From 360be006badcee00e26f975f9bfc2fdb4b8458d8 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Thu, 19 Sep 2024 18:45:13 +0000 Subject: [PATCH] build based on f3ee422 --- dev/404.html | 8 +-- dev/UserGuide/cache.html | 10 ++-- dev/UserGuide/chunk.html | 10 ++-- dev/UserGuide/combine.html | 10 ++-- dev/UserGuide/compute.html | 30 +++++----- dev/UserGuide/convert.html | 48 ++++++++-------- dev/UserGuide/create.html | 10 ++-- dev/UserGuide/faq.html | 42 +++++++------- dev/UserGuide/group.html | 14 ++--- dev/UserGuide/read.html | 14 ++--- dev/UserGuide/select.html | 10 ++-- dev/UserGuide/types.html | 10 ++-- dev/UserGuide/write.html | 34 +++++------ dev/api.html | 22 +++---- ...L-.js => UserGuide_compute.md.BLzjfYSH.js} | 18 +++--- ... => UserGuide_compute.md.BLzjfYSH.lean.js} | 18 +++--- ...jR.js => UserGuide_convert.md.BTPZ4pOo.js} | 36 ++++++------ ... => UserGuide_convert.md.BTPZ4pOo.lean.js} | 36 ++++++------ ...xbU8uL.js => UserGuide_faq.md.C4SnKlqV.js} | 30 +++++----- ...n.js => UserGuide_faq.md.C4SnKlqV.lean.js} | 30 +++++----- ...3ZFU.js => UserGuide_group.md.j3EByEps.js} | 2 +- ...js => UserGuide_group.md.j3EByEps.lean.js} | 2 +- ...a96ai.js => UserGuide_read.md.C3NhNh2F.js} | 2 +- ....js => UserGuide_read.md.C3NhNh2F.lean.js} | 2 +- ...TqhY.js => UserGuide_write.md.DPsjh-L9.js} | 24 ++++---- ...js => UserGuide_write.md.DPsjh-L9.lean.js} | 24 ++++---- dev/assets/api.md.Czz2cpaE.js | 13 ----- dev/assets/api.md.Czz2cpaE.lean.js | 13 ----- dev/assets/api.md.D6o6S_4N.js | 10 ++++ dev/assets/api.md.D6o6S_4N.lean.js | 10 ++++ .../{app.By04Axg6.js => app.B_iBQCZV.js} | 2 +- dev/assets/bvbfegb.Bcoj2HvJ.jpeg | Bin 38202 -> 0 bytes dev/assets/bvtlvgr.B7sKb-Og.png | Bin 0 -> 80591 bytes .../chunks/@localSearchIndexroot.3d9PN5N8.js | 1 + .../chunks/@localSearchIndexroot.DyoBPTFo.js | 1 - 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+ - + - +
- + \ No newline at end of file diff --git a/dev/UserGuide/cache.html b/dev/UserGuide/cache.html index 7201e009..112d9135 100644 --- a/dev/UserGuide/cache.html +++ b/dev/UserGuide/cache.html @@ -6,14 +6,14 @@ Caching YAXArrays | YAXArrays.jl - + - + - + - + @@ -23,7 +23,7 @@ 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/dev/UserGuide/chunk.html b/dev/UserGuide/chunk.html index 66cd5603..5c00dab0 100644 --- a/dev/UserGuide/chunk.html +++ b/dev/UserGuide/chunk.html @@ -6,14 +6,14 @@ Chunk YAXArrays | YAXArrays.jl - + - + - + - + @@ -116,7 +116,7 @@ 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/dev/UserGuide/combine.html b/dev/UserGuide/combine.html index 1c0bc2ed..e65c435e 100644 --- a/dev/UserGuide/combine.html +++ b/dev/UserGuide/combine.html @@ -6,14 +6,14 @@ Combine YAXArrays | YAXArrays.jl - + - + - + - + @@ -46,7 +46,7 @@ ├───────────────────────────────────────────────────── file size ┤ file size: 96.0 bytes └────────────────────────────────────────────────────────────────┘ - + \ No newline at end of file diff --git a/dev/UserGuide/compute.html b/dev/UserGuide/compute.html index c70efd9a..a730e413 100644 --- a/dev/UserGuide/compute.html +++ b/dev/UserGuide/compute.html @@ -6,14 +6,14 @@ Compute YAXArrays | YAXArrays.jl - + - + - + - - + + @@ -39,7 +39,7 @@ :origin => "user guide" ├─────────────────────────────────────────────────────────────────── file size ┤ file size: 35.16 KB -└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8329685182217375
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
╭──────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.025324579638537736
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,
@@ -222,14 +222,14 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0       1.28571     1.57143     …  4.42857    4.71429    5.0
-  1.0  0.910983  0.26493     0.60037        0.712525   0.219355   0.191605
-  2.0  0.809596  0.820355    0.00974435     0.904112   0.812749   0.0646667
-  3.0  0.990385  0.618667    0.416924       0.175702   0.831285   0.838209
-  ⋮                                      ⋱                        ⋮
-  8.0  0.855011  0.00146845  0.644835       0.0311724  0.885093   0.706774
-  9.0  0.327124  0.739298    0.14481        0.844681   0.0182801  0.604438
- 10.0  0.401121  0.406059    0.637282    …  0.12146    0.356502   0.814431

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
+  ↓ →  1.0       1.28571    1.57143    …  4.42857   4.71429   5.0
+  1.0  0.375821  0.560766   0.884426      0.890564  0.228407  0.912908
+  2.0  0.255229  0.375382   0.403859      0.274981  0.426353  0.518274
+  3.0  0.42107   0.922232   0.565869      0.829112  0.627852  0.487919
+  ⋮                                    ⋱                      ⋮
+  8.0  0.655588  0.304365   0.0466898     0.854413  0.177804  0.253668
+  9.0  0.947859  0.0858109  0.13364       0.394292  0.470232  0.420855
+ 10.0  0.248372  0.196275   0.297128   …  0.173796  0.190084  0.633491

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
 points_of_regions = map(enumerate(regions)) do (i,region)
     region => findall(isequal(region), regions_mat)
 end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
@@ -283,7 +283,7 @@
 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/dev/UserGuide/convert.html b/dev/UserGuide/convert.html index 8b8c0449..aedcefbc 100644 --- a/dev/UserGuide/convert.html +++ b/dev/UserGuide/convert.html @@ -6,14 +6,14 @@ Convert YAXArrays | YAXArrays.jl - + - + - + - - + + @@ -31,11 +31,11 @@ ├────────────────────────────────────────────────────────── file size ┤ file size: 400.0 bytes └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.719749   0.151377   0.981562  0.0519894  …  0.0591867  0.290459  0.770549
- 0.606585   0.0871085  0.887682  0.763154      0.3112     0.460844  0.783917
- 0.738476   0.821196   0.86214   0.706696      0.539204   0.942384  0.87077
- 0.284879   0.427933   0.814206  0.837723      0.595169   0.56795   0.739883
- 0.0115923  0.365387   0.761887  0.550946      0.94926    0.993443  0.520799

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
+ 0.524633  0.370482  0.412361  0.196559  …  0.238624  0.424653  0.40762
+ 0.594816  0.780489  0.54155   0.945465     0.644569  0.189047  0.183471
+ 0.81136   0.329104  0.749983  0.924787     0.201735  0.548653  0.58484
+ 0.41026   0.104324  0.141143  0.357791     0.134209  0.698667  0.56521
+ 0.874662  0.582269  0.421896  0.133941     0.477973  0.851218  0.513574

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -63,13 +63,13 @@
 
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  25          26         27          28         29          30
- 25     0.3178      0.325229   0.422698    0.255545   0.0550086   0.274048
- 26     0.0299239   0.355731   0.237918    0.433276   0.714768    0.219044
- 27     0.706141    0.700227   0.330262    0.620018   0.690047    0.322318
- 28     0.517699    0.127488   0.0768364   0.631014   0.292472    0.486169
- 29     0.243928    0.486085   0.843537    0.200422   0.115995    0.794568
- 30     0.42652     0.547011   0.718885    0.67165    0.226868    0.276756

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
+  ↓ →  25         26         27         28          29         30
+ 25     0.786711   0.189037   0.169373   0.965898    0.325074   0.400512
+ 26     0.311602   0.827386   0.362321   0.165219    0.670214   0.0315953
+ 27     0.229491   0.865433   0.133521   0.157064    0.508858   0.0284624
+ 28     0.273382   0.143182   0.489026   0.890739    0.697903   0.454512
+ 29     0.158818   0.402114   0.156629   0.0693534   0.121691   0.521615
+ 30     0.430677   0.386331   0.397729   0.65228     0.812053   0.515333

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
@@ -90,13 +90,13 @@
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       11.0        12.0       13.0       14.0        15.0
- 1     0.521161   0.0742493   0.111182   0.879808   0.369484    0.728395
- 2     0.12119    0.834542    0.952605   0.881662   0.825379    0.985806
- 3     0.883008   0.815705    0.726456   0.749105   0.0862858   0.114351
- 4     0.86782    0.70031     0.951969   0.931922   0.919482    0.0926064
- 5     0.939667   0.728204    0.883542   0.876702   0.374626    0.26991

INFO

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

- + ↓ → 10.0 11.0 12.0 13.0 14.0 15.0 + 1 0.462248 0.834717 0.21044 0.691133 0.483961 0.0254053 + 2 0.517977 0.329037 0.624566 0.779341 0.16948 0.176568 + 3 0.730595 0.895435 0.0725453 0.498221 0.563674 0.866756 + 4 0.0453567 0.479338 0.493645 0.887853 0.546619 0.956343 + 5 0.00499699 0.886718 0.251572 0.908424 0.228948 0.840967

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/dev/UserGuide/create.html b/dev/UserGuide/create.html index 92b66a8c..04929fb5 100644 --- a/dev/UserGuide/create.html +++ b/dev/UserGuide/create.html @@ -6,14 +6,14 @@ Create YAXArrays and Datasets | YAXArrays.jl - + - + - + - + @@ -66,7 +66,7 @@ a2, a3 Properties: Dict(:origin => "user guide") - + \ No newline at end of file diff --git a/dev/UserGuide/faq.html b/dev/UserGuide/faq.html index f6814a47..363452bf 100644 --- a/dev/UserGuide/faq.html +++ b/dev/UserGuide/faq.html @@ -6,14 +6,14 @@ Frequently Asked Questions (FAQ) | YAXArrays.jl - + - + - + - - + + @@ -214,20 +214,20 @@ None Variables with additional axes: Additional Axes: - (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points) + (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points) Variables: longitudes Additional Axes: - (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points) + (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points, + → Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points) Variables: - latitudes + temperature Additional Axes: - (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points, - → Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points) + (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points) Variables: - temperature

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"])
+  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_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
@@ -237,18 +237,18 @@
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  longitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
   latitudes

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}
@@ -297,7 +297,7 @@
 fig, ax, obj = heatmap(classes;
     colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
 cbar = Colorbar(fig[1,2], obj)
-fig

Now we define the input cubes that will be considered for the iterable table

julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+fig

Now we define the input cubes that will be considered for the iterable table

julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
@@ -358,7 +358,7 @@
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
@@ -370,14 +370,14 @@
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
 variable 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.

- + \ No newline at end of file diff --git a/dev/UserGuide/group.html b/dev/UserGuide/group.html index 35a0f3de..b75d1227 100644 --- a/dev/UserGuide/group.html +++ b/dev/UserGuide/group.html @@ -6,14 +6,14 @@ Group YAXArrays and Datasets | YAXArrays.jl - + - + - + - - + + @@ -207,8 +207,8 @@ colgap!(fig.layout, 5) rowgap!(fig.layout, 5) fig -end

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

- +end

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

+ \ No newline at end of file diff --git a/dev/UserGuide/read.html b/dev/UserGuide/read.html index 0d1ea97d..046b2ff9 100644 --- a/dev/UserGuide/read.html +++ b/dev/UserGuide/read.html @@ -6,14 +6,14 @@ Read YAXArrays and Datasets | YAXArrays.jl - + - + - + - - + + @@ -95,9 +95,9 @@ using Downloads: download path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif") -# ds = open_dataset(path) # this is broken +ds = open_dataset(path) nothing
[ Info: new driver key :gdal, updating backendlist.
- + \ No newline at end of file diff --git a/dev/UserGuide/select.html b/dev/UserGuide/select.html index c39e7225..439a7e0b 100644 --- a/dev/UserGuide/select.html +++ b/dev/UserGuide/select.html @@ -6,14 +6,14 @@ Select YAXArrays and Datasets | YAXArrays.jl - + - + - + - + @@ -312,7 +312,7 @@ 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/dev/UserGuide/types.html b/dev/UserGuide/types.html index 0813644a..77f20138 100644 --- a/dev/UserGuide/types.html +++ b/dev/UserGuide/types.html @@ -6,20 +6,20 @@ Types | YAXArrays.jl - + - + - + - +
Skip to content

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/dev/UserGuide/write.html b/dev/UserGuide/write.html index 9fcbb4cd..1f6ea2f5 100644 --- a/dev/UserGuide/write.html +++ b/dev/UserGuide/write.html @@ -6,14 +6,14 @@ Write YAXArrays and Datasets | YAXArrays.jl - + - + - + - - + + @@ -34,7 +34,7 @@ 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"…)

Write Zarr

Save a single YAXArray to a directory:

julia
using Zarr
 savecube(ds.tos, "tos.zarr", driver=:zarr)

Save an entire Dataset to a directory:

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

Write NetCDF

Save a single YAXArray to a directory:

julia
using NetCDF
-savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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.savedatasetFunction.

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)))
+savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -72,19 +72,19 @@
 Variables: 
 skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset

Now, we show how to start updating the array values. In order to do it we need to open the dataset first with writing w rights as follows:

julia
ds_open = zopen("skeleton.zarr", "w")
 ds_array = ds_open["skeleton"]
ZArray{Float32} of size 5 x 4 x 5

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
+ 0.359879  0.538008   0.587219  0.740689
+ 0.859139  0.0239619  0.131405  0.652891
+ 0.246337  0.351488   0.684753  0.414051
+ 0.831863  0.496363   0.440468  0.879778
+ 0.729017  0.172771   0.24201   0.972012

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

indeed, those entries had been updated.

- + 0.359879 0.538008 0.587219 0.740689 + 0.859139 0.0239619 0.131405 0.652891 + 0.246337 0.351488 0.684753 0.414051 + 0.831863 0.496363 0.440468 0.879778 + 0.729017 0.172771 0.24201 0.972012

indeed, those entries had been updated.

+ \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index ac0ad49e..956952e3 100644 --- a/dev/api.html +++ b/dev/api.html @@ -6,32 +6,32 @@ API Reference | YAXArrays.jl - + - + - + - - + + -
Skip to content

API Reference

This section describes all available functions of this package.

Public API

# YAXArrays.getAxisMethod.
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.CubesModule.

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.YAXArrayType.
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.caxesFunction.

Returns the axes of a Cube

source


# YAXArrays.Cubes.caxesMethod.
julia
caxes

Embeds Cube inside a new Cube

source


# YAXArrays.Cubes.concatenatecubesMethod.
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.readcubedataMethod.
julia
readcubedata(cube)

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

source


# YAXArrays.Cubes.setchunksMethod.
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.subsetcubeFunction.

This function calculates a subset of a cube's data

source


# YAXArrays.DAT.InDimsType.
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.MovingWindowType.
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.OutDimsMethod.
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.CubeTableMethod.
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.cubefittableMethod.
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.fittableMethod.
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.mapCubeMethod.
julia
mapCube(fun, cube, addargs...;kwargs...)
+    
Skip to content

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.mapCubeMethod.
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.DatasetType.
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.
+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.DatasetMethod.

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.CubeMethod.
julia
Cube(ds::Dataset; joinname="Variable")

Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.

source


# YAXArrays.Datasets.open_datasetMethod.

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.savecubeMethod.
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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.savedatasetMethod.

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_datasetMethod.

to_dataset(c;datasetaxis = "Variable", 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.YAXDefaultsConstant.

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.findAxisMethod.
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.getOutAxisMethod.
julia
getOutAxis

source


# YAXArrays.get_descriptorMethod.
julia
get_descriptor(a)

Get the descriptor of an Axis. This is used to dispatch on the descriptor.

source


# YAXArrays.match_axisMethod.
julia
match_axis

Internal function

Extended Help

Match the Axis based on the AxisDescriptor.
+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="Variable")

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.savecube Method
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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 = "Variable", 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.CleanMeType.
julia
mutable struct CleanMe

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

source


# YAXArrays.Cubes.cleanMethod.
julia
clean(c::CleanMe)

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

source


# YAXArrays.Cubes.copydataMethod.
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.optifuncMethod.
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.DATConfigType.

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

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

  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} 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.InputCubeType.

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.OutputCubeType.

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.YAXColumnType.
julia
YAXColumn

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

  • inarBC

  • inds

source


# YAXArrays.DAT.cmpcachmissesMethod.

Function that compares two cache miss specifiers by their importance

source


# YAXArrays.DAT.getFrontPermMethod.

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

source


# YAXArrays.DAT.getLoopCacheSizeMethod.

Calculate optimal Cache size to DAT operation

source


# YAXArrays.DAT.getOuttypeMethod.
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source


# YAXArrays.DAT.getloopchunksMethod.
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.permuteloopaxesMethod.
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.setchunksMethod.
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.collectfromhandleMethod.

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

source


# YAXArrays.Datasets.createdatasetMethod.

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="Variable" 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.getarrayinfoMethod.

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

source


# YAXArrays.Datasets.testrangeMethod.

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:

source

# YAXArrays.DAT.InputCube Type

Internal representation of an input cube for DAT operations

source

# YAXArrays.DAT.OutputCube Type

Internal representation of an output cube for DAT operations

Fields

source

# YAXArrays.DAT.YAXColumn Type
julia
YAXColumn

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

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:

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

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

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/dev/assets/UserGuide_compute.md.l32-sEL-.js b/dev/assets/UserGuide_compute.md.BLzjfYSH.js similarity index 94% rename from dev/assets/UserGuide_compute.md.l32-sEL-.js rename to dev/assets/UserGuide_compute.md.BLzjfYSH.js index 4d50f5bb..7593a0cd 100644 --- a/dev/assets/UserGuide_compute.md.l32-sEL-.js +++ b/dev/assets/UserGuide_compute.md.BLzjfYSH.js @@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g :origin => "user guide" ├─────────────────────────────────────────────────────────────────── file size ┤ file size: 35.16 KB -└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8329685182217375
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
╭──────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.025324579638537736
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,
@@ -202,14 +202,14 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0       1.28571     1.57143     …  4.42857    4.71429    5.0
-  1.0  0.910983  0.26493     0.60037        0.712525   0.219355   0.191605
-  2.0  0.809596  0.820355    0.00974435     0.904112   0.812749   0.0646667
-  3.0  0.990385  0.618667    0.416924       0.175702   0.831285   0.838209
-  ⋮                                      ⋱                        ⋮
-  8.0  0.855011  0.00146845  0.644835       0.0311724  0.885093   0.706774
-  9.0  0.327124  0.739298    0.14481        0.844681   0.0182801  0.604438
- 10.0  0.401121  0.406059    0.637282    …  0.12146    0.356502   0.814431

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
+  ↓ →  1.0       1.28571    1.57143    …  4.42857   4.71429   5.0
+  1.0  0.375821  0.560766   0.884426      0.890564  0.228407  0.912908
+  2.0  0.255229  0.375382   0.403859      0.274981  0.426353  0.518274
+  3.0  0.42107   0.922232   0.565869      0.829112  0.627852  0.487919
+  ⋮                                    ⋱                      ⋮
+  8.0  0.655588  0.304365   0.0466898     0.854413  0.177804  0.253668
+  9.0  0.947859  0.0858109  0.13364       0.394292  0.470232  0.420855
+ 10.0  0.248372  0.196275   0.297128   …  0.173796  0.190084  0.633491

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
 points_of_regions = map(enumerate(regions)) do (i,region)
     region => findall(isequal(region), regions_mat)
 end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
diff --git a/dev/assets/UserGuide_compute.md.l32-sEL-.lean.js b/dev/assets/UserGuide_compute.md.BLzjfYSH.lean.js
similarity index 94%
rename from dev/assets/UserGuide_compute.md.l32-sEL-.lean.js
rename to dev/assets/UserGuide_compute.md.BLzjfYSH.lean.js
index 4d50f5bb..7593a0cd 100644
--- a/dev/assets/UserGuide_compute.md.l32-sEL-.lean.js
+++ b/dev/assets/UserGuide_compute.md.BLzjfYSH.lean.js
@@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g
   :origin => "user guide"
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8329685182217375
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
╭──────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.025324579638537736
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,
@@ -202,14 +202,14 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0       1.28571     1.57143     …  4.42857    4.71429    5.0
-  1.0  0.910983  0.26493     0.60037        0.712525   0.219355   0.191605
-  2.0  0.809596  0.820355    0.00974435     0.904112   0.812749   0.0646667
-  3.0  0.990385  0.618667    0.416924       0.175702   0.831285   0.838209
-  ⋮                                      ⋱                        ⋮
-  8.0  0.855011  0.00146845  0.644835       0.0311724  0.885093   0.706774
-  9.0  0.327124  0.739298    0.14481        0.844681   0.0182801  0.604438
- 10.0  0.401121  0.406059    0.637282    …  0.12146    0.356502   0.814431

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
+  ↓ →  1.0       1.28571    1.57143    …  4.42857   4.71429   5.0
+  1.0  0.375821  0.560766   0.884426      0.890564  0.228407  0.912908
+  2.0  0.255229  0.375382   0.403859      0.274981  0.426353  0.518274
+  3.0  0.42107   0.922232   0.565869      0.829112  0.627852  0.487919
+  ⋮                                    ⋱                      ⋮
+  8.0  0.655588  0.304365   0.0466898     0.854413  0.177804  0.253668
+  9.0  0.947859  0.0858109  0.13364       0.394292  0.470232  0.420855
+ 10.0  0.248372  0.196275   0.297128   …  0.173796  0.190084  0.633491

Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.

julia
regions = ["A", "B", "C", "D"]
 points_of_regions = map(enumerate(regions)) do (i,region)
     region => findall(isequal(region), regions_mat)
 end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
diff --git a/dev/assets/UserGuide_convert.md.CaREO6jR.js b/dev/assets/UserGuide_convert.md.BTPZ4pOo.js
similarity index 80%
rename from dev/assets/UserGuide_convert.md.CaREO6jR.js
rename to dev/assets/UserGuide_convert.md.BTPZ4pOo.js
index 63e4ca7c..6bcf75b2 100644
--- a/dev/assets/UserGuide_convert.md.CaREO6jR.js
+++ b/dev/assets/UserGuide_convert.md.BTPZ4pOo.js
@@ -11,11 +11,11 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c
 ├────────────────────────────────────────────────────────── file size ┤ 
   file size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.719749   0.151377   0.981562  0.0519894  …  0.0591867  0.290459  0.770549
- 0.606585   0.0871085  0.887682  0.763154      0.3112     0.460844  0.783917
- 0.738476   0.821196   0.86214   0.706696      0.539204   0.942384  0.87077
- 0.284879   0.427933   0.814206  0.837723      0.595169   0.56795   0.739883
- 0.0115923  0.365387   0.761887  0.550946      0.94926    0.993443  0.520799

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
+ 0.524633  0.370482  0.412361  0.196559  …  0.238624  0.424653  0.40762
+ 0.594816  0.780489  0.54155   0.945465     0.644569  0.189047  0.183471
+ 0.81136   0.329104  0.749983  0.924787     0.201735  0.548653  0.58484
+ 0.41026   0.104324  0.141143  0.357791     0.134209  0.698667  0.56521
+ 0.874662  0.582269  0.421896  0.133941     0.477973  0.851218  0.513574

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -43,13 +43,13 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c
 
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  25          26         27          28         29          30
- 25     0.3178      0.325229   0.422698    0.255545   0.0550086   0.274048
- 26     0.0299239   0.355731   0.237918    0.433276   0.714768    0.219044
- 27     0.706141    0.700227   0.330262    0.620018   0.690047    0.322318
- 28     0.517699    0.127488   0.0768364   0.631014   0.292472    0.486169
- 29     0.243928    0.486085   0.843537    0.200422   0.115995    0.794568
- 30     0.42652     0.547011   0.718885    0.67165    0.226868    0.276756

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
+  ↓ →  25         26         27         28          29         30
+ 25     0.786711   0.189037   0.169373   0.965898    0.325074   0.400512
+ 26     0.311602   0.827386   0.362321   0.165219    0.670214   0.0315953
+ 27     0.229491   0.865433   0.133521   0.157064    0.508858   0.0284624
+ 28     0.273382   0.143182   0.489026   0.890739    0.697903   0.454512
+ 29     0.158818   0.402114   0.156629   0.0693534   0.121691   0.521615
+ 30     0.430677   0.386331   0.397729   0.65228     0.812053   0.515333

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
@@ -70,9 +70,9 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       11.0        12.0       13.0       14.0        15.0
- 1     0.521161   0.0742493   0.111182   0.879808   0.369484    0.728395
- 2     0.12119    0.834542    0.952605   0.881662   0.825379    0.985806
- 3     0.883008   0.815705    0.726456   0.749105   0.0862858   0.114351
- 4     0.86782    0.70031     0.951969   0.931922   0.919482    0.0926064
- 5     0.939667   0.728204    0.883542   0.876702   0.374626    0.26991

INFO

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

`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default}; + ↓ → 10.0 11.0 12.0 13.0 14.0 15.0 + 1 0.462248 0.834717 0.21044 0.691133 0.483961 0.0254053 + 2 0.517977 0.329037 0.624566 0.779341 0.16948 0.176568 + 3 0.730595 0.895435 0.0725453 0.498221 0.563674 0.866756 + 4 0.0453567 0.479338 0.493645 0.887853 0.546619 0.956343 + 5 0.00499699 0.886718 0.251572 0.908424 0.228948 0.840967

INFO

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

`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default}; diff --git a/dev/assets/UserGuide_convert.md.CaREO6jR.lean.js b/dev/assets/UserGuide_convert.md.BTPZ4pOo.lean.js similarity index 80% rename from dev/assets/UserGuide_convert.md.CaREO6jR.lean.js rename to dev/assets/UserGuide_convert.md.BTPZ4pOo.lean.js index 63e4ca7c..6bcf75b2 100644 --- a/dev/assets/UserGuide_convert.md.CaREO6jR.lean.js +++ b/dev/assets/UserGuide_convert.md.BTPZ4pOo.lean.js @@ -11,11 +11,11 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c ├────────────────────────────────────────────────────────── file size ┤ file size: 400.0 bytes └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.719749   0.151377   0.981562  0.0519894  …  0.0591867  0.290459  0.770549
- 0.606585   0.0871085  0.887682  0.763154      0.3112     0.460844  0.783917
- 0.738476   0.821196   0.86214   0.706696      0.539204   0.942384  0.87077
- 0.284879   0.427933   0.814206  0.837723      0.595169   0.56795   0.739883
- 0.0115923  0.365387   0.761887  0.550946      0.94926    0.993443  0.520799

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
+ 0.524633  0.370482  0.412361  0.196559  …  0.238624  0.424653  0.40762
+ 0.594816  0.780489  0.54155   0.945465     0.644569  0.189047  0.183471
+ 0.81136   0.329104  0.749983  0.924787     0.201735  0.548653  0.58484
+ 0.41026   0.104324  0.141143  0.357791     0.134209  0.698667  0.56521
+ 0.874662  0.582269  0.421896  0.133941     0.477973  0.851218  0.513574

Convert Raster

A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:

julia
using Rasters
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -43,13 +43,13 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c
 
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  25          26         27          28         29          30
- 25     0.3178      0.325229   0.422698    0.255545   0.0550086   0.274048
- 26     0.0299239   0.355731   0.237918    0.433276   0.714768    0.219044
- 27     0.706141    0.700227   0.330262    0.620018   0.690047    0.322318
- 28     0.517699    0.127488   0.0768364   0.631014   0.292472    0.486169
- 29     0.243928    0.486085   0.843537    0.200422   0.115995    0.794568
- 30     0.42652     0.547011   0.718885    0.67165    0.226868    0.276756

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
+  ↓ →  25         26         27         28          29         30
+ 25     0.786711   0.189037   0.169373   0.965898    0.325074   0.400512
+ 26     0.311602   0.827386   0.362321   0.165219    0.670214   0.0315953
+ 27     0.229491   0.865433   0.133521   0.157064    0.508858   0.0284624
+ 28     0.273382   0.143182   0.489026   0.890739    0.697903   0.454512
+ 29     0.158818   0.402114   0.156629   0.0693534   0.121691   0.521615
+ 30     0.430677   0.386331   0.397729   0.65228     0.812053   0.515333

Convert DimArray

A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.

Convert DimArray to YAXArray:

julia
using DimensionalData
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
@@ -70,9 +70,9 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       11.0        12.0       13.0       14.0        15.0
- 1     0.521161   0.0742493   0.111182   0.879808   0.369484    0.728395
- 2     0.12119    0.834542    0.952605   0.881662   0.825379    0.985806
- 3     0.883008   0.815705    0.726456   0.749105   0.0862858   0.114351
- 4     0.86782    0.70031     0.951969   0.931922   0.919482    0.0926064
- 5     0.939667   0.728204    0.883542   0.876702   0.374626    0.26991

INFO

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

`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default}; + ↓ → 10.0 11.0 12.0 13.0 14.0 15.0 + 1 0.462248 0.834717 0.21044 0.691133 0.483961 0.0254053 + 2 0.517977 0.329037 0.624566 0.779341 0.16948 0.176568 + 3 0.730595 0.895435 0.0725453 0.498221 0.563674 0.866756 + 4 0.0453567 0.479338 0.493645 0.887853 0.546619 0.956343 + 5 0.00499699 0.886718 0.251572 0.908424 0.228948 0.840967

INFO

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

`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default}; diff --git a/dev/assets/UserGuide_faq.md.D4xbU8uL.js b/dev/assets/UserGuide_faq.md.C4SnKlqV.js similarity index 98% rename from dev/assets/UserGuide_faq.md.D4xbU8uL.js rename to dev/assets/UserGuide_faq.md.C4SnKlqV.js index 10f0017e..e0c1b383 100644 --- a/dev/assets/UserGuide_faq.md.D4xbU8uL.js +++ b/dev/assets/UserGuide_faq.md.C4SnKlqV.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/bvbfegb.Bcoj2HvJ.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
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/pjhcdwy.-oFrkB6k.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 ┐
@@ -194,20 +194,20 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  latitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
-  temperature

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"])
+  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_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
@@ -217,18 +217,18 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  longitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
   latitudes

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}
@@ -338,7 +338,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
@@ -350,7 +350,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
 variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
diff --git a/dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js b/dev/assets/UserGuide_faq.md.C4SnKlqV.lean.js
similarity index 98%
rename from dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js
rename to dev/assets/UserGuide_faq.md.C4SnKlqV.lean.js
index 10f0017e..e0c1b383 100644
--- a/dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js
+++ b/dev/assets/UserGuide_faq.md.C4SnKlqV.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/bvbfegb.Bcoj2HvJ.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
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/pjhcdwy.-oFrkB6k.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 ┐
@@ -194,20 +194,20 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  latitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
-  temperature

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"])
+  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_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
@@ -217,18 +217,18 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  longitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 3, …, 94, 100] ForwardOrdered Irregular Points)
   Variables: 
   latitudes

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}
@@ -338,7 +338,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
@@ -350,7 +350,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t
   (time     Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
 variable 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,
 variable Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
diff --git a/dev/assets/UserGuide_group.md.DBOT3ZFU.js b/dev/assets/UserGuide_group.md.j3EByEps.js
similarity index 99%
rename from dev/assets/UserGuide_group.md.DBOT3ZFU.js
rename to dev/assets/UserGuide_group.md.j3EByEps.js
index db3a3291..9c272fd8 100644
--- a/dev/assets/UserGuide_group.md.DBOT3ZFU.js
+++ b/dev/assets/UserGuide_group.md.j3EByEps.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/gqwibql.CBBZcGwj.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 h(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.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/omgwzta.CBBZcGwj.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 h(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
 using NetCDF
 using Downloads
 using Dates
diff --git a/dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js b/dev/assets/UserGuide_group.md.j3EByEps.lean.js
similarity index 99%
rename from dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js
rename to dev/assets/UserGuide_group.md.j3EByEps.lean.js
index db3a3291..9c272fd8 100644
--- a/dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js
+++ b/dev/assets/UserGuide_group.md.j3EByEps.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/gqwibql.CBBZcGwj.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 h(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.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/omgwzta.CBBZcGwj.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 h(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
 using NetCDF
 using Downloads
 using Dates
diff --git a/dev/assets/UserGuide_read.md.B25a96ai.js b/dev/assets/UserGuide_read.md.C3NhNh2F.js
similarity index 98%
rename from dev/assets/UserGuide_read.md.B25a96ai.js
rename to dev/assets/UserGuide_read.md.C3NhNh2F.js
index bfb70f21..9cc9b7f8 100644
--- a/dev/assets/UserGuide_read.md.B25a96ai.js
+++ b/dev/assets/UserGuide_read.md.C3NhNh2F.js
@@ -75,5 +75,5 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const c
 using Downloads: download
 
 path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
-# ds = open_dataset(path) # this is broken
+ds = open_dataset(path)
 nothing
[ Info: new driver key :gdal, updating backendlist.
`,21)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default}; diff --git a/dev/assets/UserGuide_read.md.B25a96ai.lean.js b/dev/assets/UserGuide_read.md.C3NhNh2F.lean.js similarity index 98% rename from dev/assets/UserGuide_read.md.B25a96ai.lean.js rename to dev/assets/UserGuide_read.md.C3NhNh2F.lean.js index bfb70f21..9cc9b7f8 100644 --- a/dev/assets/UserGuide_read.md.B25a96ai.lean.js +++ b/dev/assets/UserGuide_read.md.C3NhNh2F.lean.js @@ -75,5 +75,5 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const c using Downloads: download path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif") -# ds = open_dataset(path) # this is broken +ds = open_dataset(path) nothing
[ Info: new driver key :gdal, updating backendlist.
`,21)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default}; diff --git a/dev/assets/UserGuide_write.md.CxpbTqhY.js b/dev/assets/UserGuide_write.md.DPsjh-L9.js similarity index 86% rename from dev/assets/UserGuide_write.md.CxpbTqhY.js rename to dev/assets/UserGuide_write.md.DPsjh-L9.js index bc8d0e93..1e1df744 100644 --- a/dev/assets/UserGuide_write.md.CxpbTqhY.js +++ b/dev/assets/UserGuide_write.md.DPsjh-L9.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),n={name:"UserGuide/write.md"};function l(p,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[e(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
+import{_ as n,c as l,a2 as e,j as i,a,G as p,B as h,o as k}from"./chunks/framework.BgERvflD.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,u,E){const t=h("Badge");return k(),l("div",null,[s[8]||(s[8]=e(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -14,7 +14,7 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g
 
 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"…)

Write Zarr

Save a single YAXArray to a directory:

julia
using Zarr
 savecube(ds.tos, "tos.zarr", driver=:zarr)

Save an entire Dataset to a directory:

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

Write NetCDF

Save a single YAXArray to a directory:

julia
using NetCDF
-savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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.savedatasetFunction.

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)))
+savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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

`,19)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},"#",-1)),s[1]||(s[1]=a()),s[2]||(s[2]=i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset",-1)),s[3]||(s[3]=a()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[4]||(s[4]=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[5]||(s[5]=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[6]||(s[6]=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[7]||(s[7]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[9]||(s[9]=e(`

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
@@ -52,15 +52,15 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g
 Variables: 
 skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset

Now, we show how to start updating the array values. In order to do it we need to open the dataset first with writing w rights as follows:

julia
ds_open = zopen("skeleton.zarr", "w")
 ds_array = ds_open["skeleton"]
ZArray{Float32} of size 5 x 4 x 5

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
+ 0.359879  0.538008   0.587219  0.740689
+ 0.859139  0.0239619  0.131405  0.652891
+ 0.246337  0.351488   0.684753  0.414051
+ 0.831863  0.496363   0.440468  0.879778
+ 0.729017  0.172771   0.24201   0.972012

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

indeed, those entries had been updated.

`,56)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default}; + 0.359879 0.538008 0.587219 0.740689 + 0.859139 0.0239619 0.131405 0.652891 + 0.246337 0.351488 0.684753 0.414051 + 0.831863 0.496363 0.440468 0.879778 + 0.729017 0.172771 0.24201 0.972012

indeed, those entries had been updated.

`,35))])}const f=n(d,[["render",o]]);export{v as __pageData,f as default}; diff --git a/dev/assets/UserGuide_write.md.CxpbTqhY.lean.js b/dev/assets/UserGuide_write.md.DPsjh-L9.lean.js similarity index 86% rename from dev/assets/UserGuide_write.md.CxpbTqhY.lean.js rename to dev/assets/UserGuide_write.md.DPsjh-L9.lean.js index bc8d0e93..1e1df744 100644 --- a/dev/assets/UserGuide_write.md.CxpbTqhY.lean.js +++ b/dev/assets/UserGuide_write.md.DPsjh-L9.lean.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),n={name:"UserGuide/write.md"};function l(p,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[e(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
+import{_ as n,c as l,a2 as e,j as i,a,G as p,B as h,o as k}from"./chunks/framework.BgERvflD.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,u,E){const t=h("Badge");return k(),l("div",null,[s[8]||(s[8]=e(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -14,7 +14,7 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g
 
 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"…)

Write Zarr

Save a single YAXArray to a directory:

julia
using Zarr
 savecube(ds.tos, "tos.zarr", driver=:zarr)

Save an entire Dataset to a directory:

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

Write NetCDF

Save a single YAXArray to a directory:

julia
using NetCDF
-savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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.savedatasetFunction.

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)))
+savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

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

`,19)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},"#",-1)),s[1]||(s[1]=a()),s[2]||(s[2]=i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset",-1)),s[3]||(s[3]=a()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[4]||(s[4]=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[5]||(s[5]=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[6]||(s[6]=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[7]||(s[7]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[9]||(s[9]=e(`

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
@@ -52,15 +52,15 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const g
 Variables: 
 skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset

Now, we show how to start updating the array values. In order to do it we need to open the dataset first with writing w rights as follows:

julia
ds_open = zopen("skeleton.zarr", "w")
 ds_array = ds_open["skeleton"]
ZArray{Float32} of size 5 x 4 x 5

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
+ 0.359879  0.538008   0.587219  0.740689
+ 0.859139  0.0239619  0.131405  0.652891
+ 0.246337  0.351488   0.684753  0.414051
+ 0.831863  0.496363   0.440468  0.879778
+ 0.729017  0.172771   0.24201   0.972012

we can verify is this working by loading again directly from disk

julia
ds_open = open_dataset("skeleton.zarr")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.846743  0.0140747  0.97676    0.145289
- 0.575242  0.52048    0.538205   0.859388
- 0.26917   0.59869    0.921279   0.107742
- 0.242777  0.064759   0.732488   0.754006
- 0.204435  0.401787   0.0058406  0.674613

indeed, those entries had been updated.

`,56)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default}; + 0.359879 0.538008 0.587219 0.740689 + 0.859139 0.0239619 0.131405 0.652891 + 0.246337 0.351488 0.684753 0.414051 + 0.831863 0.496363 0.440468 0.879778 + 0.729017 0.172771 0.24201 0.972012

indeed, those entries had been updated.

`,35))])}const f=n(d,[["render",o]]);export{v as __pageData,f as default}; diff --git a/dev/assets/api.md.Czz2cpaE.js b/dev/assets/api.md.Czz2cpaE.js deleted file mode 100644 index df42b8cf..00000000 --- a/dev/assets/api.md.Czz2cpaE.js +++ /dev/null @@ -1,13 +0,0 @@ -import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"};function o(l,e,d,n,p,c){return t(),s("div",null,e[0]||(e[0]=[i(`

API Reference

This section describes all available functions of this package.

Public API

# YAXArrays.getAxisMethod.
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.CubesModule.

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.YAXArrayType.
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.caxesFunction.

Returns the axes of a Cube

source


# YAXArrays.Cubes.caxesMethod.
julia
caxes

Embeds Cube inside a new Cube

source


# YAXArrays.Cubes.concatenatecubesMethod.
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.readcubedataMethod.
julia
readcubedata(cube)

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

source


# YAXArrays.Cubes.setchunksMethod.
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.subsetcubeFunction.

This function calculates a subset of a cube's data

source


# YAXArrays.DAT.InDimsType.
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.MovingWindowType.
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.OutDimsMethod.
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.CubeTableMethod.
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.cubefittableMethod.
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.fittableMethod.
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.mapCubeMethod.
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.mapCubeMethod.
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.DatasetType.
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.DatasetMethod.

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.CubeMethod.
julia
Cube(ds::Dataset; joinname="Variable")

Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.

source


# YAXArrays.Datasets.open_datasetMethod.

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.savecubeMethod.
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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.savedatasetMethod.

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_datasetMethod.

to_dataset(c;datasetaxis = "Variable", 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.YAXDefaultsConstant.

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.findAxisMethod.
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.getOutAxisMethod.
julia
getOutAxis

source


# YAXArrays.get_descriptorMethod.
julia
get_descriptor(a)

Get the descriptor of an Axis. This is used to dispatch on the descriptor.

source


# YAXArrays.match_axisMethod.
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.CleanMeType.
julia
mutable struct CleanMe

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

source


# YAXArrays.Cubes.cleanMethod.
julia
clean(c::CleanMe)

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

source


# YAXArrays.Cubes.copydataMethod.
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.optifuncMethod.
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.DATConfigType.

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

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

  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} 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.InputCubeType.

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.OutputCubeType.

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.YAXColumnType.
julia
YAXColumn

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

  • inarBC

  • inds

source


# YAXArrays.DAT.cmpcachmissesMethod.

Function that compares two cache miss specifiers by their importance

source


# YAXArrays.DAT.getFrontPermMethod.

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

source


# YAXArrays.DAT.getLoopCacheSizeMethod.

Calculate optimal Cache size to DAT operation

source


# YAXArrays.DAT.getOuttypeMethod.
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source


# YAXArrays.DAT.getloopchunksMethod.
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.permuteloopaxesMethod.
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.setchunksMethod.
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.collectfromhandleMethod.

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

source


# YAXArrays.Datasets.createdatasetMethod.

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="Variable" 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.getarrayinfoMethod.

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

source


# YAXArrays.Datasets.testrangeMethod.

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

source


`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default}; diff --git a/dev/assets/api.md.Czz2cpaE.lean.js b/dev/assets/api.md.Czz2cpaE.lean.js deleted file mode 100644 index df42b8cf..00000000 --- a/dev/assets/api.md.Czz2cpaE.lean.js +++ /dev/null @@ -1,13 +0,0 @@ -import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"};function o(l,e,d,n,p,c){return t(),s("div",null,e[0]||(e[0]=[i(`

API Reference

This section describes all available functions of this package.

Public API

# YAXArrays.getAxisMethod.
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.CubesModule.

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.YAXArrayType.
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.caxesFunction.

Returns the axes of a Cube

source


# YAXArrays.Cubes.caxesMethod.
julia
caxes

Embeds Cube inside a new Cube

source


# YAXArrays.Cubes.concatenatecubesMethod.
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.readcubedataMethod.
julia
readcubedata(cube)

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

source


# YAXArrays.Cubes.setchunksMethod.
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.subsetcubeFunction.

This function calculates a subset of a cube's data

source


# YAXArrays.DAT.InDimsType.
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.MovingWindowType.
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.OutDimsMethod.
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.CubeTableMethod.
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.cubefittableMethod.
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.fittableMethod.
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.mapCubeMethod.
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.mapCubeMethod.
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.DatasetType.
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.DatasetMethod.

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.CubeMethod.
julia
Cube(ds::Dataset; joinname="Variable")

Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.

source


# YAXArrays.Datasets.open_datasetMethod.

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.savecubeMethod.
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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.savedatasetMethod.

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_datasetMethod.

to_dataset(c;datasetaxis = "Variable", 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.YAXDefaultsConstant.

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.findAxisMethod.
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.getOutAxisMethod.
julia
getOutAxis

source


# YAXArrays.get_descriptorMethod.
julia
get_descriptor(a)

Get the descriptor of an Axis. This is used to dispatch on the descriptor.

source


# YAXArrays.match_axisMethod.
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.CleanMeType.
julia
mutable struct CleanMe

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

source


# YAXArrays.Cubes.cleanMethod.
julia
clean(c::CleanMe)

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

source


# YAXArrays.Cubes.copydataMethod.
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.optifuncMethod.
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.DATConfigType.

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

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

  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} 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.InputCubeType.

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.OutputCubeType.

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.YAXColumnType.
julia
YAXColumn

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

  • inarBC

  • inds

source


# YAXArrays.DAT.cmpcachmissesMethod.

Function that compares two cache miss specifiers by their importance

source


# YAXArrays.DAT.getFrontPermMethod.

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

source


# YAXArrays.DAT.getLoopCacheSizeMethod.

Calculate optimal Cache size to DAT operation

source


# YAXArrays.DAT.getOuttypeMethod.
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source


# YAXArrays.DAT.getloopchunksMethod.
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.permuteloopaxesMethod.
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.setchunksMethod.
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.collectfromhandleMethod.

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

source


# YAXArrays.Datasets.createdatasetMethod.

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="Variable" 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.getarrayinfoMethod.

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

source


# YAXArrays.Datasets.testrangeMethod.

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

source


`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default}; diff --git a/dev/assets/api.md.D6o6S_4N.js b/dev/assets/api.md.D6o6S_4N.js new file mode 100644 index 00000000..8d0478ea --- /dev/null +++ b/dev/assets/api.md.D6o6S_4N.js @@ -0,0 +1,10 @@ +import{_ as n,c as o,j as e,a as t,G as i,a2 as l,B as r,o as p}from"./chunks/framework.BgERvflD.js";const bs=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:""},b={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={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:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={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:""},ts={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const a=r("Badge");return p(),o("div",null,[s[258]||(s[258]=e("h1",{id:"API-Reference",tabindex:"-1"},[t("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[259]||(s[259]=e("p",null,"This section describes all available functions of this package.",-1)),s[260]||(s[260]=e("h2",{id:"Public-API",tabindex:"-1"},[t("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}"},"#",-1)),s[1]||(s[1]=t()),s[2]||(s[2]=e("span",{class:"jlbinding"},"YAXArrays.getAxis",-1)),s[3]||(s[3]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[4]||(s[4]=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

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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

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julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",A,[e("summary",null,[s[27]||(s[27]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},"#",-1)),s[28]||(s[28]=t()),s[29]||(s[29]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes",-1)),s[30]||(s[30]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[31]||(s[31]=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",c,[e("summary",null,[s[32]||(s[32]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},"#",-1)),s[33]||(s[33]=t()),s[34]||(s[34]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata",-1)),s[35]||(s[35]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[36]||(s[36]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[37]||(s[37]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},"#",-1)),s[38]||(s[38]=t()),s[39]||(s[39]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks",-1)),s[40]||(s[40]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=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[42]||(s[42]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},"#",-1)),s[43]||(s[43]=t()),s[44]||(s[44]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube",-1)),s[45]||(s[45]=t()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[46]||(s[46]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[47]||(s[47]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},"#",-1)),s[49]||(s[49]=t()),s[50]||(s[50]=e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims",-1)),s[51]||(s[51]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[52]||(s[52]=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[53]||(s[53]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},"#",-1)),s[54]||(s[54]=t()),s[55]||(s[55]=e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow",-1)),s[56]||(s[56]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[57]||(s[57]=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",C,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},"#",-1)),s[59]||(s[59]=t()),s[60]||(s[60]=e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims",-1)),s[61]||(s[61]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[62]||(s[62]=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",D,[e("summary",null,[s[63]||(s[63]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},"#",-1)),s[64]||(s[64]=t()),s[65]||(s[65]=e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable",-1)),s[66]||(s[66]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=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",E,[e("summary",null,[s[68]||(s[68]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},"#",-1)),s[69]||(s[69]=t()),s[70]||(s[70]=e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable",-1)),s[71]||(s[71]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=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[73]||(s[73]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},"#",-1)),s[74]||(s[74]=t()),s[75]||(s[75]=e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable",-1)),s[76]||(s[76]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[77]||(s[77]=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[78]||(s[78]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},"#",-1)),s[79]||(s[79]=t()),s[80]||(s[80]=e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube",-1)),s[81]||(s[81]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[82]||(s[82]=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[83]||(s[83]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},"#",-1)),s[84]||(s[84]=t()),s[85]||(s[85]=e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube",-1)),s[86]||(s[86]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=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[88]||(s[88]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},"#",-1)),s[89]||(s[89]=t()),s[90]||(s[90]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset",-1)),s[91]||(s[91]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=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[93]||(s[93]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},"#",-1)),s[94]||(s[94]=t()),s[95]||(s[95]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset",-1)),s[96]||(s[96]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[97]||(s[97]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[98]||(s[98]=e("p",null,[t("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),t(" a and a list of named YAXArrays cubes...")],-1)),s[99]||(s[99]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},"#",-1)),s[101]||(s[101]=t()),s[102]||(s[102]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube",-1)),s[103]||(s[103]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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[105]||(s[105]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},"#",-1)),s[106]||(s[106]=t()),s[107]||(s[107]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset",-1)),s[108]||(s[108]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[109]||(s[109]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[110]||(s[110]=e("p",null,[t("Open the dataset at "),e("code",null,"g"),t(" with the given "),e("code",null,"driver"),t(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[111]||(s[111]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L330-L335",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},"#",-1)),s[113]||(s[113]=t()),s[114]||(s[114]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube",-1)),s[115]||(s[115]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[116]||(s[116]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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",M,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},"#",-1)),s[118]||(s[118]=t()),s[119]||(s[119]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset",-1)),s[120]||(s[120]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[121]||(s[121]=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[122]||(s[122]=e("p",null,[t("Saves a Dataset into a file at "),e("code",null,"path"),t(" with the format given by "),e("code",null,"driver"),t(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[123]||(s[123]=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[124]||(s[124]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[125]||(s[125]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},"#",-1)),s[126]||(s[126]=t()),s[127]||(s[127]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset",-1)),s[128]||(s[128]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[130]||(s[130]=e("p",null,[t(`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[131]||(s[131]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[261]||(s[261]=e("h2",{id:"Internal-API",tabindex:"-1"},[t("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[132]||(s[132]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},"#",-1)),s[133]||(s[133]=t()),s[134]||(s[134]=e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults",-1)),s[135]||(s[135]=t()),i(a,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[136]||(s[136]=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",B,[e("summary",null,[s[137]||(s[137]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},"#",-1)),s[138]||(s[138]=t()),s[139]||(s[139]=e("span",{class:"jlbinding"},"YAXArrays.findAxis",-1)),s[140]||(s[140]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=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",J,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},"#",-1)),s[143]||(s[143]=t()),s[144]||(s[144]=e("span",{class:"jlbinding"},"YAXArrays.getOutAxis",-1)),s[145]||(s[145]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},"#",-1)),s[148]||(s[148]=t()),s[149]||(s[149]=e("span",{class:"jlbinding"},"YAXArrays.get_descriptor",-1)),s[150]||(s[150]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=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[152]||(s[152]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},"#",-1)),s[153]||(s[153]=t()),s[154]||(s[154]=e("span",{class:"jlbinding"},"YAXArrays.match_axis",-1)),s[155]||(s[155]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=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[157]||(s[157]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},"#",-1)),s[158]||(s[158]=t()),s[159]||(s[159]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe",-1)),s[160]||(s[160]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=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[162]||(s[162]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},"#",-1)),s[163]||(s[163]=t()),s[164]||(s[164]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean",-1)),s[165]||(s[165]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[166]||(s[166]=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",q,[e("summary",null,[s[167]||(s[167]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},"#",-1)),s[168]||(s[168]=t()),s[169]||(s[169]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata",-1)),s[170]||(s[170]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[171]||(s[171]=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[172]||(s[172]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},"#",-1)),s[173]||(s[173]=t()),s[174]||(s[174]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc",-1)),s[175]||(s[175]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[176]||(s[176]=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[177]||(s[177]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},"#",-1)),s[178]||(s[178]=t()),s[179]||(s[179]=e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig",-1)),s[180]||(s[180]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[181]||(s[181]=l('

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

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

  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} 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",R,[e("summary",null,[s[182]||(s[182]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},"#",-1)),s[183]||(s[183]=t()),s[184]||(s[184]=e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube",-1)),s[185]||(s[185]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[186]||(s[186]=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",W,[e("summary",null,[s[187]||(s[187]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},"#",-1)),s[188]||(s[188]=t()),s[189]||(s[189]=e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube",-1)),s[190]||(s[190]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[191]||(s[191]=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",U,[e("summary",null,[s[192]||(s[192]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},"#",-1)),s[193]||(s[193]=t()),s[194]||(s[194]=e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn",-1)),s[195]||(s[195]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[196]||(s[196]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[197]||(s[197]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},"#",-1)),s[198]||(s[198]=t()),s[199]||(s[199]=e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses",-1)),s[200]||(s[200]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[201]||(s[201]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[202]||(s[202]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L949-L951",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[203]||(s[203]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},"#",-1)),s[204]||(s[204]=t()),s[205]||(s[205]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm",-1)),s[206]||(s[206]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[207]||(s[207]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[208]||(s[208]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L1194",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[209]||(s[209]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},"#",-1)),s[210]||(s[210]=t()),s[211]||(s[211]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize",-1)),s[212]||(s[212]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[213]||(s[213]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[214]||(s[214]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L1048",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[215]||(s[215]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},"#",-1)),s[216]||(s[216]=t()),s[217]||(s[217]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype",-1)),s[218]||(s[218]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[219]||(s[219]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[220]||(s[220]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},"#",-1)),s[221]||(s[221]=t()),s[222]||(s[222]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks",-1)),s[223]||(s[223]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[224]||(s[224]=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",_,[e("summary",null,[s[225]||(s[225]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},"#",-1)),s[226]||(s[226]=t()),s[227]||(s[227]=e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes",-1)),s[228]||(s[228]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[229]||(s[229]=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",ss,[e("summary",null,[s[230]||(s[230]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},"#",-1)),s[231]||(s[231]=t()),s[232]||(s[232]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks",-1)),s[233]||(s[233]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[234]||(s[234]=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",es,[e("summary",null,[s[235]||(s[235]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},"#",-1)),s[236]||(s[236]=t()),s[237]||(s[237]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle",-1)),s[238]||(s[238]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[239]||(s[239]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[240]||(s[240]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L463-L465",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[241]||(s[241]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},"#",-1)),s[242]||(s[242]=t()),s[243]||(s[243]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset",-1)),s[244]||(s[244]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[245]||(s[245]=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="Variable" 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",as,[e("summary",null,[s[246]||(s[246]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},"#",-1)),s[247]||(s[247]=t()),s[248]||(s[248]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo",-1)),s[249]||(s[249]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[250]||(s[250]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[251]||(s[251]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L432-L434",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",is,[e("summary",null,[s[252]||(s[252]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},"#",-1)),s[253]||(s[253]=t()),s[254]||(s[254]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange",-1)),s[255]||(s[255]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[256]||(s[256]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[257]||(s[257]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L302",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const hs=n(d,[["render",ls]]);export{bs as __pageData,hs as default}; diff --git a/dev/assets/api.md.D6o6S_4N.lean.js b/dev/assets/api.md.D6o6S_4N.lean.js new file mode 100644 index 00000000..8d0478ea --- /dev/null +++ b/dev/assets/api.md.D6o6S_4N.lean.js @@ -0,0 +1,10 @@ +import{_ as n,c as o,j as e,a as t,G as i,a2 as l,B as r,o as p}from"./chunks/framework.BgERvflD.js";const bs=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:""},b={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={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:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={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:""},ts={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const a=r("Badge");return p(),o("div",null,[s[258]||(s[258]=e("h1",{id:"API-Reference",tabindex:"-1"},[t("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[259]||(s[259]=e("p",null,"This section describes all available functions of this package.",-1)),s[260]||(s[260]=e("h2",{id:"Public-API",tabindex:"-1"},[t("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}"},"#",-1)),s[1]||(s[1]=t()),s[2]||(s[2]=e("span",{class:"jlbinding"},"YAXArrays.getAxis",-1)),s[3]||(s[3]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[4]||(s[4]=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",b,[e("summary",null,[s[5]||(s[5]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},"#",-1)),s[6]||(s[6]=t()),s[7]||(s[7]=e("span",{class:"jlbinding"},"YAXArrays.Cubes",-1)),s[8]||(s[8]=t()),i(a,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[9]||(s[9]=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[10]||(s[10]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[s[11]||(s[11]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},"#",-1)),s[12]||(s[12]=t()),s[13]||(s[13]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray",-1)),s[14]||(s[14]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[15]||(s[15]=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",y,[e("summary",null,[s[16]||(s[16]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},"#",-1)),s[17]||(s[17]=t()),s[18]||(s[18]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes",-1)),s[19]||(s[19]=t()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[20]||(s[20]=e("p",null,"Returns the axes of a Cube",-1)),s[21]||(s[21]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",g,[e("summary",null,[s[22]||(s[22]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},"#",-1)),s[23]||(s[23]=t()),s[24]||(s[24]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes",-1)),s[25]||(s[25]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[26]||(s[26]=l('
julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",A,[e("summary",null,[s[27]||(s[27]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},"#",-1)),s[28]||(s[28]=t()),s[29]||(s[29]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes",-1)),s[30]||(s[30]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[31]||(s[31]=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",c,[e("summary",null,[s[32]||(s[32]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},"#",-1)),s[33]||(s[33]=t()),s[34]||(s[34]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata",-1)),s[35]||(s[35]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[36]||(s[36]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[37]||(s[37]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},"#",-1)),s[38]||(s[38]=t()),s[39]||(s[39]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks",-1)),s[40]||(s[40]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=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[42]||(s[42]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},"#",-1)),s[43]||(s[43]=t()),s[44]||(s[44]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube",-1)),s[45]||(s[45]=t()),i(a,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[46]||(s[46]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[47]||(s[47]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},"#",-1)),s[49]||(s[49]=t()),s[50]||(s[50]=e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims",-1)),s[51]||(s[51]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[52]||(s[52]=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[53]||(s[53]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},"#",-1)),s[54]||(s[54]=t()),s[55]||(s[55]=e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow",-1)),s[56]||(s[56]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[57]||(s[57]=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",C,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},"#",-1)),s[59]||(s[59]=t()),s[60]||(s[60]=e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims",-1)),s[61]||(s[61]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[62]||(s[62]=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",D,[e("summary",null,[s[63]||(s[63]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},"#",-1)),s[64]||(s[64]=t()),s[65]||(s[65]=e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable",-1)),s[66]||(s[66]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=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",E,[e("summary",null,[s[68]||(s[68]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},"#",-1)),s[69]||(s[69]=t()),s[70]||(s[70]=e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable",-1)),s[71]||(s[71]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=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[73]||(s[73]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},"#",-1)),s[74]||(s[74]=t()),s[75]||(s[75]=e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable",-1)),s[76]||(s[76]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[77]||(s[77]=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[78]||(s[78]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},"#",-1)),s[79]||(s[79]=t()),s[80]||(s[80]=e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube",-1)),s[81]||(s[81]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[82]||(s[82]=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[83]||(s[83]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},"#",-1)),s[84]||(s[84]=t()),s[85]||(s[85]=e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube",-1)),s[86]||(s[86]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=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[88]||(s[88]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},"#",-1)),s[89]||(s[89]=t()),s[90]||(s[90]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset",-1)),s[91]||(s[91]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[92]||(s[92]=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[93]||(s[93]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},"#",-1)),s[94]||(s[94]=t()),s[95]||(s[95]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset",-1)),s[96]||(s[96]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[97]||(s[97]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[98]||(s[98]=e("p",null,[t("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),t(" a and a list of named YAXArrays cubes...")],-1)),s[99]||(s[99]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},"#",-1)),s[101]||(s[101]=t()),s[102]||(s[102]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube",-1)),s[103]||(s[103]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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[105]||(s[105]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},"#",-1)),s[106]||(s[106]=t()),s[107]||(s[107]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset",-1)),s[108]||(s[108]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[109]||(s[109]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[110]||(s[110]=e("p",null,[t("Open the dataset at "),e("code",null,"g"),t(" with the given "),e("code",null,"driver"),t(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[111]||(s[111]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L330-L335",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},"#",-1)),s[113]||(s[113]=t()),s[114]||(s[114]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube",-1)),s[115]||(s[115]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[116]||(s[116]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

  • datasetaxis="Variable" 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",M,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},"#",-1)),s[118]||(s[118]=t()),s[119]||(s[119]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset",-1)),s[120]||(s[120]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[121]||(s[121]=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[122]||(s[122]=e("p",null,[t("Saves a Dataset into a file at "),e("code",null,"path"),t(" with the format given by "),e("code",null,"driver"),t(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[123]||(s[123]=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[124]||(s[124]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[125]||(s[125]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},"#",-1)),s[126]||(s[126]=t()),s[127]||(s[127]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset",-1)),s[128]||(s[128]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[130]||(s[130]=e("p",null,[t(`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[131]||(s[131]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[261]||(s[261]=e("h2",{id:"Internal-API",tabindex:"-1"},[t("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[132]||(s[132]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},"#",-1)),s[133]||(s[133]=t()),s[134]||(s[134]=e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults",-1)),s[135]||(s[135]=t()),i(a,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[136]||(s[136]=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",B,[e("summary",null,[s[137]||(s[137]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},"#",-1)),s[138]||(s[138]=t()),s[139]||(s[139]=e("span",{class:"jlbinding"},"YAXArrays.findAxis",-1)),s[140]||(s[140]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=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",J,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},"#",-1)),s[143]||(s[143]=t()),s[144]||(s[144]=e("span",{class:"jlbinding"},"YAXArrays.getOutAxis",-1)),s[145]||(s[145]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},"#",-1)),s[148]||(s[148]=t()),s[149]||(s[149]=e("span",{class:"jlbinding"},"YAXArrays.get_descriptor",-1)),s[150]||(s[150]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=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[152]||(s[152]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},"#",-1)),s[153]||(s[153]=t()),s[154]||(s[154]=e("span",{class:"jlbinding"},"YAXArrays.match_axis",-1)),s[155]||(s[155]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=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[157]||(s[157]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},"#",-1)),s[158]||(s[158]=t()),s[159]||(s[159]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe",-1)),s[160]||(s[160]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[161]||(s[161]=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[162]||(s[162]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},"#",-1)),s[163]||(s[163]=t()),s[164]||(s[164]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean",-1)),s[165]||(s[165]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[166]||(s[166]=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",q,[e("summary",null,[s[167]||(s[167]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},"#",-1)),s[168]||(s[168]=t()),s[169]||(s[169]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata",-1)),s[170]||(s[170]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[171]||(s[171]=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[172]||(s[172]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},"#",-1)),s[173]||(s[173]=t()),s[174]||(s[174]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc",-1)),s[175]||(s[175]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[176]||(s[176]=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[177]||(s[177]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},"#",-1)),s[178]||(s[178]=t()),s[179]||(s[179]=e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig",-1)),s[180]||(s[180]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[181]||(s[181]=l('

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

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

  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} 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",R,[e("summary",null,[s[182]||(s[182]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},"#",-1)),s[183]||(s[183]=t()),s[184]||(s[184]=e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube",-1)),s[185]||(s[185]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[186]||(s[186]=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",W,[e("summary",null,[s[187]||(s[187]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},"#",-1)),s[188]||(s[188]=t()),s[189]||(s[189]=e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube",-1)),s[190]||(s[190]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[191]||(s[191]=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",U,[e("summary",null,[s[192]||(s[192]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},"#",-1)),s[193]||(s[193]=t()),s[194]||(s[194]=e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn",-1)),s[195]||(s[195]=t()),i(a,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[196]||(s[196]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[197]||(s[197]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},"#",-1)),s[198]||(s[198]=t()),s[199]||(s[199]=e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses",-1)),s[200]||(s[200]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[201]||(s[201]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[202]||(s[202]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L949-L951",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[203]||(s[203]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},"#",-1)),s[204]||(s[204]=t()),s[205]||(s[205]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm",-1)),s[206]||(s[206]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[207]||(s[207]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[208]||(s[208]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L1194",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[209]||(s[209]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},"#",-1)),s[210]||(s[210]=t()),s[211]||(s[211]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize",-1)),s[212]||(s[212]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[213]||(s[213]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[214]||(s[214]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DAT/DAT.jl#L1048",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[215]||(s[215]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},"#",-1)),s[216]||(s[216]=t()),s[217]||(s[217]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype",-1)),s[218]||(s[218]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[219]||(s[219]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[220]||(s[220]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},"#",-1)),s[221]||(s[221]=t()),s[222]||(s[222]=e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks",-1)),s[223]||(s[223]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[224]||(s[224]=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",_,[e("summary",null,[s[225]||(s[225]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},"#",-1)),s[226]||(s[226]=t()),s[227]||(s[227]=e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes",-1)),s[228]||(s[228]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[229]||(s[229]=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",ss,[e("summary",null,[s[230]||(s[230]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},"#",-1)),s[231]||(s[231]=t()),s[232]||(s[232]=e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks",-1)),s[233]||(s[233]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[234]||(s[234]=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",es,[e("summary",null,[s[235]||(s[235]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},"#",-1)),s[236]||(s[236]=t()),s[237]||(s[237]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle",-1)),s[238]||(s[238]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[239]||(s[239]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[240]||(s[240]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/f3ee42291d83d349ef73db1807b36bc4a65b719a/src/DatasetAPI/Datasets.jl#L463-L465",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[241]||(s[241]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},"#",-1)),s[242]||(s[242]=t()),s[243]||(s[243]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset",-1)),s[244]||(s[244]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[245]||(s[245]=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="Variable" 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",as,[e("summary",null,[s[246]||(s[246]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},"#",-1)),s[247]||(s[247]=t()),s[248]||(s[248]=e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo",-1)),s[249]||(s[249]=t()),i(a,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[250]||(s[250]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray 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DiskArrays.jl

Get your chunks!

Named Dimensions

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Select & Index!

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Chunkerd, compressed !

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Scientific binary data.

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GDAL in Julia.

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geospatial data in Julia.

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GRIBDatasets.jl

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Array-oriented data

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Get the temperature map at the first point in time:

julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
- 0.283062  0.335574  0.84576    0.00226881  …  0.437392  0.794276   0.155916
- 0.269069  0.986456  0.285493   0.703045       0.913638  0.519026   0.679047
- 0.74739   0.414422  0.0345087  0.589595       0.383664  0.0368185  0.600453
- 0.252273  0.595459  0.178107   0.846899       0.693244  0.428467   0.473834
- 0.325824  0.559687  0.714469   0.370229       0.168069  0.252106   0.590456
- 0.34699   0.600163  0.590449   0.86603     …  0.954538  0.0204375  0.99651
- 0.403281  0.190715  0.388749   0.555087       0.944345  0.586174   0.128577
- 0.647865  0.310074  0.405095   0.775146       0.744436  0.0191769  0.333272
- 0.311947  0.12766   0.883386   0.544461       0.810824  0.5773     0.633293
- 0.121612  0.130216  0.897246   0.643835       0.867361  0.945154   0.433286

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.

`,21)]))}const E=a(p,[["render",l]]);export{g as __pageData,E as default}; + 0.282992 0.772715 0.874017 0.735243 … 0.20286 0.762541 0.140078 + 0.97516 0.442654 0.934867 0.304814 0.384193 0.712073 0.560473 + 0.742948 0.551508 0.149414 0.336329 0.857719 0.872124 0.633045 + 0.204985 0.229143 0.856273 0.977741 0.608939 0.498316 0.319783 + 0.771077 0.328615 0.909593 0.975608 0.317198 0.48182 0.173363 + 0.252929 0.240246 0.992895 0.179911 … 0.0654663 0.856681 0.704105 + 0.734549 0.965872 0.163054 0.963513 0.533294 0.090707 0.576244 + 0.472979 0.382731 0.392958 0.679289 0.508627 0.558638 0.41574 + 0.769307 0.654118 0.0588592 0.0840825 0.0814768 0.0764857 0.211975 + 0.652476 0.256378 0.14925 0.386857 0.236513 0.240129 0.752586

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.

`,21)]))}const E=a(p,[["render",l]]);export{g as __pageData,E as default}; diff --git a/dev/assets/get_started.md.QDrVCaRy.lean.js b/dev/assets/get_started.md.C-S_oDsF.lean.js similarity index 93% rename from dev/assets/get_started.md.QDrVCaRy.lean.js rename to dev/assets/get_started.md.C-S_oDsF.lean.js index b724d9b4..689645ee 100644 --- a/dev/assets/get_started.md.QDrVCaRy.lean.js +++ b/dev/assets/get_started.md.C-S_oDsF.lean.js @@ -44,13 +44,13 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g ├─────────────────────────────────────────────────────────────────── file size ┤ file size: 46.88 KB └──────────────────────────────────────────────────────────────────────────────┘

Get the temperature map at the first point in time:

julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
- 0.283062  0.335574  0.84576    0.00226881  …  0.437392  0.794276   0.155916
- 0.269069  0.986456  0.285493   0.703045       0.913638  0.519026   0.679047
- 0.74739   0.414422  0.0345087  0.589595       0.383664  0.0368185  0.600453
- 0.252273  0.595459  0.178107   0.846899       0.693244  0.428467   0.473834
- 0.325824  0.559687  0.714469   0.370229       0.168069  0.252106   0.590456
- 0.34699   0.600163  0.590449   0.86603     …  0.954538  0.0204375  0.99651
- 0.403281  0.190715  0.388749   0.555087       0.944345  0.586174   0.128577
- 0.647865  0.310074  0.405095   0.775146       0.744436  0.0191769  0.333272
- 0.311947  0.12766   0.883386   0.544461       0.810824  0.5773     0.633293
- 0.121612  0.130216  0.897246   0.643835       0.867361  0.945154   0.433286

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.

`,21)]))}const E=a(p,[["render",l]]);export{g as __pageData,E as default}; + 0.282992 0.772715 0.874017 0.735243 … 0.20286 0.762541 0.140078 + 0.97516 0.442654 0.934867 0.304814 0.384193 0.712073 0.560473 + 0.742948 0.551508 0.149414 0.336329 0.857719 0.872124 0.633045 + 0.204985 0.229143 0.856273 0.977741 0.608939 0.498316 0.319783 + 0.771077 0.328615 0.909593 0.975608 0.317198 0.48182 0.173363 + 0.252929 0.240246 0.992895 0.179911 … 0.0654663 0.856681 0.704105 + 0.734549 0.965872 0.163054 0.963513 0.533294 0.090707 0.576244 + 0.472979 0.382731 0.392958 0.679289 0.508627 0.558638 0.41574 + 0.769307 0.654118 0.0588592 0.0840825 0.0814768 0.0764857 0.211975 + 0.652476 0.256378 0.14925 0.386857 0.236513 0.240129 0.752586

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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var(--vp-c-purple-soft);--vp-c-warning-1: var(--vp-c-yellow-1);--vp-c-warning-2: var(--vp-c-yellow-2);--vp-c-warning-3: var(--vp-c-yellow-3);--vp-c-warning-soft: var(--vp-c-yellow-soft);--vp-c-danger-1: var(--vp-c-red-1);--vp-c-danger-2: var(--vp-c-red-2);--vp-c-danger-3: var(--vp-c-red-3);--vp-c-danger-soft: var(--vp-c-red-soft);--vp-c-caution-1: var(--vp-c-red-1);--vp-c-caution-2: var(--vp-c-red-2);--vp-c-caution-3: var(--vp-c-red-3);--vp-c-caution-soft: var(--vp-c-red-soft)}:root{--vp-font-family-base: "Inter", ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--vp-font-family-mono: ui-monospace, "Menlo", "Monaco", "Consolas", "Liberation Mono", "Courier New", monospace;font-optical-sizing:auto}:root:where(:lang(zh)){--vp-font-family-base: "Punctuation SC", "Inter", ui-sans-serif, system-ui, "PingFang SC", "Noto Sans CJK SC", "Noto Sans SC", "Heiti SC", "Microsoft YaHei", "DengXian", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"}:root{--vp-shadow-1: 0 1px 2px rgba(0, 0, 0, .04), 0 1px 2px rgba(0, 0, 0, .06);--vp-shadow-2: 0 3px 12px rgba(0, 0, 0, .07), 0 1px 4px rgba(0, 0, 0, .07);--vp-shadow-3: 0 12px 32px rgba(0, 0, 0, .1), 0 2px 6px rgba(0, 0, 0, .08);--vp-shadow-4: 0 14px 44px rgba(0, 0, 0, .12), 0 3px 9px rgba(0, 0, 0, .12);--vp-shadow-5: 0 18px 56px rgba(0, 0, 0, .16), 0 4px 12px rgba(0, 0, 0, .16)}:root{--vp-z-index-footer: 10;--vp-z-index-local-nav: 20;--vp-z-index-nav: 30;--vp-z-index-layout-top: 40;--vp-z-index-backdrop: 50;--vp-z-index-sidebar: 60}@media (min-width: 960px){:root{--vp-z-index-sidebar: 25}}:root{--vp-layout-max-width: 1440px}:root{--vp-header-anchor-symbol: "#"}:root{--vp-code-line-height: 1.7;--vp-code-font-size: .875em;--vp-code-color: var(--vp-c-brand-1);--vp-code-link-color: var(--vp-c-brand-1);--vp-code-link-hover-color: var(--vp-c-brand-2);--vp-code-bg: var(--vp-c-default-soft);--vp-code-block-color: var(--vp-c-text-2);--vp-code-block-bg: var(--vp-c-bg-alt);--vp-code-block-divider-color: var(--vp-c-gutter);--vp-code-lang-color: var(--vp-c-text-3);--vp-code-line-highlight-color: var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: transparent;--vp-button-sponsor-active-border: var(--vp-c-sponsor);--vp-button-sponsor-active-text: var(--vp-c-sponsor);--vp-button-sponsor-active-bg: transparent}:root{--vp-custom-block-font-size: 14px;--vp-custom-block-code-font-size: 13px;--vp-custom-block-info-border: transparent;--vp-custom-block-info-text: var(--vp-c-text-1);--vp-custom-block-info-bg: var(--vp-c-default-soft);--vp-custom-block-info-code-bg: var(--vp-c-default-soft);--vp-custom-block-note-border: transparent;--vp-custom-block-note-text: var(--vp-c-text-1);--vp-custom-block-note-bg: var(--vp-c-default-soft);--vp-custom-block-note-code-bg: var(--vp-c-default-soft);--vp-custom-block-tip-border: transparent;--vp-custom-block-tip-text: var(--vp-c-text-1);--vp-custom-block-tip-bg: var(--vp-c-tip-soft);--vp-custom-block-tip-code-bg: var(--vp-c-tip-soft);--vp-custom-block-important-border: transparent;--vp-custom-block-important-text: var(--vp-c-text-1);--vp-custom-block-important-bg: var(--vp-c-important-soft);--vp-custom-block-important-code-bg: var(--vp-c-important-soft);--vp-custom-block-warning-border: transparent;--vp-custom-block-warning-text: var(--vp-c-text-1);--vp-custom-block-warning-bg: var(--vp-c-warning-soft);--vp-custom-block-warning-code-bg: var(--vp-c-warning-soft);--vp-custom-block-danger-border: transparent;--vp-custom-block-danger-text: var(--vp-c-text-1);--vp-custom-block-danger-bg: var(--vp-c-danger-soft);--vp-custom-block-danger-code-bg: var(--vp-c-danger-soft);--vp-custom-block-caution-border: transparent;--vp-custom-block-caution-text: var(--vp-c-text-1);--vp-custom-block-caution-bg: var(--vp-c-caution-soft);--vp-custom-block-caution-code-bg: var(--vp-c-caution-soft);--vp-custom-block-details-border: var(--vp-custom-block-info-border);--vp-custom-block-details-text: var(--vp-custom-block-info-text);--vp-custom-block-details-bg: var(--vp-custom-block-info-bg);--vp-custom-block-details-code-bg: var(--vp-custom-block-info-code-bg)}:root{--vp-input-border-color: var(--vp-c-border);--vp-input-bg-color: var(--vp-c-bg-alt);--vp-input-switch-bg-color: var(--vp-c-default-soft)}:root{--vp-nav-height: 64px;--vp-nav-bg-color: var(--vp-c-bg);--vp-nav-screen-bg-color: var(--vp-c-bg);--vp-nav-logo-height: 24px}.hide-nav{--vp-nav-height: 0px}.hide-nav .VPSidebar{--vp-nav-height: 22px}:root{--vp-local-nav-bg-color: var(--vp-c-bg)}:root{--vp-sidebar-width: 272px;--vp-sidebar-bg-color: var(--vp-c-bg-alt)}:root{--vp-backdrop-bg-color: rgba(0, 0, 0, .6)}:root{--vp-home-hero-name-color: var(--vp-c-brand-1);--vp-home-hero-name-background: transparent;--vp-home-hero-image-background-image: none;--vp-home-hero-image-filter: none}:root{--vp-badge-info-border: transparent;--vp-badge-info-text: var(--vp-c-text-2);--vp-badge-info-bg: var(--vp-c-default-soft);--vp-badge-tip-border: transparent;--vp-badge-tip-text: var(--vp-c-tip-1);--vp-badge-tip-bg: var(--vp-c-tip-soft);--vp-badge-warning-border: transparent;--vp-badge-warning-text: var(--vp-c-warning-1);--vp-badge-warning-bg: var(--vp-c-warning-soft);--vp-badge-danger-border: transparent;--vp-badge-danger-text: var(--vp-c-danger-1);--vp-badge-danger-bg: var(--vp-c-danger-soft)}:root{--vp-carbon-ads-text-color: var(--vp-c-text-1);--vp-carbon-ads-poweredby-color: var(--vp-c-text-2);--vp-carbon-ads-bg-color: var(--vp-c-bg-soft);--vp-carbon-ads-hover-text-color: var(--vp-c-brand-1);--vp-carbon-ads-hover-poweredby-color: var(--vp-c-text-1)}:root{--vp-local-search-bg: var(--vp-c-bg);--vp-local-search-result-bg: var(--vp-c-bg);--vp-local-search-result-border: var(--vp-c-divider);--vp-local-search-result-selected-bg: 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a/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.js +++ b/dev/assets/tutorials_mean_seasonal_cycle.md.BzuHeqdd.js @@ -1,4 +1,4 @@ -import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l="/YAXArrays.jl/dev/assets/lmtpexd.kQgSETCy.png",k="/YAXArrays.jl/dev/assets/lrwnyyz.DO8psrwy.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`
julia
using CairoMakie
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l="/YAXArrays.jl/dev/assets/bvtlvgr.B7sKb-Og.png",k="/YAXArrays.jl/dev/assets/qdvxbdz.CBRoxrfm.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`

Mean Seasonal Cycle for a single pixel

julia
using CairoMakie
 CairoMakie.activate!()
 using Dates
 using Statistics

We define the data span. For simplicity, three non-leap years were selected.

julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
@@ -37,26 +37,26 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.03950128816140833
- -0.06543078292209109
-  0.06894364536016301
- -0.003940775453868335
-  0.10226947160678594
- -0.011679430378217809
-  0.029110414351034442
-  0.17108855806206869
-  0.14665533881137394
-  0.09710853712741103
+  0.03178074978231279
+  0.029590806608405934
+  0.023243868368917397
+  0.04866794589829804
+  0.16391218709689828
+  0.17620816946585505
+  0.20133289850289035
+  0.030633516515381543
+  0.16486681026072406
+  0.17817659167931454
 
- -0.15771433632463785
- -0.1858403579779676
- -0.015974661080283597
- -0.019829128922060204
- -0.14261633679073402
- -0.08238381041801922
-  0.0042454457004389495
-  0.029577960933055997
- -0.042256901873424846

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+ -0.12846746837916714
+ -0.20523841276145796
+ -0.19283190893569083
+ -0.03370928988141961
+ -0.13659926292850597
+  0.03537384135592742
+ -0.08836741623056867
+ -0.061819963168312016
+  0.054328424108148254

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.BzuHeqdd.lean.js
similarity index 95%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.BzuHeqdd.lean.js
index 313c7a01..be32568d 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.BzuHeqdd.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l="/YAXArrays.jl/dev/assets/lmtpexd.kQgSETCy.png",k="/YAXArrays.jl/dev/assets/lrwnyyz.DO8psrwy.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`

Mean Seasonal Cycle for a single pixel

julia
using CairoMakie
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l="/YAXArrays.jl/dev/assets/bvtlvgr.B7sKb-Og.png",k="/YAXArrays.jl/dev/assets/qdvxbdz.CBRoxrfm.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`

Mean Seasonal Cycle for a single pixel

julia
using CairoMakie
 CairoMakie.activate!()
 using Dates
 using Statistics

We define the data span. For simplicity, three non-leap years were selected.

julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
@@ -37,26 +37,26 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BgERvflD.js";const l
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.03950128816140833
- -0.06543078292209109
-  0.06894364536016301
- -0.003940775453868335
-  0.10226947160678594
- -0.011679430378217809
-  0.029110414351034442
-  0.17108855806206869
-  0.14665533881137394
-  0.09710853712741103
+  0.03178074978231279
+  0.029590806608405934
+  0.023243868368917397
+  0.04866794589829804
+  0.16391218709689828
+  0.17620816946585505
+  0.20133289850289035
+  0.030633516515381543
+  0.16486681026072406
+  0.17817659167931454
 
- -0.15771433632463785
- -0.1858403579779676
- -0.015974661080283597
- -0.019829128922060204
- -0.14261633679073402
- -0.08238381041801922
-  0.0042454457004389495
-  0.029577960933055997
- -0.042256901873424846

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+ -0.12846746837916714
+ -0.20523841276145796
+ -0.19283190893569083
+ -0.03370928988141961
+ -0.13659926292850597
+  0.03537384135592742
+ -0.08836741623056867
+ -0.061819963168312016
+  0.054328424108148254

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
diff --git a/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js b/dev/assets/tutorials_plottingmaps.md.Df01Syq_.js
similarity index 99%
rename from dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js
rename to dev/assets/tutorials_plottingmaps.md.Df01Syq_.js
index c48e1281..7fb2a9a8 100644
--- a/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js
+++ b/dev/assets/tutorials_plottingmaps.md.Df01Syq_.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/uhhydvj.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/zreiqqa.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/buxplhm.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`

Plotting maps

As test data we use the CMIP6 Scenarios.

julia
using Zarr, YAXArrays, Dates
+import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/pxjoxmj.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/imbpujw.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/njmjijs.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`

Plotting maps

As test data we use the CMIP6 Scenarios.

julia
using Zarr, YAXArrays, Dates
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
diff --git a/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js b/dev/assets/tutorials_plottingmaps.md.Df01Syq_.lean.js
similarity index 99%
rename from dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js
rename to dev/assets/tutorials_plottingmaps.md.Df01Syq_.lean.js
index c48e1281..7fb2a9a8 100644
--- a/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js
+++ b/dev/assets/tutorials_plottingmaps.md.Df01Syq_.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/uhhydvj.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/zreiqqa.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/buxplhm.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`

Plotting maps

As test data we use the CMIP6 Scenarios.

julia
using Zarr, YAXArrays, Dates
+import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BgERvflD.js";const t="/YAXArrays.jl/dev/assets/pxjoxmj.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/imbpujw.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/njmjijs.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`

Plotting maps

As test data we use the CMIP6 Scenarios.

julia
using Zarr, YAXArrays, Dates
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
diff --git a/dev/development/contribute.html b/dev/development/contribute.html
index 8b0d97a7..73202972 100644
--- a/dev/development/contribute.html
+++ b/dev/development/contribute.html
@@ -6,14 +6,14 @@
     Contribute to YAXArrays.jl | YAXArrays.jl
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
-    
+    
     
     
   
@@ -21,7 +21,7 @@
     
Skip to content

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.

- + \ No newline at end of file diff --git a/dev/development/contributors.html b/dev/development/contributors.html index 17f23d38..4afe1788 100644 --- a/dev/development/contributors.html +++ b/dev/development/contributors.html @@ -6,20 +6,20 @@ YAXArrays.jl - + - + - + - - + +
Skip to content

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.

- + \ No newline at end of file diff --git a/dev/get_started.html b/dev/get_started.html index 1d1f6209..37572498 100644 --- a/dev/get_started.html +++ b/dev/get_started.html @@ -6,14 +6,14 @@ Getting Started | YAXArrays.jl - + - + - + - - + + @@ -64,17 +64,17 @@ ├─────────────────────────────────────────────────────────────────── file size ┤ file size: 46.88 KB └──────────────────────────────────────────────────────────────────────────────┘

Get the temperature map at the first point in time:

julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
- 0.283062  0.335574  0.84576    0.00226881  …  0.437392  0.794276   0.155916
- 0.269069  0.986456  0.285493   0.703045       0.913638  0.519026   0.679047
- 0.74739   0.414422  0.0345087  0.589595       0.383664  0.0368185  0.600453
- 0.252273  0.595459  0.178107   0.846899       0.693244  0.428467   0.473834
- 0.325824  0.559687  0.714469   0.370229       0.168069  0.252106   0.590456
- 0.34699   0.600163  0.590449   0.86603     …  0.954538  0.0204375  0.99651
- 0.403281  0.190715  0.388749   0.555087       0.944345  0.586174   0.128577
- 0.647865  0.310074  0.405095   0.775146       0.744436  0.0191769  0.333272
- 0.311947  0.12766   0.883386   0.544461       0.810824  0.5773     0.633293
- 0.121612  0.130216  0.897246   0.643835       0.867361  0.945154   0.433286

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.

Mean Seasonal Cycle for a single pixel

- + 0.282992 0.772715 0.874017 0.735243 … 0.20286 0.762541 0.140078 + 0.97516 0.442654 0.934867 0.304814 0.384193 0.712073 0.560473 + 0.742948 0.551508 0.149414 0.336329 0.857719 0.872124 0.633045 + 0.204985 0.229143 0.856273 0.977741 0.608939 0.498316 0.319783 + 0.771077 0.328615 0.909593 0.975608 0.317198 0.48182 0.173363 + 0.252929 0.240246 0.992895 0.179911 … 0.0654663 0.856681 0.704105 + 0.734549 0.965872 0.163054 0.963513 0.533294 0.090707 0.576244 + 0.472979 0.382731 0.392958 0.679289 0.508627 0.558638 0.41574 + 0.769307 0.654118 0.0588592 0.0840825 0.0814768 0.0764857 0.211975 + 0.652476 0.256378 0.14925 0.386857 0.236513 0.240129 0.752586

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.

+ \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index 2726bf1d..99ec3f72 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api.md":"Czz2cpaE","development_contribute.md":"ClrIjhyO","development_contributors.md":"CLIwhHUf","get_started.md":"QDrVCaRy","index.md":"GdAm7ifr","tutorials_mean_seasonal_cycle.md":"gJhMR6dH","tutorials_other_tutorials.md":"CO_GVBJJ","tutorials_plottingmaps.md":"BxZGI0zc","userguide_cache.md":"ClYVtu-M","userguide_chunk.md":"QsQQbTJH","userguide_combine.md":"DYPIjlvr","userguide_compute.md":"l32-sEL-","userguide_convert.md":"CaREO6jR","userguide_create.md":"r5QE-6jo","userguide_faq.md":"D4xbU8uL","userguide_group.md":"DBOT3ZFU","userguide_read.md":"B25a96ai","userguide_select.md":"DwHkhEFI","userguide_types.md":"R6FLmeYW","userguide_write.md":"CxpbTqhY"} +{"api.md":"D6o6S_4N","development_contribute.md":"ClrIjhyO","development_contributors.md":"DpEx_YjL","get_started.md":"C-S_oDsF","index.md":"GdAm7ifr","tutorials_mean_seasonal_cycle.md":"BzuHeqdd","tutorials_other_tutorials.md":"CO_GVBJJ","tutorials_plottingmaps.md":"Df01Syq_","userguide_cache.md":"ClYVtu-M","userguide_chunk.md":"QsQQbTJH","userguide_combine.md":"DYPIjlvr","userguide_compute.md":"BLzjfYSH","userguide_convert.md":"BTPZ4pOo","userguide_create.md":"r5QE-6jo","userguide_faq.md":"C4SnKlqV","userguide_group.md":"j3EByEps","userguide_read.md":"C3NhNh2F","userguide_select.md":"DwHkhEFI","userguide_types.md":"R6FLmeYW","userguide_write.md":"DPsjh-L9"} diff --git a/dev/index.html b/dev/index.html index f485c849..eef4838a 100644 --- a/dev/index.html +++ b/dev/index.html @@ -6,20 +6,20 @@ YAXArrays.jl - + - + - + - + - + \ No newline at end of file diff --git a/dev/tutorials/mean_seasonal_cycle.html b/dev/tutorials/mean_seasonal_cycle.html index 61752ada..0e4083eb 100644 --- a/dev/tutorials/mean_seasonal_cycle.html +++ b/dev/tutorials/mean_seasonal_cycle.html @@ -6,14 +6,14 @@ Mean Seasonal Cycle for a single pixel | YAXArrays.jl - + - + - + - - + + @@ -29,7 +29,7 @@ ) ax.xticklabelrotation = π / 4 ax.xticklabelalign = (:right, :center) -fig

Define the cube

julia
julia> using YAXArrays, DimensionalData
+fig

Define the cube

julia
julia> using YAXArrays, DimensionalData
 
 julia> axes = (Dim{: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}
@@ -57,26 +57,26 @@
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.03950128816140833
- -0.06543078292209109
-  0.06894364536016301
- -0.003940775453868335
-  0.10226947160678594
- -0.011679430378217809
-  0.029110414351034442
-  0.17108855806206869
-  0.14665533881137394
-  0.09710853712741103
+  0.03178074978231279
+  0.029590806608405934
+  0.023243868368917397
+  0.04866794589829804
+  0.16391218709689828
+  0.17620816946585505
+  0.20133289850289035
+  0.030633516515381543
+  0.16486681026072406
+  0.17817659167931454
 
- -0.15771433632463785
- -0.1858403579779676
- -0.015974661080283597
- -0.019829128922060204
- -0.14261633679073402
- -0.08238381041801922
-  0.0042454457004389495
-  0.029577960933055997
- -0.042256901873424846

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+ -0.12846746837916714
+ -0.20523841276145796
+ -0.19283190893569083
+ -0.03370928988141961
+ -0.13659926292850597
+  0.03537384135592742
+ -0.08836741623056867
+ -0.061819963168312016
+  0.054328424108148254

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
@@ -89,8 +89,8 @@
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
 fig
-current_figure()

- +current_figure()

+ \ No newline at end of file diff --git a/dev/tutorials/other_tutorials.html b/dev/tutorials/other_tutorials.html index acf95225..6e19590a 100644 --- a/dev/tutorials/other_tutorials.html +++ b/dev/tutorials/other_tutorials.html @@ -6,20 +6,20 @@ Other tutorials | YAXArrays.jl - + - + - + - +
Skip to content

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

- + \ No newline at end of file diff --git a/dev/tutorials/plottingmaps.html b/dev/tutorials/plottingmaps.html index 0799e395..329adc5f 100644 --- a/dev/tutorials/plottingmaps.html +++ b/dev/tutorials/plottingmaps.html @@ -6,14 +6,14 @@ Plotting maps | YAXArrays.jl - + - + - + - - + + @@ -44,19 +44,19 @@ fig, ax, plt = heatmap(ct1_slice; colormap = :seaborn_icefire_gradient, axis = (; aspect=DataAspect()), figure = (; size = (1200,600), fontsize=24)) -fig

Wintri Projection

Some transformations

julia
δlon = (lon[2]-lon[1])/2
+fig

Wintri Projection

Some transformations

julia
δlon = (lon[2]-lon[1])/2
 nlon = lon .- 180 .+ δlon
 ndata = circshift(data, (192,1))

and add Coastlines with GeoMakie.coastlines(),

julia
fig = Figure(;size=(1200,600))
 ax = GeoAxis(fig[1,1])
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig

Moll projection

julia
fig = Figure(; size=(1200,600))
+fig

Moll projection

julia
fig = Figure(; size=(1200,600))
 ax = GeoAxis(fig[1,1]; dest = "+proj=moll")
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig

3D sphere plot

julia
using Bonito, WGLMakie
+fig

3D sphere plot

julia
using Bonito, WGLMakie
 Page(exportable=true, offline=true)
 
 WGLMakie.activate!()
@@ -72,7 +72,7 @@
 zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
 rotate!(ax.scene, 2.5)
 fig
- + \ No newline at end of file