diff --git a/dev/404.html b/dev/404.html index 47ffeaf9..c25d7ccd 100644 --- a/dev/404.html +++ b/dev/404.html @@ -8,7 +8,7 @@ - + @@ -18,7 +18,7 @@
- + \ No newline at end of file diff --git a/dev/UserGuide/cache.html b/dev/UserGuide/cache.html index aa96fd87..bd193038 100644 --- a/dev/UserGuide/cache.html +++ b/dev/UserGuide/cache.html @@ -8,9 +8,9 @@ - + - + @@ -25,7 +25,7 @@ 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 4b45c976..8f66d484 100644 --- a/dev/UserGuide/chunk.html +++ b/dev/UserGuide/chunk.html @@ -8,9 +8,9 @@ - + - + @@ -118,7 +118,7 @@ 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 d9148153..cd065138 100644 --- a/dev/UserGuide/combine.html +++ b/dev/UserGuide/combine.html @@ -8,11 +8,11 @@ - + - + - + @@ -30,8 +30,8 @@ ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points ├───────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├──────────────────────────────────────────────────────────── file size ┤ - file size: 96.0 bytes +├───────────────────────────────────────────────────── loaded in memory ┤ + data size: 96.0 bytes └───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension

Here we use concatenatecubes to combine two arrays of different variables that have the same dimensions. The resulting array combined has an additional dimension variable indicating from which array the element values originates. Note that using a Dataset instead is a more flexible approach in handling different variables.

julia
using YAXArrays
 
 temperature = YAXArray((Dim{:time}(1:6),), rand(6))
@@ -45,10 +45,10 @@
   → variable Categorical{String} ["temp", "prep"] ReverseOrdered
 ├────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├───────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └────────────────────────────────────────────────────────────────┘
- + \ No newline at end of file diff --git a/dev/UserGuide/compute.html b/dev/UserGuide/compute.html index 273995b6..7e943bc4 100644 --- a/dev/UserGuide/compute.html +++ b/dev/UserGuide/compute.html @@ -8,11 +8,11 @@ - + - + - + @@ -39,9 +39,9 @@ ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{Symbol, String} with 1 entry: :origin => "user guide" -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 35.16 KB -└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8807820337001978
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
╭──────────────────────────────╮
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8897936156214767
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,
@@ -50,8 +50,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
@@ -64,8 +64,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices

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

julia
import Statistics: mean
 mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
 │ 10×15 YAXArray{Union{Missing, Float64},2} │
@@ -74,16 +74,16 @@
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
 │ 30-element YAXArray{Union{Missing, Float64},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

mapCube

mapCube is the most flexible way to apply a function over subsets of an array. Dimensions may be added or removed.

Operations over several YAXArrays

Here, we will define a simple function, that will take as input several YAXArrays. But first, let's load the necessary packages.

julia
using YAXArrays, Zarr
 using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
@@ -93,16 +93,16 @@
 lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 120.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat = YAXArray(Dim{:lat}(range(1, 10)))
╭──────────────────────────────╮
 10-element YAXArray{Int64,1}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
 lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 80.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
 time = YAXArray(Dim{:time}(tspan))
╭─────────────────────────────╮
 │ 30-element YAXArray{Date,1} │
@@ -110,8 +110,8 @@
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

note that the following can be extended to arbitrary YAXArrays with additional data and dimensions.

Let's generate a new cube using mapCube and saving the output directly into disk.

julia
julia> gen_cube = mapCube(g, (lon, lat, time);
            indims = (InDims(), InDims(), InDims("time")),
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
@@ -126,8 +126,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

Note that currently the time axis in the output cube goes first.

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -157,8 +157,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -199,8 +199,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims

In the following function, note how the outputs are defined first and the inputs later.

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
     xout_one .= f1.(xin_one)
     xout_two .= f2.(xin_one)
@@ -228,8 +228,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "name" => "plus_two"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
 yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
 │ 4×3×2 YAXArray{Int64,3} │
@@ -240,8 +240,8 @@
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "2d dimensional test cube"
-├────────────────────────────────────────────────────────────── file size ┤
-  file size: 192.0 bytes
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
 └─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
     xout_one .= f1.(xin_one)
     xout_two .= f2mix.(xin_one, xin_two)
@@ -274,8 +274,8 @@
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
 ├───────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────── file size ┤
-  file size: 672.0 bytes
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
 └───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
 data = rand(3.0:5.0, 5, 4, 3)
 
@@ -292,8 +292,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
     s = sum(abs.(xin_xyz))
     return xin_xyt.^2 .+ s
@@ -313,8 +313,8 @@
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Creating a vector array

Here we transform a raster array with spatial dimension lat and lon into a vector array having just one spatial dimension i.e. region. First, create the raster array:

julia
using YAXArrays
 using DimensionalData
 using Dates
@@ -333,8 +333,8 @@
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Then, create a Matrix with the same spatial dimensions indicating to which region each point belongs to:

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
     1 <= lon < 10 && 1 <= lat < 5 && return "A"
     1 <= lon < 10 && 5 <= lat < 10 && return "B"
@@ -395,8 +395,8 @@
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

This gives us a vector array with only one spatial dimension, i.e. the region. Note that we still have 30 points in time. The transformation was applied for each date separately.

Hereby, xin is a 10x15 array representing a map at a given time and xout is a 4 element vector of missing values initially representing the 4 regions at that date. Then, we set each output element by the sum of all corresponding points

Distributed Computation

All map methods apply a function on all elements of all non-input dimensions separately. This allows to run each map function call in parallel. For example, we can execute each date of a time series in a different CPU thread during spatial aggregation.

The following code does a time mean over all grid points using multiple CPUs of a local machine:

julia
using YAXArrays
 using Dates
 using Distributed
@@ -426,7 +426,7 @@
 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 cbe11d2c..e080a822 100644 --- a/dev/UserGuide/convert.html +++ b/dev/UserGuide/convert.html @@ -8,11 +8,11 @@ - + - + - + @@ -30,8 +30,8 @@ → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points ├─────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├────────────────────────────────────────────────────────── file size ┤ - file size: 400.0 bytes +├─────────────────────────────────────────────────── loaded in memory ┤ + data size: 400.0 bytes └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
  0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
  0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
@@ -50,8 +50,8 @@
   ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────── file size ┤
-  file size: 7.03 KB
+├─────────────────────────────────────────── loaded in memory ┤
+  data size: 7.03 KB
 └─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
 │ 6×6×25 Raster{Float64,3} │
 ├──────────────────────────┴──────────────────────────── dims ┐
@@ -82,8 +82,8 @@
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
 │ 5×6 DimArray{Float64,2} │
 ├─────────────────────────┴────────────────────────────────── dims ┐
@@ -98,7 +98,7 @@
  3     0.522262   0.237824   0.165853    0.662295   0.327439   0.793913
  4     0.365971   0.215988   0.520744    0.096862   0.625389   0.725765
  5     0.271209   0.521769   0.858065    0.162134   0.181798   0.425153

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 8c9b0293..9b504e9d 100644 --- a/dev/UserGuide/create.html +++ b/dev/UserGuide/create.html @@ -8,11 +8,11 @@ - + - + - + @@ -29,8 +29,8 @@ ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points ├──────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────── file size ┤ - file size: 7.81 KB +├──────────────────────────────────────────────────── loaded in memory ┤ + data size: 7.81 KB └──────────────────────────────────────────────────────────────────────┘

The dimensions have only generic names, e.g. Dim_1 and only integer values. We can also specify the dimensions with custom names enabling easier access:

julia
using Dates
 
 axlist = (
@@ -49,8 +49,8 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
 → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
@@ -68,7 +68,7 @@
 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 932df078..afc09b28 100644 --- a/dev/UserGuide/faq.html +++ b/dev/UserGuide/faq.html @@ -8,11 +8,11 @@ - + - + - + @@ -29,8 +29,8 @@ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 3.91 KB +├──────────────────────────────────────────────────────────── loaded in memory ┤ + data size: 3.91 KB └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

To get the axes of a YAXArray use the dims function instead of the caxes function

julia
julia> dims(c)
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -45,8 +45,8 @@
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -54,8 +54,8 @@
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -63,8 +63,8 @@
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube

There are two options to collect values from axes. In this examples the axis ranges from 1 to 10.

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
 collect(c.axes[1].val)
10-element Vector{Int64}:
   1
@@ -83,8 +83,8 @@
 Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes

It is possible to concatenate several cubes that shared the same dimensions using the [concatenatecubes]@ref function.

Let's create two dummy cubes

julia
using YAXArrays
 axlist = (
     Dim{:time}(range(1, 20, length=20)),
@@ -105,8 +105,8 @@
 Variables Categorical{String} ["var1", "var2"] ForwardOrdered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I subset a YAXArray ( Cube ) or Dataset?

These are the three main datatypes provided by the YAXArrays libray. You can find a description of them here. A Cube is no more than a YAXArray, so, we will not explicitly tell about a Cube.

Subsetting a YAXArray

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. `a .. b`.

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))
(↓ Lon  1:10,
@@ -119,8 +119,8 @@
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 28.12 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
 │ 10×10×12 YAXArray{Int64,3} │
 ├────────────────────────────┴─────────────────────────────────────────── dims ┐
@@ -129,8 +129,8 @@
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
 │ 10×10 YAXArray{Int64,2} │
 ├─────────────────────────┴────────────────────────── dims ┐
@@ -138,8 +138,8 @@
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
 │ 10×10×8 YAXArray{Int64,3} │
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -148,8 +148,8 @@
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 6.25 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 6.25 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
 │ 5×6×36 YAXArray{Int64,3} │
 ├──────────────────────────┴───────────────────────────────────────────── dims ┐
@@ -158,8 +158,8 @@
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 8.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subsetting a Dataset

In a dataset, we can have several variables (YAXArrays) that share some or all of their dimensions.

Subsetting a Dataset whose variables share all their dimensions

This works for YAXArrays. Let's make an example.

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data
@@ -218,7 +218,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
@@ -229,7 +229,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes

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

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

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -239,11 +239,6 @@
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
-  Variables: 
-  longitudes
-
-  Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
   → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
@@ -252,6 +247,11 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
+  Variables: 
   latitudes

How do I apply map algebra?

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -260,8 +260,8 @@
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -270,8 +270,8 @@
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
        end
╭──────────────────────────────────────────────╮
@@ -282,8 +282,8 @@
 time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function?

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
 10×15 YAXArray{Int64,2}
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
@@ -291,8 +291,8 @@
 lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
 GLMakie.activate!()
 # This is how our classification map looks like
@@ -309,8 +309,8 @@
 classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 24.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
 3×20 YAXArray{Union{Missing, Float64},2}
 ├──────────────────────────────────────────┴───────────────────────────── dims ┐
@@ -318,8 +318,8 @@
 time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset

One variable name

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
 Shared Axes:
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
@@ -331,17 +331,17 @@
 None
 Variables with additional axes:
   Additional Axes: 
-  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
-  Variables: 
-  a
-
-  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
   b
 
   Additional Axes: 
+  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
@@ -386,7 +386,7 @@
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
 yax_bool = YAXArray(test_bool)
╭─────────────────────╮
@@ -396,10 +396,10 @@
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 172.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

WARNING

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

- + \ No newline at end of file diff --git a/dev/UserGuide/group.html b/dev/UserGuide/group.html index ad9c7274..08c00e00 100644 --- a/dev/UserGuide/group.html +++ b/dev/UserGuide/group.html @@ -8,11 +8,11 @@ - + - + - + @@ -97,8 +97,8 @@ ↓ Ti Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 288.0 bytes +├──────────────────────────────────────────────────────────── loaded in memory ┤ + data size: 288.0 bytes └──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, Ti => seasons(; start=December))
╭────────────────────────────────────────────────╮
 4-element DimGroupByArray{YAXArray{Int64,0},1}
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -210,7 +210,7 @@
     rowgap!(fig.layout, 5)
     fig
 end

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

- + \ No newline at end of file diff --git a/dev/UserGuide/read.html b/dev/UserGuide/read.html index 88378840..976fa176 100644 --- a/dev/UserGuide/read.html +++ b/dev/UserGuide/read.html @@ -8,11 +8,11 @@ - + - + - + @@ -56,8 +56,8 @@ "standard_name" => "air_temperature" "_FillValue" => 1.0f20 "comment" => "near-surface (usually, 2 meter) air temperature" -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 69.02 GB +├─────────────────────────────────────────────────────────────── loaded lazily ┤ + data size: 69.02 GB └──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF

Open a NetCDF file as a Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
@@ -90,8 +90,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Please note that netCDF4 uses HDF5 which is not thread-safe in Julia. Add manual locks in your own code to avoid any data-race:

julia
my_lock = ReentrantLock()
 Threads.@threads for i in 1:10
     @lock my_lock @info ds.tos[1, 1, 1]
@@ -118,7 +118,7 @@
 Blue, Green, Red
 
 Properties: Dict{String, Any}("projection" => "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")
- + \ No newline at end of file diff --git a/dev/UserGuide/select.html b/dev/UserGuide/select.html index 2c4bac34..7458ad95 100644 --- a/dev/UserGuide/select.html +++ b/dev/UserGuide/select.html @@ -8,11 +8,11 @@ - + - + - + @@ -52,8 +52,8 @@ "standard_name" => "sea_surface_temperature" "_FillValue" => 1.0f20 "original_name" => "sosstsst" -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 2.8 MB +├─────────────────────────────────────────────────────────────── loaded lazily ┤ + data size: 2.8 MB └──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
 │ 180×170×24 YAXArray{Union{Missing, Float32},3} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -72,8 +72,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Select elements

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -90,8 +90,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -108,8 +108,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
 tos[time = At(time1)]
╭─────────────────────────────────────────────╮
@@ -129,8 +129,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├───────────────────────────────────────────────────────────────────── file size ┤
-  file size: 119.53 KB
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
 └────────────────────────────────────────────────────────────────────────────────┘

Select ranges

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

julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -149,8 +149,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -169,8 +169,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
 │ 10×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -189,8 +189,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 159.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals

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

julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
 │ 45×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -209,8 +209,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -229,8 +229,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -249,8 +249,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -269,8 +269,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -289,8 +289,8 @@
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension

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

julia
collect(tos.lat)
170-element Vector{Float64}:
  -79.5
  -78.5
@@ -314,7 +314,7 @@
   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 7b09f958..771a7122 100644 --- a/dev/UserGuide/types.html +++ b/dev/UserGuide/types.html @@ -8,9 +8,9 @@ - + - + @@ -21,7 +21,7 @@
Skip to content

Types

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

YAXArray

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

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

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

Dataset

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

(Data) Cube

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

Dimension

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

- + \ No newline at end of file diff --git a/dev/UserGuide/write.html b/dev/UserGuide/write.html index d15261c8..865e5227 100644 --- a/dev/UserGuide/write.html +++ b/dev/UserGuide/write.html @@ -8,11 +8,11 @@ - + - + - + @@ -42,7 +42,7 @@ savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

netcdf compression

Save a dataset to NetCDF format with compression:

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

Comparing it to the default saved file

julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

YAXArrays.Datasets.savedataset Function

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

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

Warning

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

source

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

YAXArrays.Datasets.savedataset Function

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

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

Warning

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

source

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -69,8 +69,8 @@
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 400.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

julia
r = savecube(a, "skeleton.zarr", layername="skeleton", driver=:zarr, skeleton=true, overwrite=true)

WARNING

overwrite=true will delete your previous .zarr file before creating a new one.

Note also that if layername="skeleton" is not provided then the default name for the cube variable will be layer.

Now, we check that all the values are missing

julia
all(ismissing, r[:,:,:])
true

If using FillArrays is not possible, using the zeros function works as well, though it does allocate the array in memory.

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
 Shared Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
@@ -92,7 +92,7 @@
  0.459041  0.761553   0.367809  0.414041
  0.223574  0.898926   0.647957  0.820737
  0.457984  0.839919   0.858795  0.1921

indeed, those entries had been updated.

- + \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index d5e9faa1..91b4488c 100644 --- a/dev/api.html +++ b/dev/api.html @@ -8,11 +8,11 @@ - + - + - + @@ -20,20 +20,20 @@ -
Skip to content

API Reference

This section describes all available functions of this package.

Public API

YAXArrays.getAxis Method
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

YAXArrays.Cubes Module

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

source

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

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

YAXArrays.Cubes.caxes Function

Returns the axes of a Cube

source

YAXArrays.Cubes.caxes Method
julia
caxes

Embeds Cube inside a new Cube

source

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

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

source

YAXArrays.Cubes.readcubedata Method
julia
readcubedata(cube)

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

source

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

YAXArrays.Cubes.subsetcube Function

This function calculates a subset of a cube's data

source

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

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

Keyword arguments

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

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

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

source

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

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

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

source

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

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

YAXArrays.DAT.CubeTable Method
julia
CubeTable()

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

source

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

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

source

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

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

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

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

source

YAXArrays.DAT.mapCube Method
julia
mapCube(fun, cube, addargs...;kwargs...)
+    
Skip to content

API Reference

This section describes all available functions of this package.

Public API

YAXArrays.getAxis Method
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

YAXArrays.Cubes Module

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

source

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

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

YAXArrays.Cubes.caxes Function

Returns the axes of a Cube

source

YAXArrays.Cubes.caxes Method
julia
caxes

Embeds Cube inside a new Cube

source

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

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

source

YAXArrays.Cubes.readcubedata Method
julia
readcubedata(cube)

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

source

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

YAXArrays.Cubes.subsetcube Function

This function calculates a subset of a cube's data

source

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

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

Keyword arguments

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

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

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

source

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

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

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

source

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

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

YAXArrays.DAT.CubeTable Method
julia
CubeTable()

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

source

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

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

source

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

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

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

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

source

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

source

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

YAXArrays.DAT.InputCube Type

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

YAXArrays.DAT.OutputCube Type

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

YAXArrays.DAT.YAXColumn Type
julia
YAXColumn

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

  • inarBC

  • inds

source

YAXArrays.DAT.cmpcachmisses Method

Function that compares two cache miss specifiers by their importance

source

YAXArrays.DAT.getFrontPerm Method

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

source

YAXArrays.DAT.getLoopCacheSize Method

Calculate optimal Cache size to DAT operation

source

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

Internal function

Get the element type for the output cube

source

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

Internal function

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

source

YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)

Internal function

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

source

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

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

YAXArrays.Datasets.collectfromhandle Method

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

source

YAXArrays.Datasets.createdataset Method

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

YAXArrays.DAT.InputCube Type

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

YAXArrays.DAT.OutputCube Type

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

YAXArrays.DAT.YAXColumn Type
julia
YAXColumn

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

  • inarBC

  • inds

source

YAXArrays.DAT.cmpcachmisses Method

Function that compares two cache miss specifiers by their importance

source

YAXArrays.DAT.getFrontPerm Method

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

source

YAXArrays.DAT.getLoopCacheSize Method

Calculate optimal Cache size to DAT operation

source

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

Internal function

Get the element type for the output cube

source

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

Internal function

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

source

YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)

Internal function

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

source

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

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

YAXArrays.Datasets.collectfromhandle Method

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

source

YAXArrays.Datasets.createdataset Method

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

  • datasetaxis="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_combine.md.CyKx3Fe9.js b/dev/assets/UserGuide_combine.md.DSN4kM1g.js similarity index 97% rename from dev/assets/UserGuide_combine.md.CyKx3Fe9.js rename to dev/assets/UserGuide_combine.md.DSN4kM1g.js index be9be2e3..44b8cab2 100644 --- a/dev/assets/UserGuide_combine.md.CyKx3Fe9.js +++ b/dev/assets/UserGuide_combine.md.DSN4kM1g.js @@ -8,8 +8,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const c ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points ├───────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├──────────────────────────────────────────────────────────── file size ┤ - file size: 96.0 bytes +├───────────────────────────────────────────────────── loaded in memory ┤ + data size: 96.0 bytes └───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension

Here we use concatenatecubes to combine two arrays of different variables that have the same dimensions. The resulting array combined has an additional dimension variable indicating from which array the element values originates. Note that using a Dataset instead is a more flexible approach in handling different variables.

julia
using YAXArrays
 
 temperature = YAXArray((Dim{:time}(1:6),), rand(6))
@@ -23,6 +23,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const c
   → variable Categorical{String} ["temp", "prep"] ReverseOrdered
 ├────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├───────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └────────────────────────────────────────────────────────────────┘
`,10)]))}const E=i(t,[["render",p]]);export{c as __pageData,E as default}; diff --git a/dev/assets/UserGuide_combine.md.CyKx3Fe9.lean.js b/dev/assets/UserGuide_combine.md.DSN4kM1g.lean.js similarity index 97% rename from dev/assets/UserGuide_combine.md.CyKx3Fe9.lean.js rename to dev/assets/UserGuide_combine.md.DSN4kM1g.lean.js index be9be2e3..44b8cab2 100644 --- a/dev/assets/UserGuide_combine.md.CyKx3Fe9.lean.js +++ b/dev/assets/UserGuide_combine.md.DSN4kM1g.lean.js @@ -8,8 +8,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const c ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points ├───────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├──────────────────────────────────────────────────────────── file size ┤ - file size: 96.0 bytes +├───────────────────────────────────────────────────── loaded in memory ┤ + data size: 96.0 bytes └───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension

Here we use concatenatecubes to combine two arrays of different variables that have the same dimensions. The resulting array combined has an additional dimension variable indicating from which array the element values originates. Note that using a Dataset instead is a more flexible approach in handling different variables.

julia
using YAXArrays
 
 temperature = YAXArray((Dim{:time}(1:6),), rand(6))
@@ -23,6 +23,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const c
   → variable Categorical{String} ["temp", "prep"] ReverseOrdered
 ├────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├───────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └────────────────────────────────────────────────────────────────┘
`,10)]))}const E=i(t,[["render",p]]);export{c as __pageData,E as default}; diff --git a/dev/assets/UserGuide_compute.md.Co8RnmtU.js b/dev/assets/UserGuide_compute.md.Drf4vzB8.js similarity index 98% rename from dev/assets/UserGuide_compute.md.Co8RnmtU.js rename to dev/assets/UserGuide_compute.md.Drf4vzB8.js index ff8060d8..5a9b0513 100644 --- a/dev/assets/UserGuide_compute.md.Co8RnmtU.js +++ b/dev/assets/UserGuide_compute.md.Drf4vzB8.js @@ -17,9 +17,9 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{Symbol, String} with 1 entry: :origin => "user guide" -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 35.16 KB -└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8807820337001978
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
╭──────────────────────────────╮
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8897936156214767
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,
@@ -28,8 +28,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
@@ -42,8 +42,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices

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

julia
import Statistics: mean
 mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
 │ 10×15 YAXArray{Union{Missing, Float64},2} │
@@ -52,16 +52,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
 │ 30-element YAXArray{Union{Missing, Float64},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

mapCube

mapCube is the most flexible way to apply a function over subsets of an array. Dimensions may be added or removed.

Operations over several YAXArrays

Here, we will define a simple function, that will take as input several YAXArrays. But first, let's load the necessary packages.

julia
using YAXArrays, Zarr
 using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
@@ -71,16 +71,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 120.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat = YAXArray(Dim{:lat}(range(1, 10)))
╭──────────────────────────────╮
 10-element YAXArray{Int64,1}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
 lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 80.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
 time = YAXArray(Dim{:time}(tspan))
╭─────────────────────────────╮
 │ 30-element YAXArray{Date,1} │
@@ -88,8 +88,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

note that the following can be extended to arbitrary YAXArrays with additional data and dimensions.

Let's generate a new cube using mapCube and saving the output directly into disk.

julia
julia> gen_cube = mapCube(g, (lon, lat, time);
            indims = (InDims(), InDims(), InDims("time")),
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
@@ -104,8 +104,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

Note that currently the time axis in the output cube goes first.

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -135,8 +135,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -177,8 +177,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims

In the following function, note how the outputs are defined first and the inputs later.

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
     xout_one .= f1.(xin_one)
     xout_two .= f2.(xin_one)
@@ -206,8 +206,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "name" => "plus_two"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
 yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
 │ 4×3×2 YAXArray{Int64,3} │
@@ -218,8 +218,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "2d dimensional test cube"
-├────────────────────────────────────────────────────────────── file size ┤
-  file size: 192.0 bytes
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
 └─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
     xout_one .= f1.(xin_one)
     xout_two .= f2mix.(xin_one, xin_two)
@@ -252,8 +252,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
 ├───────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────── file size ┤
-  file size: 672.0 bytes
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
 └───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
 data = rand(3.0:5.0, 5, 4, 3)
 
@@ -270,8 +270,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
     s = sum(abs.(xin_xyz))
     return xin_xyt.^2 .+ s
@@ -291,8 +291,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Creating a vector array

Here we transform a raster array with spatial dimension lat and lon into a vector array having just one spatial dimension i.e. region. First, create the raster array:

julia
using YAXArrays
 using DimensionalData
 using Dates
@@ -311,8 +311,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Then, create a Matrix with the same spatial dimensions indicating to which region each point belongs to:

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
     1 <= lon < 10 && 1 <= lat < 5 && return "A"
     1 <= lon < 10 && 5 <= lat < 10 && return "B"
@@ -373,8 +373,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

This gives us a vector array with only one spatial dimension, i.e. the region. Note that we still have 30 points in time. The transformation was applied for each date separately.

Hereby, xin is a 10x15 array representing a map at a given time and xout is a 4 element vector of missing values initially representing the 4 regions at that date. Then, we set each output element by the sum of all corresponding points

Distributed Computation

All map methods apply a function on all elements of all non-input dimensions separately. This allows to run each map function call in parallel. For example, we can execute each date of a time series in a different CPU thread during spatial aggregation.

The following code does a time mean over all grid points using multiple CPUs of a local machine:

julia
using YAXArrays
 using Dates
 using Distributed
diff --git a/dev/assets/UserGuide_compute.md.Co8RnmtU.lean.js b/dev/assets/UserGuide_compute.md.Drf4vzB8.lean.js
similarity index 98%
rename from dev/assets/UserGuide_compute.md.Co8RnmtU.lean.js
rename to dev/assets/UserGuide_compute.md.Drf4vzB8.lean.js
index ff8060d8..5a9b0513 100644
--- a/dev/assets/UserGuide_compute.md.Co8RnmtU.lean.js
+++ b/dev/assets/UserGuide_compute.md.Drf4vzB8.lean.js
@@ -17,9 +17,9 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8807820337001978
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
╭──────────────────────────────╮
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray

julia
a[1,2,3]
0.8897936156214767
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,
@@ -28,8 +28,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map

Apply a function on every element of an array individually:

julia
offset = 5
 map(a) do x
     (x + offset) / 2 * 3
@@ -42,8 +42,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices

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

julia
import Statistics: mean
 mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
 │ 10×15 YAXArray{Union{Missing, Float64},2} │
@@ -52,16 +52,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
mapslices(mean, a, dims=("lat", "lon"))
╭────────────────────────────────────────────────╮
 │ 30-element YAXArray{Union{Missing, Float64},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

mapCube

mapCube is the most flexible way to apply a function over subsets of an array. Dimensions may be added or removed.

Operations over several YAXArrays

Here, we will define a simple function, that will take as input several YAXArrays. But first, let's load the necessary packages.

julia
using YAXArrays, Zarr
 using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
     xout .= f.(lo, la, t)
@@ -71,16 +71,16 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 120.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat = YAXArray(Dim{:lat}(range(1, 10)))
╭──────────────────────────────╮
 10-element YAXArray{Int64,1}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
 lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 80.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
 time = YAXArray(Dim{:time}(tspan))
╭─────────────────────────────╮
 │ 30-element YAXArray{Date,1} │
@@ -88,8 +88,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

note that the following can be extended to arbitrary YAXArrays with additional data and dimensions.

Let's generate a new cube using mapCube and saving the output directly into disk.

julia
julia> gen_cube = mapCube(g, (lon, lat, time);
            indims = (InDims(), InDims(), InDims("time")),
            outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
@@ -104,8 +104,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

Note that currently the time axis in the output cube goes first.

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -135,8 +135,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "missing_value" => 1.0f32
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 17.58 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
 └──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
   3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
@@ -177,8 +177,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims

In the following function, note how the outputs are defined first and the inputs later.

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
     xout_one .= f1.(xin_one)
     xout_two .= f2.(xin_one)
@@ -206,8 +206,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any} with 1 entry:
   "name" => "plus_two"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
 yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
╭─────────────────────────╮
 │ 4×3×2 YAXArray{Int64,3} │
@@ -218,8 +218,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├─────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "2d dimensional test cube"
-├────────────────────────────────────────────────────────────── file size ┤
-  file size: 192.0 bytes
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
 └─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
     xout_one .= f1.(xin_one)
     xout_two .= f2mix.(xin_one, xin_two)
@@ -252,8 +252,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
 ├───────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────── file size ┤
-  file size: 672.0 bytes
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
 └───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
 data = rand(3.0:5.0, 5, 4, 3)
 
@@ -270,8 +270,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, String} with 1 entry:
   "description" => "multi dimensional test cube"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
     s = sum(abs.(xin_xyz))
     return xin_xyt.^2 .+ s
@@ -291,8 +291,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Creating a vector array

Here we transform a raster array with spatial dimension lat and lon into a vector array having just one spatial dimension i.e. region. First, create the raster array:

julia
using YAXArrays
 using DimensionalData
 using Dates
@@ -311,8 +311,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Then, create a Matrix with the same spatial dimensions indicating to which region each point belongs to:

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
     1 <= lon < 10 && 1 <= lat < 5 && return "A"
     1 <= lon < 10 && 5 <= lat < 10 && return "B"
@@ -373,8 +373,8 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 960.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

This gives us a vector array with only one spatial dimension, i.e. the region. Note that we still have 30 points in time. The transformation was applied for each date separately.

Hereby, xin is a 10x15 array representing a map at a given time and xout is a 4 element vector of missing values initially representing the 4 regions at that date. Then, we set each output element by the sum of all corresponding points

Distributed Computation

All map methods apply a function on all elements of all non-input dimensions separately. This allows to run each map function call in parallel. For example, we can execute each date of a time series in a different CPU thread during spatial aggregation.

The following code does a time mean over all grid points using multiple CPUs of a local machine:

julia
using YAXArrays
 using Dates
 using Distributed
diff --git a/dev/assets/UserGuide_convert.md.BVA3H7Dh.js b/dev/assets/UserGuide_convert.md.TbQ9TLKq.js
similarity index 97%
rename from dev/assets/UserGuide_convert.md.BVA3H7Dh.js
rename to dev/assets/UserGuide_convert.md.TbQ9TLKq.js
index deb35c44..ec4c8e4c 100644
--- a/dev/assets/UserGuide_convert.md.BVA3H7Dh.js
+++ b/dev/assets/UserGuide_convert.md.TbQ9TLKq.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.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.DgSLhxNl.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 t(l,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)
╭──────────────────────────╮
@@ -8,8 +8,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────── file size ┤
-  file size: 400.0 bytes
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
  0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
  0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
@@ -28,8 +28,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────── file size ┤
-  file size: 7.03 KB
+├─────────────────────────────────────────── loaded in memory ┤
+  data size: 7.03 KB
 └─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
 │ 6×6×25 Raster{Float64,3} │
 ├──────────────────────────┴──────────────────────────── dims ┐
@@ -60,8 +60,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
 │ 5×6 DimArray{Float64,2} │
 ├─────────────────────────┴────────────────────────────────── dims ┐
@@ -75,4 +75,4 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
  2     0.536273   0.791138   0.47951     0.917969   0.686278   0.780824
  3     0.522262   0.237824   0.165853    0.662295   0.327439   0.793913
  4     0.365971   0.215988   0.520744    0.096862   0.625389   0.725765
- 5     0.271209   0.521769   0.858065    0.162134   0.181798   0.425153

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}; + 5 0.271209 0.521769 0.858065 0.162134 0.181798 0.425153

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",t]]);export{c as __pageData,g as default}; diff --git a/dev/assets/UserGuide_convert.md.BVA3H7Dh.lean.js b/dev/assets/UserGuide_convert.md.TbQ9TLKq.lean.js similarity index 97% rename from dev/assets/UserGuide_convert.md.BVA3H7Dh.lean.js rename to dev/assets/UserGuide_convert.md.TbQ9TLKq.lean.js index deb35c44..ec4c8e4c 100644 --- a/dev/assets/UserGuide_convert.md.BVA3H7Dh.lean.js +++ b/dev/assets/UserGuide_convert.md.TbQ9TLKq.lean.js @@ -1,4 +1,4 @@ -import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.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.DgSLhxNl.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 t(l,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)
╭──────────────────────────╮
@@ -8,8 +8,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────── file size ┤
-  file size: 400.0 bytes
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
  0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
  0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
@@ -28,8 +28,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────── file size ┤
-  file size: 7.03 KB
+├─────────────────────────────────────────── loaded in memory ┤
+  data size: 7.03 KB
 └─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
 │ 6×6×25 Raster{Float64,3} │
 ├──────────────────────────┴──────────────────────────── dims ┐
@@ -60,8 +60,8 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
   → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────── file size ┤
-  file size: 240.0 bytes
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
 └──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
 │ 5×6 DimArray{Float64,2} │
 ├─────────────────────────┴────────────────────────────────── dims ┐
@@ -75,4 +75,4 @@ import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.DgSLhxNl.js";const c
  2     0.536273   0.791138   0.47951     0.917969   0.686278   0.780824
  3     0.522262   0.237824   0.165853    0.662295   0.327439   0.793913
  4     0.365971   0.215988   0.520744    0.096862   0.625389   0.725765
- 5     0.271209   0.521769   0.858065    0.162134   0.181798   0.425153

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}; + 5 0.271209 0.521769 0.858065 0.162134 0.181798 0.425153

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",t]]);export{c as __pageData,g as default}; diff --git a/dev/assets/UserGuide_create.md.XjOA1x12.js b/dev/assets/UserGuide_create.md.CFGBwSgS.js similarity index 98% rename from dev/assets/UserGuide_create.md.XjOA1x12.js rename to dev/assets/UserGuide_create.md.CFGBwSgS.js index 6b7b3cf2..15c79ed2 100644 --- a/dev/assets/UserGuide_create.md.XjOA1x12.js +++ b/dev/assets/UserGuide_create.md.CFGBwSgS.js @@ -7,8 +7,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points ├──────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────── file size ┤ - file size: 7.81 KB +├──────────────────────────────────────────────────── loaded in memory ┤ + data size: 7.81 KB └──────────────────────────────────────────────────────────────────────┘

The dimensions have only generic names, e.g. Dim_1 and only integer values. We can also specify the dimensions with custom names enabling easier access:

julia
using Dates
 
 axlist = (
@@ -27,8 +27,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
 → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
diff --git a/dev/assets/UserGuide_create.md.XjOA1x12.lean.js b/dev/assets/UserGuide_create.md.CFGBwSgS.lean.js
similarity index 98%
rename from dev/assets/UserGuide_create.md.XjOA1x12.lean.js
rename to dev/assets/UserGuide_create.md.CFGBwSgS.lean.js
index 6b7b3cf2..15c79ed2 100644
--- a/dev/assets/UserGuide_create.md.XjOA1x12.lean.js
+++ b/dev/assets/UserGuide_create.md.CFGBwSgS.lean.js
@@ -7,8 +7,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
-  file size: 7.81 KB
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 7.81 KB
 └──────────────────────────────────────────────────────────────────────┘

The dimensions have only generic names, e.g. Dim_1 and only integer values. We can also specify the dimensions with custom names enabling easier access:

julia
using Dates
 
 axlist = (
@@ -27,8 +27,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 35.16 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
   :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
 → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
diff --git a/dev/assets/UserGuide_faq.md.BEzNQu7O.js b/dev/assets/UserGuide_faq.md.DusaRncX.js
similarity index 98%
rename from dev/assets/UserGuide_faq.md.BEzNQu7O.js
rename to dev/assets/UserGuide_faq.md.DusaRncX.js
index 255fffb8..413ed9ed 100644
--- a/dev/assets/UserGuide_faq.md.BEzNQu7O.js
+++ b/dev/assets/UserGuide_faq.md.DusaRncX.js
@@ -7,8 +7,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 3.91 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 3.91 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

To get the axes of a YAXArray use the dims function instead of the caxes function

julia
julia> dims(c)
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -23,8 +23,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -32,8 +32,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -41,8 +41,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube

There are two options to collect values from axes. In this examples the axis ranges from 1 to 10.

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
 collect(c.axes[1].val)
10-element Vector{Int64}:
   1
@@ -61,8 +61,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes

It is possible to concatenate several cubes that shared the same dimensions using the [concatenatecubes]@ref function.

Let's create two dummy cubes

julia
using YAXArrays
 axlist = (
     Dim{:time}(range(1, 20, length=20)),
@@ -83,8 +83,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Variables Categorical{String} ["var1", "var2"] ForwardOrdered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I subset a YAXArray ( Cube ) or Dataset?

These are the three main datatypes provided by the YAXArrays libray. You can find a description of them here. A Cube is no more than a YAXArray, so, we will not explicitly tell about a Cube.

Subsetting a YAXArray

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. \`a .. b\`.

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))
(↓ Lon  1:10,
@@ -97,8 +97,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 28.12 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
 │ 10×10×12 YAXArray{Int64,3} │
 ├────────────────────────────┴─────────────────────────────────────────── dims ┐
@@ -107,8 +107,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
 │ 10×10 YAXArray{Int64,2} │
 ├─────────────────────────┴────────────────────────── dims ┐
@@ -116,8 +116,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
 │ 10×10×8 YAXArray{Int64,3} │
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -126,8 +126,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 6.25 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 6.25 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
 │ 5×6×36 YAXArray{Int64,3} │
 ├──────────────────────────┴───────────────────────────────────────────── dims ┐
@@ -136,8 +136,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 8.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subsetting a Dataset

In a dataset, we can have several variables (YAXArrays) that share some or all of their dimensions.

Subsetting a Dataset whose variables share all their dimensions

This works for YAXArrays. Let's make an example.

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data
@@ -196,7 +196,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
@@ -207,7 +207,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes

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

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

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -217,11 +217,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
-  Variables: 
-  longitudes
-
-  Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
   → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
@@ -230,6 +225,11 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
+  Variables: 
   latitudes

How do I apply map algebra?

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -238,8 +238,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -248,8 +248,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
        end
╭──────────────────────────────────────────────╮
@@ -260,8 +260,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function?

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
 10×15 YAXArray{Int64,2}
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
@@ -269,8 +269,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
 GLMakie.activate!()
 # This is how our classification map looks like
@@ -287,8 +287,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 24.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
 3×20 YAXArray{Union{Missing, Float64},2}
 ├──────────────────────────────────────────┴───────────────────────────── dims ┐
@@ -296,8 +296,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset

One variable name

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
 Shared Axes:
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
@@ -309,17 +309,17 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
-  Variables: 
-  a
-
-  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
   b
 
   Additional Axes: 
+  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
@@ -364,7 +364,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
 yax_bool = YAXArray(test_bool)
╭─────────────────────╮
@@ -374,6 +374,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 172.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

WARNING

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

`,149)]))}const E=i(l,[["render",h]]);export{o as __pageData,E as default}; diff --git a/dev/assets/UserGuide_faq.md.BEzNQu7O.lean.js b/dev/assets/UserGuide_faq.md.DusaRncX.lean.js similarity index 98% rename from dev/assets/UserGuide_faq.md.BEzNQu7O.lean.js rename to dev/assets/UserGuide_faq.md.DusaRncX.lean.js index 255fffb8..413ed9ed 100644 --- a/dev/assets/UserGuide_faq.md.BEzNQu7O.lean.js +++ b/dev/assets/UserGuide_faq.md.DusaRncX.lean.js @@ -7,8 +7,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 3.91 KB +├──────────────────────────────────────────────────────────── loaded in memory ┤ + data size: 3.91 KB └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

To get the axes of a YAXArray use the dims function instead of the caxes function

julia
julia> dims(c)
(Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -23,8 +23,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -32,8 +32,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
╭───────────────────────────╮
 10×10 YAXArray{Float64,2}
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -41,8 +41,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube

There are two options to collect values from axes. In this examples the axis ranges from 1 to 10.

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
 collect(c.axes[1].val)
10-element Vector{Int64}:
   1
@@ -61,8 +61,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes

It is possible to concatenate several cubes that shared the same dimensions using the [concatenatecubes]@ref function.

Let's create two dummy cubes

julia
using YAXArrays
 axlist = (
     Dim{:time}(range(1, 20, length=20)),
@@ -83,8 +83,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 Variables Categorical{String} ["var1", "var2"] ForwardOrdered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I subset a YAXArray ( Cube ) or Dataset?

These are the three main datatypes provided by the YAXArrays libray. You can find a description of them here. A Cube is no more than a YAXArray, so, we will not explicitly tell about a Cube.

Subsetting a YAXArray

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data, however the intervals notation should be used instead, i.e. \`a .. b\`.

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))
(↓ Lon  1:10,
@@ -97,8 +97,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 28.12 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

julia
ytime = y[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
 │ 10×10×12 YAXArray{Int64,3} │
 ├────────────────────────────┴─────────────────────────────────────────── dims ┐
@@ -107,8 +107,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by a specific date

julia
ytime2 = y[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
 │ 10×10 YAXArray{Int64,2} │
 ├─────────────────────────┴────────────────────────── dims ┐
@@ -116,8 +116,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────── file size ┤
-  file size: 800.0 bytes
+├──────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
 └──────────────────────────────────────────────────────────┘

Subset YAXArray by a date range

julia
ytime3 = y[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
 │ 10×10×8 YAXArray{Int64,3} │
 ├───────────────────────────┴──────────────────────────────────────────── dims ┐
@@ -126,8 +126,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 6.25 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 6.25 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subset YAXArray by longitude and latitude

julia
ylonlat = y[Lon=1 .. 5, Lat=5 .. 10]
╭──────────────────────────╮
 │ 5×6×36 YAXArray{Int64,3} │
 ├──────────────────────────┴───────────────────────────────────────────── dims ┐
@@ -136,8 +136,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 8.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Subsetting a Dataset

In a dataset, we can have several variables (YAXArrays) that share some or all of their dimensions.

Subsetting a Dataset whose variables share all their dimensions

This works for YAXArrays. Let's make an example.

julia
using YAXArrays
 using Dates # To generate the dates of the time axis
 using DimensionalData # To use the "Between" option for selecting data
@@ -196,7 +196,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
@@ -207,7 +207,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes

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

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

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -217,11 +217,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
-  Variables: 
-  longitudes
-
-  Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points,
   → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
@@ -230,6 +225,11 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
   Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [9, 13, …, 95, 100] ForwardOrdered Irregular Points)
+  Variables: 
   latitudes

How do I apply map algebra?

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -238,8 +238,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
╭──────────────────────────────╮
 20×10×15 YAXArray{Float64,3}
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
@@ -248,8 +248,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
            (xin * π) ./ maximum(skipmissing(xin))
        end
╭──────────────────────────────────────────────╮
@@ -260,8 +260,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 23.44 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
 └──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function?

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
╭─────────────────────────╮
 10×15 YAXArray{Int64,2}
 ├─────────────────────────┴────────────────────────────────────────────── dims ┐
@@ -269,8 +269,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 1.17 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
 GLMakie.activate!()
 # This is how our classification map looks like
@@ -287,8 +287,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 24.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
╭──────────────────────────────────────────╮
 3×20 YAXArray{Union{Missing, Float64},2}
 ├──────────────────────────────────────────┴───────────────────────────── dims ┐
@@ -296,8 +296,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 480.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset

One variable name

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
 Shared Axes:
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
@@ -309,17 +309,17 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
 None
 Variables with additional axes:
   Additional Axes: 
-  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
-  Variables: 
-  a
-
-  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
   b
 
   Additional Axes: 
+  (Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
   (Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
 Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
   Variables: 
@@ -364,7 +364,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 121.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
 yax_bool = YAXArray(test_bool)
╭─────────────────────╮
@@ -374,6 +374,6 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const t
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────── memory size ┤
+├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 172.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

WARNING

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

`,149)]))}const E=i(l,[["render",h]]);export{o as __pageData,E as default}; diff --git a/dev/assets/UserGuide_group.md.CmyTrCZm.js b/dev/assets/UserGuide_group.md.B7UQBCPb.js similarity index 99% rename from dev/assets/UserGuide_group.md.CmyTrCZm.js rename to dev/assets/UserGuide_group.md.B7UQBCPb.js index 7c4b9660..d88c735f 100644 --- a/dev/assets/UserGuide_group.md.CmyTrCZm.js +++ b/dev/assets/UserGuide_group.md.B7UQBCPb.js @@ -75,8 +75,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const h ↓ Ti Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 288.0 bytes +├──────────────────────────────────────────────────────────── loaded in memory ┤ + data size: 288.0 bytes └──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, Ti => seasons(; start=December))
╭────────────────────────────────────────────────╮
 4-element DimGroupByArray{YAXArray{Int64,0},1}
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
diff --git a/dev/assets/UserGuide_group.md.CmyTrCZm.lean.js b/dev/assets/UserGuide_group.md.B7UQBCPb.lean.js
similarity index 99%
rename from dev/assets/UserGuide_group.md.CmyTrCZm.lean.js
rename to dev/assets/UserGuide_group.md.B7UQBCPb.lean.js
index 7c4b9660..d88c735f 100644
--- a/dev/assets/UserGuide_group.md.CmyTrCZm.lean.js
+++ b/dev/assets/UserGuide_group.md.B7UQBCPb.lean.js
@@ -75,8 +75,8 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.DgSLhxNl.js";const h
   ↓ Ti Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 288.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 288.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, Ti => seasons(; start=December))
╭────────────────────────────────────────────────╮
 4-element DimGroupByArray{YAXArray{Int64,0},1}
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
diff --git a/dev/assets/UserGuide_read.md.BUhYzSmC.js b/dev/assets/UserGuide_read.md.C9HWqe6o.js
similarity index 98%
rename from dev/assets/UserGuide_read.md.BUhYzSmC.js
rename to dev/assets/UserGuide_read.md.C9HWqe6o.js
index a6f8c4e8..b0145767 100644
--- a/dev/assets/UserGuide_read.md.BUhYzSmC.js
+++ b/dev/assets/UserGuide_read.md.C9HWqe6o.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k=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,h,d){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.DgSLhxNl.js";const k=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)
@@ -34,8 +34,8 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k
   "standard_name" => "air_temperature"
   "_FillValue"    => 1.0f20
   "comment"       => "near-surface (usually, 2 meter) air temperature"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 69.02 GB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 69.02 GB
 └──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF

Open a NetCDF file as a Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
@@ -68,8 +68,8 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Please note that netCDF4 uses HDF5 which is not thread-safe in Julia. Add manual locks in your own code to avoid any data-race:

julia
my_lock = ReentrantLock()
 Threads.@threads for i in 1:10
     @lock my_lock @info ds.tos[1, 1, 1]
diff --git a/dev/assets/UserGuide_read.md.BUhYzSmC.lean.js b/dev/assets/UserGuide_read.md.C9HWqe6o.lean.js
similarity index 98%
rename from dev/assets/UserGuide_read.md.BUhYzSmC.lean.js
rename to dev/assets/UserGuide_read.md.C9HWqe6o.lean.js
index a6f8c4e8..b0145767 100644
--- a/dev/assets/UserGuide_read.md.BUhYzSmC.lean.js
+++ b/dev/assets/UserGuide_read.md.C9HWqe6o.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k=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,h,d){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.DgSLhxNl.js";const k=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)
@@ -34,8 +34,8 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k
   "standard_name" => "air_temperature"
   "_FillValue"    => 1.0f20
   "comment"       => "near-surface (usually, 2 meter) air temperature"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 69.02 GB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 69.02 GB
 └──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF

Open a NetCDF file as a Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
@@ -68,8 +68,8 @@ import{_ as a,c as n,a2 as i,o as t}from"./chunks/framework.DgSLhxNl.js";const k
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Please note that netCDF4 uses HDF5 which is not thread-safe in Julia. Add manual locks in your own code to avoid any data-race:

julia
my_lock = ReentrantLock()
 Threads.@threads for i in 1:10
     @lock my_lock @info ds.tos[1, 1, 1]
diff --git a/dev/assets/UserGuide_select.md.elnGsCxh.js b/dev/assets/UserGuide_select.md.B8kuj4Uf.js
similarity index 97%
rename from dev/assets/UserGuide_select.md.elnGsCxh.js
rename to dev/assets/UserGuide_select.md.B8kuj4Uf.js
index 0bfd4c3f..a3bf8f8e 100644
--- a/dev/assets/UserGuide_select.md.elnGsCxh.js
+++ b/dev/assets/UserGuide_select.md.B8kuj4Uf.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.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.DgSLhxNl.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),n("div",null,s[0]||(s[0]=[i(`

Select YAXArrays and Datasets

The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -30,8 +30,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
 │ 180×170×24 YAXArray{Union{Missing, Float32},3} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -50,8 +50,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Select elements

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -68,8 +68,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -86,8 +86,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
 tos[time = At(time1)]
╭─────────────────────────────────────────────╮
@@ -107,8 +107,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├───────────────────────────────────────────────────────────────────── file size ┤
-  file size: 119.53 KB
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
 └────────────────────────────────────────────────────────────────────────────────┘

Select ranges

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

julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -127,8 +127,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -147,8 +147,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
 │ 10×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -167,8 +167,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 159.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals

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

julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
 │ 45×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -187,8 +187,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -207,8 +207,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -227,8 +227,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -247,8 +247,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -267,8 +267,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension

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

julia
collect(tos.lat)
170-element Vector{Float64}:
  -79.5
  -78.5
diff --git a/dev/assets/UserGuide_select.md.elnGsCxh.lean.js b/dev/assets/UserGuide_select.md.B8kuj4Uf.lean.js
similarity index 97%
rename from dev/assets/UserGuide_select.md.elnGsCxh.lean.js
rename to dev/assets/UserGuide_select.md.B8kuj4Uf.lean.js
index 0bfd4c3f..a3bf8f8e 100644
--- a/dev/assets/UserGuide_select.md.elnGsCxh.lean.js
+++ b/dev/assets/UserGuide_select.md.B8kuj4Uf.lean.js
@@ -1,4 +1,4 @@
-import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.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.DgSLhxNl.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),n("div",null,s[0]||(s[0]=[i(`

Select YAXArrays and Datasets

The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -30,8 +30,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
 │ 180×170×24 YAXArray{Union{Missing, Float32},3} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -50,8 +50,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 2.8 MB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
 └──────────────────────────────────────────────────────────────────────────────┘

Select elements

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -68,8 +68,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
╭────────────────────────────────────────────────╮
 │ 24-element YAXArray{Union{Missing, Float32},1} │
 ├────────────────────────────────────────────────┴─────────────────────── dims ┐
@@ -86,8 +86,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 96.0 bytes
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
 time1 = DateTime360Day(2001,01,16)
 tos[time = At(time1)]
╭─────────────────────────────────────────────╮
@@ -107,8 +107,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├───────────────────────────────────────────────────────────────────── file size ┤
-  file size: 119.53 KB
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
 └────────────────────────────────────────────────────────────────────────────────┘

Select ranges

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

julia
tos[lon = 1:10, lat = 1:10]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -127,8 +127,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
╭──────────────────────────────────────────────╮
 │ 10×10×24 YAXArray{Union{Missing, Float32},3} │
 ├──────────────────────────────────────────────┴───────────────────────── dims ┐
@@ -147,8 +147,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 9.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
╭───────────────────────────────────────────────╮
 │ 10×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -167,8 +167,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 159.38 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals

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

julia
tos[lon = 90 .. 180]
╭───────────────────────────────────────────────╮
 │ 45×170×24 YAXArray{Union{Missing, Float32},3} │
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -187,8 +187,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -207,8 +207,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -227,8 +227,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -247,8 +247,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
 45×170×24 YAXArray{Union{Missing, Float32},3}
 ├───────────────────────────────────────────────┴──────────────────────── dims ┐
@@ -267,8 +267,8 @@ import{_ as a,c as n,a2 as i,o as e}from"./chunks/framework.DgSLhxNl.js";const u
   "standard_name"  => "sea_surface_temperature"
   "_FillValue"     => 1.0f20
   "original_name"  => "sosstsst"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 717.19 KB
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
 └──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension

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

julia
collect(tos.lat)
170-element Vector{Float64}:
  -79.5
  -78.5
diff --git a/dev/assets/UserGuide_write.md.DgDaUFrs.js b/dev/assets/UserGuide_write.md.CPpseOm6.js
similarity index 99%
rename from dev/assets/UserGuide_write.md.DgDaUFrs.js
rename to dev/assets/UserGuide_write.md.CPpseOm6.js
index 183ea3e8..5e70b6c3 100644
--- a/dev/assets/UserGuide_write.md.DgDaUFrs.js
+++ b/dev/assets/UserGuide_write.md.CPpseOm6.js
@@ -1,4 +1,4 @@
-import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DgSLhxNl.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,E,y,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
+import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DgSLhxNl.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,y,E,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -20,7 +20,7 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

netcdf compression

Save a dataset to NetCDF format with compression:

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

Comparing it to the default saved file

julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L567-L576",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -47,8 +47,8 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 400.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

julia
r = savecube(a, "skeleton.zarr", layername="skeleton", driver=:zarr, skeleton=true, overwrite=true)

WARNING

overwrite=true will delete your previous .zarr file before creating a new one.

Note also that if layername="skeleton" is not provided then the default name for the cube variable will be layer.

Now, we check that all the values are missing

julia
all(ismissing, r[:,:,:])
true

If using FillArrays is not possible, using the zeros function works as well, though it does allocate the array in memory.

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
 Shared Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
diff --git a/dev/assets/UserGuide_write.md.DgDaUFrs.lean.js b/dev/assets/UserGuide_write.md.CPpseOm6.lean.js
similarity index 99%
rename from dev/assets/UserGuide_write.md.DgDaUFrs.lean.js
rename to dev/assets/UserGuide_write.md.CPpseOm6.lean.js
index 183ea3e8..5e70b6c3 100644
--- a/dev/assets/UserGuide_write.md.DgDaUFrs.lean.js
+++ b/dev/assets/UserGuide_write.md.CPpseOm6.lean.js
@@ -1,4 +1,4 @@
-import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DgSLhxNl.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,E,y,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
+import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framework.DgSLhxNl.js";const v=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,y,E,u){const e=h("Badge");return k(),l("div",null,[s[6]||(s[6]=t(`

Write YAXArrays and Datasets

Create an example Dataset:

julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
@@ -20,7 +20,7 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 savecube(ds.tos, "tos.nc", driver=:netcdf)

Save an entire Dataset to a directory:

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

netcdf compression

Save a dataset to NetCDF format with compression:

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

Comparing it to the default saved file

julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963858 bytes, compress: 1159914 bytes

Overwrite a Dataset

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

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

DANGER

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

Look at the doc string for more information

`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=a()),p(e,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=i("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[3]||(s[3]=i("p",null,[a("Saves a Dataset into a file at "),i("code",null,"path"),a(" with the format given by "),i("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[4]||(s[4]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Warning"),i("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[5]||(s[5]=i("p",null,[i("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L567-L576",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[7]||(s[7]=t(`

Append to a Dataset

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -47,8 +47,8 @@ import{_ as n,c as l,a2 as t,j as i,a,G as p,B as h,o as k}from"./chunks/framewo
 Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 400.0 bytes
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
 └──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

julia
r = savecube(a, "skeleton.zarr", layername="skeleton", driver=:zarr, skeleton=true, overwrite=true)

WARNING

overwrite=true will delete your previous .zarr file before creating a new one.

Note also that if layername="skeleton" is not provided then the default name for the cube variable will be layer.

Now, we check that all the values are missing

julia
all(ismissing, r[:,:,:])
true

If using FillArrays is not possible, using the zeros function works as well, though it does allocate the array in memory.

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
 Shared Axes: 
   (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
diff --git a/dev/assets/api.md.D6vP2vGi.js b/dev/assets/api.md.B_ln9Frf.js
similarity index 92%
rename from dev/assets/api.md.D6vP2vGi.js
rename to dev/assets/api.md.B_ln9Frf.js
index 0e2b803f..727c20ae 100644
--- a/dev/assets/api.md.D6vP2vGi.js
+++ b/dev/assets/api.md.B_ln9Frf.js
@@ -1,10 +1,10 @@
-import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DgSLhxNl.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const t=r("Badge");return p(),o("div",null,[s[162]||(s[162]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[163]||(s[163]=e("p",null,"This section describes all available functions of this package.",-1)),s[164]||(s[164]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",g,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)

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

source

',3))]),e("details",A,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)

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

Keyword arguments

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

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

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

source

',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)

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

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

source

',4))]),e("details",C,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

',4))]),e("details",D,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()

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

source

',3))]),e("details",E,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)

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

source

',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)

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

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

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

source

',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DgSLhxNl.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const t=r("Badge");return p(),o("div",null,[s[162]||(s[162]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[163]||(s[163]=e("p",null,"This section describes all available functions of this package.",-1)),s[164]||(s[164]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",g,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)

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

source

',3))]),e("details",A,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)

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

Keyword arguments

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

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

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

source

',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)

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

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

source

',4))]),e("details",C,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

',4))]),e("details",D,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()

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

source

',3))]),e("details",E,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)

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

source

',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)

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

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

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

source

',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
 
 Map a given function \`fun\` over slices of all cubes of the dataset \`ds\`. 
 Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
 For Datasets, only one output cube can be specified.
 In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
 
-For the specific keyword arguments see the docstring of the mapCube function for cubes.

source

`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)

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

Keyword arguments

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

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

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

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

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

  • showprog boolean indicating if a ProgressMeter shall be shown

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

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

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

  • kwargs additional keyword arguments are passed to the inner function

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

source

',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.

source

',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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

source

',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L330-L335",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

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

  • max_cache: The number of bits that are used as cache for the data handling.

  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.

  • driver: The same setting as backend.

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

source

',6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[76]||(s[76]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[77]||(s[77]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[78]||(s[78]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[81]||(s[81]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[82]||(s[82]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[83]||(s[83]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[165]||(s[165]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[86]||(s[86]=l('

Default configuration for YAXArrays, has the following fields:

  • workdir[]::String = "./" The default location for temporary cubes.

  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.

  • chunksize[]::Any = :input Set the default output chunksize.

  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.

  • cubedir[]::"" the default location for Cube() without an argument.

  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.

source

',3))]),e("details",B,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
findAxis(desc, c)

Internal function

Extended Help

Given an Axis description and a cube return the index of the Axis.

The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source

',7))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
get_descriptor(a)

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

source

',3))]),e("details",z,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l(`
julia
match_axis

Internal function

Extended Help

Match the Axis based on the AxisDescriptor.
+For the specific keyword arguments see the docstring of the mapCube function for cubes.

source

`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)

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

Keyword arguments

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

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

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

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

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

  • showprog boolean indicating if a ProgressMeter shall be shown

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

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

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

  • kwargs additional keyword arguments are passed to the inner function

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

source

',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.

source

',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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

source

',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L340-L345",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

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

  • max_cache: The number of bits that are used as cache for the data handling.

  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.

  • driver: The same setting as backend.

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

source

',6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[76]||(s[76]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[77]||(s[77]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[78]||(s[78]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L567-L576",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[81]||(s[81]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[82]||(s[82]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[83]||(s[83]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[165]||(s[165]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[86]||(s[86]=l('

Default configuration for YAXArrays, has the following fields:

  • workdir[]::String = "./" The default location for temporary cubes.

  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.

  • chunksize[]::Any = :input Set the default output chunksize.

  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.

  • cubedir[]::"" the default location for Cube() without an argument.

  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.

source

',3))]),e("details",B,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
findAxis(desc, c)

Internal function

Extended Help

Given an Axis description and a cube return the index of the Axis.

The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source

',7))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
get_descriptor(a)

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

source

',3))]),e("details",z,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l(`
julia
match_axis

Internal function

Extended Help

Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.

source

`,5))]),e("details",N,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=l('
julia
mutable struct CleanMe

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

source

',3))]),e("details",S,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
clean(c::CleanMe)

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

source

',3))]),e("details",q,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
copydata(outar, inar, copybuf)

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

source

',3))]),e("details",V,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)

Internal

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

source

',4))]),e("details",G,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

',3))]),e("details",R,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

',4))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[125]||(s[125]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[126]||(s[126]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[127]||(s[127]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[128]||(s[128]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[130]||(s[130]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[131]||(s[131]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[132]||(s[132]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[133]||(s[133]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[134]||(s[134]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getloopchunks(dc::DATConfig)

Internal function

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

source

',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
permuteloopaxes(dc)

Internal function

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

source

',4))]),e("details",ss,[e("summary",null,[s[144]||(s[144]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[145]||(s[145]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
setchunks(c::Dataset,chunks)

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',6))]),e("details",es,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[148]||(s[148]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[149]||(s[149]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[150]||(s[150]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L463-L465",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",as,[e("summary",null,[s[151]||(s[151]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[152]||(s[152]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[153]||(s[153]=l('

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

source

',6))]),e("details",ts,[e("summary",null,[s[154]||(s[154]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[155]||(s[155]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[157]||(s[157]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L432-L434",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",is,[e("summary",null,[s[158]||(s[158]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[159]||(s[159]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[160]||(s[160]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[161]||(s[161]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L302",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const hs=n(d,[["render",ls]]);export{cs as __pageData,hs as default}; +For example to disregard differences of captialisation.

source

`,5))]),e("details",N,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=l('
julia
mutable struct CleanMe

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

source

',3))]),e("details",S,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
clean(c::CleanMe)

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

source

',3))]),e("details",q,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
copydata(outar, inar, copybuf)

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

source

',3))]),e("details",V,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)

Internal

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

source

',4))]),e("details",G,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

',3))]),e("details",R,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

',4))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[125]||(s[125]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[126]||(s[126]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[127]||(s[127]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[128]||(s[128]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[130]||(s[130]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[131]||(s[131]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[132]||(s[132]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[133]||(s[133]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[134]||(s[134]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getloopchunks(dc::DATConfig)

Internal function

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

source

',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
permuteloopaxes(dc)

Internal function

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

source

',4))]),e("details",ss,[e("summary",null,[s[144]||(s[144]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[145]||(s[145]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
setchunks(c::Dataset,chunks)

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',6))]),e("details",es,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[148]||(s[148]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[149]||(s[149]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[150]||(s[150]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L473-L475",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",as,[e("summary",null,[s[151]||(s[151]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[152]||(s[152]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[153]||(s[153]=l('

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

source

',6))]),e("details",ts,[e("summary",null,[s[154]||(s[154]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[155]||(s[155]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[157]||(s[157]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L442-L444",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",is,[e("summary",null,[s[158]||(s[158]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[159]||(s[159]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[160]||(s[160]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[161]||(s[161]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const hs=n(d,[["render",ls]]);export{cs as __pageData,hs as default}; diff --git a/dev/assets/api.md.D6vP2vGi.lean.js b/dev/assets/api.md.B_ln9Frf.lean.js similarity index 92% rename from dev/assets/api.md.D6vP2vGi.lean.js rename to dev/assets/api.md.B_ln9Frf.lean.js index 0e2b803f..727c20ae 100644 --- a/dev/assets/api.md.D6vP2vGi.lean.js +++ b/dev/assets/api.md.B_ln9Frf.lean.js @@ -1,10 +1,10 @@ -import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DgSLhxNl.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const t=r("Badge");return p(),o("div",null,[s[162]||(s[162]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[163]||(s[163]=e("p",null,"This section describes all available functions of this package.",-1)),s[164]||(s[164]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",g,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)

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

source

',3))]),e("details",A,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)

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

Keyword arguments

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

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

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

source

',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)

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

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

source

',4))]),e("details",C,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

',4))]),e("details",D,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()

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

source

',3))]),e("details",E,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)

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

source

',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)

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

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

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

source

',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.DgSLhxNl.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""};function ls(ns,s,os,rs,ps,ds){const t=r("Badge");return p(),o("div",null,[s[162]||(s[162]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[163]||(s[163]=e("p",null,"This section describes all available functions of this package.",-1)),s[164]||(s[164]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",u,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)

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

  • the name as a string or symbol.

  • an Axis object

source

',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source

',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",y,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes

Embeds Cube inside a new Cube

source

',3))]),e("details",g,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)

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

source

',3))]),e("details",A,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)

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

source

',3))]),e("details",k,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)

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

Keyword arguments

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

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

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

source

',5))]),e("details",j,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)

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

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

source

',4))]),e("details",C,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)

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

  • axisdesc: List of input axis names

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

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

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

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

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

source

',4))]),e("details",D,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()

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

source

',3))]),e("details",E,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)

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

source

',3))]),e("details",T,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)

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

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

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

source

',5))]),e("details",v,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l(`
julia
mapCube(fun, cube, addargs...;kwargs...)
 
 Map a given function \`fun\` over slices of all cubes of the dataset \`ds\`. 
 Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
 For Datasets, only one output cube can be specified.
 In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
 
-For the specific keyword arguments see the docstring of the mapCube function for cubes.

source

`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)

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

Keyword arguments

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

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

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

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

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

  • showprog boolean indicating if a ProgressMeter shall be shown

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

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

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

  • kwargs additional keyword arguments are passed to the inner function

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

source

',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.

source

',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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

source

',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L330-L335",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

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

  • max_cache: The number of bits that are used as cache for the data handling.

  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.

  • driver: The same setting as backend.

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

source

',6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[76]||(s[76]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[77]||(s[77]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[78]||(s[78]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L557-L566",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[81]||(s[81]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[82]||(s[82]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[83]||(s[83]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[165]||(s[165]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[86]||(s[86]=l('

Default configuration for YAXArrays, has the following fields:

  • workdir[]::String = "./" The default location for temporary cubes.

  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.

  • chunksize[]::Any = :input Set the default output chunksize.

  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.

  • cubedir[]::"" the default location for Cube() without an argument.

  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.

source

',3))]),e("details",B,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
findAxis(desc, c)

Internal function

Extended Help

Given an Axis description and a cube return the index of the Axis.

The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source

',7))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
get_descriptor(a)

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

source

',3))]),e("details",z,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l(`
julia
match_axis

Internal function

Extended Help

Match the Axis based on the AxisDescriptor.
+For the specific keyword arguments see the docstring of the mapCube function for cubes.

source

`,2))]),e("details",X,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)

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

Keyword arguments

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

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

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

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

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

  • showprog boolean indicating if a ProgressMeter shall be shown

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

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

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

  • kwargs additional keyword arguments are passed to the inner function

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

source

',6))]),e("details",Y,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=l('
julia
Dataset object which stores an `OrderedDict` of YAXArrays with Symbol keys.\na dictionary of CubeAxes and a Dictionary of general properties.\nA dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.

source

',2))]),e("details",x,[e("summary",null,[s[57]||(s[57]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[58]||(s[58]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[59]||(s[59]=e("p",null,"Dataset(; properties = Dict{String,Any}, cubes...)",-1)),s[60]||(s[60]=e("p",null,[a("Construct a YAXArray Dataset with global attributes "),e("code",null,"properties"),a(" a and a list of named YAXArrays cubes...")],-1)),s[61]||(s[61]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L28-L32",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",F,[e("summary",null,[s[62]||(s[62]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[63]||(s[63]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[64]||(s[64]=l('
julia
Cube(ds::Dataset; joinname="Variable")

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

source

',3))]),e("details",w,[e("summary",null,[s[65]||(s[65]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[66]||(s[66]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[67]||(s[67]=e("p",null,"open_dataset(g; driver=:all)",-1)),s[68]||(s[68]=e("p",null,[a("Open the dataset at "),e("code",null,"g"),a(" with the given "),e("code",null,"driver"),a(". The default driver will search for available drivers and tries to detect the useable driver from the filename extension.")],-1)),s[69]||(s[69]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L340-L345",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",L,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

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

  • max_cache: The number of bits that are used as cache for the data handling.

  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.

  • driver: The same setting as backend.

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

source

',6))]),e("details",M,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=e("p",null,'savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)',-1)),s[76]||(s[76]=e("p",null,[a("Saves a Dataset into a file at "),e("code",null,"path"),a(" with the format given by "),e("code",null,"driver"),a(", i.e., driver=:netcdf or driver=:zarr.")],-1)),s[77]||(s[77]=e("div",{class:"warning custom-block"},[e("p",{class:"custom-block-title"},"Warning"),e("p",null,"overwrite = true, deletes ALL your data and it will create a new file.")],-1)),s[78]||(s[78]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L567-L576",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",O,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[81]||(s[81]=e("p",null,'to_dataset(c;datasetaxis = "Variable", layername = "layer")',-1)),s[82]||(s[82]=e("p",null,[a(`Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name `),e("code",null,"layername")],-1)),s[83]||(s[83]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L45-L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),s[165]||(s[165]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[84]||(s[84]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[85]||(s[85]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[86]||(s[86]=l('

Default configuration for YAXArrays, has the following fields:

  • workdir[]::String = "./" The default location for temporary cubes.

  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.

  • chunksize[]::Any = :input Set the default output chunksize.

  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.

  • cubedir[]::"" the default location for Cube() without an argument.

  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.

source

',3))]),e("details",B,[e("summary",null,[s[87]||(s[87]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[88]||(s[88]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[89]||(s[89]=l('
julia
findAxis(desc, c)

Internal function

Extended Help

Given an Axis description and a cube return the index of the Axis.

The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source

',7))]),e("details",J,[e("summary",null,[s[90]||(s[90]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[91]||(s[91]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[92]||(s[92]=l('
julia
getOutAxis

source

',2))]),e("details",P,[e("summary",null,[s[93]||(s[93]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[94]||(s[94]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[95]||(s[95]=l('
julia
get_descriptor(a)

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

source

',3))]),e("details",z,[e("summary",null,[s[96]||(s[96]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[97]||(s[97]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[98]||(s[98]=l(`
julia
match_axis

Internal function

Extended Help

Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.

source

`,5))]),e("details",N,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=l('
julia
mutable struct CleanMe

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

source

',3))]),e("details",S,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
clean(c::CleanMe)

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

source

',3))]),e("details",q,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
copydata(outar, inar, copybuf)

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

source

',3))]),e("details",V,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)

Internal

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

source

',4))]),e("details",G,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

',3))]),e("details",R,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

',4))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[125]||(s[125]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[126]||(s[126]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[127]||(s[127]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[128]||(s[128]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[130]||(s[130]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[131]||(s[131]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[132]||(s[132]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[133]||(s[133]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[134]||(s[134]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getloopchunks(dc::DATConfig)

Internal function

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

source

',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
permuteloopaxes(dc)

Internal function

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

source

',4))]),e("details",ss,[e("summary",null,[s[144]||(s[144]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[145]||(s[145]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
setchunks(c::Dataset,chunks)

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',6))]),e("details",es,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[148]||(s[148]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[149]||(s[149]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[150]||(s[150]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L463-L465",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",as,[e("summary",null,[s[151]||(s[151]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[152]||(s[152]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[153]||(s[153]=l('

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

source

',6))]),e("details",ts,[e("summary",null,[s[154]||(s[154]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[155]||(s[155]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[157]||(s[157]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L432-L434",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",is,[e("summary",null,[s[158]||(s[158]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[159]||(s[159]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[160]||(s[160]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[161]||(s[161]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/44a9d700c3b73f9c69cd5cc0412f628797643df5/src/DatasetAPI/Datasets.jl#L302",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const hs=n(d,[["render",ls]]);export{cs as __pageData,hs as default}; +For example to disregard differences of captialisation.

source

`,5))]),e("details",N,[e("summary",null,[s[99]||(s[99]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[100]||(s[100]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[101]||(s[101]=l('
julia
mutable struct CleanMe

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

source

',3))]),e("details",S,[e("summary",null,[s[102]||(s[102]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[103]||(s[103]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[104]||(s[104]=l('
julia
clean(c::CleanMe)

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

source

',3))]),e("details",q,[e("summary",null,[s[105]||(s[105]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[106]||(s[106]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[107]||(s[107]=l('
julia
copydata(outar, inar, copybuf)

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

source

',3))]),e("details",V,[e("summary",null,[s[108]||(s[108]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[109]||(s[109]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[110]||(s[110]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)

Internal

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

source

',4))]),e("details",G,[e("summary",null,[s[111]||(s[111]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[112]||(s[112]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[113]||(s[113]=l('

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source

',3))]),e("details",R,[e("summary",null,[s[114]||(s[114]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[115]||(s[115]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[116]||(s[116]=l('

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source

',3))]),e("details",W,[e("summary",null,[s[117]||(s[117]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[118]||(s[118]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[119]||(s[119]=l('

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source

',4))]),e("details",U,[e("summary",null,[s[120]||(s[120]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[121]||(s[121]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[122]||(s[122]=l('
julia
YAXColumn

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

  • inarBC

  • inds

source

',4))]),e("details",$,[e("summary",null,[s[123]||(s[123]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[124]||(s[124]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[125]||(s[125]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[126]||(s[126]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L957-L959",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[127]||(s[127]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[128]||(s[128]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[129]||(s[129]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[130]||(s[130]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L1202",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",K,[e("summary",null,[s[131]||(s[131]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[132]||(s[132]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[133]||(s[133]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[134]||(s[134]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DAT/DAT.jl#L1056",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[135]||(s[135]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[136]||(s[136]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[137]||(s[137]=l('
julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

',4))]),e("details",Q,[e("summary",null,[s[138]||(s[138]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[139]||(s[139]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[140]||(s[140]=l('
julia
getloopchunks(dc::DATConfig)

Internal function

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

source

',4))]),e("details",_,[e("summary",null,[s[141]||(s[141]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[142]||(s[142]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[143]||(s[143]=l('
julia
permuteloopaxes(dc)

Internal function

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

source

',4))]),e("details",ss,[e("summary",null,[s[144]||(s[144]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[145]||(s[145]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[146]||(s[146]=l('
julia
setchunks(c::Dataset,chunks)

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source

',6))]),e("details",es,[e("summary",null,[s[147]||(s[147]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[148]||(s[148]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[149]||(s[149]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[150]||(s[150]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L473-L475",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",as,[e("summary",null,[s[151]||(s[151]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[152]||(s[152]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[153]||(s[153]=l('

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

source

',6))]),e("details",ts,[e("summary",null,[s[154]||(s[154]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[155]||(s[155]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[156]||(s[156]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[157]||(s[157]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L442-L444",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",is,[e("summary",null,[s[158]||(s[158]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[159]||(s[159]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[160]||(s[160]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[161]||(s[161]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/4635df48ccc186291b899e2c19ca2146ef806bd9/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const hs=n(d,[["render",ls]]);export{cs as __pageData,hs as default}; diff --git a/dev/assets/app.Ld_wjof0.js b/dev/assets/app.rDKFvbi4.js similarity index 95% rename from dev/assets/app.Ld_wjof0.js rename to dev/assets/app.rDKFvbi4.js index 1d2c7b26..ec156587 100644 --- a/dev/assets/app.Ld_wjof0.js +++ b/dev/assets/app.rDKFvbi4.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.C2TXdxqr.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.DgSLhxNl.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 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geospatial data in Julia.

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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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Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
using DimensionalData
 
 # axes or dimensions with name and tick values
@@ -41,8 +41,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   "y"      => "latitude"
   "x"      => "longitude"
   "origin" => "YAXArrays.jl example"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data 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:
  0.325997   0.624506   0.655204  0.142733  …  0.587477   0.626919  0.606561
  0.0972941  0.0693719  0.828299  0.531092     0.884949   0.060422  0.265107
diff --git a/dev/assets/get_started.md.0W6cNOC_.lean.js b/dev/assets/get_started.md.0ob1QuZx.lean.js
similarity index 98%
rename from dev/assets/get_started.md.0W6cNOC_.lean.js
rename to dev/assets/get_started.md.0ob1QuZx.lean.js
index 162fe22f..2d6e7981 100644
--- a/dev/assets/get_started.md.0W6cNOC_.lean.js
+++ b/dev/assets/get_started.md.0ob1QuZx.lean.js
@@ -7,8 +7,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
 ├─────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├────────────────────────────────────────────────────────── file size ┤
-  file size: 48.0 bytes
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 48.0 bytes
 └─────────────────────────────────────────────────────────────────────┘

Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
using DimensionalData
 
 # axes or dimensions with name and tick values
@@ -41,8 +41,8 @@ import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DgSLhxNl.js";const g
   "y"      => "latitude"
   "x"      => "longitude"
   "origin" => "YAXArrays.jl example"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data 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:
  0.325997   0.624506   0.655204  0.142733  …  0.587477   0.626919  0.606561
  0.0972941  0.0693719  0.828299  0.531092     0.884949   0.060422  0.265107
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.js b/dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.js
similarity index 99%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.js
index 11a35eb4..cb0c2caf 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.js
@@ -17,8 +17,8 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.DgSLhxNl.js";const l
 Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 8.55 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.55 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
     ## filterig by month-day
     monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.lean.js
similarity index 99%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.lean.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.lean.js
index 11a35eb4..cb0c2caf 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.Dkm6Wu6i.lean.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.D9XqWxXS.lean.js
@@ -17,8 +17,8 @@ import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.DgSLhxNl.js";const l
 Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 8.55 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.55 KB
 └──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
     ## filterig by month-day
     monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
diff --git a/dev/development/contribute.html b/dev/development/contribute.html
index a6a7db78..06ca186e 100644
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+++ b/dev/development/contribute.html
@@ -8,9 +8,9 @@
     
     
     
-    
+    
     
-    
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Contribute to YAXArrays.jl

Pull requests and bug reports are always welcome at the YAXArrays.jl GitHub repository.

Contribute to Documentation

Contributing with examples can be done by first creating a new file example here

new file

  • your_new_file.md at docs/src/UserGuide/

Once this is done you need to add a new entry here at the appropriate level.

add entry to docs

Your new entry should look like:

  • { text: 'Your title example', link: '/UserGuide/your_new_file.md' }

Build docs locally

If you want to take a look at the docs locally before doing a PR follow the next steps:

Install the dependencies in your system, locate yourself at the docs level folder, then do

sh
npm i

Then simply go to your docs env and activate it, i.e.

sh
docs> julia
 julia> ]
 pkg> activate .

Next, run the scripts. Generate files and build docs by running:

sh
include("make.jl")

Now go to your terminal in the same path docs> and run:

sh
npm run docs:dev

This should ouput http://localhost:5173/YAXArrays.jl/, copy/paste this into your browser and you are all set.

- + \ No newline at end of file diff --git a/dev/development/contributors.html b/dev/development/contributors.html index 11446845..93c98796 100644 --- a/dev/development/contributors.html +++ b/dev/development/contributors.html @@ -8,11 +8,11 @@ - + - + - + @@ -21,7 +21,7 @@
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Contributors

Current core contributors

They have taking the lead for the ongoing organizational maintenance and technical direction of , and .

Fabian Gans

Fabian Gans

Geoscientific Programmer

Felix Cremer

Felix Cremer

PhD Candidate in Remote Sensing

Rafael Schouten

Rafael Schouten

Spatial/ecological modelling

Lazaro Alonso

Lazaro Alonso

Scientist. Data Visualization

Our valuable contributors

We appreciate all contributions from the Julia community so that this ecosystem can thrive.

- + \ No newline at end of file diff --git a/dev/get_started.html b/dev/get_started.html index 02263b70..4584bd68 100644 --- a/dev/get_started.html +++ b/dev/get_started.html @@ -8,11 +8,11 @@ - + - + - + @@ -29,8 +29,8 @@ → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points ├─────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├────────────────────────────────────────────────────────── file size ┤ - file size: 48.0 bytes +├─────────────────────────────────────────────────── loaded in memory ┤ + data size: 48.0 bytes └─────────────────────────────────────────────────────────────────────┘

Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
using DimensionalData
 
 # axes or dimensions with name and tick values
@@ -63,8 +63,8 @@
   "y"      => "latitude"
   "x"      => "longitude"
   "origin" => "YAXArrays.jl example"
-├─────────────────────────────────────────────────────────────────── file size ┤
-  file size: 46.88 KB
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data 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:
  0.325997   0.624506   0.655204  0.142733  …  0.587477   0.626919  0.606561
  0.0972941  0.0693719  0.828299  0.531092     0.884949   0.060422  0.265107
@@ -76,7 +76,7 @@
  0.853962   0.461652   0.438885  0.665833     0.0743642  0.81705   0.00997173
  0.515079   0.283788   0.258509  0.807171     0.234116   0.404819  0.3785
  0.864937   0.0916764  0.235899  0.047283     0.768363   0.937012  0.732556

Updates

TIP

The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.

You may check the installed version with:

julia
pkg> st YAXArrays

INFO

With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.

- + \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index a97afb6b..2da6a762 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api.md":"D6vP2vGi","development_contribute.md":"CWxNBB30","development_contributors.md":"CxgtnG29","get_started.md":"0W6cNOC_","index.md":"C1AQoR-g","tutorials_mean_seasonal_cycle.md":"Dkm6Wu6i","tutorials_other_tutorials.md":"8UuNb0p3","tutorials_plottingmaps.md":"Ew7MjOB_","userguide_cache.md":"DpJd_JXl","userguide_chunk.md":"DJ6iFmHa","userguide_combine.md":"CyKx3Fe9","userguide_compute.md":"Co8RnmtU","userguide_convert.md":"BVA3H7Dh","userguide_create.md":"XjOA1x12","userguide_faq.md":"BEzNQu7O","userguide_group.md":"CmyTrCZm","userguide_read.md":"BUhYzSmC","userguide_select.md":"elnGsCxh","userguide_types.md":"Cb003_ux","userguide_write.md":"DgDaUFrs"} +{"api.md":"B_ln9Frf","development_contribute.md":"CWxNBB30","development_contributors.md":"DrwWmkLi","get_started.md":"0ob1QuZx","index.md":"C1AQoR-g","tutorials_mean_seasonal_cycle.md":"D9XqWxXS","tutorials_other_tutorials.md":"8UuNb0p3","tutorials_plottingmaps.md":"Ew7MjOB_","userguide_cache.md":"DpJd_JXl","userguide_chunk.md":"DJ6iFmHa","userguide_combine.md":"DSN4kM1g","userguide_compute.md":"Drf4vzB8","userguide_convert.md":"TbQ9TLKq","userguide_create.md":"CFGBwSgS","userguide_faq.md":"DusaRncX","userguide_group.md":"B7UQBCPb","userguide_read.md":"C9HWqe6o","userguide_select.md":"B8kuj4Uf","userguide_types.md":"Cb003_ux","userguide_write.md":"CPpseOm6"} diff --git a/dev/index.html b/dev/index.html index 48775cad..2dd1bda5 100644 --- a/dev/index.html +++ b/dev/index.html @@ -8,9 +8,9 @@ - + - + @@ -21,7 +21,7 @@ - + \ No newline at end of file diff --git a/dev/tutorials/mean_seasonal_cycle.html b/dev/tutorials/mean_seasonal_cycle.html index 8d96a91e..30e6bff9 100644 --- a/dev/tutorials/mean_seasonal_cycle.html +++ b/dev/tutorials/mean_seasonal_cycle.html @@ -8,11 +8,11 @@ - + - + - + @@ -39,8 +39,8 @@ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points ├──────────────────────────────────────────────────────────────────── metadata ┤ Dict{String, Any}() -├─────────────────────────────────────────────────────────────────── file size ┤ - file size: 8.55 KB +├──────────────────────────────────────────────────────────── loaded in memory ┤ + data size: 8.55 KB └──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
     ## filterig by month-day
     monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
@@ -92,7 +92,7 @@
 ax.xticklabelalign = (:right, :center)
 fig
 current_figure()

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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 1637f0c6..de4cdb9f 100644 --- a/dev/tutorials/plottingmaps.html +++ b/dev/tutorials/plottingmaps.html @@ -8,9 +8,9 @@ - + - + @@ -74,7 +74,7 @@ zoom!(ax.scene, cameracontrols(ax.scene), 0.5) rotate!(ax.scene, 2.5) fig - + \ No newline at end of file