the package DimensionalData:

julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0

which is equivalent to:

julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/types.html b/previews/PR479/UserGuide/types.html index 0b2cf7f2..e4199c6c 100644 --- a/previews/PR479/UserGuide/types.html +++ b/previews/PR479/UserGuide/types.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Types ​

This section describes the data structures used to work with n-dimensional arrays in YAXArrays.

YAXArray ​

An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.

A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.

A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.

Dataset ​

A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.

(Data) Cube ​

A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020

Dimension ​

A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.

- + \ No newline at end of file diff --git a/previews/PR479/UserGuide/write.html b/previews/PR479/UserGuide/write.html index 8e85e230..8a736c2b 100644 --- a/previews/PR479/UserGuide/write.html +++ b/previews/PR479/UserGuide/write.html @@ -9,11 +9,11 @@ - + - + - + @@ -43,7 +43,7 @@ savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

julia
savedataset(ds, path="ds.nc", driver=:netcdf)

netcdf compression ​

Save a dataset to NetCDF format with compression:

julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)

Comparing it to the default saved file

julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes

Overwrite a Dataset ​

If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset

julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)

DANGER

Again, setting overwrite will delete all your previous saved data.

Look at the doc string for more information

YAXArrays.Datasets.savedataset Function

savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)

Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.

Warning

overwrite = true, deletes ALL your data and it will create a new file.

source

Append to a Dataset ​

New variables can be added to an existing dataset using the append=true keyword.

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes

Overwrite a Dataset ​

If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset

julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)

DANGER

Again, setting overwrite will delete all your previous saved data.

Look at the doc string for more information

YAXArrays.Datasets.savedataset Function
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)

Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.

Warning

overwrite = true, deletes ALL your data and it will create a new file.

source

Append to a Dataset ​

New variables can be added to an existing dataset using the append=true keyword.

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -62,9 +62,8 @@
   Variables: 
   z
 
-Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
╭───────────────────────────────────────────╮
-│ 5×4×5 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -93,7 +92,7 @@
  0.891257  0.0887544  0.630526  0.782494
  0.948244  0.195437   0.102333  0.669125
  0.102816  0.781572   0.527401  0.719692

indeed, those entries had been updated.

- + \ No newline at end of file diff --git a/previews/PR479/api.html b/previews/PR479/api.html index 30902db9..469a5f5e 100644 --- a/previews/PR479/api.html +++ b/previews/PR479/api.html @@ -9,11 +9,11 @@ - + - + - + @@ -21,22 +21,15 @@ -
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

API Reference ​

This section describes all available functions of this package.

Public API ​

YAXArrays.getAxis Method
julia
getAxis(desc, c)

Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source

YAXArrays.Cubes Module

The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that

source

YAXArrays.Cubes.YAXArray Type
julia
YAXArray{T,N}

An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.

Fields

  • axes: Tuple of Dimensions containing the Axes of the Cube

  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope

source

YAXArrays.Cubes.caxes Function

Returns the axes of a Cube

source

YAXArrays.Cubes.caxes Method
julia
caxes

Embeds Cube inside a new Cube

source

YAXArrays.Cubes.concatenatecubes Method
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)

Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis

source

YAXArrays.Cubes.readcubedata Method
julia
readcubedata(cube)

Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.

source

YAXArrays.Cubes.setchunks Method
julia
setchunks(c::YAXArray,chunks)

Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes

source

YAXArrays.Cubes.subsetcube Function

This function calculates a subset of a cube's data

source

YAXArrays.DAT.InDims Type
julia
InDims(axisdesc...;...)

Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.

Keyword arguments

  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray

  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.

  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas

source

YAXArrays.DAT.MovingWindow Type
julia
MovingWindow(desc, pre, after)

Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.

For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.

source

YAXArrays.DAT.OutDims Method
julia
OutDims(axisdesc;...)

Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.

  • axisdesc: List of input axis names

  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist

  • update : specifies wether the function operates inplace or if an output is returned

  • artype : specifies the Array type inside the inner function that is mapped over

  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions

  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used

source

YAXArrays.DAT.CubeTable Method
julia
CubeTable()

Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.

source

YAXArrays.DAT.cubefittable Method
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)

Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.

source

YAXArrays.DAT.fittable Method
julia
fittable(tab,o,fitsym;by=(),weight=nothing)

Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.

For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:

julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))

source

YAXArrays.DAT.mapCube Method
julia
mapCube(fun, cube, addargs...;kwargs...)
-
-Map a given function `fun` over slices of all cubes of the dataset `ds`. 
-Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
-For Datasets, only one output cube can be specified.
-In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
-
-For the specific keyword arguments see the docstring of the mapCube function for cubes.

source

YAXArrays.DAT.mapCube Method
julia
mapCube(fun, cube, addargs...;kwargs...)

Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.

Keyword arguments

  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.

  • indims::InDims List of input cube descriptors of type InDims for each input data cube.

  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.

  • inplace does the function write to an output array inplace or return a single value> defaults to true

  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.

  • showprog boolean indicating if a ProgressMeter shall be shown

  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments

  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.

  • loopchunksize determines the chunk sizes of variables which are looped over, a dict

  • kwargs additional keyword arguments are passed to the inner function

The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.

source

YAXArrays.Datasets.Dataset Type
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
API Reference ​
This section describes all available functions of this package.
Public API ​
YAXArrays.getAxis Method
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
YAXArrays.Cubes Module
The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that
source
YAXArrays.Cubes.YAXArray Type
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
YAXArrays.Cubes.caxes Function
Returns the axes of a Cube
source
YAXArrays.Cubes.caxes Method
julia
caxes
Embeds Cube inside a new Cube
source
YAXArrays.Cubes.concatenatecubes Method
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
YAXArrays.Cubes.readcubedata Method
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
YAXArrays.Cubes.setchunks Method
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
YAXArrays.Cubes.subsetcube Function
This function calculates a subset of a cube's data
source
YAXArrays.DAT.InDims Type
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
YAXArrays.DAT.MovingWindow Type
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
YAXArrays.DAT.OutDims Method
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
YAXArrays.DAT.CubeTable Method
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
YAXArrays.DAT.cubefittable Method
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
YAXArrays.DAT.fittable Method
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
YAXArrays.DAT.mapCube Method
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
YAXArrays.DAT.mapCube Method
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
YAXArrays.Datasets.Dataset Type
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.
 a dictionary of CubeAxes and a Dictionary of general properties.
-A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
YAXArrays.Datasets.Dataset Method
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
YAXArrays.Datasets.Cube Method
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
YAXArrays.Datasets.open_dataset Method
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source
YAXArrays.Datasets.open_mfdataset Method
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
YAXArrays.Datasets.Dataset Method
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
YAXArrays.Datasets.Cube Method
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
YAXArrays.Datasets.open_dataset Method
julia
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source
YAXArrays.Datasets.open_mfdataset Method
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
 open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
YAXArrays.Datasets.savecube Method
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
YAXArrays.Datasets.savedataset Method
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
YAXArrays.Datasets.to_dataset Method
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source
Internal API ​
YAXArrays.YAXDefaults Constant
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
YAXArrays.findAxis Method
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
YAXArrays.getOutAxis Method
julia
getOutAxis
source
YAXArrays.get_descriptor Method
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
YAXArrays.match_axis Method
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
YAXArrays.Datasets.savecube Method
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
YAXArrays.Datasets.savedataset Method
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
YAXArrays.Datasets.to_dataset Method
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source
Internal API ​
YAXArrays.YAXDefaults Constant
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
YAXArrays.findAxis Method
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
YAXArrays.getOutAxis Method
julia
getOutAxis
source
YAXArrays.get_descriptor Method
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
YAXArrays.match_axis Method
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
YAXArrays.Cubes.CleanMe Type
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
YAXArrays.Cubes.clean Method
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
YAXArrays.Cubes.copydata Method
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
YAXArrays.Cubes.optifunc Method
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
YAXArrays.DAT.DATConfig Type
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
YAXArrays.DAT.InputCube Type
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
YAXArrays.DAT.OutputCube Type
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
YAXArrays.DAT.YAXColumn Type
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
YAXArrays.DAT.cmpcachmisses Method
Function that compares two cache miss specifiers by their importance
source
YAXArrays.DAT.getFrontPerm Method
Calculate an axis permutation that brings the wanted dimensions to the front
source
YAXArrays.DAT.getLoopCacheSize Method
Calculate optimal Cache size to DAT operation
source
YAXArrays.DAT.getOuttype Method
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
YAXArrays.DAT.getloopchunks Method
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
-This computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
YAXArrays.Cubes.setchunks Method
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
YAXArrays.Datasets.collectfromhandle Method
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source
YAXArrays.Datasets.createdataset Method
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
YAXArrays.Datasets.getarrayinfo Method
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source
YAXArrays.Datasets.testrange Method
Test if data in x can be approximated by a step range
source

-    
+For example to disregard differences of captialisation.

source

YAXArrays.Cubes.CleanMe Type
julia
mutable struct CleanMe

Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step

source

YAXArrays.Cubes.clean Method
julia
clean(c::CleanMe)

finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.

source

YAXArrays.Cubes.copydata Method
julia
copydata(outar, inar, copybuf)

Internal function which copies the data from the input inar into the output outar at the copybuf positions.

source

YAXArrays.Cubes.optifunc Method
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)

Internal

This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.

source

YAXArrays.DAT.DATConfig Type

Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:

  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes

  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes

  • allInAxes::Vector: List of all axes of the input cubes

  • LoopAxes::Vector: List of axes that are looped through

  • ispar::Bool: Flag whether the computation is parallelized

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

  • max_cache::Any: Maximal size of the in memory cache

  • fu::Any: Inner function which is computed

  • inplace::Bool: Flag whether the computation happens in place

  • include_loopvars::Bool:

  • ntr::Any:

  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9

  • addargs::Any: Additional arguments for the inner function

  • kwargs::Any: Additional keyword arguments for the inner function

source

YAXArrays.DAT.InputCube Type

Internal representation of an input cube for DAT operations

  • cube: The input data

  • desc: The input description given by the user/registration

  • axesSmall: List of axes that were actually selected through the description

  • icolon

  • colonperm

  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts

  • cachesize: Number of elements to keep in cache along each axis

  • window

  • iwindow

  • windowloopinds

  • iall

source

YAXArrays.DAT.OutputCube Type

Internal representation of an output cube for DAT operations

Fields

  • cube: The actual outcube cube, once it is generated

  • cube_unpermuted: The unpermuted output cube

  • desc: The description of the output axes as given by users or registration

  • axesSmall: The list of output axes determined through the description

  • allAxes: List of all the axes of the cube

  • loopinds: Index of the loop axes that are broadcasted for this output cube

  • innerchunks

  • outtype: Elementtype of the outputcube

source

YAXArrays.DAT.YAXColumn Type
julia
YAXColumn

A struct representing a single column of a YAXArray partitioned Table # Fields

  • inarBC

  • inds

source

YAXArrays.DAT.cmpcachmisses Method

Function that compares two cache miss specifiers by their importance

source

YAXArrays.DAT.getFrontPerm Method

Calculate an axis permutation that brings the wanted dimensions to the front

source

YAXArrays.DAT.getLoopCacheSize Method

Calculate optimal Cache size to DAT operation

source

YAXArrays.DAT.getOuttype Method
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

YAXArrays.DAT.getloopchunks Method
julia
getloopchunks(dc::DATConfig)

Internal function

Returns the chunks that can be looped over toghether for all dimensions.
+This computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)

source

YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)

Internal function

Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.

source

YAXArrays.Cubes.setchunks Method
julia
setchunks(c::Dataset,chunks)

Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:

  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks

  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks

  • a description of the desired variable chunks applied to all members of the Dataset

where a description of the desired variable chunks can take one of the following forms:

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes

source

YAXArrays.Datasets.collectfromhandle Method

Extracts a YAXArray from a dataset handle that was just created from a arrayinfo

source

YAXArrays.Datasets.createdataset Method
julia
function createdataset(DS::Type,axlist; kwargs...)

Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.

Keyword arguments

  • path="" location where the new cube is stored

  • T=Union{Float32,Missing} data type of the target cube

  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array

  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks

  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?

  • overwrite::Bool=false overwrite cube if it already exists

  • properties=Dict{String,Any}() additional cube properties

  • globalproperties=Dict{String,Any} global attributes to be added to the dataset

  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value

  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays

  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found

source

YAXArrays.Datasets.getarrayinfo Method

Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair

source

YAXArrays.Datasets.testrange Method

Test if data in x can be approximated by a step range

source

+ \ No newline at end of file diff --git a/previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.js b/previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.js similarity index 94% rename from previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.js rename to previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.js index 796b2629..d9450c3b 100644 --- a/previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.js +++ b/previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.js @@ -1,11 +1,10 @@ -import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`

Combine YAXArrays ​

Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.

cat along an existing dimension ​

Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.

julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function h(l,s,p,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
 first_half = YAXArray((YAX.time(1:6),), rand(6))
 second_half = YAXArray((YAX.time(7:12),), rand(6))
-whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
-│ 12-element YAXArray{Float64,1} │
-├────────────────────────────────┴──────────────────────────────── dims ┐
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
   ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
 ├───────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -18,13 +17,12 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E
 precipitation = YAXArray((YAX.time(1:6),), rand(6))
 cubes = [temperature,precipitation]
 var_axis = Variables(["temp", "prep"])
-combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
-│ 6×2 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────── dims ┐
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
   ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
   → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
 ├─────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
-└─────────────────────────────────────────────────────────────────┘
`,10)]))}const c=i(t,[["render",p]]);export{E as __pageData,c as default};
+└─────────────────────────────────────────────────────────────────┘
`,10)]))}const g=i(t,[["render",h]]);export{E as __pageData,g as default}; diff --git a/previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.lean.js b/previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.lean.js similarity index 94% rename from previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.lean.js rename to previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.lean.js index 796b2629..d9450c3b 100644 --- a/previews/PR479/assets/UserGuide_combine.md.B3kKJwRR.lean.js +++ b/previews/PR479/assets/UserGuide_combine.md.BYUU3tRz.lean.js @@ -1,11 +1,10 @@ -import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`

Combine YAXArrays ​

Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.

cat along an existing dimension ​

Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.

julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function h(l,s,p,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
 first_half = YAXArray((YAX.time(1:6),), rand(6))
 second_half = YAXArray((YAX.time(7:12),), rand(6))
-whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
-│ 12-element YAXArray{Float64,1} │
-├────────────────────────────────┴──────────────────────────────── dims ┐
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
   ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
 ├───────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -18,13 +17,12 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const E
 precipitation = YAXArray((YAX.time(1:6),), rand(6))
 cubes = [temperature,precipitation]
 var_axis = Variables(["temp", "prep"])
-combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
-│ 6×2 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────── dims ┐
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
   ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
   → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
 ├─────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
-└─────────────────────────────────────────────────────────────────┘
`,10)]))}const c=i(t,[["render",p]]);export{E as __pageData,c as default};
+└─────────────────────────────────────────────────────────────────┘
`,10)]))}const g=i(t,[["render",h]]);export{E as __pageData,g as default}; diff --git a/previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.js b/previews/PR479/assets/UserGuide_compute.md.CEww8r0_.js similarity index 95% rename from previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.js rename to previews/PR479/assets/UserGuide_compute.md.CEww8r0_.js index b6407648..9a292e9e 100644 --- a/previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.js +++ b/previews/PR479/assets/UserGuide_compute.md.CEww8r0_.js @@ -9,9 +9,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g ) data = rand(30, 10, 15) properties = Dict(:origin => "user guide") -a = YAXArray(axlist, data, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -20,9 +19,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.062032476785460866
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.053923887690016103
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -34,9 +32,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
-end
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -46,18 +43,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

This keeps all dimensions unchanged. Note, that here we can not access neighboring elements. In this case, we can use mapslices or mapCube instead. Each element of the array is processed individually.

The code runs very fast, because map applies the function lazily. Actual computation will be performed only on demand, e.g. when elements were explicitly requested or further computations were performed.

mapslices ​

Reduce the time dimension by calculating the average value of all points in time:

julia
import Statistics: mean
-mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
-│ 10×15 YAXArray{Union{Missing, Float64},2} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 1.17 KB
-└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
-│ 30-element YAXArray{Union{Missing, Float64},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
┌ 30-element YAXArray{Union{Missing, Float64}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -67,26 +62,23 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 using YAXArrays: YAXArrays as YAX
 using Dates

Define function in space and time

julia
f(lo, la, t) = (lo + la + Dates.dayofyear(t))
f (generic function with 1 method)

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
-end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
╭──────────────────────────────╮
-│ 15-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 120.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
╭──────────────────────────────╮
-│ 10-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
-time_yax = YAXArray(YAX.time(tspan))
╭─────────────────────────────╮
-│ 30-element YAXArray{Date,1} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -97,9 +89,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
@@ -128,9 +119,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
            outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
@@ -171,9 +161,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 data = rand(1:5, 5, 4, 3, 2)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3×2 YAXArray{Int64,4} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -200,9 +189,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 outdims_two = OutDims("Time"; properties=properties_two)
 outdims_flat = OutDims(;) # it will get the default \`layer\` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
     indims = indims_one,
-    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
╭───────────────────────────────────────────╮
-│ 5×4×3×2 YAXArray{Union{Missing, Int64},4} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -213,9 +201,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
-yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
-│ 4×3×2 YAXArray{Int64,3} │
-├─────────────────────────┴───────────────────────────────────────── dims ┐
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
   ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
@@ -248,9 +235,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 data = rand(3.0:5.0, 5, 4, 3)
 
 axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
-yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
╭───────────────────────────╮
-│ 4×3×7 YAXArray{Float64,3} │
-├───────────────────────────┴───────────────────────── dims ┐
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
   ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
@@ -265,9 +251,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     lon(1:4), lat(1:3),)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3 YAXArray{Float64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -287,9 +272,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     return nothing
 end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
     indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
-    outdims = OutDims("Time"))
╭───────────────────────────────────────────╮
-│ 5×4×3 YAXArray{Union{Missing, Float64},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -308,9 +292,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     lat(range(1, 5, length=15)),
 )
 data = rand(30, 10, 15)
-raster_arr = YAXArray(axlist, data)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -324,9 +307,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     10 <= lon < 15 && 1 <= lat < 5 && return "C"
     return "D"
 end
-regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
╭──────────────────────────╮
-│ 10×15 DimArray{String,2} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
@@ -340,9 +322,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
   7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
- 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
-│ 10×15 DimArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -371,9 +352,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
         # aggregate values of points in the current region at the current date
         xout[region_pos] = sum(view(xin, points))
     end
-end
╭──────────────────────────────────────────╮
-│ 4×30 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
diff --git a/previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.lean.js b/previews/PR479/assets/UserGuide_compute.md.CEww8r0_.lean.js
similarity index 95%
rename from previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.lean.js
rename to previews/PR479/assets/UserGuide_compute.md.CEww8r0_.lean.js
index b6407648..9a292e9e 100644
--- a/previews/PR479/assets/UserGuide_compute.md.GnwBFM_7.lean.js
+++ b/previews/PR479/assets/UserGuide_compute.md.CEww8r0_.lean.js
@@ -9,9 +9,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 )
 data = rand(30, 10, 15)
 properties = Dict(:origin => "user guide")
-a = YAXArray(axlist, data, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -20,9 +19,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.062032476785460866
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.053923887690016103
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -34,9 +32,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
-end
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -46,18 +43,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

This keeps all dimensions unchanged. Note, that here we can not access neighboring elements. In this case, we can use mapslices or mapCube instead. Each element of the array is processed individually.

The code runs very fast, because map applies the function lazily. Actual computation will be performed only on demand, e.g. when elements were explicitly requested or further computations were performed.

mapslices ​

Reduce the time dimension by calculating the average value of all points in time:

julia
import Statistics: mean
-mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
-│ 10×15 YAXArray{Union{Missing, Float64},2} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 1.17 KB
-└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
-│ 30-element YAXArray{Union{Missing, Float64},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘

There is no time dimension left, because there is only one value left after averaging all time steps. We can also calculate spatial means resulting in one value per time step:

julia
mapslices(mean, a, dims=("lat", "lon"))
┌ 30-element YAXArray{Union{Missing, Float64}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -67,26 +62,23 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 using YAXArrays: YAXArrays as YAX
 using Dates

Define function in space and time

julia
f(lo, la, t) = (lo + la + Dates.dayofyear(t))
f (generic function with 1 method)

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
-end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
╭──────────────────────────────╮
-│ 15-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 120.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
╭──────────────────────────────╮
-│ 10-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
-time_yax = YAXArray(YAX.time(tspan))
╭─────────────────────────────╮
-│ 30-element YAXArray{Date,1} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -97,9 +89,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
@@ -128,9 +119,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
            outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
            outtype = Float32)
            # max_cache=1e9
-       )
╭──────────────────────────────────────────────╮
-│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
   → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
@@ -171,9 +161,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 data = rand(1:5, 5, 4, 3, 2)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3×2 YAXArray{Int64,4} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -200,9 +189,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 outdims_two = OutDims("Time"; properties=properties_two)
 outdims_flat = OutDims(;) # it will get the default \`layer\` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
     indims = indims_one,
-    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
╭───────────────────────────────────────────╮
-│ 5×4×3×2 YAXArray{Union{Missing, Int64},4} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
@@ -213,9 +201,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
-yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
-│ 4×3×2 YAXArray{Int64,3} │
-├─────────────────────────┴───────────────────────────────────────── dims ┐
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
   → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
   ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
@@ -248,9 +235,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 data = rand(3.0:5.0, 5, 4, 3)
 
 axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
-yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
╭───────────────────────────╮
-│ 4×3×7 YAXArray{Float64,3} │
-├───────────────────────────┴───────────────────────── dims ┐
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
   ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
@@ -265,9 +251,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     lon(1:4), lat(1:3),)
 
 properties = Dict("description" => "multi dimensional test cube")
-yax_test = YAXArray(axlist, data, properties)
╭───────────────────────────╮
-│ 5×4×3 YAXArray{Float64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -287,9 +272,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     return nothing
 end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
     indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
-    outdims = OutDims("Time"))
╭───────────────────────────────────────────╮
-│ 5×4×3 YAXArray{Union{Missing, Float64},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
   → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
@@ -308,9 +292,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     lat(range(1, 5, length=15)),
 )
 data = rand(30, 10, 15)
-raster_arr = YAXArray(axlist, data)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -324,9 +307,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
     10 <= lon < 15 && 1 <= lat < 5 && return "C"
     return "D"
 end
-regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
╭──────────────────────────╮
-│ 10×15 DimArray{String,2} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────────────────────────┘
@@ -340,9 +322,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
   7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
   9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
- 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
-│ 10×15 DimArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -371,9 +352,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
         # aggregate values of points in the current region at the current date
         xout[region_pos] = sum(view(xin, points))
     end
-end
╭──────────────────────────────────────────╮
-│ 4×30 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
diff --git a/previews/PR479/assets/UserGuide_convert.md.CObFCPzI.js b/previews/PR479/assets/UserGuide_convert.md.mv4euI22.js
similarity index 95%
rename from previews/PR479/assets/UserGuide_convert.md.CObFCPzI.js
rename to previews/PR479/assets/UserGuide_convert.md.mv4euI22.js
index 304bd862..564bc7af 100644
--- a/previews/PR479/assets/UserGuide_convert.md.CObFCPzI.js
+++ b/previews/PR479/assets/UserGuide_convert.md.mv4euI22.js
@@ -1,9 +1,8 @@
-import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"};function t(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),t={name:"UserGuide/convert.md"};function p(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
-a = YAXArray(m)
╭──────────────────────────╮
-│ 5×10 YAXArray{Float64,2} │
-├──────────────────────────┴──────────────────────────────────── dims ┐
+a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -24,18 +23,16 @@ import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
-a = yaxconvert(YAXArray, dim_arr)
╭─────────────────────────╮
-│ 5×6 YAXArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────── loaded in memory ┤
   data size: 240.0 bytes
-└──────────────────────────────────────────────────────────────────┘
Convert YAXArray to DimArray:
julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
-│ 5×6 DimArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────┘
Convert YAXArray to DimArray:
julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
@@ -46,4 +43,4 @@ import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c
  2     0.203714   0.770949    0.731779    0.71314    0.687891   0.435994
  3     0.492817   0.718667    0.0702532   0.926096   0.225542   0.100622
  4     0.268675   0.0566881   0.916686    0.973332   0.744521   0.052264
- 5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,23)]))}const g=a(p,[["render",t]]);export{c as __pageData,g as default};
+ 5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,23)]))}const g=a(t,[["render",p]]);export{c as __pageData,g as default};
diff --git a/previews/PR479/assets/UserGuide_convert.md.CObFCPzI.lean.js b/previews/PR479/assets/UserGuide_convert.md.mv4euI22.lean.js
similarity index 95%
rename from previews/PR479/assets/UserGuide_convert.md.CObFCPzI.lean.js
rename to previews/PR479/assets/UserGuide_convert.md.mv4euI22.lean.js
index 304bd862..564bc7af 100644
--- a/previews/PR479/assets/UserGuide_convert.md.CObFCPzI.lean.js
+++ b/previews/PR479/assets/UserGuide_convert.md.mv4euI22.lean.js
@@ -1,9 +1,8 @@
-import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"};function t(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),t={name:"UserGuide/convert.md"};function p(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
-a = YAXArray(m)
╭──────────────────────────╮
-│ 5×10 YAXArray{Float64,2} │
-├──────────────────────────┴──────────────────────────────────── dims ┐
+a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -24,18 +23,16 @@ import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
-a = yaxconvert(YAXArray, dim_arr)
╭─────────────────────────╮
-│ 5×6 YAXArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────── loaded in memory ┤
   data size: 240.0 bytes
-└──────────────────────────────────────────────────────────────────┘
Convert YAXArray to DimArray:
julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
-│ 5×6 DimArray{Float64,2} │
-├─────────────────────────┴────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────┘
Convert YAXArray to DimArray:
julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
   ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
@@ -46,4 +43,4 @@ import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const c
  2     0.203714   0.770949    0.731779    0.71314    0.687891   0.435994
  3     0.492817   0.718667    0.0702532   0.926096   0.225542   0.100622
  4     0.268675   0.0566881   0.916686    0.973332   0.744521   0.052264
- 5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,23)]))}const g=a(p,[["render",t]]);export{c as __pageData,g as default};
+ 5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,23)]))}const g=a(t,[["render",p]]);export{c as __pageData,g as default};
diff --git a/previews/PR479/assets/UserGuide_create.md.C7ebbtn2.js b/previews/PR479/assets/UserGuide_create.md.ClN01XDL.js
similarity index 96%
rename from previews/PR479/assets/UserGuide_create.md.C7ebbtn2.js
rename to previews/PR479/assets/UserGuide_create.md.ClN01XDL.js
index 304d7f74..cffa1253 100644
--- a/previews/PR479/assets/UserGuide_create.md.C7ebbtn2.js
+++ b/previews/PR479/assets/UserGuide_create.md.ClN01XDL.js
@@ -1,9 +1,8 @@
 import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
-│ 10×20×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────── dims ┐
+a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -20,9 +19,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 )
 data2 = rand(30, 10, 15)
 properties = Dict(:origin => "user guide")
-a2 = YAXArray(axlist, data2, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
diff --git a/previews/PR479/assets/UserGuide_create.md.C7ebbtn2.lean.js b/previews/PR479/assets/UserGuide_create.md.ClN01XDL.lean.js
similarity index 96%
rename from previews/PR479/assets/UserGuide_create.md.C7ebbtn2.lean.js
rename to previews/PR479/assets/UserGuide_create.md.ClN01XDL.lean.js
index 304d7f74..cffa1253 100644
--- a/previews/PR479/assets/UserGuide_create.md.C7ebbtn2.lean.js
+++ b/previews/PR479/assets/UserGuide_create.md.ClN01XDL.lean.js
@@ -1,9 +1,8 @@
 import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
-│ 10×20×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────── dims ┐
+a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -20,9 +19,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const g
 )
 data2 = rand(30, 10, 15)
 properties = Dict(:origin => "user guide")
-a2 = YAXArray(axlist, data2, properties)
╭──────────────────────────────╮
-│ 30×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
diff --git a/previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.js b/previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.js
similarity index 93%
rename from previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.js
rename to previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.js
index aa9cfc23..f2bc8f7d 100644
--- a/previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.js
+++ b/previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.js
@@ -1,7 +1,6 @@
 import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/utzwahn.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
-using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
-│ 10×10×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -16,27 +15,24 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
INFO
Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.
rebuild ​
As an example let's consider the following
julia
using YAXArrays
 using DimensionalData
 
-c = YAXArray(ones(Int, 10,10))
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────── dims ┐
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────┘
then creating a new c with the same structure (axes) but different values is done by
julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────┘
then creating a new c with the same structure (axes) but different values is done by
julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly
julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly
julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -54,9 +50,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   7
   8
   9
- 10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+ 10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -76,9 +71,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ds1 = YAXArray(axlist, data1)
 
 data2 = rand(20, 10, 15)
-ds2 = YAXArray(axlist, data2)
Now we can concatenate ds1 and ds2:
julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+ds2 = YAXArray(axlist, data2)
Now we can concatenate ds1 and ds2:
julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
@@ -91,9 +85,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. \`a .. b\`.
Define the time span of the YAXArray
julia
t = Date("2020-01-01"):Month(1):Date("2022-12-31")
Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")
create YAXArray axes
julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
 → Lat  1:10,
-↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
-│ 10×10×36 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -101,9 +94,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 28.12 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Now we subset the YAXArray by any dimension.
Subset YAXArray by years
julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
-│ 10×10×12 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Now we subset the YAXArray by any dimension.
Subset YAXArray by years
julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
┌ 10×10×12 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -111,18 +103,16 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by a specific date
julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by a specific date
julia
ytime2 = y[Time=At(Date("2021-05-01"))]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────── dims ┐
   ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────┘
Subset YAXArray by a date range
julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
-│ 10×10×8 YAXArray{Int64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────┘
Subset YAXArray by a date range
julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
┌ 10×10×8 YAXArray{Int64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -130,9 +120,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 6.25 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by longitude and latitude
julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
-│ 5×6×36 YAXArray{Int64,3} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by longitude and latitude
julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
┌ 5×6×36 YAXArray{Int64, 3} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -198,7 +187,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -209,7 +198,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -232,9 +221,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -242,9 +230,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Cubes with more than 3 dimensions
julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Cubes with more than 3 dimensions
julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -254,9 +241,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘
To add some complexity, we will multiply each value for π and then divided for the sum of each time step. We will use the ds1 cube for this purpose.
julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
-       end
╭──────────────────────────────────────────────╮
-│ 10×15×20 YAXArray{Union{Missing, Float64},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
   ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
@@ -264,9 +250,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘
How do I use the CubeTable function? ​
The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.
Here we will use the ds1 Cube defined previously and we create a mask for data classification.
Cube containing a mask with classes 1, 2 and 3.
julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
-│ 10×15 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
How do I use the CubeTable function? ​
The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.
Here we will use the ds1 Cube defined previously and we create a mask for data classification.
Cube containing a mask with classes 1, 2 and 3.
julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -283,17 +268,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
-first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
-│ 3-element YAXArray{Union{Missing, Float64},1} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 24.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
-│ 3×20 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
   → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -311,15 +294,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
   Variables: 
-  c
+  a
 
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
-  a
+  c
 
   Additional Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -342,7 +325,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
@@ -354,16 +337,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock3
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
 
 Variables: 
 Stock1, Stock2, Stock3
now they are printed together, showing that is exactly the same axis structure for all variables.
Create a YAXArray with unions containing Strings ​
julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
-yax_string = YAXArray(test_x)
╭──────────────────────────────────────╮
-│ 2×2 YAXArray{Union{Int64, String},2} │
-├──────────────────────────────────────┴──────────────────────── dims ┐
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -371,9 +353,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
or simply with an Any type
julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
-yax_bool = YAXArray(test_bool)
╭─────────────────────╮
-│ 3×3 YAXArray{Any,2} │
-├─────────────────────┴───────────────────────────────────────── dims ┐
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
diff --git a/previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.lean.js b/previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.lean.js
similarity index 93%
rename from previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.lean.js
rename to previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.lean.js
index aa9cfc23..f2bc8f7d 100644
--- a/previews/PR479/assets/UserGuide_faq.md.C9UN1j4H.lean.js
+++ b/previews/PR479/assets/UserGuide_faq.md.Co6gBYzG.lean.js
@@ -1,7 +1,6 @@
 import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/utzwahn.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
-using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
-│ 10×10×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────────────── dims ┐
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -16,27 +15,24 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
INFO
Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.
rebuild ​
As an example let's consider the following
julia
using YAXArrays
 using DimensionalData
 
-c = YAXArray(ones(Int, 10,10))
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────── dims ┐
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────┘
then creating a new c with the same structure (axes) but different values is done by
julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────┘
then creating a new c with the same structure (axes) but different values is done by
julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly
julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
-│ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
note that the type is now Float64. Or, we could create a new structure but using the dimensions from yax explicitly
julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -54,9 +50,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   7
   8
   9
- 10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+ 10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -76,9 +71,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ds1 = YAXArray(axlist, data1)
 
 data2 = rand(20, 10, 15)
-ds2 = YAXArray(axlist, data2)
Now we can concatenate ds1 and ds2:
julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+ds2 = YAXArray(axlist, data2)
Now we can concatenate ds1 and ds2:
julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
@@ -91,9 +85,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. \`a .. b\`.
Define the time span of the YAXArray
julia
t = Date("2020-01-01"):Month(1):Date("2022-12-31")
Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")
create YAXArray axes
julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
 → Lat  1:10,
-↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
-│ 10×10×36 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -101,9 +94,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 28.12 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Now we subset the YAXArray by any dimension.
Subset YAXArray by years
julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
-│ 10×10×12 YAXArray{Int64,3} │
-├────────────────────────────┴─────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Now we subset the YAXArray by any dimension.
Subset YAXArray by years
julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
┌ 10×10×12 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -111,18 +103,16 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by a specific date
julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
-│ 10×10 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by a specific date
julia
ytime2 = y[Time=At(Date("2021-05-01"))]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────── dims ┐
   ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────── loaded in memory ┤
   data size: 800.0 bytes
-└──────────────────────────────────────────────────────────┘
Subset YAXArray by a date range
julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
-│ 10×10×8 YAXArray{Int64,3} │
-├───────────────────────────┴──────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────┘
Subset YAXArray by a date range
julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
┌ 10×10×8 YAXArray{Int64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
@@ -130,9 +120,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 6.25 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by longitude and latitude
julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
-│ 5×6×36 YAXArray{Int64,3} │
-├──────────────────────────┴───────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Subset YAXArray by longitude and latitude
julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
┌ 5×6×36 YAXArray{Int64, 3} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
   → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
@@ -198,7 +187,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -209,7 +198,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -232,9 +221,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -242,9 +230,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Cubes with more than 3 dimensions
julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
-│ 20×10×15 YAXArray{Float64,3} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Cubes with more than 3 dimensions
julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
@@ -254,9 +241,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘
To add some complexity, we will multiply each value for π and then divided for the sum of each time step. We will use the ds1 cube for this purpose.
julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
-       end
╭──────────────────────────────────────────────╮
-│ 10×15×20 YAXArray{Union{Missing, Float64},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
   ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
@@ -264,9 +250,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 23.44 KB
-└──────────────────────────────────────────────────────────────────────────────┘
How do I use the CubeTable function? ​
The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.
Here we will use the ds1 Cube defined previously and we create a mask for data classification.
Cube containing a mask with classes 1, 2 and 3.
julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
-│ 10×15 YAXArray{Int64,2} │
-├─────────────────────────┴────────────────────────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
How do I use the CubeTable function? ​
The function "CubeTable" creates an iterable table and the result is a DataCube. It is therefore very handy for grouping data and computing statistics by class. It uses OnlineStats.jl to calculate statistics, and weighted statistics can be calculated as well.
Here we will use the ds1 Cube defined previously and we create a mask for data classification.
Cube containing a mask with classes 1, 2 and 3.
julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -283,17 +268,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
-first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
-│ 3-element YAXArray{Union{Missing, Float64},1} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 24.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
-│ 3×20 YAXArray{Union{Missing, Float64},2} │
-├──────────────────────────────────────────┴───────────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
We can also use more than one criteria for grouping the values. In the next example, the mean is calculated for each class and timestep.
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
   → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -311,15 +294,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
   Variables: 
-  c
+  a
 
   Additional Axes: 
-  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
-  a
+  c
 
   Additional Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -342,7 +325,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
@@ -354,16 +337,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock3
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
 
 Variables: 
 Stock1, Stock2, Stock3
now they are printed together, showing that is exactly the same axis structure for all variables.
Create a YAXArray with unions containing Strings ​
julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
-yax_string = YAXArray(test_x)
╭──────────────────────────────────────╮
-│ 2×2 YAXArray{Union{Int64, String},2} │
-├──────────────────────────────────────┴──────────────────────── dims ┐
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -371,9 +353,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
 ├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
or simply with an Any type
julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
-yax_bool = YAXArray(test_bool)
╭─────────────────────╮
-│ 3×3 YAXArray{Any,2} │
-├─────────────────────┴───────────────────────────────────────── dims ┐
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
diff --git a/previews/PR479/assets/UserGuide_group.md.C2yVVeKj.js b/previews/PR479/assets/UserGuide_group.md.CyxshSz7.js
similarity index 83%
rename from previews/PR479/assets/UserGuide_group.md.C2yVVeKj.js
rename to previews/PR479/assets/UserGuide_group.md.CyxshSz7.js
index be8edb3d..215173c6 100644
--- a/previews/PR479/assets/UserGuide_group.md.C2yVVeKj.js
+++ b/previews/PR479/assets/UserGuide_group.md.CyxshSz7.js
@@ -1,16 +1,44 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/lhmmpxn.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),h={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/lhmmpxn.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using YAXArrays: YAXArrays as YAX
 using NetCDF
 using Downloads
 using Dates
 using Statistics
[ Info: new driver key :netcdf, updating backendlist.
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
 filename = Downloads.download(url_path, "rasm.nc")
-ds_o = Cube(filename)
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
axs = dims(ds_o) # get the dimensions
-data = ds_o.data[:,:,:] # read the data
-_FillValue = ds_o.properties["_FillValue"]
-data = replace(data, _FillValue => NaN)
-# create new YAXArray
-ds = YAXArray(axs, data)
GroupBy: seasons ​
function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘
GroupBy: seasons ​
function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
     # calculate weights 
     tempo = dims(ds, :time)
     month_length = YAXArray((tempo,), daysinmonth.(tempo))
@@ -29,8 +57,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
╭──────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+end
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -42,8 +69,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  275×205×9 YAXArray
  :Mar_Apr_May  275×205×9 YAXArray
  :Jun_Jul_Aug  275×205×9 YAXArray
- :Sep_Oct_Nov  275×205×9 YAXArray
And the mean per season is calculated as follows
julia
julia> mean_g = mean.(g_ds, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  275×205×9 YAXArray
Note how we are referencing the \`time\` dimension via \`YAX.time\`. This approach is used to avoid name clashes with \`time\` (\`Time\`) from \`Base\` (\`Dates\`). For convenience, we have defined the \`Dimensions\` \`time\` and \`Time\` in \`YAXArrays.jl\`, which are only accessible when explicitly called.
And the mean per season is calculated as follows
julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -53,8 +79,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]
dropdims ​
Note that now the time dimension has length one, we can use dropdims to remove it
julia
julia> mean_g = dropdims.(mean_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]
dropdims ​
Note that now the time dimension has length one, we can use dropdims to remove it
julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -70,16 +95,14 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Mar_Apr_May
  :Jun_Jul_Aug
  :Sep_Oct_Nov
Next, we will weight this grouping by days/month in each group.
GroupBy: weight ​
Create a YAXArray for the month length
julia
tempo = dims(ds, :time)
-month_length = YAXArray((tempo,), daysinmonth.(tempo))
╭──────────────────────────────╮
-│ 36-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 288.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
Now group it by season
julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
╭────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Int64,0},1} │
+└──────────────────────────────────────────────────────────────────────────────┘
Now group it by season
julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -91,8 +114,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  9-element YAXArray
  :Mar_Apr_May  9-element YAXArray
  :Jun_Jul_Aug  9-element YAXArray
- :Sep_Oct_Nov  9-element YAXArray
Get the number of days per season
julia
julia> sum_days = sum.(g_tempo, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Int64, 1, Vector{Int64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  9-element YAXArray
Get the number of days per season
julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -102,8 +124,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  [270]
  :Mar_Apr_May  [276]
  :Jun_Jul_Aug  [276]
- :Sep_Oct_Nov  [273]
weights ​
Weight the seasonal groups by sum_days
julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} groupby │
+ :Sep_Oct_Nov  [273]
weights ​
Weight the seasonal groups by sum_days
julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -113,9 +134,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
  :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
  :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
- :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]
Verify that the sum per season is 1
julia
julia> sum.(weights)
╭───────────────────────────────╮
-│ 4-element DimArray{Float64,1} │
-├───────────────────────────────┴──────────────────────────────────────── dims ┐
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]
Verify that the sum per season is 1
julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, Any} with 1 entry:
@@ -124,8 +144,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  1.0
  :Mar_Apr_May  1.0
  :Jun_Jul_Aug  1.0
- :Sep_Oct_Nov  1.0
weighted seasons ​
Now, let's weight the seasons
julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov  1.0
weighted seasons ​
Now, let's weight the seasons
julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -137,8 +156,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
  :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]
apply a sum over the time dimension and drop it
julia
julia> weighted_g = sum.(g_dsW, dims = :time);
 
-julia> weighted_g = dropdims.(weighted_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -148,8 +166,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]
Calculate the differences
julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]
Calculate the differences
julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -188,4 +205,4 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.
',63)]))}const N=i(h,[["render",l]]);export{y as __pageData,N as default};
+end
which shows a good agreement with the results first published by Joe Hamman.
',66)]))}const N=i(l,[["render",p]]);export{y as __pageData,N as default};
diff --git a/previews/PR479/assets/UserGuide_group.md.C2yVVeKj.lean.js b/previews/PR479/assets/UserGuide_group.md.CyxshSz7.lean.js
similarity index 83%
rename from previews/PR479/assets/UserGuide_group.md.C2yVVeKj.lean.js
rename to previews/PR479/assets/UserGuide_group.md.CyxshSz7.lean.js
index be8edb3d..215173c6 100644
--- a/previews/PR479/assets/UserGuide_group.md.C2yVVeKj.lean.js
+++ b/previews/PR479/assets/UserGuide_group.md.CyxshSz7.lean.js
@@ -1,16 +1,44 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/lhmmpxn.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),h={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t="/YAXArrays.jl/previews/PR479/assets/lhmmpxn.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using YAXArrays: YAXArrays as YAX
 using NetCDF
 using Downloads
 using Dates
 using Statistics
[ Info: new driver key :netcdf, updating backendlist.
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
 filename = Downloads.download(url_path, "rasm.nc")
-ds_o = Cube(filename)
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
axs = dims(ds_o) # get the dimensions
-data = ds_o.data[:,:,:] # read the data
-_FillValue = ds_o.properties["_FillValue"]
-data = replace(data, _FillValue => NaN)
-# create new YAXArray
-ds = YAXArray(axs, data)
GroupBy: seasons ​
function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘
GroupBy: seasons ​
function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
     # calculate weights 
     tempo = dims(ds, :time)
     month_length = YAXArray((tempo,), daysinmonth.(tempo))
@@ -29,8 +57,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
╭──────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+end
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -42,8 +69,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  275×205×9 YAXArray
  :Mar_Apr_May  275×205×9 YAXArray
  :Jun_Jul_Aug  275×205×9 YAXArray
- :Sep_Oct_Nov  275×205×9 YAXArray
And the mean per season is calculated as follows
julia
julia> mean_g = mean.(g_ds, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  275×205×9 YAXArray
Note how we are referencing the \`time\` dimension via \`YAX.time\`. This approach is used to avoid name clashes with \`time\` (\`Time\`) from \`Base\` (\`Dates\`). For convenience, we have defined the \`Dimensions\` \`time\` and \`Time\` in \`YAXArrays.jl\`, which are only accessible when explicitly called.
And the mean per season is calculated as follows
julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -53,8 +79,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]
dropdims ​
Note that now the time dimension has length one, we can use dropdims to remove it
julia
julia> mean_g = dropdims.(mean_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]
dropdims ​
Note that now the time dimension has length one, we can use dropdims to remove it
julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -70,16 +95,14 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Mar_Apr_May
  :Jun_Jul_Aug
  :Sep_Oct_Nov
Next, we will weight this grouping by days/month in each group.
GroupBy: weight ​
Create a YAXArray for the month length
julia
tempo = dims(ds, :time)
-month_length = YAXArray((tempo,), daysinmonth.(tempo))
╭──────────────────────────────╮
-│ 36-element YAXArray{Int64,1} │
-├──────────────────────────────┴───────────────────────────────────────── dims ┐
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 288.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
Now group it by season
julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
╭────────────────────────────────────────────────╮
-│ 4-element DimGroupByArray{YAXArray{Int64,0},1} │
+└──────────────────────────────────────────────────────────────────────────────┘
Now group it by season
julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -91,8 +114,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  9-element YAXArray
  :Mar_Apr_May  9-element YAXArray
  :Jun_Jul_Aug  9-element YAXArray
- :Sep_Oct_Nov  9-element YAXArray
Get the number of days per season
julia
julia> sum_days = sum.(g_tempo, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Int64, 1, Vector{Int64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+ :Sep_Oct_Nov  9-element YAXArray
Get the number of days per season
julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -102,8 +124,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  [270]
  :Mar_Apr_May  [276]
  :Jun_Jul_Aug  [276]
- :Sep_Oct_Nov  [273]
weights ​
Weight the seasonal groups by sum_days
julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} groupby │
+ :Sep_Oct_Nov  [273]
weights ​
Weight the seasonal groups by sum_days
julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -113,9 +134,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
  :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
  :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
- :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]
Verify that the sum per season is 1
julia
julia> sum.(weights)
╭───────────────────────────────╮
-│ 4-element DimArray{Float64,1} │
-├───────────────────────────────┴──────────────────────────────────────── dims ┐
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]
Verify that the sum per season is 1
julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, Any} with 1 entry:
@@ -124,8 +144,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  1.0
  :Mar_Apr_May  1.0
  :Jun_Jul_Aug  1.0
- :Sep_Oct_Nov  1.0
weighted seasons ​
Now, let's weight the seasons
julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov  1.0
weighted seasons ​
Now, let's weight the seasons
julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -137,8 +156,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
  :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]
apply a sum over the time dimension and drop it
julia
julia> weighted_g = sum.(g_dsW, dims = :time);
 
-julia> weighted_g = dropdims.(weighted_g, dims=:time)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -148,8 +166,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
  :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
  :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
  :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
- :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]
Calculate the differences
julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────────────────────────────────╮
-│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]
Calculate the differences
julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
 ├──────────────────────────────────────────────────────────────────────── dims ┤
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -188,4 +205,4 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const t
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.
',63)]))}const N=i(h,[["render",l]]);export{y as __pageData,N as default};
+end
which shows a good agreement with the results first published by Joe Hamman.
',66)]))}const N=i(l,[["render",p]]);export{y as __pageData,N as default};
diff --git a/previews/PR479/assets/UserGuide_read.md.De0OvTWP.js b/previews/PR479/assets/UserGuide_read.md.m9LR7MXT.js
similarity index 95%
rename from previews/PR479/assets/UserGuide_read.md.De0OvTWP.js
rename to previews/PR479/assets/UserGuide_read.md.m9LR7MXT.js
index b1e8ce1e..4f0ecda4 100644
--- a/previews/PR479/assets/UserGuide_read.md.De0OvTWP.js
+++ b/previews/PR479/assets/UserGuide_read.md.m9LR7MXT.js
@@ -16,9 +16,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
   Variables: 
   tas
 
-Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
╭────────────────────────────────────╮
-│ 384×192×251288 YAXArray{Float32,3} │
-├────────────────────────────────────┴─────────────────────────────────── dims ┐
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
   ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
@@ -50,9 +49,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 Variables: 
 tos
 
-Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -95,9 +93,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 Variables: 
 Blue, Green, Red
 
-Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")
Load data into memory ​
For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.
readcubedata ​
`,28)),i(p,null,{default:e(()=>[i(t,{label:"single variable"},{default:e(()=>n[0]||(n[0]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos)")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")
Load data into memory ​
For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.
readcubedata ​
`,28)),i(p,null,{default:e(()=>[i(t,{label:"single variable"},{default:e(()=>n[0]||(n[0]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos)")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
@@ -115,9 +112,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 `),s("span",{class:"line"},[s("span",null,'  "original_name"  => "sosstsst"')]),a(`
 `),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  data size: 2.8 MB")]),a(`
-`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),i(t,{label:"with the `:` operator"},{default:e(()=>n[1]||(n[1]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos[:, :, :]")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),i(t,{label:"with the `:` operator"},{default:e(()=>n[1]||(n[1]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos[:, :, :]")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
@@ -136,9 +132,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 `),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  data size: 2.8 MB")]),a(`
 `),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1),s("p",null,"In this case, you should know in advance how many dimensions there are and how long they are, which shouldn't be hard to determine since this information is already displayed when querying such variables.",-1)])),_:1}),i(t,{label:"Complete Dataset"},{default:e(()=>n[2]||(n[2]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded "),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds)")]),a(`
-`),s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded["),s("span",{style:{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"}},'"tos"'),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"] "),s("span",{style:{"--shiki-light":"#6A737D","--shiki-dark":"#6A737D"}},"# Load the variable of interest; the loaded status is shown for each variable.")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+`),s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded["),s("span",{style:{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"}},'"tos"'),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"] "),s("span",{style:{"--shiki-light":"#6A737D","--shiki-dark":"#6A737D"}},"# Load the variable of interest; the loaded status is shown for each variable.")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
diff --git a/previews/PR479/assets/UserGuide_read.md.De0OvTWP.lean.js b/previews/PR479/assets/UserGuide_read.md.m9LR7MXT.lean.js
similarity index 95%
rename from previews/PR479/assets/UserGuide_read.md.De0OvTWP.lean.js
rename to previews/PR479/assets/UserGuide_read.md.m9LR7MXT.lean.js
index b1e8ce1e..4f0ecda4 100644
--- a/previews/PR479/assets/UserGuide_read.md.De0OvTWP.lean.js
+++ b/previews/PR479/assets/UserGuide_read.md.m9LR7MXT.lean.js
@@ -16,9 +16,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
   Variables: 
   tas
 
-Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
╭────────────────────────────────────╮
-│ 384×192×251288 YAXArray{Float32,3} │
-├────────────────────────────────────┴─────────────────────────────────── dims ┐
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
   ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
@@ -50,9 +49,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 Variables: 
 tos
 
-Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -95,9 +93,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 Variables: 
 Blue, Green, Red
 
-Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")
Load data into memory ​
For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.
readcubedata ​
`,28)),i(p,null,{default:e(()=>[i(t,{label:"single variable"},{default:e(()=>n[0]||(n[0]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos)")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")
Load data into memory ​
For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.
readcubedata ​
`,28)),i(p,null,{default:e(()=>[i(t,{label:"single variable"},{default:e(()=>n[0]||(n[0]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos)")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
@@ -115,9 +112,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 `),s("span",{class:"line"},[s("span",null,'  "original_name"  => "sosstsst"')]),a(`
 `),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  data size: 2.8 MB")]),a(`
-`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),i(t,{label:"with the `:` operator"},{default:e(()=>n[1]||(n[1]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos[:, :, :]")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),i(t,{label:"with the `:` operator"},{default:e(()=>n[1]||(n[1]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos[:, :, :]")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
@@ -136,9 +132,8 @@ import{_ as o,c as r,a2 as d,G as i,w as e,j as s,a,B as l,o as h}from"./chunks/
 `),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  data size: 2.8 MB")]),a(`
 `),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1),s("p",null,"In this case, you should know in advance how many dimensions there are and how long they are, which shouldn't be hard to determine since this information is already displayed when querying such variables.",-1)])),_:1}),i(t,{label:"Complete Dataset"},{default:e(()=>n[2]||(n[2]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded "),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds)")]),a(`
-`),s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded["),s("span",{style:{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"}},'"tos"'),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"] "),s("span",{style:{"--shiki-light":"#6A737D","--shiki-dark":"#6A737D"}},"# Load the variable of interest; the loaded status is shown for each variable.")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"╭────────────────────────────────────────────────╮")]),a(`
-`),s("span",{class:"line"},[s("span",null,"│ 180×170×24 YAXArray{Union{Missing, Float32},3} │")]),a(`
-`),s("span",{class:"line"},[s("span",null,"├────────────────────────────────────────────────┴─────────────────────── dims ┐")]),a(`
+`),s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded["),s("span",{style:{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"}},'"tos"'),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"] "),s("span",{style:{"--shiki-light":"#6A737D","--shiki-dark":"#6A737D"}},"# Load the variable of interest; the loaded status is shown for each variable.")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(`
+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(`
 `),s("span",{class:"line"},[s("span",null,"  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(`
diff --git a/previews/PR479/assets/UserGuide_select.md.rxBehFDA.js b/previews/PR479/assets/UserGuide_select.md.wOOEByPV.js
similarity index 89%
rename from previews/PR479/assets/UserGuide_select.md.rxBehFDA.js
rename to previews/PR479/assets/UserGuide_select.md.wOOEByPV.js
index acae7330..f97e0189 100644
--- a/previews/PR479/assets/UserGuide_select.md.rxBehFDA.js
+++ b/previews/PR479/assets/UserGuide_select.md.wOOEByPV.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),i("div",null,s[0]||(s[0]=[n(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -12,9 +12,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
 Variables: 
 tos
 
-Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Select a YAXArray ​
Get the sea surface temperature of the Dataset:
julia
tos = ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Select a YAXArray ​
Get the sea surface temperature of the Dataset:
julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -32,9 +31,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘
which is the same as:
julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
which is the same as:
julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -52,9 +50,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘
Select elements ​
Using positional integer indexing:
julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Select elements ​
Using positional integer indexing:
julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -70,9 +67,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -90,9 +86,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘
Using special types:
julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
-tos[time = At(time1)]
╭─────────────────────────────────────────────╮
-│ 180×170 YAXArray{Union{Missing, Float32},2} │
-├─────────────────────────────────────────────┴───────────────── dims ┐
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────────────────┴ metadata ┐
@@ -109,9 +104,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├───────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 119.53 KB
-└────────────────────────────────────────────────────────────────────────────────┘
Select ranges ​
Here we subset an interval of a dimension using positional integer indexing.
julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└────────────────────────────────────────────────────────────────────────────────┘
Select ranges ​
Here we subset an interval of a dimension using positional integer indexing.
julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -129,9 +123,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -149,9 +142,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Read more about the At selector in the package DimensionalData. Get values within a tolerances:
julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
-│ 10×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Read more about the At selector in the package DimensionalData. Get values within a tolerances:
julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -169,9 +161,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 159.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Closed and open intervals ​
Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:
julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Closed and open intervals ​
Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:
julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -189,9 +180,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.
julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.
julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -209,9 +199,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -229,9 +218,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -249,9 +237,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -269,26 +256,21 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
See tutorials for use cases.
Get a dimension ​
Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:
julia
collect(tos.lat)
170-element Vector{Float64}:
- -79.5
- -78.5
- -77.5
- -76.5
- -75.5
- -74.5
- -73.5
- -72.5
- -71.5
- -70.5
-   ⋮
-  81.5
-  82.5
-  83.5
-  84.5
-  85.5
-  86.5
-  87.5
-  88.5
-  89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+└──────────────────────────────────────────────────────────────────────────────┘
See tutorials for use cases.
Get a dimension ​
Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:
julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  89.5   89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
which is equivalent to:
julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default};
diff --git a/previews/PR479/assets/UserGuide_select.md.rxBehFDA.lean.js b/previews/PR479/assets/UserGuide_select.md.wOOEByPV.lean.js
similarity index 89%
rename from previews/PR479/assets/UserGuide_select.md.rxBehFDA.lean.js
rename to previews/PR479/assets/UserGuide_select.md.wOOEByPV.lean.js
index acae7330..f97e0189 100644
--- a/previews/PR479/assets/UserGuide_select.md.rxBehFDA.lean.js
+++ b/previews/PR479/assets/UserGuide_select.md.wOOEByPV.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.DYY3HcdR.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),i("div",null,s[0]||(s[0]=[n(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -12,9 +12,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
 Variables: 
 tos
 
-Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Select a YAXArray ​
Get the sea surface temperature of the Dataset:
julia
tos = ds.tos
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Select a YAXArray ​
Get the sea surface temperature of the Dataset:
julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -32,9 +31,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘
which is the same as:
julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
-│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
which is the same as:
julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -52,9 +50,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 2.8 MB
-└──────────────────────────────────────────────────────────────────────────────┘
Select elements ​
Using positional integer indexing:
julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Select elements ​
Using positional integer indexing:
julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -70,9 +67,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
-└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
-│ 24-element YAXArray{Union{Missing, Float32},1} │
-├────────────────────────────────────────────────┴─────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
   ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 10 entries:
@@ -90,9 +86,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘
Using special types:
julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
-tos[time = At(time1)]
╭─────────────────────────────────────────────╮
-│ 180×170 YAXArray{Union{Missing, Float32},2} │
-├─────────────────────────────────────────────┴───────────────── dims ┐
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
   ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────────────────┴ metadata ┐
@@ -109,9 +104,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├───────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 119.53 KB
-└────────────────────────────────────────────────────────────────────────────────┘
Select ranges ​
Here we subset an interval of a dimension using positional integer indexing.
julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└────────────────────────────────────────────────────────────────────────────────┘
Select ranges ​
Here we subset an interval of a dimension using positional integer indexing.
julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -129,9 +123,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
-│ 10×10×24 YAXArray{Union{Missing, Float32},3} │
-├──────────────────────────────────────────────┴───────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Same but using named indexing:
julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -149,9 +142,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 9.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Read more about the At selector in the package DimensionalData. Get values within a tolerances:
julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
-│ 10×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Read more about the At selector in the package DimensionalData. Get values within a tolerances:
julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -169,9 +161,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 159.38 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Closed and open intervals ​
Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:
julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Closed and open intervals ​
Although a Between(a,b) function is available in DimensionalData, is recommended to use instead the a .. b notation:
julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -189,9 +180,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.
julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
This describes a closed interval in which all points were included. More selectors from DimensionalData are available, such as Touches, Near, Where and Contains.
julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -209,9 +199,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -229,9 +218,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -249,9 +237,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
-│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────────┴──────────────────────── dims ┐
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
   → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
   ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
@@ -269,26 +256,21 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DYY3HcdR.js";const u
   "original_name"  => "sosstsst"
 ├─────────────────────────────────────────────────────────────── loaded lazily ┤
   data size: 717.19 KB
-└──────────────────────────────────────────────────────────────────────────────┘
See tutorials for use cases.
Get a dimension ​
Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:
julia
collect(tos.lat)
170-element Vector{Float64}:
- -79.5
- -78.5
- -77.5
- -76.5
- -75.5
- -74.5
- -73.5
- -72.5
- -71.5
- -70.5
-   ⋮
-  81.5
-  82.5
-  83.5
-  84.5
-  85.5
-  86.5
-  87.5
-  88.5
-  89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+└──────────────────────────────────────────────────────────────────────────────┘
See tutorials for use cases.
Get a dimension ​
Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:
julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  89.5   89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
which is equivalent to:
julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default};
diff --git a/previews/PR479/assets/UserGuide_write.md.CslDCk8B.js b/previews/PR479/assets/UserGuide_write.md.BMZhOtZh.js
similarity index 86%
rename from previews/PR479/assets/UserGuide_write.md.CslDCk8B.js
rename to previews/PR479/assets/UserGuide_write.md.BMZhOtZh.js
index 178553f5..6f051ace 100644
--- a/previews/PR479/assets/UserGuide_write.md.CslDCk8B.js
+++ b/previews/PR479/assets/UserGuide_write.md.BMZhOtZh.js
@@ -1,4 +1,4 @@
-import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DYY3HcdR.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,y,E,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
+import{_ as e,c as n,a2 as a,j as i,a as l,G as p,B as h,o as k}from"./chunks/framework.DYY3HcdR.js";const C=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,E,c,y,u){const t=h("Badge");return k(),n("div",null,[s[3]||(s[3]=a(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -20,7 +20,7 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
netcdf compression ​
Save a dataset to NetCDF format with compression:
julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)
Comparing it to the default saved file
julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L637-L646",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -39,9 +39,8 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
   Variables: 
   z
 
-Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
╭───────────────────────────────────────────╮
-│ 5×4×5 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -69,4 +68,4 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
  0.414006  0.66729    0.125769  0.00277787
  0.891257  0.0887544  0.630526  0.782494
  0.948244  0.195437   0.102333  0.669125
- 0.102816  0.781572   0.527401  0.719692
indeed, those entries had been updated.
`,35))])}const C=n(d,[["render",o]]);export{v as __pageData,C as default};
+ 0.102816  0.781572   0.527401  0.719692
indeed, those entries had been updated.
`,35))])}const v=e(d,[["render",o]]);export{C as __pageData,v as default};
diff --git a/previews/PR479/assets/UserGuide_write.md.CslDCk8B.lean.js b/previews/PR479/assets/UserGuide_write.md.BMZhOtZh.lean.js
similarity index 86%
rename from previews/PR479/assets/UserGuide_write.md.CslDCk8B.lean.js
rename to previews/PR479/assets/UserGuide_write.md.BMZhOtZh.lean.js
index 178553f5..6f051ace 100644
--- a/previews/PR479/assets/UserGuide_write.md.CslDCk8B.lean.js
+++ b/previews/PR479/assets/UserGuide_write.md.BMZhOtZh.lean.js
@@ -1,4 +1,4 @@
-import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DYY3HcdR.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,y,E,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
+import{_ as e,c as n,a2 as a,j as i,a as l,G as p,B as h,o as k}from"./chunks/framework.DYY3HcdR.js";const C=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,E,c,y,u){const t=h("Badge");return k(),n("div",null,[s[3]||(s[3]=a(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -20,7 +20,7 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
netcdf compression ​
Save a dataset to NetCDF format with compression:
julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)
Comparing it to the default saved file
julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L637-L646",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -39,9 +39,8 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
   Variables: 
   z
 
-Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
╭───────────────────────────────────────────╮
-│ 5×4×5 YAXArray{Union{Missing, Float32},3} │
-├───────────────────────────────────────────┴──────────────────────────── dims ┐
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
@@ -69,4 +68,4 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
  0.414006  0.66729    0.125769  0.00277787
  0.891257  0.0887544  0.630526  0.782494
  0.948244  0.195437   0.102333  0.669125
- 0.102816  0.781572   0.527401  0.719692
indeed, those entries had been updated.
`,35))])}const C=n(d,[["render",o]]);export{v as __pageData,C as default};
+ 0.102816  0.781572   0.527401  0.719692
indeed, those entries had been updated.
`,35))])}const v=e(d,[["render",o]]);export{C as __pageData,v as default};
diff --git a/previews/PR479/assets/api.md.DRceqLPS.js b/previews/PR479/assets/api.md.DRceqLPS.js
new file mode 100644
index 00000000..7a06b188
--- /dev/null
+++ b/previews/PR479/assets/api.md.DRceqLPS.js
@@ -0,0 +1,5 @@
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DYY3HcdR.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[156]||(s[156]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[157]||(s[157]=e("p",null,"This section describes all available functions of this package.",-1)),s[158]||(s[158]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",u,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",T,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
',2))]),e("details",F,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[60]||(s[60]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[61]||(s[61]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[62]||(s[62]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[63]||(s[63]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[64]||(s[64]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[65]||(s[65]=l('
julia
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source
',3))]),e("details",L,[e("summary",null,[s[66]||(s[66]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[67]||(s[67]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[68]||(s[68]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[69]||(s[69]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[70]||(s[70]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[71]||(s[71]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[72]||(s[72]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[73]||(s[73]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[74]||(s[74]=l('
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[75]||(s[75]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[76]||(s[76]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[77]||(s[77]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source
',3))]),s[159]||(s[159]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[78]||(s[78]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[79]||(s[79]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[80]||(s[80]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[81]||(s[81]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[82]||(s[82]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[83]||(s[83]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",P,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[86]||(s[86]=l('
julia
getOutAxis
source
',2))]),e("details",q,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+This is used to find different axes and to make certain axis description the same.
+For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",R,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[101]||(s[101]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",V,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",$,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",H,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[119]||(s[119]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[120]||(s[120]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[121]||(s[121]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[122]||(s[122]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[123]||(s[123]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[124]||(s[124]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[125]||(s[125]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[126]||(s[126]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[127]||(s[127]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[128]||(s[128]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[129]||(s[129]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[130]||(s[130]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[131]||(s[131]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[132]||(s[132]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[133]||(s[133]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[134]||(s[134]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[144]||(s[144]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L543-L545",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[145]||(s[145]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[146]||(s[146]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[147]||(s[147]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[148]||(s[148]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[149]||(s[149]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[150]||(s[150]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[151]||(s[151]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L512-L514",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[152]||(s[152]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[153]||(s[153]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[154]||(s[154]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[155]||(s[155]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{cs as __pageData,bs as default};
diff --git a/previews/PR479/assets/api.md.DRceqLPS.lean.js b/previews/PR479/assets/api.md.DRceqLPS.lean.js
new file mode 100644
index 00000000..7a06b188
--- /dev/null
+++ b/previews/PR479/assets/api.md.DRceqLPS.lean.js
@@ -0,0 +1,5 @@
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DYY3HcdR.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[156]||(s[156]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[157]||(s[157]=e("p",null,"This section describes all available functions of this package.",-1)),s[158]||(s[158]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",u,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",T,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
',2))]),e("details",F,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[60]||(s[60]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[61]||(s[61]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[62]||(s[62]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[63]||(s[63]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[64]||(s[64]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[65]||(s[65]=l('
julia
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source
',3))]),e("details",L,[e("summary",null,[s[66]||(s[66]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[67]||(s[67]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[68]||(s[68]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[69]||(s[69]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[70]||(s[70]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[71]||(s[71]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[72]||(s[72]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[73]||(s[73]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[74]||(s[74]=l('
julia
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[75]||(s[75]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[76]||(s[76]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[77]||(s[77]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source
',3))]),s[159]||(s[159]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[78]||(s[78]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[79]||(s[79]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[80]||(s[80]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[81]||(s[81]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[82]||(s[82]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[83]||(s[83]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",P,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[86]||(s[86]=l('
julia
getOutAxis
source
',2))]),e("details",q,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+This is used to find different axes and to make certain axis description the same.
+For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[95]||(s[95]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",R,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[101]||(s[101]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",V,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[107]||(s[107]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[110]||(s[110]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",$,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",H,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[119]||(s[119]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[120]||(s[120]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[121]||(s[121]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[122]||(s[122]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[123]||(s[123]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[124]||(s[124]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[125]||(s[125]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[126]||(s[126]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[127]||(s[127]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[128]||(s[128]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[129]||(s[129]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[130]||(s[130]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[131]||(s[131]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[132]||(s[132]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[133]||(s[133]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[134]||(s[134]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[144]||(s[144]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L543-L545",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[145]||(s[145]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[146]||(s[146]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[147]||(s[147]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[148]||(s[148]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[149]||(s[149]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[150]||(s[150]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[151]||(s[151]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L512-L514",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[152]||(s[152]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[153]||(s[153]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[154]||(s[154]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[155]||(s[155]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/453ee3395962cfd60967d1803469f03ddbc9fb0f/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{cs as __pageData,bs as default};
diff --git a/previews/PR479/assets/api.md.LtcwYcT6.js b/previews/PR479/assets/api.md.LtcwYcT6.js
deleted file mode 100644
index 2fffeaa4..00000000
--- a/previews/PR479/assets/api.md.LtcwYcT6.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DYY3HcdR.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,us){const t=r("Badge");return p(),o("div",null,[s[165]||(s[165]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=e("p",null,"This section describes all available functions of this package.",-1)),s[167]||(s[167]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",h,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",k,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",E,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
-
-Map a given function \`fun\` over slices of all cubes of the dataset \`ds\`. 
-Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
-For Datasets, only one output cube can be specified.
-In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
-
-For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L408-L413",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
-open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[79]||(s[79]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[80]||(s[80]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[81]||(s[81]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L637-L646",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=e("p",null,'to_dataset(c;datasetaxis = "Variables", layername = "layer")',-1)),s[85]||(s[85]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[86]||(s[86]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[168]||(s[168]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[89]||(s[89]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
getOutAxis
source
',2))]),e("details",q,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[101]||(s[101]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
-This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",R,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",V,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[113]||(s[113]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",H,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[133]||(s[133]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[134]||(s[134]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[135]||(s[135]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[136]||(s[136]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[137]||(s[137]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[144]||(s[144]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[145]||(s[145]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[148]||(s[148]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[149]||(s[149]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[150]||(s[150]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[151]||(s[151]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[152]||(s[152]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[153]||(s[153]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L543-L545",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[154]||(s[154]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[155]||(s[155]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=l('
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',6))]),e("details",is,[e("summary",null,[s[157]||(s[157]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[158]||(s[158]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[159]||(s[159]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[160]||(s[160]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L512-L514",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[161]||(s[161]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[162]||(s[162]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[163]||(s[163]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[164]||(s[164]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{cs as __pageData,bs as default};
diff --git a/previews/PR479/assets/api.md.LtcwYcT6.lean.js b/previews/PR479/assets/api.md.LtcwYcT6.lean.js
deleted file mode 100644
index 2fffeaa4..00000000
--- a/previews/PR479/assets/api.md.LtcwYcT6.lean.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DYY3HcdR.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,us){const t=r("Badge");return p(),o("div",null,[s[165]||(s[165]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[166]||(s[166]=e("p",null,"This section describes all available functions of this package.",-1)),s[167]||(s[167]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",h,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",k,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",E,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
-
-Map a given function \`fun\` over slices of all cubes of the dataset \`ds\`. 
-Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
-For Datasets, only one output cube can be specified.
-In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
-
-For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source
',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L408-L413",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
-open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[79]||(s[79]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[80]||(s[80]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[81]||(s[81]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L637-L646",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=e("p",null,'to_dataset(c;datasetaxis = "Variables", layername = "layer")',-1)),s[85]||(s[85]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[86]||(s[86]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[168]||(s[168]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[89]||(s[89]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
getOutAxis
source
',2))]),e("details",q,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[101]||(s[101]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
-This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[104]||(s[104]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",R,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",V,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[113]||(s[113]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[125]||(s[125]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",H,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[133]||(s[133]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[134]||(s[134]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[135]||(s[135]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[136]||(s[136]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[137]||(s[137]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/1aeb627f3d8705a950182240b938bee4c9e3e42a/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
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julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
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julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
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function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
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diff --git a/previews/PR479/assets/app.Cujj9Dqk.js b/previews/PR479/assets/app.CTp7ev_O.js
similarity index 95%
rename from previews/PR479/assets/app.Cujj9Dqk.js
rename to previews/PR479/assets/app.CTp7ev_O.js
index 725ef669..f69a70e4 100644
--- a/previews/PR479/assets/app.Cujj9Dqk.js
+++ b/previews/PR479/assets/app.CTp7ev_O.js
@@ -1 +1 @@
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+import{R as p}from"./chunks/theme.6qbPyd-G.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.DYY3HcdR.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp};
diff --git a/previews/PR479/assets/chunks/@localSearchIndexroot.DBpPZp1N.js b/previews/PR479/assets/chunks/@localSearchIndexroot.DBpPZp1N.js
deleted file mode 100644
index 74f1a899..00000000
--- a/previews/PR479/assets/chunks/@localSearchIndexroot.DBpPZp1N.js
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Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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similarity index 98%
rename from previews/PR479/assets/development_contributors.md.CBWqoCWc.js
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diff --git a/previews/PR479/assets/development_contributors.md.CBWqoCWc.lean.js b/previews/PR479/assets/development_contributors.md.CStJtQsj.lean.js
similarity index 98%
rename from previews/PR479/assets/development_contributors.md.CBWqoCWc.lean.js
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Data Visualization",links:[{icon:"github",link:"https://github.com/lazarusA"},{icon:"x",link:"https://twitter.com/LazarusAlon"},{icon:"linkedin",link:"https://www.linkedin.com/in/lazaro-alonso/"},{icon:"mastodon",link:"https://julialang.social/@LazaroAlonso"}]}];return(d,a)=>{const i=c("font");return b(),h("div",null,[r(n(g),null,{default:s(()=>[r(n(u),null,{title:s(()=>a[0]||(a[0]=[e("Contributors")])),lead:s(()=>[a[8]||(a[8]=t("strong",null,"Current core contributors ",-1)),a[9]||(a[9]=e()),a[10]||(a[10]=t("br",null,null,-1)),t("div",p,[a[4]||(a[4]=e(" They have taking the lead for the ongoing organizational maintenance and technical direction of ")),r(i,{color:"orange"},{default:s(()=>a[1]||(a[1]=[e("YAXArrays.jl")])),_:1}),a[5]||(a[5]=e(", ")),r(i,{color:"orange"},{default:s(()=>a[2]||(a[2]=[e("DiskArrays.jl")])),_:1}),a[6]||(a[6]=e(" and ")),r(i,{color:"orange"},{default:s(()=>a[3]||(a[3]=[e("DimensionalData.jl")])),_:1}),a[7]||(a[7]=e(". 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diff --git a/previews/PR479/assets/get_started.md.D4AEcaZh.js b/previews/PR479/assets/get_started.md.DOZH6HEp.js
similarity index 96%
rename from previews/PR479/assets/get_started.md.D4AEcaZh.js
rename to previews/PR479/assets/get_started.md.DOZH6HEp.js
index f8ad07b2..9518b819 100644
--- a/previews/PR479/assets/get_started.md.D4AEcaZh.js
+++ b/previews/PR479/assets/get_started.md.DOZH6HEp.js
@@ -1,9 +1,8 @@
 import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a = YAXArray(rand(2,3))
╭─────────────────────────╮
-│ 2×3 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────────── dims ┐
+a = YAXArray(rand(2,3))
┌ 2×3 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -28,9 +27,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const E
     "y" => "latitude",
 );
 
-a2 = YAXArray(axlist, data, props)
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
diff --git a/previews/PR479/assets/get_started.md.D4AEcaZh.lean.js b/previews/PR479/assets/get_started.md.DOZH6HEp.lean.js
similarity index 96%
rename from previews/PR479/assets/get_started.md.D4AEcaZh.lean.js
rename to previews/PR479/assets/get_started.md.DOZH6HEp.lean.js
index f8ad07b2..9518b819 100644
--- a/previews/PR479/assets/get_started.md.D4AEcaZh.lean.js
+++ b/previews/PR479/assets/get_started.md.DOZH6HEp.lean.js
@@ -1,9 +1,8 @@
 import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const E=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a = YAXArray(rand(2,3))
╭─────────────────────────╮
-│ 2×3 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────────── dims ┐
+a = YAXArray(rand(2,3))
┌ 2×3 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -28,9 +27,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYY3HcdR.js";const E
     "y" => "latitude",
 );
 
-a2 = YAXArray(axlist, data, props)
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
diff --git a/previews/PR479/assets/index.md.C6f_t179.js b/previews/PR479/assets/index.md.Bckrm96e.js
similarity index 93%
rename from previews/PR479/assets/index.md.C6f_t179.js
rename to previews/PR479/assets/index.md.Bckrm96e.js
index 365c2d26..f525745b 100644
--- a/previews/PR479/assets/index.md.C6f_t179.js
+++ b/previews/PR479/assets/index.md.Bckrm96e.js
@@ -1,8 +1,8 @@
-import{_ as s,c as a,a2 as t,o as e}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.","link":"/tutorials/plottingmaps"},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),l={name:"index.md"};function n(p,i,h,r,k,o){return e(),a("div",null,i[0]||(i[0]=[t(`
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
+import{_ as s,c as a,a2 as t,o as e}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.","link":"/tutorials/plottingmaps"},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),l={name:"index.md"};function n(p,i,h,k,r,o){return e(),a("div",null,i[0]||(i[0]=[t(`
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
 julia> Pkg.add("YAXArrays.jl")
 # or
 julia> ] # ']' should be pressed
-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
diff --git a/previews/PR479/assets/index.md.C6f_t179.lean.js b/previews/PR479/assets/index.md.Bckrm96e.lean.js
similarity index 93%
rename from previews/PR479/assets/index.md.C6f_t179.lean.js
rename to previews/PR479/assets/index.md.Bckrm96e.lean.js
index 365c2d26..f525745b 100644
--- a/previews/PR479/assets/index.md.C6f_t179.lean.js
+++ b/previews/PR479/assets/index.md.Bckrm96e.lean.js
@@ -1,8 +1,8 @@
-import{_ as s,c as a,a2 as t,o as e}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.","link":"/tutorials/plottingmaps"},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),l={name:"index.md"};function n(p,i,h,r,k,o){return e(),a("div",null,i[0]||(i[0]=[t(`
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
+import{_ as s,c as a,a2 as t,o as e}from"./chunks/framework.DYY3HcdR.js";const g=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.","link":"/tutorials/plottingmaps"},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),l={name:"index.md"};function n(p,i,h,k,r,o){return e(),a("div",null,i[0]||(i[0]=[t(`
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
 julia> Pkg.add("YAXArrays.jl")
 # or
 julia> ] # ']' should be pressed
-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
diff --git a/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.js b/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.js
similarity index 98%
rename from previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.js
rename to previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.js
index c5ccc020..fe4cf2e4 100644
--- a/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.js
+++ b/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.js
@@ -13,9 +13,8 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.DYY3HcdR.js";const l
 
 julia> using YAXArrays: YAXArrays as YAX
 
-julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
-│ 1095-element YAXArray{Float64,1} │
-├──────────────────────────────────┴───────────────────────────────────── dims ┐
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
diff --git a/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.lean.js b/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.lean.js
similarity index 98%
rename from previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.lean.js
rename to previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.lean.js
index c5ccc020..fe4cf2e4 100644
--- a/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.DY61i3Ri.lean.js
+++ b/previews/PR479/assets/tutorials_mean_seasonal_cycle.md.BIq0le5H.lean.js
@@ -13,9 +13,8 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.DYY3HcdR.js";const l
 
 julia> using YAXArrays: YAXArrays as YAX
 
-julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
-│ 1095-element YAXArray{Float64,1} │
-├──────────────────────────────────┴───────────────────────────────────── dims ┐
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
diff --git a/previews/PR479/development/contribute.html b/previews/PR479/development/contribute.html
index 5063dcde..6803a588 100644
--- a/previews/PR479/development/contribute.html
+++ b/previews/PR479/development/contribute.html
@@ -9,9 +9,9 @@
     
     
     
-    
+    
     
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@@ -24,7 +24,7 @@
     
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Contribute to YAXArrays.jl ​
Pull requests and bug reports are always welcome at the YAXArrays.jl GitHub repository.
Contribute to Documentation ​
Contributing with examples can be done by first creating a new file example here
new file
  • your_new_file.md at docs/src/UserGuide/
Once this is done you need to add a new entry here at the appropriate level.
add entry to docs
Your new entry should look like:
  • { text: 'Your title example', link: '/UserGuide/your_new_file.md' }
Build docs locally ​
If you want to take a look at the docs locally before doing a PR follow the next steps:
Install the dependencies in your system, locate yourself at the docs level folder, then do
sh
npm i
Then simply go to your docs env and activate it, i.e.
sh
docs> julia
 julia> ]
 pkg> activate .
Next, run the scripts. Generate files and build docs by running:
sh
include("make.jl")
Now go to your terminal in the same path docs> and run:
sh
npm run docs:dev
This should ouput http://localhost:5173/YAXArrays.jl/, copy/paste this into your browser and you are all set.

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diff --git a/previews/PR479/development/contributors.html b/previews/PR479/development/contributors.html
index 63172fec..4ef959a9 100644
--- a/previews/PR479/development/contributors.html
+++ b/previews/PR479/development/contributors.html
@@ -9,11 +9,11 @@
     
     
     
-    
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@@ -22,7 +22,7 @@
   
   
     
YAXArrays.jl
Appearance
Contributors
Current core contributors  
 They have taking the lead for the ongoing organizational maintenance and technical direction of ,  and . 
Fabian Gans
Fabian Gans
Geoscientific Programmer
Felix Cremer
Felix Cremer
PhD Candidate in Remote Sensing
Rafael Schouten
Rafael Schouten
Spatial/ecological modelling
Lazaro Alonso
Lazaro Alonso
Scientist. Data Visualization
Our valuable contributors
 We appreciate all contributions from the Julia community so that this ecosystem can thrive.

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diff --git a/previews/PR479/get_started.html b/previews/PR479/get_started.html
index 4ff19012..eef923d0 100644
--- a/previews/PR479/get_started.html
+++ b/previews/PR479/get_started.html
@@ -9,11 +9,11 @@
     
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -24,9 +24,8 @@
     
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
-a = YAXArray(rand(2,3))
╭─────────────────────────╮
-│ 2×3 YAXArray{Float64,2} │
-├─────────────────────────┴───────────────────────────────────── dims ┐
+a = YAXArray(rand(2,3))
┌ 2×3 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
@@ -51,9 +50,8 @@
     "y" => "latitude",
 );
 
-a2 = YAXArray(axlist, data, props)
╭────────────────────────────────╮
-│ 20×10×15×2 YAXArray{Float64,4} │
-├────────────────────────────────┴─────────────────────────────────────── dims ┐
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
   ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
   → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
   ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
@@ -76,7 +74,7 @@
  0.647058  0.657324  0.475594   0.63291      0.405317  0.263789   0.641411
  0.223412  0.771583  0.119937   0.771179     0.45015   0.991786   0.663392
  0.607943  0.13068   0.711506   0.629872     0.457345  0.319698   0.0900259
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.

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\ No newline at end of file
diff --git a/previews/PR479/hashmap.json b/previews/PR479/hashmap.json
index 22659b30..0bf839ce 100644
--- a/previews/PR479/hashmap.json
+++ b/previews/PR479/hashmap.json
@@ -1 +1 @@
-{"api.md":"LtcwYcT6","development_contribute.md":"CkpgxKi9","development_contributors.md":"CBWqoCWc","get_started.md":"D4AEcaZh","index.md":"C6f_t179","tutorials_mean_seasonal_cycle.md":"DY61i3Ri","tutorials_other_tutorials.md":"k1cIf9TO","tutorials_plottingmaps.md":"DfxpCqPP","userguide_cache.md":"BPLL4v-g","userguide_chunk.md":"BmJaxpwh","userguide_combine.md":"B3kKJwRR","userguide_compute.md":"GnwBFM_7","userguide_convert.md":"CObFCPzI","userguide_create.md":"C7ebbtn2","userguide_faq.md":"C9UN1j4H","userguide_group.md":"C2yVVeKj","userguide_read.md":"De0OvTWP","userguide_select.md":"rxBehFDA","userguide_types.md":"C9xprOE-","userguide_write.md":"CslDCk8B"}
+{"api.md":"DRceqLPS","development_contribute.md":"CkpgxKi9","development_contributors.md":"CStJtQsj","get_started.md":"DOZH6HEp","index.md":"Bckrm96e","tutorials_mean_seasonal_cycle.md":"BIq0le5H","tutorials_other_tutorials.md":"k1cIf9TO","tutorials_plottingmaps.md":"DfxpCqPP","userguide_cache.md":"BPLL4v-g","userguide_chunk.md":"BmJaxpwh","userguide_combine.md":"BYUU3tRz","userguide_compute.md":"CEww8r0_","userguide_convert.md":"mv4euI22","userguide_create.md":"ClN01XDL","userguide_faq.md":"Co6gBYzG","userguide_group.md":"CyxshSz7","userguide_read.md":"m9LR7MXT","userguide_select.md":"wOOEByPV","userguide_types.md":"C9xprOE-","userguide_write.md":"BMZhOtZh"}
diff --git a/previews/PR479/index.html b/previews/PR479/index.html
index 5859c13f..93612bcd 100644
--- a/previews/PR479/index.html
+++ b/previews/PR479/index.html
@@ -9,11 +9,11 @@
     
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -25,12 +25,12 @@
 julia> Pkg.add("YAXArrays.jl")
 # or
 julia> ] # ']' should be pressed
-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
 Pkg.add(["GLMakie", "GeoMakie", "AlgebraOfGraphics", "DimensionalData"])

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+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR479/tutorials/mean_seasonal_cycle.html b/previews/PR479/tutorials/mean_seasonal_cycle.html
index c17a1640..17047cd2 100644
--- a/previews/PR479/tutorials/mean_seasonal_cycle.html
+++ b/previews/PR479/tutorials/mean_seasonal_cycle.html
@@ -9,11 +9,11 @@
     
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -36,9 +36,8 @@
 
 julia> using YAXArrays: YAXArrays as YAX
 
-julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
-│ 1095-element YAXArray{Float64,1} │
-├──────────────────────────────────┴───────────────────────────────────── dims ┐
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
   ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
@@ -95,7 +94,7 @@
 ax.xticklabelalign = (:right, :center)
 fig
 current_figure()

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diff --git a/previews/PR479/tutorials/other_tutorials.html b/previews/PR479/tutorials/other_tutorials.html
index ad25939d..dbaa3b13 100644
--- a/previews/PR479/tutorials/other_tutorials.html
+++ b/previews/PR479/tutorials/other_tutorials.html
@@ -9,9 +9,9 @@
     
     
     
-    
+    
     
-    
+    
     
     
     
@@ -22,7 +22,7 @@
   
   
     
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Other tutorials ​
If you are interested in learning how to work with YAXArrays for different use cases you can follow along one of the following tutorials.
  • Currently the overview tutorial is located at ESDLTutorials Repository
  • You can find further tutorial videos at the EO College. Beware that the syntax in the video tutorials might be slightly changed.
  • the other tutorials are still work in progress.
General overview of the functionality of YAXArrays ​
This tutorial provides a broad overview about the features of YAXArrays.
Table-style iteration over YAXArrays ​
Work in progress
Sometimes you want to combine the data that is represented in the data cube with other datasets, which are best described as a data frame. In this tutorial you will learn how to use the Tables.jl interface to iterate over the data in the YAXArray.
Combining multiple tiff files into a zarr based datacube ​

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diff --git a/previews/PR479/tutorials/plottingmaps.html b/previews/PR479/tutorials/plottingmaps.html
index c80a7bc6..f3245f15 100644
--- a/previews/PR479/tutorials/plottingmaps.html
+++ b/previews/PR479/tutorials/plottingmaps.html
@@ -9,9 +9,9 @@
     
     
     
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@@ -75,7 +75,7 @@
 zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
 rotate!(ax.scene, 2.5)
 fig
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