From 24d96a64d7c962910fd729a4b3671fb39bcc605a Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Thu, 19 Sep 2024 06:30:00 +0000 Subject: [PATCH] build based on b4b08dd --- dev/404.html | 4 +- dev/UserGuide/cache.html | 10 ++-- dev/UserGuide/chunk.html | 10 ++-- dev/UserGuide/combine.html | 10 ++-- dev/UserGuide/compute.html | 28 +++++----- dev/UserGuide/convert.html | 44 +++++++-------- dev/UserGuide/create.html | 10 ++-- dev/UserGuide/faq.html | 42 +++++++------- dev/UserGuide/group.html | 14 ++--- dev/UserGuide/read.html | 12 ++-- dev/UserGuide/select.html | 10 ++-- dev/UserGuide/types.html | 10 ++-- dev/UserGuide/write.html | 32 +++++------ dev/api.html | 20 +++---- ..._VAX.js => UserGuide_cache.md.ClYVtu-M.js} | 2 +- ...js => UserGuide_cache.md.ClYVtu-M.lean.js} | 2 +- ...JboX.js => UserGuide_chunk.md.QsQQbTJH.js} | 2 +- ...js => UserGuide_chunk.md.QsQQbTJH.lean.js} | 2 +- ...rm.js => UserGuide_combine.md.DYPIjlvr.js} | 2 +- ... => UserGuide_combine.md.DYPIjlvr.lean.js} | 2 +- 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- + \ No newline at end of file diff --git a/dev/UserGuide/cache.html b/dev/UserGuide/cache.html index 93aef40f..7201e009 100644 --- a/dev/UserGuide/cache.html +++ b/dev/UserGuide/cache.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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 d90c31bb..66cd5603 100644 --- a/dev/UserGuide/chunk.html +++ b/dev/UserGuide/chunk.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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 96316ea7..1c0bc2ed 100644 --- a/dev/UserGuide/combine.html +++ b/dev/UserGuide/combine.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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 039e418e..c70efd9a 100644 --- a/dev/UserGuide/compute.html +++ b/dev/UserGuide/compute.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -39,7 +39,7 @@ :origin => "user guide" ├─────────────────────────────────────────────────────────────────── file size ┤ file size: 35.16 KB -└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.34093180898718256
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]
0.8329685182217375
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
╭──────────────────────────────╮
 │ 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  0.423959  0.495718   0.126617     0.16858   0.454909  0.338821
-  2.0  0.609098  0.140488   0.729186     0.984254  0.924728  0.837976
-  3.0  0.710902  0.214893   0.702405     0.953086  0.35839   0.796689
-  ⋮                                   ⋱                      ⋮
-  8.0  0.275995  0.0626282  0.180195     0.175184  0.449547  0.703896
-  9.0  0.902076  0.655641   0.964787     0.025293  0.104898  0.579358
- 10.0  0.285525  0.813113   0.104357  …  0.054352  0.222979  0.944919

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  0.910983  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"]
 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 e7839320..8b8c0449 100644 --- a/dev/UserGuide/convert.html +++ b/dev/UserGuide/convert.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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}:
- 0.275953   0.908733  0.945473  0.89082   …  0.506143  0.646264   0.484504
- 0.931359   0.79898   0.492366  0.354019     0.380954  0.902789   0.775352
- 0.195112   0.149747  0.587823  0.299036     0.952645  0.0481227  0.372878
- 0.0126228  0.291247  0.417136  0.362148     0.834957  0.942395   0.855535
- 0.957847   0.505572  0.075497  0.812221     0.678015  0.209058   0.845177

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
+ 0.719749   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
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -64,12 +64,12 @@
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
   ↓ →  25          26         27          28         29          30
- 25     0.508968    0.201      0.228329    0.443921   0.0016043   0.744147
- 26     0.426614    0.457045   0.360031    0.854556   0.873199    0.202457
- 27     0.898824    0.295065   0.443947    0.689034   0.782729    0.236862
- 28     0.233642    0.598441   0.928433    0.821712   0.428044    0.00341717
- 29     0.249775    0.287776   0.0391094   0.998243   0.155019    0.835979
- 30     0.0270115   0.952619   0.356225    0.983686   0.0318083   0.337743

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     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
 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     0.375071    0.10778     0.136866    0.801031    0.945303   0.322938
- 2     0.255362    0.475875    0.891795    0.146681    0.355027   0.747662
- 3     0.883687    0.909186    0.604756    0.943467    0.5221     0.623636
- 4     0.0809511   0.918395    0.0535527   0.908792    0.406759   0.345457
- 5     0.54991     0.0401898   0.790472    0.0259303   0.383191   0.171133

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 0.521161 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.

+ \ No newline at end of file diff --git a/dev/UserGuide/create.html b/dev/UserGuide/create.html index ae8e4c0c..92b66a8c 100644 --- a/dev/UserGuide/create.html +++ b/dev/UserGuide/create.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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 c35e899f..f6814a47 100644 --- a/dev/UserGuide/faq.html +++ b/dev/UserGuide/faq.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -214,20 +214,20 @@ None Variables with additional axes: Additional Axes: - (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points) + (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points) Variables: longitudes Additional Axes: - (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points, - → Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points) + (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points) Variables: - temperature + latitudes Additional Axes: - (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points) + (↓ 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) Variables: - latitudes

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  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"])
 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
@@ -237,20 +237,20 @@
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ 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)
   Variables: 
-  latitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
+  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} │
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
@@ -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: 
-  Stock1
+  Stock3
 
   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: 
-  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
+  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
 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 c926a394..35a0f3de 100644 --- a/dev/UserGuide/group.html +++ b/dev/UserGuide/group.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -22,7 +22,7 @@ using NetCDF using Downloads using Dates -using Statistics

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

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

WARNING

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

julia
axs = dims(ds_o) # get the dimensions
 data = ds_o.data[:,:,:] # read the data
@@ -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 49da3036..0d1ea97d 100644 --- a/dev/UserGuide/read.html +++ b/dev/UserGuide/read.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -96,8 +96,8 @@ path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif") # ds = open_dataset(path) # this is broken -nothing - +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 991c5320..c39e7225 100644 --- a/dev/UserGuide/select.html +++ b/dev/UserGuide/select.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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 fa183a03..0813644a 100644 --- a/dev/UserGuide/types.html +++ b/dev/UserGuide/types.html @@ -8,18 +8,18 @@ - + - - - + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Types ​

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

YAXArray ​

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

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

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

Dataset ​

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

(Data) Cube ​

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

Dimension ​

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

- + \ No newline at end of file diff --git a/dev/UserGuide/write.html b/dev/UserGuide/write.html index f9b5ad56..9fcbb4cd 100644 --- a/dev/UserGuide/write.html +++ b/dev/UserGuide/write.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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.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)))
+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}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158

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

julia
ds_open = open_dataset("skeleton.zarr")
+ 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")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158

indeed, those entries had been updated.

- + 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.

+ \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index 5d839bb3..ac0ad49e 100644 --- a/dev/api.html +++ b/dev/api.html @@ -8,30 +8,30 @@ - + - - - + + + -
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

API Reference ​

This section describes all available functions of this package.

Public API ​

# YAXArrays.getAxis — Method.
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source


# YAXArrays.Cubes — Module.

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

source


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

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source


# YAXArrays.Cubes.caxes — Function.

Returns the axes of a Cube

source


# YAXArrays.Cubes.caxes — Method.
julia
caxes

Embeds Cube inside a new Cube

source


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

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

source


# YAXArrays.Cubes.readcubedata — Method.
julia
readcubedata(cube)

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

source


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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source


# YAXArrays.Cubes.subsetcube — Function.

This function calculates a subset of a cube's data

source


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

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

Keyword arguments

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

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

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

source


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

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

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

source


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

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

  • axisdesc: List of input axis names

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

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

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

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

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

source


# YAXArrays.DAT.CubeTable — Method.
julia
CubeTable()

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

source


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

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

source


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

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

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

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

source


# YAXArrays.DAT.mapCube — Method.
julia
mapCube(fun, cube, addargs...;kwargs...)
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
API Reference ​
This section describes all available functions of this package.
Public API ​
# YAXArrays.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

# YAXArrays.Cubes.YAXArray — Type. 
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source

# YAXArrays.Cubes.caxes — Function. 
Returns the axes of a Cube
source

# YAXArrays.Cubes.caxes — Method. 
julia
caxes
Embeds Cube inside a new Cube
source

# YAXArrays.Cubes.concatenatecubes — Method. 
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source

# YAXArrays.Cubes.readcubedata — Method. 
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Cubes.subsetcube — Function. 
This function calculates a subset of a cube's data
source

# YAXArrays.DAT.InDims — Type. 
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source

# YAXArrays.DAT.MovingWindow — Type. 
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source

# YAXArrays.DAT.OutDims — Method. 
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source

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

# YAXArrays.DAT.cubefittable — Method. 
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source

# YAXArrays.DAT.fittable — Method. 
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source

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

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.
+For the specific keyword arguments see the docstring of the mapCube function for cubes.
source

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB``` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.
 a dictionary of CubeAxes and a Dictionary of general properties.
-A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source

# YAXArrays.Datasets.Dataset — Method. 
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="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.
+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.CleanMe — Type. 
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source

# YAXArrays.Cubes.clean — Method. 
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source

# YAXArrays.Cubes.copydata — Method. 
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source

# YAXArrays.Cubes.optifunc — Method. 
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::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.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

# YAXArrays.DAT.OutputCube — Type. 
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source

# YAXArrays.DAT.YAXColumn — Type. 
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source

# YAXArrays.DAT.cmpcachmisses — Method. 
Function that compares two cache miss specifiers by their importance
source

# YAXArrays.DAT.getFrontPerm — Method. 
Calculate an axis permutation that brings the wanted dimensions to the front
source

# YAXArrays.DAT.getLoopCacheSize — Method. 
Calculate optimal Cache size to DAT operation
source

# YAXArrays.DAT.getOuttype — Method. 
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source

# YAXArrays.DAT.getloopchunks — Method. 
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
-This computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source

# YAXArrays.DAT.permuteloopaxes — Method. 
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Datasets.collectfromhandle — Method. 
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="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.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

# YAXArrays.Datasets.testrange — Method. 
Test if data in x can be approximated by a step range
source


-    
+For example to disregard differences of captialisation.

source


# YAXArrays.Cubes.CleanMe — Type.
julia
mutable struct CleanMe

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

source


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

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

source


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

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

source


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

Internal

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

source


# YAXArrays.DAT.DATConfig — Type.

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

  • incubes::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.InputCube — Type.

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source


# YAXArrays.DAT.OutputCube — Type.

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source


# YAXArrays.DAT.YAXColumn — Type.
julia
YAXColumn

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

  • inarBC

  • inds

source


# YAXArrays.DAT.cmpcachmisses — Method.

Function that compares two cache miss specifiers by their importance

source


# YAXArrays.DAT.getFrontPerm — Method.

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

source


# YAXArrays.DAT.getLoopCacheSize — Method.

Calculate optimal Cache size to DAT operation

source


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

Internal function

Get the element type for the output cube

source


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

Internal function

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

source


# YAXArrays.DAT.permuteloopaxes — Method.
julia
permuteloopaxes(dc)

Internal function

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

source


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

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source


# YAXArrays.Datasets.collectfromhandle — Method.

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

source


# YAXArrays.Datasets.createdataset — Method.

function createdataset(DS::Type,axlist; kwargs...)

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

Keyword arguments

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

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

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

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

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

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

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

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

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

  • datasetaxis="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.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_cache.md.D4lc_VAX.js b/dev/assets/UserGuide_cache.md.ClYVtu-M.js similarity index 96% rename from dev/assets/UserGuide_cache.md.D4lc_VAX.js rename to dev/assets/UserGuide_cache.md.ClYVtu-M.js index 14ca1f42..2963718e 100644 --- a/dev/assets/UserGuide_cache.md.D4lc_VAX.js +++ b/dev/assets/UserGuide_cache.md.ClYVtu-M.js @@ -1,4 +1,4 @@ -import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`

Caching YAXArrays ​

For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:

julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
 ds = open_dataset("path/to/source")
 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
diff --git a/dev/assets/UserGuide_cache.md.D4lc_VAX.lean.js b/dev/assets/UserGuide_cache.md.ClYVtu-M.lean.js
similarity index 96%
rename from dev/assets/UserGuide_cache.md.D4lc_VAX.lean.js
rename to dev/assets/UserGuide_cache.md.ClYVtu-M.lean.js
index 14ca1f42..2963718e 100644
--- a/dev/assets/UserGuide_cache.md.D4lc_VAX.lean.js
+++ b/dev/assets/UserGuide_cache.md.ClYVtu-M.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.BgERvflD.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
 ds = open_dataset("path/to/source")
 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
diff --git a/dev/assets/UserGuide_chunk.md.BA5tJboX.js b/dev/assets/UserGuide_chunk.md.QsQQbTJH.js
similarity index 99%
rename from dev/assets/UserGuide_chunk.md.BA5tJboX.js
rename to dev/assets/UserGuide_chunk.md.QsQQbTJH.js
index e7a6f54e..1589b974 100644
--- a/dev/assets/UserGuide_chunk.md.BA5tJboX.js
+++ b/dev/assets/UserGuide_chunk.md.QsQQbTJH.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
 a = YAXArray(rand(10,20))
 a_chunked = setchunks(a, (5,10))
 a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
diff --git a/dev/assets/UserGuide_chunk.md.BA5tJboX.lean.js b/dev/assets/UserGuide_chunk.md.QsQQbTJH.lean.js
similarity index 99%
rename from dev/assets/UserGuide_chunk.md.BA5tJboX.lean.js
rename to dev/assets/UserGuide_chunk.md.QsQQbTJH.lean.js
index e7a6f54e..1589b974 100644
--- a/dev/assets/UserGuide_chunk.md.BA5tJboX.lean.js
+++ b/dev/assets/UserGuide_chunk.md.QsQQbTJH.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
 a = YAXArray(rand(10,20))
 a_chunked = setchunks(a, (5,10))
 a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
diff --git a/dev/assets/UserGuide_combine.md.CAw7m9rm.js b/dev/assets/UserGuide_combine.md.DYPIjlvr.js
similarity index 98%
rename from dev/assets/UserGuide_combine.md.CAw7m9rm.js
rename to dev/assets/UserGuide_combine.md.DYPIjlvr.js
index 4e1b5149..eaf1338e 100644
--- a/dev/assets/UserGuide_combine.md.CAw7m9rm.js
+++ b/dev/assets/UserGuide_combine.md.DYPIjlvr.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 
 first_half = YAXArray((Dim{:time}(1:6),), rand(6))
 second_half = YAXArray((Dim{:time}(7:12),), rand(6))
diff --git a/dev/assets/UserGuide_combine.md.CAw7m9rm.lean.js b/dev/assets/UserGuide_combine.md.DYPIjlvr.lean.js
similarity index 98%
rename from dev/assets/UserGuide_combine.md.CAw7m9rm.lean.js
rename to dev/assets/UserGuide_combine.md.DYPIjlvr.lean.js
index 4e1b5149..eaf1338e 100644
--- a/dev/assets/UserGuide_combine.md.CAw7m9rm.lean.js
+++ b/dev/assets/UserGuide_combine.md.DYPIjlvr.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 
 first_half = YAXArray((Dim{:time}(1:6),), rand(6))
 second_half = YAXArray((Dim{:time}(7:12),), rand(6))
diff --git a/dev/assets/UserGuide_compute.md.DTZ8X-wl.js b/dev/assets/UserGuide_compute.md.l32-sEL-.js
similarity index 94%
rename from dev/assets/UserGuide_compute.md.DTZ8X-wl.js
rename to dev/assets/UserGuide_compute.md.l32-sEL-.js
index a1984eb1..4d50f5bb 100644
--- a/dev/assets/UserGuide_compute.md.DTZ8X-wl.js
+++ b/dev/assets/UserGuide_compute.md.l32-sEL-.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
 using Dates
 
 axlist = (
@@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g
   :origin => "user guide"
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.34093180898718256
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]
0.8329685182217375
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
╭──────────────────────────────╮
 │ 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.BEF5B6Ex.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0       1.28571    1.57143   …  4.42857   4.71429   5.0
-  1.0  0.423959  0.495718   0.126617     0.16858   0.454909  0.338821
-  2.0  0.609098  0.140488   0.729186     0.984254  0.924728  0.837976
-  3.0  0.710902  0.214893   0.702405     0.953086  0.35839   0.796689
-  ⋮                                   ⋱                      ⋮
-  8.0  0.275995  0.0626282  0.180195     0.175184  0.449547  0.703896
-  9.0  0.902076  0.655641   0.964787     0.025293  0.104898  0.579358
- 10.0  0.285525  0.813113   0.104357  …  0.054352  0.222979  0.944919
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  0.910983  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"]
 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.DTZ8X-wl.lean.js b/dev/assets/UserGuide_compute.md.l32-sEL-.lean.js
similarity index 94%
rename from dev/assets/UserGuide_compute.md.DTZ8X-wl.lean.js
rename to dev/assets/UserGuide_compute.md.l32-sEL-.lean.js
index a1984eb1..4d50f5bb 100644
--- a/dev/assets/UserGuide_compute.md.DTZ8X-wl.lean.js
+++ b/dev/assets/UserGuide_compute.md.l32-sEL-.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
 using Dates
 
 axlist = (
@@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g
   :origin => "user guide"
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.34093180898718256
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]
0.8329685182217375
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
╭──────────────────────────────╮
 │ 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.BEF5B6Ex.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0       1.28571    1.57143   …  4.42857   4.71429   5.0
-  1.0  0.423959  0.495718   0.126617     0.16858   0.454909  0.338821
-  2.0  0.609098  0.140488   0.729186     0.984254  0.924728  0.837976
-  3.0  0.710902  0.214893   0.702405     0.953086  0.35839   0.796689
-  ⋮                                   ⋱                      ⋮
-  8.0  0.275995  0.0626282  0.180195     0.175184  0.449547  0.703896
-  9.0  0.902076  0.655641   0.964787     0.025293  0.104898  0.579358
- 10.0  0.285525  0.813113   0.104357  …  0.054352  0.222979  0.944919
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  0.910983  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"]
 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.DYAQ-3eU.js b/dev/assets/UserGuide_convert.md.CaREO6jR.js
similarity index 85%
rename from dev/assets/UserGuide_convert.md.DYAQ-3eU.js
rename to dev/assets/UserGuide_convert.md.CaREO6jR.js
index d96ff746..63e4ca7c 100644
--- a/dev/assets/UserGuide_convert.md.DYAQ-3eU.js
+++ b/dev/assets/UserGuide_convert.md.CaREO6jR.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
 a = YAXArray(m)
╭──────────────────────────╮
@@ -11,11 +11,11 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c
 ├────────────────────────────────────────────────────────── file size ┤ 
   file size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
Convert YAXArray to Base.Array:
julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.275953   0.908733  0.945473  0.89082   …  0.506143  0.646264   0.484504
- 0.931359   0.79898   0.492366  0.354019     0.380954  0.902789   0.775352
- 0.195112   0.149747  0.587823  0.299036     0.952645  0.0481227  0.372878
- 0.0126228  0.291247  0.417136  0.362148     0.834957  0.942395   0.855535
- 0.957847   0.505572  0.075497  0.812221     0.678015  0.209058   0.845177
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
+ 0.719749   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
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -44,12 +44,12 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
   ↓ →  25          26         27          28         29          30
- 25     0.508968    0.201      0.228329    0.443921   0.0016043   0.744147
- 26     0.426614    0.457045   0.360031    0.854556   0.873199    0.202457
- 27     0.898824    0.295065   0.443947    0.689034   0.782729    0.236862
- 28     0.233642    0.598441   0.928433    0.821712   0.428044    0.00341717
- 29     0.249775    0.287776   0.0391094   0.998243   0.155019    0.835979
- 30     0.0270115   0.952619   0.356225    0.983686   0.0318083   0.337743
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     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
 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.BEF5B6Ex.js";const c
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0        11.0        12.0        13.0        14.0       15.0
- 1     0.375071    0.10778     0.136866    0.801031    0.945303   0.322938
- 2     0.255362    0.475875    0.891795    0.146681    0.355027   0.747662
- 3     0.883687    0.909186    0.604756    0.943467    0.5221     0.623636
- 4     0.0809511   0.918395    0.0535527   0.908792    0.406759   0.345457
- 5     0.54991     0.0401898   0.790472    0.0259303   0.383191   0.171133
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     0.521161   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};
diff --git a/dev/assets/UserGuide_convert.md.DYAQ-3eU.lean.js b/dev/assets/UserGuide_convert.md.CaREO6jR.lean.js
similarity index 85%
rename from dev/assets/UserGuide_convert.md.DYAQ-3eU.lean.js
rename to dev/assets/UserGuide_convert.md.CaREO6jR.lean.js
index d96ff746..63e4ca7c 100644
--- a/dev/assets/UserGuide_convert.md.DYAQ-3eU.lean.js
+++ b/dev/assets/UserGuide_convert.md.CaREO6jR.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
 a = YAXArray(m)
╭──────────────────────────╮
@@ -11,11 +11,11 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c
 ├────────────────────────────────────────────────────────── file size ┤ 
   file size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
Convert YAXArray to Base.Array:
julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.275953   0.908733  0.945473  0.89082   …  0.506143  0.646264   0.484504
- 0.931359   0.79898   0.492366  0.354019     0.380954  0.902789   0.775352
- 0.195112   0.149747  0.587823  0.299036     0.952645  0.0481227  0.372878
- 0.0126228  0.291247  0.417136  0.362148     0.834957  0.942395   0.855535
- 0.957847   0.505572  0.075497  0.812221     0.678015  0.209058   0.845177
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
+ 0.719749   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
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -44,12 +44,12 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.BEF5B6Ex.js";const c
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
   ↓ →  25          26         27          28         29          30
- 25     0.508968    0.201      0.228329    0.443921   0.0016043   0.744147
- 26     0.426614    0.457045   0.360031    0.854556   0.873199    0.202457
- 27     0.898824    0.295065   0.443947    0.689034   0.782729    0.236862
- 28     0.233642    0.598441   0.928433    0.821712   0.428044    0.00341717
- 29     0.249775    0.287776   0.0391094   0.998243   0.155019    0.835979
- 30     0.0270115   0.952619   0.356225    0.983686   0.0318083   0.337743
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     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
 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.BEF5B6Ex.js";const c
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0        11.0        12.0        13.0        14.0       15.0
- 1     0.375071    0.10778     0.136866    0.801031    0.945303   0.322938
- 2     0.255362    0.475875    0.891795    0.146681    0.355027   0.747662
- 3     0.883687    0.909186    0.604756    0.943467    0.5221     0.623636
- 4     0.0809511   0.918395    0.0535527   0.908792    0.406759   0.345457
- 5     0.54991     0.0401898   0.790472    0.0259303   0.383191   0.171133
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     0.521161   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};
diff --git a/dev/assets/UserGuide_create.md.BL_P2KiQ.js b/dev/assets/UserGuide_create.md.r5QE-6jo.js
similarity index 99%
rename from dev/assets/UserGuide_create.md.BL_P2KiQ.js
rename to dev/assets/UserGuide_create.md.r5QE-6jo.js
index 78009fca..9e5b723f 100644
--- a/dev/assets/UserGuide_create.md.BL_P2KiQ.js
+++ b/dev/assets/UserGuide_create.md.r5QE-6jo.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
 │ 10×20×5 YAXArray{Float64,3} │
 ├─────────────────────────────┴────────────────────────────────── dims ┐
diff --git a/dev/assets/UserGuide_create.md.BL_P2KiQ.lean.js b/dev/assets/UserGuide_create.md.r5QE-6jo.lean.js
similarity index 99%
rename from dev/assets/UserGuide_create.md.BL_P2KiQ.lean.js
rename to dev/assets/UserGuide_create.md.r5QE-6jo.lean.js
index 78009fca..9e5b723f 100644
--- a/dev/assets/UserGuide_create.md.BL_P2KiQ.lean.js
+++ b/dev/assets/UserGuide_create.md.r5QE-6jo.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
 │ 10×20×5 YAXArray{Float64,3} │
 ├─────────────────────────────┴────────────────────────────────── dims ┐
diff --git a/dev/assets/UserGuide_faq.md.3ZSYUGcl.js b/dev/assets/UserGuide_faq.md.D4xbU8uL.js
similarity index 97%
rename from dev/assets/UserGuide_faq.md.3ZSYUGcl.js
rename to dev/assets/UserGuide_faq.md.D4xbU8uL.js
index 861d1b6a..10f0017e 100644
--- a/dev/assets/UserGuide_faq.md.3ZSYUGcl.js
+++ b/dev/assets/UserGuide_faq.md.D4xbU8uL.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const t="/YAXArrays.jl/dev/assets/efsodgu.jxZi8ZiH.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/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
 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.BEF5B6Ex.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  latitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ 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)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  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"])
 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
@@ -217,20 +217,20 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ 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)
   Variables: 
-  latitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
+  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} │
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
@@ -338,7 +338,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.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
+  Stock3
 
   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.BEF5B6Ex.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
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
+  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
 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.3ZSYUGcl.lean.js b/dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js
similarity index 97%
rename from dev/assets/UserGuide_faq.md.3ZSYUGcl.lean.js
rename to dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js
index 861d1b6a..10f0017e 100644
--- a/dev/assets/UserGuide_faq.md.3ZSYUGcl.lean.js
+++ b/dev/assets/UserGuide_faq.md.D4xbU8uL.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const t="/YAXArrays.jl/dev/assets/efsodgu.jxZi8ZiH.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/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
 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.BEF5B6Ex.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  latitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ 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)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  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"])
 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
@@ -217,20 +217,20 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ 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)
   Variables: 
-  latitudes
+  temperature
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 6, …, 93, 94] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [8, 10, …, 94, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
+  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} │
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
@@ -338,7 +338,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.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
+  Stock3
 
   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.BEF5B6Ex.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
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
+  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
 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.CmN1GMIM.js b/dev/assets/UserGuide_group.md.DBOT3ZFU.js
similarity index 97%
rename from dev/assets/UserGuide_group.md.CmN1GMIM.js
rename to dev/assets/UserGuide_group.md.DBOT3ZFU.js
index ea6f6271..db3a3291 100644
--- a/dev/assets/UserGuide_group.md.CmN1GMIM.js
+++ b/dev/assets/UserGuide_group.md.DBOT3ZFU.js
@@ -1,8 +1,8 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const h="/YAXArrays.jl/dev/assets/qfkjmwc.CBBZcGwj.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),t={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.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
 using NetCDF
 using Downloads
 using Dates
-using Statistics
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
 filename = Downloads.download(url_path, "rasm.nc")
 ds_o = Cube(filename)
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
axs = dims(ds_o) # get the dimensions
 data = ds_o.data[:,:,:] # read the data
@@ -187,4 +187,4 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const h
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.
',62)]))}const N=i(t,[["render",l]]);export{y as __pageData,N as default};
+end
which shows a good agreement with the results first published by Joe Hamman.
',63)]))}const N=i(l,[["render",h]]);export{y as __pageData,N as default};
diff --git a/dev/assets/UserGuide_group.md.CmN1GMIM.lean.js b/dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js
similarity index 97%
rename from dev/assets/UserGuide_group.md.CmN1GMIM.lean.js
rename to dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js
index ea6f6271..db3a3291 100644
--- a/dev/assets/UserGuide_group.md.CmN1GMIM.lean.js
+++ b/dev/assets/UserGuide_group.md.DBOT3ZFU.lean.js
@@ -1,8 +1,8 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const h="/YAXArrays.jl/dev/assets/qfkjmwc.CBBZcGwj.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),t={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.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
 using NetCDF
 using Downloads
 using Dates
-using Statistics
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
 filename = Downloads.download(url_path, "rasm.nc")
 ds_o = Cube(filename)
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
axs = dims(ds_o) # get the dimensions
 data = ds_o.data[:,:,:] # read the data
@@ -187,4 +187,4 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.BEF5B6Ex.js";const h
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.
',62)]))}const N=i(t,[["render",l]]);export{y as __pageData,N as default};
+end
which shows a good agreement with the results first published by Joe Hamman.
',63)]))}const N=i(l,[["render",h]]);export{y as __pageData,N as default};
diff --git a/dev/assets/UserGuide_read.md.3Sna00BO.js b/dev/assets/UserGuide_read.md.B25a96ai.js
similarity index 96%
rename from dev/assets/UserGuide_read.md.3Sna00BO.js
rename to dev/assets/UserGuide_read.md.B25a96ai.js
index b5d1eb45..bfb70f21 100644
--- a/dev/assets/UserGuide_read.md.3Sna00BO.js
+++ b/dev/assets/UserGuide_read.md.B25a96ai.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return t(),n("div",null,s[0]||(s[0]=[i(`
Read YAXArrays and Datasets ​
This section describes how to read files, URLs, and directories into YAXArrays and datasets.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return t(),n("div",null,s[0]||(s[0]=[i(`
Read YAXArrays and Datasets ​
This section describes how to read files, URLs, and directories into YAXArrays and datasets.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
 using Zarr
 path="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
 store = zopen(path, consolidated=true)
@@ -76,4 +76,4 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.js";const c
 
 path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
 # ds = open_dataset(path) # this is broken
-nothing
`,20)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default};
+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.3Sna00BO.lean.js b/dev/assets/UserGuide_read.md.B25a96ai.lean.js
similarity index 96%
rename from dev/assets/UserGuide_read.md.3Sna00BO.lean.js
rename to dev/assets/UserGuide_read.md.B25a96ai.lean.js
index b5d1eb45..bfb70f21 100644
--- a/dev/assets/UserGuide_read.md.3Sna00BO.lean.js
+++ b/dev/assets/UserGuide_read.md.B25a96ai.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return t(),n("div",null,s[0]||(s[0]=[i(`
Read YAXArrays and Datasets ​
This section describes how to read files, URLs, and directories into YAXArrays and datasets.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return t(),n("div",null,s[0]||(s[0]=[i(`
Read YAXArrays and Datasets ​
This section describes how to read files, URLs, and directories into YAXArrays and datasets.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
 using Zarr
 path="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
 store = zopen(path, consolidated=true)
@@ -76,4 +76,4 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.js";const c
 
 path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
 # ds = open_dataset(path) # this is broken
-nothing
`,20)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default};
+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_select.md.CoUJUkad.js b/dev/assets/UserGuide_select.md.DwHkhEFI.js
similarity index 99%
rename from dev/assets/UserGuide_select.md.CoUJUkad.js
rename to dev/assets/UserGuide_select.md.DwHkhEFI.js
index 2a172ee2..461fa20b 100644
--- a/dev/assets/UserGuide_select.md.CoUJUkad.js
+++ b/dev/assets/UserGuide_select.md.DwHkhEFI.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,k,d){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,k,d){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
diff --git a/dev/assets/UserGuide_select.md.CoUJUkad.lean.js b/dev/assets/UserGuide_select.md.DwHkhEFI.lean.js
similarity index 99%
rename from dev/assets/UserGuide_select.md.CoUJUkad.lean.js
rename to dev/assets/UserGuide_select.md.DwHkhEFI.lean.js
index 2a172ee2..461fa20b 100644
--- a/dev/assets/UserGuide_select.md.CoUJUkad.lean.js
+++ b/dev/assets/UserGuide_select.md.DwHkhEFI.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,k,d){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,k,d){return e(),n("div",null,s[0]||(s[0]=[i(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
diff --git a/dev/assets/UserGuide_types.md.on_E3BSH.js b/dev/assets/UserGuide_types.md.R6FLmeYW.js
similarity index 97%
rename from dev/assets/UserGuide_types.md.on_E3BSH.js
rename to dev/assets/UserGuide_types.md.R6FLmeYW.js
index 0c2d2194..a2cf68ec 100644
--- a/dev/assets/UserGuide_types.md.on_E3BSH.js
+++ b/dev/assets/UserGuide_types.md.R6FLmeYW.js
@@ -1 +1 @@
-import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
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.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
+import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
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.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
diff --git a/dev/assets/UserGuide_types.md.on_E3BSH.lean.js b/dev/assets/UserGuide_types.md.R6FLmeYW.lean.js
similarity index 97%
rename from dev/assets/UserGuide_types.md.on_E3BSH.lean.js
rename to dev/assets/UserGuide_types.md.R6FLmeYW.lean.js
index 0c2d2194..a2cf68ec 100644
--- a/dev/assets/UserGuide_types.md.on_E3BSH.lean.js
+++ b/dev/assets/UserGuide_types.md.R6FLmeYW.lean.js
@@ -1 +1 @@
-import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
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.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
+import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
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.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
diff --git a/dev/assets/UserGuide_write.md.D_CbCNNM.js b/dev/assets/UserGuide_write.md.CxpbTqhY.js
similarity index 94%
rename from dev/assets/UserGuide_write.md.D_CbCNNM.js
rename to dev/assets/UserGuide_write.md.CxpbTqhY.js
index e38f5e7a..bc8d0e93 100644
--- a/dev/assets/UserGuide_write.md.D_CbCNNM.js
+++ b/dev/assets/UserGuide_write.md.CxpbTqhY.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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 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
 using NetCDF
 using Downloads: download
 
@@ -14,7 +14,7 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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.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)))
+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
@@ -52,15 +52,15 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158
we can verify is this working by loading again directly from disk
julia
ds_open = open_dataset("skeleton.zarr")
+ 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")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158
indeed, those entries had been updated.
`,56)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default};
+ 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};
diff --git a/dev/assets/UserGuide_write.md.D_CbCNNM.lean.js b/dev/assets/UserGuide_write.md.CxpbTqhY.lean.js
similarity index 94%
rename from dev/assets/UserGuide_write.md.D_CbCNNM.lean.js
rename to dev/assets/UserGuide_write.md.CxpbTqhY.lean.js
index e38f5e7a..bc8d0e93 100644
--- a/dev/assets/UserGuide_write.md.D_CbCNNM.lean.js
+++ b/dev/assets/UserGuide_write.md.CxpbTqhY.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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 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
 using NetCDF
 using Downloads: download
 
@@ -14,7 +14,7 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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.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)))
+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
@@ -52,15 +52,15 @@ import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.BEF5B6Ex.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}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158
we can verify is this working by loading again directly from disk
julia
ds_open = open_dataset("skeleton.zarr")
+ 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")
 ds_array = ds_open["skeleton"]
 ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
- 0.945332   0.00105304  0.319603  0.102103
- 0.0325738  0.474451    0.427189  0.0370204
- 0.925434   0.449373    0.637887  0.743935
- 0.793632   0.256353    0.564111  0.349282
- 0.982386   0.602297    0.766762  0.0892158
indeed, those entries had been updated.
`,56)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default};
+ 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};
diff --git a/dev/assets/api.md.CFKx0BAq.js b/dev/assets/api.md.Czz2cpaE.js
similarity index 91%
rename from dev/assets/api.md.CFKx0BAq.js
rename to dev/assets/api.md.Czz2cpaE.js
index 85afab8c..df42b8cf 100644
--- a/dev/assets/api.md.CFKx0BAq.js
+++ b/dev/assets/api.md.Czz2cpaE.js
@@ -1,13 +1,13 @@
-import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.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,n,d,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.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...)
+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.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

# YAXArrays.Cubes.YAXArray — Type. 
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source

# YAXArrays.Cubes.caxes — Function. 
Returns the axes of a Cube
source

# YAXArrays.Cubes.caxes — Method. 
julia
caxes
Embeds Cube inside a new Cube
source

# YAXArrays.Cubes.concatenatecubes — Method. 
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source

# YAXArrays.Cubes.readcubedata — Method. 
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Cubes.subsetcube — Function. 
This function calculates a subset of a cube's data
source

# YAXArrays.DAT.InDims — Type. 
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source

# YAXArrays.DAT.MovingWindow — Type. 
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source

# YAXArrays.DAT.OutDims — Method. 
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source

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

# YAXArrays.DAT.cubefittable — Method. 
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source

# YAXArrays.DAT.fittable — Method. 
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source

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

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB\`\`\` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an \`OrderedDict\` of YAXArrays with Symbol keys.
+For the specific keyword arguments see the docstring of the mapCube function for cubes.
source

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB\`\`\` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an \`OrderedDict\` of YAXArrays with Symbol keys.
 a dictionary of CubeAxes and a Dictionary of general properties.
-A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source

# YAXArrays.Datasets.Dataset — Method. 
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="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.
+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.CleanMe — Type. 
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source

# YAXArrays.Cubes.clean — Method. 
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source

# YAXArrays.Cubes.copydata — Method. 
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source

# YAXArrays.Cubes.optifunc — Method. 
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::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.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

# YAXArrays.DAT.OutputCube — Type. 
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source

# YAXArrays.DAT.YAXColumn — Type. 
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source

# YAXArrays.DAT.cmpcachmisses — Method. 
Function that compares two cache miss specifiers by their importance
source

# YAXArrays.DAT.getFrontPerm — Method. 
Calculate an axis permutation that brings the wanted dimensions to the front
source

# YAXArrays.DAT.getLoopCacheSize — Method. 
Calculate optimal Cache size to DAT operation
source

# YAXArrays.DAT.getOuttype — Method. 
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source

# YAXArrays.DAT.getloopchunks — Method. 
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
-This computation of the size of the chunks is handled by [\`DiskArrays.approx_chunksize\`](@ref)
source

# YAXArrays.DAT.permuteloopaxes — Method. 
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Datasets.collectfromhandle — Method. 
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="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.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

`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default};
+For example to disregard differences of captialisation.
source

# YAXArrays.Cubes.CleanMe — Type. 
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source

# YAXArrays.Cubes.clean — Method. 
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source

# YAXArrays.Cubes.copydata — Method. 
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source

# YAXArrays.Cubes.optifunc — Method. 
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::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.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

# YAXArrays.DAT.OutputCube — Type. 
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source

# YAXArrays.DAT.YAXColumn — Type. 
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source

# YAXArrays.DAT.cmpcachmisses — Method. 
Function that compares two cache miss specifiers by their importance
source

# YAXArrays.DAT.getFrontPerm — Method. 
Calculate an axis permutation that brings the wanted dimensions to the front
source

# YAXArrays.DAT.getLoopCacheSize — Method. 
Calculate optimal Cache size to DAT operation
source

# YAXArrays.DAT.getOuttype — Method. 
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source

# YAXArrays.DAT.getloopchunks — Method. 
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
+This computation of the size of the chunks is handled by [\`DiskArrays.approx_chunksize\`](@ref)
source

# YAXArrays.DAT.permuteloopaxes — Method. 
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Datasets.collectfromhandle — Method. 
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="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.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

`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default};
diff --git a/dev/assets/api.md.CFKx0BAq.lean.js b/dev/assets/api.md.Czz2cpaE.lean.js
similarity index 91%
rename from dev/assets/api.md.CFKx0BAq.lean.js
rename to dev/assets/api.md.Czz2cpaE.lean.js
index 85afab8c..df42b8cf 100644
--- a/dev/assets/api.md.CFKx0BAq.lean.js
+++ b/dev/assets/api.md.Czz2cpaE.lean.js
@@ -1,13 +1,13 @@
-import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.BEF5B6Ex.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,n,d,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.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...)
+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.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

# YAXArrays.Cubes.YAXArray — Type. 
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source

# YAXArrays.Cubes.caxes — Function. 
Returns the axes of a Cube
source

# YAXArrays.Cubes.caxes — Method. 
julia
caxes
Embeds Cube inside a new Cube
source

# YAXArrays.Cubes.concatenatecubes — Method. 
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source

# YAXArrays.Cubes.readcubedata — Method. 
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Cubes.subsetcube — Function. 
This function calculates a subset of a cube's data
source

# YAXArrays.DAT.InDims — Type. 
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source

# YAXArrays.DAT.MovingWindow — Type. 
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source

# YAXArrays.DAT.OutDims — Method. 
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source

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

# YAXArrays.DAT.cubefittable — Method. 
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source

# YAXArrays.DAT.fittable — Method. 
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source

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

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB\`\`\` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an \`OrderedDict\` of YAXArrays with Symbol keys.
+For the specific keyword arguments see the docstring of the mapCube function for cubes.
source

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" ormax_cache=1GB\`\`\` defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source

# YAXArrays.Datasets.Dataset — Type. 
julia
Dataset object which stores an \`OrderedDict\` of YAXArrays with Symbol keys.
 a dictionary of CubeAxes and a Dictionary of general properties.
-A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source

# YAXArrays.Datasets.Dataset — Method. 
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="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.
+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.CleanMe — Type. 
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source

# YAXArrays.Cubes.clean — Method. 
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source

# YAXArrays.Cubes.copydata — Method. 
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source

# YAXArrays.Cubes.optifunc — Method. 
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::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.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

# YAXArrays.DAT.OutputCube — Type. 
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source

# YAXArrays.DAT.YAXColumn — Type. 
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source

# YAXArrays.DAT.cmpcachmisses — Method. 
Function that compares two cache miss specifiers by their importance
source

# YAXArrays.DAT.getFrontPerm — Method. 
Calculate an axis permutation that brings the wanted dimensions to the front
source

# YAXArrays.DAT.getLoopCacheSize — Method. 
Calculate optimal Cache size to DAT operation
source

# YAXArrays.DAT.getOuttype — Method. 
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source

# YAXArrays.DAT.getloopchunks — Method. 
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
-This computation of the size of the chunks is handled by [\`DiskArrays.approx_chunksize\`](@ref)
source

# YAXArrays.DAT.permuteloopaxes — Method. 
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Datasets.collectfromhandle — Method. 
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="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.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

`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default};
+For example to disregard differences of captialisation.
source

# YAXArrays.Cubes.CleanMe — Type. 
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source

# YAXArrays.Cubes.clean — Method. 
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source

# YAXArrays.Cubes.copydata — Method. 
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source

# YAXArrays.Cubes.optifunc — Method. 
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::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.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

# YAXArrays.DAT.OutputCube — Type. 
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source

# YAXArrays.DAT.YAXColumn — Type. 
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source

# YAXArrays.DAT.cmpcachmisses — Method. 
Function that compares two cache miss specifiers by their importance
source

# YAXArrays.DAT.getFrontPerm — Method. 
Calculate an axis permutation that brings the wanted dimensions to the front
source

# YAXArrays.DAT.getLoopCacheSize — Method. 
Calculate optimal Cache size to DAT operation
source

# YAXArrays.DAT.getOuttype — Method. 
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source

# YAXArrays.DAT.getloopchunks — Method. 
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
+This computation of the size of the chunks is handled by [\`DiskArrays.approx_chunksize\`](@ref)
source

# YAXArrays.DAT.permuteloopaxes — Method. 
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source

# YAXArrays.Cubes.setchunks — Method. 
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source

# YAXArrays.Datasets.collectfromhandle — Method. 
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="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.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

`,100)]))}const b=a(r,[["render",o]]);export{u as __pageData,b as default};
diff --git a/dev/assets/app.Dd7V_cwz.js b/dev/assets/app.By04Axg6.js
similarity index 90%
rename from dev/assets/app.Dd7V_cwz.js
rename to dev/assets/app.By04Axg6.js
index 8b21ff0c..9c92d0fa 100644
--- a/dev/assets/app.Dd7V_cwz.js
+++ b/dev/assets/app.By04Axg6.js
@@ -1 +1 @@
-import{R as p}from"./chunks/theme.CrY9T1lC.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.BEF5B6Ex.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp};
+import{R as p}from"./chunks/theme.Cr_eIWnh.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.BgERvflD.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp};
diff --git a/dev/assets/jvfserh.96k_BqPR.jpeg b/dev/assets/buxplhm.96k_BqPR.jpeg
similarity index 100%
rename from dev/assets/jvfserh.96k_BqPR.jpeg
rename to dev/assets/buxplhm.96k_BqPR.jpeg
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new file mode 100644
index 00000000..83f911d6
--- /dev/null
+++ b/dev/assets/chunks/framework.BgERvflD.js
@@ -0,0 +1,18 @@
+/**
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diff --git a/dev/assets/chunks/theme.CrY9T1lC.js b/dev/assets/chunks/theme.Cr_eIWnh.js
similarity index 99%
rename from dev/assets/chunks/theme.CrY9T1lC.js
rename to dev/assets/chunks/theme.Cr_eIWnh.js
index f98c2b79..dee7c21e 100644
--- a/dev/assets/chunks/theme.CrY9T1lC.js
+++ b/dev/assets/chunks/theme.Cr_eIWnh.js
@@ -1,2 +1,2 @@
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Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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diff --git a/dev/assets/development_contribute.md.D93SjwYJ.js b/dev/assets/development_contribute.md.ClrIjhyO.js
similarity index 97%
rename from dev/assets/development_contribute.md.D93SjwYJ.js
rename to dev/assets/development_contribute.md.ClrIjhyO.js
index d2b1ecbd..eaa7f34c 100644
--- a/dev/assets/development_contribute.md.D93SjwYJ.js
+++ b/dev/assets/development_contribute.md.ClrIjhyO.js
@@ -1,3 +1,3 @@
-import{_ as s,c as t,a2 as a,o as i}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Contribute to YAXArrays.jl","description":"","frontmatter":{},"headers":[],"relativePath":"development/contribute.md","filePath":"development/contribute.md","lastUpdated":null}'),l={name:"development/contribute.md"};function o(n,e,r,p,d,h){return i(),t("div",null,e[0]||(e[0]=[a(`
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
+import{_ as s,c as t,a2 as a,o as i}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Contribute to YAXArrays.jl","description":"","frontmatter":{},"headers":[],"relativePath":"development/contribute.md","filePath":"development/contribute.md","lastUpdated":null}'),l={name:"development/contribute.md"};function o(n,e,r,p,d,h){return i(),t("div",null,e[0]||(e[0]=[a(`
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.
`,18)]))}const k=s(l,[["render",o]]);export{u as __pageData,k as default};
diff --git a/dev/assets/development_contribute.md.D93SjwYJ.lean.js b/dev/assets/development_contribute.md.ClrIjhyO.lean.js
similarity index 97%
rename from dev/assets/development_contribute.md.D93SjwYJ.lean.js
rename to dev/assets/development_contribute.md.ClrIjhyO.lean.js
index d2b1ecbd..eaa7f34c 100644
--- a/dev/assets/development_contribute.md.D93SjwYJ.lean.js
+++ b/dev/assets/development_contribute.md.ClrIjhyO.lean.js
@@ -1,3 +1,3 @@
-import{_ as s,c as t,a2 as a,o as i}from"./chunks/framework.BEF5B6Ex.js";const u=JSON.parse('{"title":"Contribute to YAXArrays.jl","description":"","frontmatter":{},"headers":[],"relativePath":"development/contribute.md","filePath":"development/contribute.md","lastUpdated":null}'),l={name:"development/contribute.md"};function o(n,e,r,p,d,h){return i(),t("div",null,e[0]||(e[0]=[a(`
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
+import{_ as s,c as t,a2 as a,o as i}from"./chunks/framework.BgERvflD.js";const u=JSON.parse('{"title":"Contribute to YAXArrays.jl","description":"","frontmatter":{},"headers":[],"relativePath":"development/contribute.md","filePath":"development/contribute.md","lastUpdated":null}'),l={name:"development/contribute.md"};function o(n,e,r,p,d,h){return i(),t("div",null,e[0]||(e[0]=[a(`
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.
`,18)]))}const k=s(l,[["render",o]]);export{u as __pageData,k as default};
diff --git a/dev/assets/development_contributors.md.B5lFKVw2.js b/dev/assets/development_contributors.md.B5lFKVw2.js
deleted file mode 100644
index 88744f43..00000000
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diff --git a/dev/assets/get_started.md.uTuJfEQI.js b/dev/assets/get_started.md.QDrVCaRy.js
similarity index 85%
rename from dev/assets/get_started.md.uTuJfEQI.js
rename to dev/assets/get_started.md.QDrVCaRy.js
index aa42e995..b724d9b4 100644
--- a/dev/assets/get_started.md.uTuJfEQI.js
+++ b/dev/assets/get_started.md.QDrVCaRy.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 
 a = YAXArray(rand(2,3))
╭─────────────────────────╮
 │ 2×3 YAXArray{Float64,2} │
@@ -44,13 +44,13 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.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:
- 0.223565  0.0384967  0.0320741  …  0.4987      0.794938   0.691496
- 0.935566  0.782947   0.706604      0.938401    0.921743   0.308591
- 0.126334  0.577554   0.681419      0.427631    0.182168   0.703209
- 0.945316  0.458639   0.353072      0.00538829  0.215085   0.950853
- 0.849879  0.0536238  0.744735      0.550354    0.47603    0.458454
- 0.127101  0.146446   0.36855    …  0.0407253   0.907397   0.892194
- 0.395165  0.295574   0.229972      0.767487    0.440327   0.566444
- 0.723242  0.728315   0.0496509     0.311457    0.435237   0.179984
- 0.816066  0.843854   0.65768       0.379213    0.647377   0.139809
- 0.261949  0.99486    0.515162      0.0445932   0.0616085  0.742585
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};
+ 0.283062  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};
diff --git a/dev/assets/get_started.md.uTuJfEQI.lean.js b/dev/assets/get_started.md.QDrVCaRy.lean.js
similarity index 85%
rename from dev/assets/get_started.md.uTuJfEQI.lean.js
rename to dev/assets/get_started.md.QDrVCaRy.lean.js
index aa42e995..b724d9b4 100644
--- a/dev/assets/get_started.md.uTuJfEQI.lean.js
+++ b/dev/assets/get_started.md.QDrVCaRy.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BgERvflD.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 
 a = YAXArray(rand(2,3))
╭─────────────────────────╮
 │ 2×3 YAXArray{Float64,2} │
@@ -44,13 +44,13 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.BEF5B6Ex.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:
- 0.223565  0.0384967  0.0320741  …  0.4987      0.794938   0.691496
- 0.935566  0.782947   0.706604      0.938401    0.921743   0.308591
- 0.126334  0.577554   0.681419      0.427631    0.182168   0.703209
- 0.945316  0.458639   0.353072      0.00538829  0.215085   0.950853
- 0.849879  0.0536238  0.744735      0.550354    0.47603    0.458454
- 0.127101  0.146446   0.36855    …  0.0407253   0.907397   0.892194
- 0.395165  0.295574   0.229972      0.767487    0.440327   0.566444
- 0.723242  0.728315   0.0496509     0.311457    0.435237   0.179984
- 0.816066  0.843854   0.65768       0.379213    0.647377   0.139809
- 0.261949  0.99486    0.515162      0.0445932   0.0616085  0.742585
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};
+ 0.283062  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};
diff --git a/dev/assets/qfkjmwc.CBBZcGwj.png b/dev/assets/gqwibql.CBBZcGwj.png
similarity index 100%
rename from dev/assets/qfkjmwc.CBBZcGwj.png
rename to dev/assets/gqwibql.CBBZcGwj.png
diff --git a/dev/assets/index.md.uTPfWlYl.js b/dev/assets/index.md.GdAm7ifr.js
similarity index 96%
rename from dev/assets/index.md.uTPfWlYl.js
rename to dev/assets/index.md.GdAm7ifr.js
index 46274336..d3042e05 100644
--- a/dev/assets/index.md.uTPfWlYl.js
+++ b/dev/assets/index.md.GdAm7ifr.js
@@ -1 +1 @@
-import{_ as e,c as t,o as a}from"./chunks/framework.BEF5B6Ex.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
+import{_ as e,c as t,o as a}from"./chunks/framework.BgERvflD.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
diff --git a/dev/assets/index.md.uTPfWlYl.lean.js b/dev/assets/index.md.GdAm7ifr.lean.js
similarity index 96%
rename from dev/assets/index.md.uTPfWlYl.lean.js
rename to dev/assets/index.md.GdAm7ifr.lean.js
index 46274336..d3042e05 100644
--- a/dev/assets/index.md.uTPfWlYl.lean.js
+++ b/dev/assets/index.md.GdAm7ifr.lean.js
@@ -1 +1 @@
-import{_ as e,c as t,o as a}from"./chunks/framework.BEF5B6Ex.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
+import{_ as e,c as t,o as a}from"./chunks/framework.BgERvflD.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
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diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.C-JLJykP.js b/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.js
similarity index 95%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.C-JLJykP.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.js
index 6d330114..313c7a01 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.C-JLJykP.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BEF5B6Ex.js";const l="/YAXArrays.jl/dev/assets/wxsstem.C5BSYN7j.png",k="/YAXArrays.jl/dev/assets/pdlvjuo.OibBte0v.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/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
 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.BEF5B6Ex.js";const l
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.03130398287109743
- -0.06059678887906461
-  0.08125105691657612
-  0.019357118933250848
- -0.03069412445788597
-  0.09901993559674853
-  0.2642946301720222
-  0.0724785553501333
-  0.21244822988355164
-  0.17115548211003082
+ -0.03950128816140833
+ -0.06543078292209109
+  0.06894364536016301
+ -0.003940775453868335
+  0.10226947160678594
+ -0.011679430378217809
+  0.029110414351034442
+  0.17108855806206869
+  0.14665533881137394
+  0.09710853712741103
   ⋮
- -0.21072435162838157
- -0.15326750862815794
- -0.12667116885178392
- -0.03650284859673286
- -0.06920309673015511
-  0.006977886140959999
- -0.10555520526290235
-  0.022285515523567546
- -0.005783221950539453
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,
+ -0.15771433632463785
+ -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,
     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.C-JLJykP.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js
similarity index 95%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.C-JLJykP.lean.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js
index 6d330114..313c7a01 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.C-JLJykP.lean.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.gJhMR6dH.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.BEF5B6Ex.js";const l="/YAXArrays.jl/dev/assets/wxsstem.C5BSYN7j.png",k="/YAXArrays.jl/dev/assets/pdlvjuo.OibBte0v.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/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
 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.BEF5B6Ex.js";const l
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.03130398287109743
- -0.06059678887906461
-  0.08125105691657612
-  0.019357118933250848
- -0.03069412445788597
-  0.09901993559674853
-  0.2642946301720222
-  0.0724785553501333
-  0.21244822988355164
-  0.17115548211003082
+ -0.03950128816140833
+ -0.06543078292209109
+  0.06894364536016301
+ -0.003940775453868335
+  0.10226947160678594
+ -0.011679430378217809
+  0.029110414351034442
+  0.17108855806206869
+  0.14665533881137394
+  0.09710853712741103
   ⋮
- -0.21072435162838157
- -0.15326750862815794
- -0.12667116885178392
- -0.03650284859673286
- -0.06920309673015511
-  0.006977886140959999
- -0.10555520526290235
-  0.022285515523567546
- -0.005783221950539453
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,
+ -0.15771433632463785
+ -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,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
diff --git a/dev/assets/tutorials_other_tutorials.md.By_HW9oi.js b/dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.js
similarity index 97%
rename from dev/assets/tutorials_other_tutorials.md.By_HW9oi.js
rename to dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.js
index 21427780..33837df8 100644
--- a/dev/assets/tutorials_other_tutorials.md.By_HW9oi.js
+++ b/dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.js
@@ -1 +1 @@
-import{_ as t,c as a,a2 as r,o}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
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 ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
+import{_ as t,c as a,a2 as r,o}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
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 ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
diff --git a/dev/assets/tutorials_other_tutorials.md.By_HW9oi.lean.js b/dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.lean.js
similarity index 97%
rename from dev/assets/tutorials_other_tutorials.md.By_HW9oi.lean.js
rename to dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.lean.js
index 21427780..33837df8 100644
--- a/dev/assets/tutorials_other_tutorials.md.By_HW9oi.lean.js
+++ b/dev/assets/tutorials_other_tutorials.md.CO_GVBJJ.lean.js
@@ -1 +1 @@
-import{_ as t,c as a,a2 as r,o}from"./chunks/framework.BEF5B6Ex.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
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 ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
+import{_ as t,c as a,a2 as r,o}from"./chunks/framework.BgERvflD.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
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 ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
diff --git a/dev/assets/tutorials_plottingmaps.md.DQHCoagw.js b/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js
similarity index 99%
rename from dev/assets/tutorials_plottingmaps.md.DQHCoagw.js
rename to dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js
index 454e9f96..c48e1281 100644
--- a/dev/assets/tutorials_plottingmaps.md.DQHCoagw.js
+++ b/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BEF5B6Ex.js";const t="/YAXArrays.jl/dev/assets/repxpch.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/ubqzrxd.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/jvfserh.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/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
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
diff --git a/dev/assets/tutorials_plottingmaps.md.DQHCoagw.lean.js b/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js
similarity index 99%
rename from dev/assets/tutorials_plottingmaps.md.DQHCoagw.lean.js
rename to dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js
index 454e9f96..c48e1281 100644
--- a/dev/assets/tutorials_plottingmaps.md.DQHCoagw.lean.js
+++ b/dev/assets/tutorials_plottingmaps.md.BxZGI0zc.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.BEF5B6Ex.js";const t="/YAXArrays.jl/dev/assets/repxpch.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/ubqzrxd.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/jvfserh.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/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
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
diff --git a/dev/assets/repxpch.xrZxBsPv.jpeg b/dev/assets/uhhydvj.xrZxBsPv.jpeg
similarity index 100%
rename from dev/assets/repxpch.xrZxBsPv.jpeg
rename to dev/assets/uhhydvj.xrZxBsPv.jpeg
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YAXArrays.jl
Appearance

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

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\ No newline at end of file
diff --git a/dev/development/contributors.html b/dev/development/contributors.html
index 99dcdf57..17f23d38 100644
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+++ b/dev/development/contributors.html
@@ -8,18 +8,18 @@
     
     
     
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YAXArrays.jl
Appearance
Contributors
Current core contributors  
 They have taking the lead for the ongoing organizational maintenance and technical direction of ,  and . 
Fabian Gans
Fabian Gans
Geoscientific Programmer
Felix Cremer
Felix Cremer
PhD Candidate in Remote Sensing
Rafael Schouten
Rafael Schouten
Spatial/ecological modelling
Lazaro Alonso
Lazaro Alonso
Scientist. Data Visualization
Our valuable contributors
 We appreciate all contributions from the Julia community so that this ecosystem can thrive.

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diff --git a/dev/get_started.html b/dev/get_started.html
index 53632187..1d1f6209 100644
--- a/dev/get_started.html
+++ b/dev/get_started.html
@@ -8,11 +8,11 @@
     
     
     
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@@ -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:
- 0.223565  0.0384967  0.0320741  …  0.4987      0.794938   0.691496
- 0.935566  0.782947   0.706604      0.938401    0.921743   0.308591
- 0.126334  0.577554   0.681419      0.427631    0.182168   0.703209
- 0.945316  0.458639   0.353072      0.00538829  0.215085   0.950853
- 0.849879  0.0536238  0.744735      0.550354    0.47603    0.458454
- 0.127101  0.146446   0.36855    …  0.0407253   0.907397   0.892194
- 0.395165  0.295574   0.229972      0.767487    0.440327   0.566444
- 0.723242  0.728315   0.0496509     0.311457    0.435237   0.179984
- 0.816066  0.843854   0.65768       0.379213    0.647377   0.139809
- 0.261949  0.99486    0.515162      0.0445932   0.0616085  0.742585
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.

-    
+ 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.

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diff --git a/dev/hashmap.json b/dev/hashmap.json
index 09e36226..2726bf1d 100644
--- a/dev/hashmap.json
+++ b/dev/hashmap.json
@@ -1 +1 @@
-{"api.md":"CFKx0BAq","development_contribute.md":"D93SjwYJ","development_contributors.md":"B5lFKVw2","get_started.md":"uTuJfEQI","index.md":"uTPfWlYl","tutorials_mean_seasonal_cycle.md":"C-JLJykP","tutorials_other_tutorials.md":"By_HW9oi","tutorials_plottingmaps.md":"DQHCoagw","userguide_cache.md":"D4lc_VAX","userguide_chunk.md":"BA5tJboX","userguide_combine.md":"CAw7m9rm","userguide_compute.md":"DTZ8X-wl","userguide_convert.md":"DYAQ-3eU","userguide_create.md":"BL_P2KiQ","userguide_faq.md":"3ZSYUGcl","userguide_group.md":"CmN1GMIM","userguide_read.md":"3Sna00BO","userguide_select.md":"CoUJUkad","userguide_types.md":"on_E3BSH","userguide_write.md":"D_CbCNNM"}
+{"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"}
diff --git a/dev/index.html b/dev/index.html
index 5bfef373..f485c849 100644
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+++ b/dev/index.html
@@ -8,18 +8,18 @@
     
     
     
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diff --git a/dev/tutorials/mean_seasonal_cycle.html b/dev/tutorials/mean_seasonal_cycle.html
index 849955ef..61752ada 100644
--- a/dev/tutorials/mean_seasonal_cycle.html
+++ b/dev/tutorials/mean_seasonal_cycle.html
@@ -8,11 +8,11 @@
     
     
     
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@@ -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}:
- -0.03130398287109743
- -0.06059678887906461
-  0.08125105691657612
-  0.019357118933250848
- -0.03069412445788597
-  0.09901993559674853
-  0.2642946301720222
-  0.0724785553501333
-  0.21244822988355164
-  0.17115548211003082
+ -0.03950128816140833
+ -0.06543078292209109
+  0.06894364536016301
+ -0.003940775453868335
+  0.10226947160678594
+ -0.011679430378217809
+  0.029110414351034442
+  0.17108855806206869
+  0.14665533881137394
+  0.09710853712741103
   ⋮
- -0.21072435162838157
- -0.15326750862815794
- -0.12667116885178392
- -0.03650284859673286
- -0.06920309673015511
-  0.006977886140959999
- -0.10555520526290235
-  0.022285515523567546
- -0.005783221950539453
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,
+ -0.15771433632463785
+ -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,
     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()

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+ \ No newline at end of file diff --git a/dev/tutorials/other_tutorials.html b/dev/tutorials/other_tutorials.html index 321d0762..acf95225 100644 --- a/dev/tutorials/other_tutorials.html +++ b/dev/tutorials/other_tutorials.html @@ -8,18 +8,18 @@ - + - - - + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Other tutorials ​

If you are interested in learning how to work with YAXArrays for different use cases you can follow along one of the following tutorials.

  • Currently the overview tutorial is located at ESDLTutorials Repository

  • You can find further tutorial videos at the EO College. Beware that the syntax in the video tutorials might be slightly changed.

  • the other tutorials are still work in progress.

General overview of the functionality of YAXArrays ​

This tutorial provides a broad overview about the features of YAXArrays.

Table-style iteration over YAXArrays ​

Work in progress

Sometimes you want to combine the data that is represented in the data cube with other datasets, which are best described as a data frame. In this tutorial you will learn how to use the Tables.jl interface to iterate over the data in the YAXArray.

Combining multiple tiff files into a zarr based datacube ​

- + \ No newline at end of file diff --git a/dev/tutorials/plottingmaps.html b/dev/tutorials/plottingmaps.html index db95006c..0799e395 100644 --- a/dev/tutorials/plottingmaps.html +++ b/dev/tutorials/plottingmaps.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -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