From df49d3ff03c5a96bb3f1d0ef183278235cc639c4 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Tue, 10 Sep 2024 19:17:43 +0000 Subject: [PATCH] build based on 07a30ea --- dev/404.html | 4 +- dev/UserGuide/cache.html | 12 +- dev/UserGuide/chunk.html | 32 +- dev/UserGuide/combine.html | 12 +- dev/UserGuide/compute.html | 122 +++++++- dev/UserGuide/convert.html | 48 +-- dev/UserGuide/create.html | 20 +- dev/UserGuide/faq.html | 94 +++--- dev/UserGuide/group.html | 14 +- dev/UserGuide/read.html | 20 +- dev/UserGuide/select.html | 16 +- dev/UserGuide/types.html | 12 +- dev/UserGuide/write.html | 34 +- dev/api.html | 20 +- ...evW4.js => UserGuide_cache.md.BxJ3IRnF.js} | 4 +- .../UserGuide_cache.md.BxJ3IRnF.lean.js | 5 + .../UserGuide_cache.md.aYjxevW4.lean.js | 1 - ...cenL.js => UserGuide_chunk.md.DQmK3Tmu.js} | 24 +- .../UserGuide_chunk.md.DQmK3Tmu.lean.js | 83 +++++ .../UserGuide_chunk.md.DZgkcenL.lean.js | 1 - ..._r.js => UserGuide_combine.md.Bs3BBbp0.js} | 4 +- 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- + \ No newline at end of file diff --git a/dev/UserGuide/cache.html b/dev/UserGuide/cache.html index abaf45fb..12d55e72 100644 --- a/dev/UserGuide/cache.html +++ b/dev/UserGuide/cache.html @@ -8,21 +8,21 @@ - + - - - + + + -
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Caching YAXArrays ​

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

julia
using YAXArrays, Zarr
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
 ds = open_dataset("path/to/source")
 cachesize = 500 #MB
 cache(ds,maxsize = cachesize)
The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays
julia
yax = ds.avariable
 cache(yax,maxsize = 1000)

-    
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diff --git a/dev/UserGuide/chunk.html b/dev/UserGuide/chunk.html
index 67222609..bde41d54 100644
--- a/dev/UserGuide/chunk.html
+++ b/dev/UserGuide/chunk.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
 a = YAXArray(rand(10,20))
 a_chunked = setchunks(a, (5,10))
 a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -46,14 +46,14 @@
  (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
Saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables: 
 x
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 y
 z
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
-  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
Set chunking by Variable ​
The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above
julia
using YAXArrays, Zarr
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking by Variable ​
The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above
julia
using YAXArrays, Zarr
 ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
 dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
 Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -70,14 +70,14 @@
  (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables: 
 x
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 y
 z
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
-  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
Set chunking for all variables ​
The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.
julia
using YAXArrays, Zarr
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking for all variables ​
The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.
julia
using YAXArrays, Zarr
 ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
 dschunked = setchunks(ds,(5,10))
 Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -94,13 +94,13 @@
  (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)
saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-→ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 Variables: 
 x
 y
 z
Suggestions on how to improve or add to these examples is welcome.

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diff --git a/dev/UserGuide/combine.html b/dev/UserGuide/combine.html
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--- a/dev/UserGuide/combine.html
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-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 
 first_half = YAXArray((Dim{:time}(1:6),), rand(6))
 second_half = YAXArray((Dim{:time}(7:12),), rand(6))
@@ -45,7 +45,7 @@
 ├───────────────────────────────────────────────────── file size ┤ 
   file size: 96.0 bytes
 └────────────────────────────────────────────────────────────────┘

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/compute.html b/dev/UserGuide/compute.html
index 00184552..623d055a 100644
--- a/dev/UserGuide/compute.html
+++ b/dev/UserGuide/compute.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
 using Dates
 
 axlist = (
@@ -38,7 +38,7 @@
   :origin => "user guide"
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.39569774546730574
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0
WARNING
Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.
Arithmetics ​
Add a value to all elements of an array and save it as a new array:
julia
a2 = a .+ 5
╭──────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.2141174000417737
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,
@@ -81,7 +81,97 @@
   Dict{String, Any}()
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file 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.
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
+└──────────────────────────────────────────────────────────────────────────────┘
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)
+end
g (generic function with 1 method)
INFO
Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.
Here, we do create YAXArrays only with the desired dimensions as
julia
julia> lon = YAXArray(Dim{:lon}(range(1, 15)))
╭──────────────────────────────╮
+│ 15-element YAXArray{Int64,1} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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} │
+├─────────────────────────────┴────────────────────────────────────────── 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
+└──────────────────────────────────────────────────────────────────────────────┘
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,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
but, we can generate a another cube with a different output order as follows
julia
julia> gen_cube = mapCube(g, (lon, lat, time);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
which outputs the same as the gen_cube.data[1, :, :] called above.
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
 
@@ -131,14 +221,14 @@
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0        1.28571   1.57143     …  4.42857   4.71429     5.0
-  1.0  0.274566   0.534793  0.854262       0.110146  0.57064     0.19585
-  2.0  0.227196   0.1771    0.795952       0.395735  0.00766601  0.099476
-  3.0  0.382234   0.848547  0.249912       0.454773  0.0948468   0.0196535
-  ⋮                                     ⋱                        ⋮
-  8.0  0.0827623  0.791032  0.00929636     0.217969  0.028745    0.878439
-  9.0  0.219269   0.903682  0.861747       0.926123  0.689873    0.690915
- 10.0  0.142275   0.381645  0.284095    …  0.896381  0.501022    0.086277
Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.
julia
regions = ["A", "B", "C", "D"]
+  ↓ →  1.0       1.28571    1.57143   …  4.42857   4.71429   5.0
+  1.0  0.111806  0.730896   0.390622     0.345419  0.886959  0.0322648
+  2.0  0.945136  0.2867     0.885515     0.636107  0.178756  0.535162
+  3.0  0.807073  0.0658503  0.951478     0.771082  0.110929  0.576982
+  ⋮                                   ⋱                      ⋮
+  8.0  0.571501  0.479832   0.401882     0.899889  0.917739  0.609885
+  9.0  0.225987  0.0376093  0.812919     0.80386   0.443763  0.226869
+ 10.0  0.335462  0.392626   0.980182  …  0.409798  0.437697  0.0388753
Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.
julia
regions = ["A", "B", "C", "D"]
 points_of_regions = map(enumerate(regions)) do (i,region)
     region => findall(isequal(region), regions_mat)
 end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
@@ -192,7 +282,7 @@
 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 3ab20eb2..fc51ee57 100644
--- a/dev/UserGuide/convert.html
+++ b/dev/UserGuide/convert.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
 a = YAXArray(m)
╭──────────────────────────╮
@@ -30,11 +30,11 @@
 ├────────────────────────────────────────────────────────── file size ┤ 
   file size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
Convert YAXArray to Base.Array:
julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.249608  0.00552544  0.56213   0.519169   …  0.450066  0.51539    0.574653
- 0.19875   0.470067    0.103211  0.856547      0.19776   0.260932   0.695295
- 0.528707  0.666155    0.839119  0.0321818     0.919695  0.836565   0.641424
- 0.665448  0.684161    0.158104  0.858738      0.243106  0.602045   0.671807
- 0.159698  0.848549    0.48712   0.0557879     0.511201  0.0943564  0.432317
Convert Raster ​
A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:
julia
using Rasters
+ 0.982681  0.30872    0.991662  0.812941  …  0.785487  0.200921  0.56509
+ 0.255901  0.191475   0.91863   0.286567     0.697089  0.578499  0.559458
+ 0.802815  0.0792955  0.49635   0.153314     0.519502  0.595256  0.982369
+ 0.838723  0.418527   0.945523  0.681937     0.654553  0.869916  0.0806691
+ 0.8037    0.726055   0.370371  0.726514     0.46623   0.371407  0.795256
Convert Raster ​
A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:
julia
using Rasters
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -62,13 +62,13 @@
 
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  25         26           27          28         29          30
- 25     0.940483   0.894856     0.496038    0.450453   0.630924    0.754794
- 26     0.437037   0.216039     0.301316    0.248116   0.810228    0.313254
- 27     0.896249   0.989218     0.0477079   0.89122    0.0261803   0.206706
- 28     0.708967   0.351027     0.26031     0.470138   0.840873    0.0133172
- 29     0.892941   0.00686917   0.035698    0.939528   0.510782    0.198741
- 30     0.646887   0.418673     0.907052    0.230079   0.02116     0.635883
Convert DimArray ​
A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.
Convert DimArray to YAXArray:
julia
using DimensionalData
+  ↓ →  25           26         27         28         29         30
+ 25     0.668356     0.238835   0.436044   0.152703   0.468086   0.158513
+ 26     0.906684     0.345557   0.739191   0.21762    0.76884    0.112142
+ 27     0.370883     0.471202   0.216082   0.40658    0.790483   0.033994
+ 28     0.469495     0.263308   0.951713   0.373742   0.307866   0.490098
+ 29     0.00465577   0.208669   0.982017   0.897845   0.337278   0.828301
+ 30     0.778578     0.948093   0.894081   0.818217   0.106047   0.92191
Convert DimArray ​
A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.
Convert DimArray to YAXArray:
julia
using DimensionalData
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
@@ -89,13 +89,13 @@
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       11.0         12.0       13.0       14.0       15.0
- 1     0.308299   0.979944     0.813157   0.953172   0.18757    0.325188
- 2     0.372918   0.669206     0.946524   0.937779   0.810681   0.552949
- 3     0.261139   0.298312     0.662771   0.246061   0.773001   0.274429
- 4     0.402285   0.597315     0.43242    0.229361   0.868584   0.56698
- 5     0.987524   0.00346434   0.355755   0.961872   0.702858   0.491176
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.

-    
+ ↓ →  10.0       11.0        12.0        13.0        14.0       15.0
+ 1     0.1495     0.0195499   0.921394    0.193496    0.445246   0.506692
+ 2     0.888258   0.318317    0.6742      0.607539    0.234382   0.722903
+ 3     0.464692   0.405837    0.610329    0.432667    0.387209   0.353383
+ 4     0.657613   0.583472    0.379975    0.896276    0.425406   0.238368
+ 5     0.404644   0.951713    0.0752454   0.0759995   0.412653   0.930755
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 ceae0894..601b68ec 100644
--- a/dev/UserGuide/create.html
+++ b/dev/UserGuide/create.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
 │ 10×20×5 YAXArray{Float64,3} │
 ├─────────────────────────────┴────────────────────────────────── dims ┐
@@ -49,23 +49,23 @@
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file 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,
+  :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,
-↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
Create a Dataset ​
julia
data3 = rand(30, 10, 15)
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
Create a Dataset ​
julia
data3 = rand(30, 10, 15)
 a3 = YAXArray(axlist, data3, properties)
 
 arrays = Dict(:a2 => a2, :a3 => a3)
 ds = Dataset(; properties, arrays...)
YAXArray Dataset
 Shared Axes: 
-↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
 → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
-↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
 Variables: 
 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 5d00fb74..643874c1 100644
--- a/dev/UserGuide/faq.html
+++ b/dev/UserGuide/faq.html
@@ -8,31 +8,31 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
 using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
 │ 10×10×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────── dims ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
-├──────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 3.91 KB
-└──────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+└──────────────────────────────────────────────────────────────────────────────┘
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,
+↗ 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,
 → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
INFO
Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.
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)
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
INFO
Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.
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
   2
@@ -45,14 +45,14 @@
   9
  10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
╭───────────────────────────╮
 │ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴────────────────────────── dims ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
-├────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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)),
     Dim{:lon}(range(1, 10, length=10)),
@@ -76,9 +76,9 @@
   file 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,
+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,
 → Lat  1:10,
-↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
 │ 10×10×36 YAXArray{Int64,3} │
 ├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
@@ -139,16 +139,16 @@
 
 ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
 Shared Axes: 
-↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+(↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
 → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
-↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
 Variables: 
 var1
 var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
 Shared Axes: 
-↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+(↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
 → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
-↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
 Variables: 
 var1
 var2
Subsetting a Dataset whose variables share some but not all of their dimensions ​
In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.
But we can also subset a variable by the values of another variable with which it shares some dimensions.
Warning
If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.
Let's make an example.
julia
using YAXArrays
@@ -166,12 +166,12 @@
 
 ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
 Shared Axes: 
-↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points
+(↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
 Variables: 
 longitudes
 latitudes
 temperature
-  ↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
Select all points between 20ºN and 85ºN, and 0ºE to 180ºE
julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
Select all points between 20ºN and 85ºN, and 0ºE to 180ºE
julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
                              ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
                              ) # Where
               ] # ds
YAXArray Dataset
@@ -179,12 +179,12 @@
 ()
 Variables: 
 longitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 latitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 temperature
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
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"])
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
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
@@ -194,12 +194,12 @@
 ()
 Variables: 
 longitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 latitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 temperature
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
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)
╭──────────────────────────────╮
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
 │ 20×10×15 YAXArray{Float64,3} │
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
@@ -246,30 +246,30 @@
 fig, ax, obj = heatmap(classes;
     colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
 cbar = Colorbar(fig[1,2], obj)
-fig
Now we define the input cubes that will be considered for the iterable table
julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+fig
Now we define the input cubes that will be considered for the iterable table
julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
 first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
 │ 3-element YAXArray{Union{Missing, Float64},1} │
-├───────────────────────────────────────────────┴────────────── dims ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
-├────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file 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))
╭──────────────────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
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 ┐
+├──────────────────────────────────────────┴───────────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
   → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
-├───────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 480.0 bytes
-└───────────────────────────────────────────────────────────────────────┘
How do I assing variable names to YAXArrays in a Dataset ​
One variable name ​
julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
+└──────────────────────────────────────────────────────────────────────────────┘
How do I assing 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
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables:
 a
Multiple variable names ​
julia
keylist = (:a, :b, :c)
 varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
@@ -277,14 +277,14 @@
 ()
 Variables:
 a
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 b
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
 c
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points

-    
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/group.html b/dev/UserGuide/group.html
index 54cb540c..7b269a8e 100644
--- a/dev/UserGuide/group.html
+++ b/dev/UserGuide/group.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using NetCDF
 using Downloads
 using Dates
@@ -206,8 +206,8 @@
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.

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

+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/read.html b/dev/UserGuide/read.html
index 82be61b0..5715837f 100644
--- a/dev/UserGuide/read.html
+++ b/dev/UserGuide/read.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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)
@@ -27,9 +27,9 @@
 Variables: 
 height
 tas
-  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
 
 Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
╭────────────────────────────────────╮
 │ 384×192×251288 YAXArray{Float32,3} │
@@ -58,9 +58,9 @@
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
@@ -91,7 +91,7 @@
 path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
 # ds = open_dataset(path) # this is broken
 nothing

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/select.html b/dev/UserGuide/select.html
index 1a16e58f..1b1ae3f3 100644
--- a/dev/UserGuide/select.html
+++ b/dev/UserGuide/select.html
@@ -8,25 +8,25 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
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
 
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
@@ -310,7 +310,7 @@
   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 724d4bb9..f031efe9 100644
--- a/dev/UserGuide/types.html
+++ b/dev/UserGuide/types.html
@@ -8,17 +8,17 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Types ​
This section describes the data structures used to work with n-dimensional arrays in YAXArrays.
YAXArray ​
An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.
A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.
A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.
Dataset ​
A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.
(Data) Cube ​
A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020
Dimension ​
A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.

-    
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Types ​
This section describes the data structures used to work with n-dimensional arrays in YAXArrays.
YAXArray ​
An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.
A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.
A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.
Dataset ​
A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.
(Data) Cube ​
A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020
Dimension ​
A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.

+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/write.html b/dev/UserGuide/write.html
index d6fa4db6..bb65e6ef 100644
--- a/dev/UserGuide/write.html
+++ b/dev/UserGuide/write.html
@@ -8,55 +8,55 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
 Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Write Zarr ​
Save a single YAXArray to a directory:
julia
using Zarr
 savecube(ds.tos, "tos.zarr", driver=:zarr)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.zarr", driver=:zarr)
Write NetCDF ​
Save a single YAXArray to a directory:
julia
using NetCDF
-savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
# YAXArrays.Datasets.savedataset — Function. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
# YAXArrays.Datasets.savedataset — Function. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 ()
 Variables:
 tos
-  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 z
-  ↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
-  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
 
 Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Int32}, 10, 20))
╭─────────────────────────────────────────╮
 │ 10×20 YAXArray{Union{Missing, Int32},2} │
-├─────────────────────────────────────────┴────────────────────── dims ┐
+├─────────────────────────────────────────┴────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
-├──────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────┘
and save them as
julia
r = savecube(a, "skeleton.zarr", driver=:zarr, skeleton=true)
and 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.

-    
+└──────────────────────────────────────────────────────────────────────────────┘
and save them as
julia
r = savecube(a, "skeleton.zarr", driver=:zarr, skeleton=true)
and 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.

+    
     
   
 
\ No newline at end of file
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index e45a5ce4..2fc337c8 100644
--- a/dev/api.html
+++ b/dev/api.html
@@ -8,29 +8,29 @@
     
     
     
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+    
     
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-    
-    
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YAXArrays.jl
Appearance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="Variable")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source

# YAXArrays.Datasets.open_dataset — Method. 
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source

# YAXArrays.Datasets.savecube — Method. 
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source

# YAXArrays.Datasets.savedataset — Method. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

# YAXArrays.Datasets.to_dataset — Method. 
to_dataset(c;datasetaxis = "Variable", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source

Internal API ​
# YAXArrays.YAXDefaults — Constant. 
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source

# YAXArrays.findAxis — Method. 
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

# YAXArrays.getOutAxis — Method. 
julia
getOutAxis
source

# YAXArrays.get_descriptor — Method. 
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source

# YAXArrays.match_axis — Method. 
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source

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

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="Variable")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source

# YAXArrays.Datasets.open_dataset — Method. 
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source

# YAXArrays.Datasets.savecube — Method. 
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source

# YAXArrays.Datasets.savedataset — Method. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

# YAXArrays.Datasets.to_dataset — Method. 
to_dataset(c;datasetaxis = "Variable", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source

Internal API ​
# YAXArrays.YAXDefaults — Constant. 
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source

# YAXArrays.findAxis — Method. 
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

# YAXArrays.getOutAxis — Method. 
julia
getOutAxis
source

# YAXArrays.get_descriptor — Method. 
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source

# YAXArrays.match_axis — Method. 
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source

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

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

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

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

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::Tuple{Vararg{YAXArrays.DAT.InputCube, NIN}} where NIN: The input data cubes
  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source

# YAXArrays.DAT.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source

# YAXArrays.Datasets.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

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


-    
+For example to disregard differences of captialisation.
source

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

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

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

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

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::Tuple{Vararg{YAXArrays.DAT.InputCube, NIN}} where NIN: The input data cubes
  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source

# YAXArrays.DAT.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source

# YAXArrays.Datasets.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

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


+    
     
   
 
\ No newline at end of file
diff --git a/dev/assets/UserGuide_cache.md.aYjxevW4.js b/dev/assets/UserGuide_cache.md.BxJ3IRnF.js
similarity index 64%
rename from dev/assets/UserGuide_cache.md.aYjxevW4.js
rename to dev/assets/UserGuide_cache.md.BxJ3IRnF.js
index 19d5d4b1..a9b6363d 100644
--- a/dev/assets/UserGuide_cache.md.aYjxevW4.js
+++ b/dev/assets/UserGuide_cache.md.BxJ3IRnF.js
@@ -1,5 +1,5 @@
-import{_ as s,c as a,o as i,a4 as e}from"./chunks/framework._h4tgM90.js";const E=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),t={name:"UserGuide/cache.md"},n=e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.Bwkmg46C.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
 ds = open_dataset("path/to/source")
 cachesize = 500 #MB
 cache(ds,maxsize = cachesize)
The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays
julia
yax = ds.avariable
-cache(yax,maxsize = 1000)
`,5),h=[n];function l(p,r,c,d,k,o){return i(),a("div",null,h)}const y=s(t,[["render",l]]);export{E as __pageData,y as default};
+cache(yax,maxsize = 1000)
`,5)]))}const g=a(n,[["render",h]]);export{o as __pageData,g as default};
diff --git a/dev/assets/UserGuide_cache.md.BxJ3IRnF.lean.js b/dev/assets/UserGuide_cache.md.BxJ3IRnF.lean.js
new file mode 100644
index 00000000..a9b6363d
--- /dev/null
+++ b/dev/assets/UserGuide_cache.md.BxJ3IRnF.lean.js
@@ -0,0 +1,5 @@
+import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.Bwkmg46C.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`
Caching YAXArrays ​
For some applications like interactive plotting of large datasets it can not be avoided that the same data must be accessed several times. In these cases it can be useful to store recently accessed data in a cache. In YAXArrays this can be easily achieved using the cache function. For example, if we open a large dataset from a remote source and want to keep data in a cache of size 500MB one can use:
julia
using YAXArrays, Zarr
+ds = open_dataset("path/to/source")
+cachesize = 500 #MB
+cache(ds,maxsize = cachesize)
The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays
julia
yax = ds.avariable
+cache(yax,maxsize = 1000)
`,5)]))}const g=a(n,[["render",h]]);export{o as __pageData,g as default};
diff --git a/dev/assets/UserGuide_cache.md.aYjxevW4.lean.js b/dev/assets/UserGuide_cache.md.aYjxevW4.lean.js
deleted file mode 100644
index 0a3a8c8a..00000000
--- a/dev/assets/UserGuide_cache.md.aYjxevW4.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as i,a4 as e}from"./chunks/framework._h4tgM90.js";const E=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),t={name:"UserGuide/cache.md"},n=e("",5),h=[n];function l(p,r,c,d,k,o){return i(),a("div",null,h)}const y=s(t,[["render",l]]);export{E as __pageData,y as default};
diff --git a/dev/assets/UserGuide_chunk.md.DZgkcenL.js b/dev/assets/UserGuide_chunk.md.DQmK3Tmu.js
similarity index 86%
rename from dev/assets/UserGuide_chunk.md.DZgkcenL.js
rename to dev/assets/UserGuide_chunk.md.DQmK3Tmu.js
index caa3aefd..92d48368 100644
--- a/dev/assets/UserGuide_chunk.md.DZgkcenL.js
+++ b/dev/assets/UserGuide_chunk.md.DQmK3Tmu.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),p={name:"UserGuide/chunk.md"},h=n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
+import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),h={name:"UserGuide/chunk.md"};function l(t,s,e,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
 a = YAXArray(rand(10,20))
 a_chunked = setchunks(a, (5,10))
 a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -27,14 +27,14 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const y
  (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
Saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables: 
 x
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 y
 z
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
-  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
Set chunking by Variable ​
The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above
julia
using YAXArrays, Zarr
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking by Variable ​
The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above
julia
using YAXArrays, Zarr
 ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
 dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
 Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -51,14 +51,14 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const y
  (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables: 
 x
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 y
 z
-  ↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
-  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
Set chunking for all variables ​
The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.
julia
using YAXArrays, Zarr
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking for all variables ​
The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.
julia
using YAXArrays, Zarr
 ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
 dschunked = setchunks(ds,(5,10))
 Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
@@ -75,9 +75,9 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const y
  (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)
saving...
julia
f = tempname()
 savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
 Shared Axes: 
-↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-→ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
 Variables: 
 x
 y
-z
Suggestions on how to improve or add to these examples is welcome.
`,36),l=[h];function t(e,k,r,d,E,g){return i(),a("div",null,l)}const o=s(p,[["render",t]]);export{y as __pageData,o as default};
+z
Suggestions on how to improve or add to these examples is welcome.
`,36)]))}const y=a(h,[["render",l]]);export{g as __pageData,y as default};
diff --git a/dev/assets/UserGuide_chunk.md.DQmK3Tmu.lean.js b/dev/assets/UserGuide_chunk.md.DQmK3Tmu.lean.js
new file mode 100644
index 00000000..92d48368
--- /dev/null
+++ b/dev/assets/UserGuide_chunk.md.DQmK3Tmu.lean.js
@@ -0,0 +1,83 @@
+import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),h={name:"UserGuide/chunk.md"};function l(t,s,e,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`
Chunk YAXArrays ​
Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.
To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.
Chunking YAXArrays ​
julia
using YAXArrays, Zarr
+a = YAXArray(rand(10,20))
+a_chunked = setchunks(a, (5,10))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)
And the saved file is also splitted into Chunks.
julia
f = tempname()
+savecube(a_chunked, f, backend=:zarr)
+Cube(f).chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)
Alternatively chunk sizes can be given by dimension name, so the following results in the same chunks:
julia
a_chunked = setchunks(a, (Dim_2=10, Dim_1=5))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)
Chunking Datasets ​
Setchunks can also be applied to a Dataset.
Set Chunks by Axis ​
Set chunk size for each axis occuring in a Dataset. This will be applied to all variables in the dataset:
julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds, Dict("Dim_1"=>5, "Dim_2"=>10, "Dim_3"=>2))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
Saving...
julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+Variables: 
+x
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+y
+z
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking by Variable ​
The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above
julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)
saving...
julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+Variables: 
+x
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+y
+z
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
Set chunking for all variables ​
The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.
julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
+dschunked = setchunks(ds,(5,10))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:1)   (1:5, 11:20, 1:1)
+ (6:10, 1:10, 1:1)  (6:10, 11:20, 1:1)
+
+[:, :, 2] =
+ (1:5, 1:10, 2:2)   (1:5, 11:20, 2:2)
+ (6:10, 1:10, 2:2)  (6:10, 11:20, 2:2)
+
+[:, :, 3] =
+ (1:5, 1:10, 3:3)   (1:5, 11:20, 3:3)
+ (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)
saving...
julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+Variables: 
+x
+y
+z
Suggestions on how to improve or add to these examples is welcome.
`,36)]))}const y=a(h,[["render",l]]);export{g as __pageData,y as default};
diff --git a/dev/assets/UserGuide_chunk.md.DZgkcenL.lean.js b/dev/assets/UserGuide_chunk.md.DZgkcenL.lean.js
deleted file mode 100644
index 047cca7f..00000000
--- a/dev/assets/UserGuide_chunk.md.DZgkcenL.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),p={name:"UserGuide/chunk.md"},h=n("",36),l=[h];function t(e,k,r,d,E,g){return i(),a("div",null,l)}const o=s(p,[["render",t]]);export{y as __pageData,o as default};
diff --git a/dev/assets/UserGuide_combine.md.CgXxcV_r.js b/dev/assets/UserGuide_combine.md.Bs3BBbp0.js
similarity index 85%
rename from dev/assets/UserGuide_combine.md.CgXxcV_r.js
rename to dev/assets/UserGuide_combine.md.Bs3BBbp0.js
index ae2509e5..2523e94a 100644
--- a/dev/assets/UserGuide_combine.md.CgXxcV_r.js
+++ b/dev/assets/UserGuide_combine.md.Bs3BBbp0.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const g=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),e={name:"UserGuide/combine.md"},t=n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
 
 first_half = YAXArray((Dim{:time}(1:6),), rand(6))
 second_half = YAXArray((Dim{:time}(7:12),), rand(6))
@@ -25,4 +25,4 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const g
   Dict{String, Any}()
 ├───────────────────────────────────────────────────── file size ┤ 
   file size: 96.0 bytes
-└────────────────────────────────────────────────────────────────┘
`,10),p=[t];function l(h,k,r,d,o,c){return a(),i("div",null,p)}const y=s(e,[["render",l]]);export{g as __pageData,y as default};
+└────────────────────────────────────────────────────────────────┘
`,10)]))}const E=i(t,[["render",p]]);export{c as __pageData,E as default};
diff --git a/dev/assets/UserGuide_combine.md.Bs3BBbp0.lean.js b/dev/assets/UserGuide_combine.md.Bs3BBbp0.lean.js
new file mode 100644
index 00000000..2523e94a
--- /dev/null
+++ b/dev/assets/UserGuide_combine.md.Bs3BBbp0.lean.js
@@ -0,0 +1,28 @@
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function p(l,s,h,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`
Combine YAXArrays ​
Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.
cat along an existing dimension ​
Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.
julia
using YAXArrays
+
+first_half = YAXArray((Dim{:time}(1:6),), rand(6))
+second_half = YAXArray((Dim{:time}(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
+│ 12-element YAXArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── file size ┤ 
+  file 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))
+precipitation = YAXArray((Dim{:time}(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Dim{:variable}(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
+│ 6×2 YAXArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time     Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → variable Categorical{String} ["temp", "prep"] ReverseOrdered
+├────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── file size ┤ 
+  file 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.CgXxcV_r.lean.js b/dev/assets/UserGuide_combine.md.CgXxcV_r.lean.js
deleted file mode 100644
index f1bb2409..00000000
--- a/dev/assets/UserGuide_combine.md.CgXxcV_r.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const g=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),e={name:"UserGuide/combine.md"},t=n("",10),p=[t];function l(h,k,r,d,o,c){return a(),i("div",null,p)}const y=s(e,[["render",l]]);export{g as __pageData,y as default};
diff --git a/dev/assets/UserGuide_compute.md.CLq4k1Y9.js b/dev/assets/UserGuide_compute.md.BPtHMXa2.js
similarity index 56%
rename from dev/assets/UserGuide_compute.md.CLq4k1Y9.js
rename to dev/assets/UserGuide_compute.md.BPtHMXa2.js
index f5bacbf6..e71a0a9b 100644
--- a/dev/assets/UserGuide_compute.md.CLq4k1Y9.js
+++ b/dev/assets/UserGuide_compute.md.BPtHMXa2.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),t={name:"UserGuide/compute.md"},p=n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
 using Dates
 
 axlist = (
@@ -19,7 +19,7 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c
   :origin => "user guide"
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.39569774546730574
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0
WARNING
Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.
Arithmetics ​
Add a value to all elements of an array and save it as a new array:
julia
a2 = a .+ 5
╭──────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.2141174000417737
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,
@@ -62,7 +62,97 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c
   Dict{String, Any}()
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file 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.
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
+└──────────────────────────────────────────────────────────────────────────────┘
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)
+end
g (generic function with 1 method)
INFO
Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.
Here, we do create YAXArrays only with the desired dimensions as
julia
julia> lon = YAXArray(Dim{:lon}(range(1, 15)))
╭──────────────────────────────╮
+│ 15-element YAXArray{Int64,1} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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} │
+├─────────────────────────────┴────────────────────────────────────────── 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
+└──────────────────────────────────────────────────────────────────────────────┘
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,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
but, we can generate a another cube with a different output order as follows
julia
julia> gen_cube = mapCube(g, (lon, lat, time);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
which outputs the same as the gen_cube.data[1, :, :] called above.
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
 
@@ -112,14 +202,14 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c
 ├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────────────────┘
-  ↓ →  1.0        1.28571   1.57143     …  4.42857   4.71429     5.0
-  1.0  0.274566   0.534793  0.854262       0.110146  0.57064     0.19585
-  2.0  0.227196   0.1771    0.795952       0.395735  0.00766601  0.099476
-  3.0  0.382234   0.848547  0.249912       0.454773  0.0948468   0.0196535
-  ⋮                                     ⋱                        ⋮
-  8.0  0.0827623  0.791032  0.00929636     0.217969  0.028745    0.878439
-  9.0  0.219269   0.903682  0.861747       0.926123  0.689873    0.690915
- 10.0  0.142275   0.381645  0.284095    …  0.896381  0.501022    0.086277
Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.
julia
regions = ["A", "B", "C", "D"]
+  ↓ →  1.0       1.28571    1.57143   …  4.42857   4.71429   5.0
+  1.0  0.111806  0.730896   0.390622     0.345419  0.886959  0.0322648
+  2.0  0.945136  0.2867     0.885515     0.636107  0.178756  0.535162
+  3.0  0.807073  0.0658503  0.951478     0.771082  0.110929  0.576982
+  ⋮                                   ⋱                      ⋮
+  8.0  0.571501  0.479832   0.401882     0.899889  0.917739  0.609885
+  9.0  0.225987  0.0376093  0.812919     0.80386   0.443763  0.226869
+ 10.0  0.335462  0.392626   0.980182  …  0.409798  0.437697  0.0388753
Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.
julia
regions = ["A", "B", "C", "D"]
 points_of_regions = map(enumerate(regions)) do (i,region)
     region => findall(isequal(region), regions_mat)
 end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
@@ -172,4 +262,4 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c
 
 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))
`,60),l=[p];function e(h,k,r,d,o,E){return a(),i("div",null,l)}const y=s(t,[["render",e]]);export{c as __pageData,y as default};
+addprocs(SlurmManager(10))
`,95)]))}const E=i(p,[["render",e]]);export{g as __pageData,E as default};
diff --git a/dev/assets/UserGuide_compute.md.BPtHMXa2.lean.js b/dev/assets/UserGuide_compute.md.BPtHMXa2.lean.js
new file mode 100644
index 00000000..e71a0a9b
--- /dev/null
+++ b/dev/assets/UserGuide_compute.md.BPtHMXa2.lean.js
@@ -0,0 +1,265 @@
+import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function e(l,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`
Compute YAXArrays ​
This section describes how to create new YAXArrays by performing operations on them.
  • Use arithmetics to add or multiply numbers to each element of an array
  • Use map to apply a more complex functions to every element of an array
  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps
  • Use mapCube to apply complex functions on an array that may change any dimensions
Let's start by creating an example dataset:
julia
using YAXArrays
+using Dates
+
+axlist = (
+    Dim{:time}(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    Dim{:lon}(range(1, 10, length=10)),
+    Dim{:lat}(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
╭──────────────────────────────╮
+│ 30×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── 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]
0.2141174000417737
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0
WARNING
Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.
Arithmetics ​
Add a value to all elements of an array and save it as a new array:
julia
a2 = a .+ 5
╭──────────────────────────────╮
+│ 30×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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
+end
╭──────────────────────────────╮
+│ 30×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
This keeps all dimensions unchanged. Note, that here we can not access neighboring elements. In this case, we can use mapslices or mapCube instead. Each element of the array is processed individually.
The code runs very fast, because map applies the function lazily. Actual computation will be performed only on demand, e.g. when elements were explicitly requested or further computations were performed.
mapslices ​
Reduce the time dimension by calculating the average value of all points in time:
julia
import Statistics: mean
+mapslices(mean, a, dims="Time")
╭───────────────────────────────────────────╮
+│ 10×15 YAXArray{Union{Missing, Float64},2} │
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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)
+end
g (generic function with 1 method)
INFO
Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.
Here, we do create YAXArrays only with the desired dimensions as
julia
julia> lon = YAXArray(Dim{:lon}(range(1, 15)))
╭──────────────────────────────╮
+│ 15-element YAXArray{Int64,1} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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} │
+├─────────────────────────────┴────────────────────────────────────────── 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
+└──────────────────────────────────────────────────────────────────────────────┘
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,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 30×15×10 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
but, we can generate a another cube with a different output order as follows
julia
julia> gen_cube = mapCube(g, (lon, lat, time);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
╭──────────────────────────────────────────────╮
+│ 15×10×30 YAXArray{Union{Missing, Float32},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0
which outputs the same as the gen_cube.data[1, :, :] called above.
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
+
+axlist = (
+    Dim{:time}(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    Dim{:lon}(range(1, 10, length=10)),
+    Dim{:lat}(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+raster_arr = YAXArray(axlist, data)
╭──────────────────────────────╮
+│ 30×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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"
+    10 <= lon < 15 && 1 <= lat < 5 && return "C"
+    return "D"
+end
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
╭──────────────────────────╮
+│ 10×15 DimArray{String,2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0   1.28571  1.57143  1.85714  …  4.14286  4.42857  4.71429  5.0
+  1.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  2.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  3.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  4.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  5.0   "A"   "A"      "A"      "A"     …   "A"      "A"      "A"      "B"
+  6.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"
which has the same spatial dimensions as the raster array at any given point in time:
julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
+│ 10×15 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571    1.57143   …  4.42857   4.71429   5.0
+  1.0  0.111806  0.730896   0.390622     0.345419  0.886959  0.0322648
+  2.0  0.945136  0.2867     0.885515     0.636107  0.178756  0.535162
+  3.0  0.807073  0.0658503  0.951478     0.771082  0.110929  0.576982
+  ⋮                                   ⋱                      ⋮
+  8.0  0.571501  0.479832   0.401882     0.899889  0.917739  0.609885
+  9.0  0.225987  0.0376093  0.812919     0.80386   0.443763  0.226869
+ 10.0  0.335462  0.392626   0.980182  …  0.409798  0.437697  0.0388753
Now we calculate the list of corresponding points for each region. This will be re-used for each point in time during the final mapCube. In addition, this avoids the allocation of unnecessary memory.
julia
regions = ["A", "B", "C", "D"]
+points_of_regions = map(enumerate(regions)) do (i,region)
+    region => findall(isequal(region), regions_mat)
+end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
+  "A" => [CartesianIndex(1, 1), CartesianIndex(2, 1), CartesianIndex(3, 1), Car…
+  "B" => [CartesianIndex(1, 15), CartesianIndex(2, 15), CartesianIndex(3, 15), …
+  "C" => [CartesianIndex(10, 1), CartesianIndex(10, 2), CartesianIndex(10, 3), …
+  "D" => [CartesianIndex(10, 15)]
Finally, we can transform the entire raster array:
julia
vector_array = mapCube(
+    raster_arr,
+    indims=InDims("lon", "lat"),
+    outdims=OutDims(Dim{:region}(regions))
+) do xout, xin
+    for (region_pos, points) in enumerate(points_of_regions.vals)
+        # aggregate values of points in the current region at the current date
+        xout[region_pos] = sum(view(xin, points))
+    end
+end
╭──────────────────────────────────────────╮
+│ 4×30 YAXArray{Union{Missing, Float64},2} │
+├──────────────────────────────────────────┴───────────────────────────── dims ┐
+  ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
+  → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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
+
+axlist = (
+    Dim{:time}(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    Dim{:lon}(range(1, 10, length=10)),
+    Dim{:lat}(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
+
+addprocs(2)
+
+@everywhere begin
+  using YAXArrays
+  using Zarr
+  using Statistics
+end
+
+@everywhere function mymean(output, pixel)
+  @show "doing a mean"
+     output[:] .= mean(pixel)
+end
+
+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))
`,95)]))}const E=i(p,[["render",e]]);export{g as __pageData,E as default};
diff --git a/dev/assets/UserGuide_compute.md.CLq4k1Y9.lean.js b/dev/assets/UserGuide_compute.md.CLq4k1Y9.lean.js
deleted file mode 100644
index 0c298165..00000000
--- a/dev/assets/UserGuide_compute.md.CLq4k1Y9.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),t={name:"UserGuide/compute.md"},p=n("",60),l=[p];function e(h,k,r,d,o,E){return a(),i("div",null,l)}const y=s(t,[["render",e]]);export{c as __pageData,y as default};
diff --git a/dev/assets/UserGuide_convert.md.CSe_wnNP.lean.js b/dev/assets/UserGuide_convert.md.CSe_wnNP.lean.js
deleted file mode 100644
index 9c91e932..00000000
--- a/dev/assets/UserGuide_convert.md.CSe_wnNP.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"},e=i("",25),l=[e];function t(r,h,d,k,o,c){return n(),a("div",null,l)}const E=s(p,[["render",t]]);export{y as __pageData,E as default};
diff --git a/dev/assets/UserGuide_convert.md.CSe_wnNP.js b/dev/assets/UserGuide_convert.md.D6pPrrfh.js
similarity index 73%
rename from dev/assets/UserGuide_convert.md.CSe_wnNP.js
rename to dev/assets/UserGuide_convert.md.D6pPrrfh.js
index b019fbe8..f14bd748 100644
--- a/dev/assets/UserGuide_convert.md.CSe_wnNP.js
+++ b/dev/assets/UserGuide_convert.md.D6pPrrfh.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"},e=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.Bwkmg46C.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
 a = YAXArray(m)
╭──────────────────────────╮
@@ -11,11 +11,11 @@ import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework._h4tgM90.js";const y
 ├────────────────────────────────────────────────────────── file size ┤ 
   file size: 400.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
Convert YAXArray to Base.Array:
julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
- 0.249608  0.00552544  0.56213   0.519169   …  0.450066  0.51539    0.574653
- 0.19875   0.470067    0.103211  0.856547      0.19776   0.260932   0.695295
- 0.528707  0.666155    0.839119  0.0321818     0.919695  0.836565   0.641424
- 0.665448  0.684161    0.158104  0.858738      0.243106  0.602045   0.671807
- 0.159698  0.848549    0.48712   0.0557879     0.511201  0.0943564  0.432317
Convert Raster ​
A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:
julia
using Rasters
+ 0.982681  0.30872    0.991662  0.812941  …  0.785487  0.200921  0.56509
+ 0.255901  0.191475   0.91863   0.286567     0.697089  0.578499  0.559458
+ 0.802815  0.0792955  0.49635   0.153314     0.519502  0.595256  0.982369
+ 0.838723  0.418527   0.945523  0.681937     0.654553  0.869916  0.0806691
+ 0.8037    0.726055   0.370371  0.726514     0.46623   0.371407  0.795256
Convert Raster ​
A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:
julia
using Rasters
 
 lon, lat = X(25:1:30), Y(25:1:30)
 time = Ti(2000:2024)
@@ -43,13 +43,13 @@ import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework._h4tgM90.js";const y
 
 └─────────────────────────────────────────────────────────────┘
 [:, :, 1]
-  ↓ →  25         26           27          28         29          30
- 25     0.940483   0.894856     0.496038    0.450453   0.630924    0.754794
- 26     0.437037   0.216039     0.301316    0.248116   0.810228    0.313254
- 27     0.896249   0.989218     0.0477079   0.89122    0.0261803   0.206706
- 28     0.708967   0.351027     0.26031     0.470138   0.840873    0.0133172
- 29     0.892941   0.00686917   0.035698    0.939528   0.510782    0.198741
- 30     0.646887   0.418673     0.907052    0.230079   0.02116     0.635883
Convert DimArray ​
A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.
Convert DimArray to YAXArray:
julia
using DimensionalData
+  ↓ →  25           26         27         28         29         30
+ 25     0.668356     0.238835   0.436044   0.152703   0.468086   0.158513
+ 26     0.906684     0.345557   0.739191   0.21762    0.76884    0.112142
+ 27     0.370883     0.471202   0.216082   0.40658    0.790483   0.033994
+ 28     0.469495     0.263308   0.951713   0.373742   0.307866   0.490098
+ 29     0.00465577   0.208669   0.982017   0.897845   0.337278   0.828301
+ 30     0.778578     0.948093   0.894081   0.818217   0.106047   0.92191
Convert DimArray ​
A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.
Convert DimArray to YAXArray:
julia
using DimensionalData
 using YAXArrayBase
 
 dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
@@ -70,9 +70,9 @@ import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework._h4tgM90.js";const y
 ├──────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
 └──────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       11.0         12.0       13.0       14.0       15.0
- 1     0.308299   0.979944     0.813157   0.953172   0.18757    0.325188
- 2     0.372918   0.669206     0.946524   0.937779   0.810681   0.552949
- 3     0.261139   0.298312     0.662771   0.246061   0.773001   0.274429
- 4     0.402285   0.597315     0.43242    0.229361   0.868584   0.56698
- 5     0.987524   0.00346434   0.355755   0.961872   0.702858   0.491176
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,25),l=[e];function t(r,h,d,k,o,c){return n(),a("div",null,l)}const E=s(p,[["render",t]]);export{y as __pageData,E as default};
+ ↓ →  10.0       11.0        12.0        13.0        14.0       15.0
+ 1     0.1495     0.0195499   0.921394    0.193496    0.445246   0.506692
+ 2     0.888258   0.318317    0.6742      0.607539    0.234382   0.722903
+ 3     0.464692   0.405837    0.610329    0.432667    0.387209   0.353383
+ 4     0.657613   0.583472    0.379975    0.896276    0.425406   0.238368
+ 5     0.404644   0.951713    0.0752454   0.0759995   0.412653   0.930755
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default};
diff --git a/dev/assets/UserGuide_convert.md.D6pPrrfh.lean.js b/dev/assets/UserGuide_convert.md.D6pPrrfh.lean.js
new file mode 100644
index 00000000..f14bd748
--- /dev/null
+++ b/dev/assets/UserGuide_convert.md.D6pPrrfh.lean.js
@@ -0,0 +1,78 @@
+import{_ as a,c as n,a2 as i,o as p}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),e={name:"UserGuide/convert.md"};function l(t,s,r,h,d,k){return p(),n("div",null,s[0]||(s[0]=[i(`
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
+
+m = rand(5,10)
+a = YAXArray(m)
╭──────────────────────────╮
+│ 5×10 YAXArray{Float64,2} │
+├──────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────────── file size ┤ 
+  file size: 400.0 bytes
+└─────────────────────────────────────────────────────────────────────┘
Convert YAXArray to Base.Array:
julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.982681  0.30872    0.991662  0.812941  …  0.785487  0.200921  0.56509
+ 0.255901  0.191475   0.91863   0.286567     0.697089  0.578499  0.559458
+ 0.802815  0.0792955  0.49635   0.153314     0.519502  0.595256  0.982369
+ 0.838723  0.418527   0.945523  0.681937     0.654553  0.869916  0.0806691
+ 0.8037    0.726055   0.370371  0.726514     0.46623   0.371407  0.795256
Convert Raster ​
A Raster as defined in Rasters.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types:
julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
╭────────────────────────────╮
+│ 6×6×25 YAXArray{Float64,3} │
+├────────────────────────────┴────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── file size ┤ 
+  file size: 7.03 KB
+└─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
+│ 6×6×25 Raster{Float64,3} │
+├──────────────────────────┴──────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── raster ┤
+  extent: Extent(X = (25, 30), Y = (25, 30), Ti = (2000, 2024))
+
+└─────────────────────────────────────────────────────────────┘
+[:, :, 1]
+  ↓ →  25           26         27         28         29         30
+ 25     0.668356     0.238835   0.436044   0.152703   0.468086   0.158513
+ 26     0.906684     0.345557   0.739191   0.21762    0.76884    0.112142
+ 27     0.370883     0.471202   0.216082   0.40658    0.790483   0.033994
+ 28     0.469495     0.263308   0.951713   0.373742   0.307866   0.490098
+ 29     0.00465577   0.208669   0.982017   0.897845   0.337278   0.828301
+ 30     0.778578     0.948093   0.894081   0.818217   0.106047   0.92191
Convert DimArray ​
A DimArray as defined in DimensionalData.jl has a same supertype of a YAXArray, i.e. AbstractDimArray, allowing easy conversion between those types.
Convert DimArray to YAXArray:
julia
using DimensionalData
+using YAXArrayBase
+
+dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
+a = yaxconvert(YAXArray, dim_arr)
╭─────────────────────────╮
+│ 5×6 YAXArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────── file size ┤ 
+  file size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────┘
Convert YAXArray to DimArray:
julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
+│ 5×6 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0       11.0        12.0        13.0        14.0       15.0
+ 1     0.1495     0.0195499   0.921394    0.193496    0.445246   0.506692
+ 2     0.888258   0.318317    0.6742      0.607539    0.234382   0.722903
+ 3     0.464692   0.405837    0.610329    0.432667    0.387209   0.353383
+ 4     0.657613   0.583472    0.379975    0.896276    0.425406   0.238368
+ 5     0.404644   0.951713    0.0752454   0.0759995   0.412653   0.930755
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.
`,25)]))}const g=a(e,[["render",l]]);export{c as __pageData,g as default};
diff --git a/dev/assets/UserGuide_create.md.JoOy7H4V.js b/dev/assets/UserGuide_create.md.D-fbNPyl.js
similarity index 83%
rename from dev/assets/UserGuide_create.md.JoOy7H4V.js
rename to dev/assets/UserGuide_create.md.D-fbNPyl.js
index 401e2cb6..b2311925 100644
--- a/dev/assets/UserGuide_create.md.JoOy7H4V.js
+++ b/dev/assets/UserGuide_create.md.D-fbNPyl.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),t={name:"UserGuide/create.md"},e=n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
 │ 10×20×5 YAXArray{Float64,3} │
 ├─────────────────────────────┴────────────────────────────────── dims ┐
@@ -30,19 +30,19 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file 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,
+  :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,
-↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
Create a Dataset ​
julia
data3 = rand(30, 10, 15)
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
Create a Dataset ​
julia
data3 = rand(30, 10, 15)
 a3 = YAXArray(axlist, data3, properties)
 
 arrays = Dict(:a2 => a2, :a3 => a3)
 ds = Dataset(; properties, arrays...)
YAXArray Dataset
 Shared Axes: 
-↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
 → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
-↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
 Variables: 
 a2
 a3
 
-Properties: Dict(:origin => "user guide")
`,16),p=[e];function l(h,k,r,d,E,g){return i(),a("div",null,p)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
+Properties: Dict(:origin => "user guide")
`,16)]))}const o=a(e,[["render",p]]);export{g as __pageData,o as default};
diff --git a/dev/assets/UserGuide_create.md.D-fbNPyl.lean.js b/dev/assets/UserGuide_create.md.D-fbNPyl.lean.js
new file mode 100644
index 00000000..b2311925
--- /dev/null
+++ b/dev/assets/UserGuide_create.md.D-fbNPyl.lean.js
@@ -0,0 +1,48 @@
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
+a1 = YAXArray(rand(10, 20, 5))
╭─────────────────────────────╮
+│ 10×20×5 YAXArray{Float64,3} │
+├─────────────────────────────┴────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────── file size ┤ 
+  file 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 = (
+    Dim{:time}(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    Dim{:lon}(range(1, 10, length=10)),
+    Dim{:lat}(range(1, 5, length=15)),
+)
+data2 = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a2 = YAXArray(axlist, data2, properties)
╭──────────────────────────────╮
+│ 30×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
Create a Dataset ​
julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared 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,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
+Variables: 
+a2
+a3
+
+Properties: Dict(:origin => "user guide")
`,16)]))}const o=a(e,[["render",p]]);export{g as __pageData,o as default};
diff --git a/dev/assets/UserGuide_create.md.JoOy7H4V.lean.js b/dev/assets/UserGuide_create.md.JoOy7H4V.lean.js
deleted file mode 100644
index ccee91e7..00000000
--- a/dev/assets/UserGuide_create.md.JoOy7H4V.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),t={name:"UserGuide/create.md"},e=n("",16),p=[e];function l(h,k,r,d,E,g){return i(),a("div",null,p)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
diff --git a/dev/assets/UserGuide_faq.md.CfSW0c59.js b/dev/assets/UserGuide_faq.md.BZaTxhVy.js
similarity index 84%
rename from dev/assets/UserGuide_faq.md.CfSW0c59.js
rename to dev/assets/UserGuide_faq.md.BZaTxhVy.js
index 003c46fa..5febf850 100644
--- a/dev/assets/UserGuide_faq.md.CfSW0c59.js
+++ b/dev/assets/UserGuide_faq.md.BZaTxhVy.js
@@ -1,19 +1,19 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t="/YAXArrays.jl/dev/assets/tlkgzfl.DA3FSb99.jpeg",c=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),e={name:"UserGuide/faq.md"},h=n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const t="/YAXArrays.jl/dev/assets/fvajudo.CbmV3u64.jpeg",E=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),h={name:"UserGuide/faq.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
 using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
 │ 10×10×5 YAXArray{Float64,3} │
-├─────────────────────────────┴────────────────────────────────── dims ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
-├──────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 3.91 KB
-└──────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+└──────────────────────────────────────────────────────────────────────────────┘
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,
+↗ 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,
 → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
INFO
Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.
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)
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
INFO
Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.
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
   2
@@ -26,14 +26,14 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
   9
  10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
╭───────────────────────────╮
 │ 10×10 YAXArray{Float64,2} │
-├───────────────────────────┴────────────────────────── dims ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
-├────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file 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
+└──────────────────────────────────────────────────────────────────────────────┘
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)),
     Dim{:lon}(range(1, 10, length=10)),
@@ -57,9 +57,9 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
   file 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,
+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,
 → Lat  1:10,
-↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01")
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
 │ 10×10×36 YAXArray{Int64,3} │
 ├────────────────────────────┴─────────────────────────────────────────── dims ┐
   ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
@@ -120,16 +120,16 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 
 ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
 Shared Axes: 
-↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+(↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
 → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
-↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
 Variables: 
 var1
 var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
 Shared Axes: 
-↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+(↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
 → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
-↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
 Variables: 
 var1
 var2
Subsetting a Dataset whose variables share some but not all of their dimensions ​
In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.
But we can also subset a variable by the values of another variable with which it shares some dimensions.
Warning
If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.
Let's make an example.
julia
using YAXArrays
@@ -147,12 +147,12 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 
 ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
 Shared Axes: 
-↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points
+(↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
 Variables: 
 longitudes
 latitudes
 temperature
-  ↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
Select all points between 20ºN and 85ºN, and 0ºE to 180ºE
julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
Select all points between 20ºN and 85ºN, and 0ºE to 180ºE
julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
                              ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
                              ) # Where
               ] # ds
YAXArray Dataset
@@ -160,12 +160,12 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 ()
 Variables: 
 longitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 latitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 temperature
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
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"])
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
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
@@ -175,12 +175,12 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 ()
 Variables: 
 longitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 latitudes
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
 temperature
-  ↓ points Sampled{Int64} [8, 13, …, 98, 100] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
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)
╭──────────────────────────────╮
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
 │ 20×10×15 YAXArray{Float64,3} │
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
@@ -233,24 +233,24 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 c_tbl = DataFrame(t[1])
 first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
 │ 3-element YAXArray{Union{Missing, Float64},1} │
-├───────────────────────────────────────────────┴────────────── dims ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
-├────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├───────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file 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))
╭──────────────────────────────────────────╮
+└──────────────────────────────────────────────────────────────────────────────┘
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 ┐
+├──────────────────────────────────────────┴───────────────────────────── dims ┐
   ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
   → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
-├───────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├──────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 480.0 bytes
-└───────────────────────────────────────────────────────────────────────┘
How do I assing variable names to YAXArrays in a Dataset ​
One variable name ​
julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
+└──────────────────────────────────────────────────────────────────────────────┘
How do I assing 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
+(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 Variables:
 a
Multiple variable names ​
julia
keylist = (:a, :b, :c)
 varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
@@ -258,10 +258,10 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t
 ()
 Variables:
 a
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
 b
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
 c
-  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
-  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
`,113),l=[h];function p(k,d,r,g,y,E){return a(),i("div",null,l)}const u=s(e,[["render",p]]);export{c as __pageData,u as default};
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
`,113)]))}const o=i(h,[["render",l]]);export{E as __pageData,o as default};
diff --git a/dev/assets/UserGuide_faq.md.BZaTxhVy.lean.js b/dev/assets/UserGuide_faq.md.BZaTxhVy.lean.js
new file mode 100644
index 00000000..5febf850
--- /dev/null
+++ b/dev/assets/UserGuide_faq.md.BZaTxhVy.lean.js
@@ -0,0 +1,267 @@
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const t="/YAXArrays.jl/dev/assets/fvajudo.CbmV3u64.jpeg",E=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),h={name:"UserGuide/faq.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
╭─────────────────────────────╮
+│ 10×10×5 YAXArray{Float64,3} │
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ 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
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
INFO
Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.
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
+  2
+  3
+  4
+  5
+  6
+  7
+  8
+  9
+ 10
to collect data from a cube works exactly the same as doing it from an array
julia
julia> c[:, :, 1]
╭───────────────────────────╮
+│ 10×10 YAXArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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)),
+    Dim{:lon}(range(1, 10, length=10)),
+    Dim{:lat}(range(1, 5, length=15))
+    )
+
+data1 = rand(20, 10, 15)
+ds1 = YAXArray(axlist, data1)
+
+data2 = rand(20, 10, 15)
+ds2 = YAXArray(axlist, data2)
Now we can concatenate ds1 and ds2:
julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
╭────────────────────────────────╮
+│ 20×10×15×2 YAXArray{Float64,4} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["var1", "var2"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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,
+→ Lat  1:10,
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))
create the YAXArray
julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
╭────────────────────────────╮
+│ 10×10×36 YAXArray{Int64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+axes = (Dim{:Lon}(1:10), Dim{:Lat}(1:10), Dim{:Time}(t))
+
+var1 = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
+var2 = YAXArray(axes, reshape((1:3600)*5, (10, 10, 36)))
+
+ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
+Shared Axes: 
+(↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+→ Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+Variables: 
+var1
+var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
+Shared Axes: 
+(↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+→ Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+Variables: 
+var1
+var2
Subsetting a Dataset whose variables share some but not all of their dimensions ​
In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.
But we can also subset a variable by the values of another variable with which it shares some dimensions.
Warning
If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.
Let's make an example.
julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" selector for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+common_axis = Dim{:points}(1:100)
+time_axis =   Dim{:Time}(t)
+
+# Note that longitudes and latitudes are not dimensions, but YAXArrays
+longitudes = YAXArray((common_axis,), rand(1:369, 100)) # 100 random values taken from 1 to 359
+latitudes  = YAXArray((common_axis,), rand(0:90, 100))  # 100 random values taken from 0 to 90
+temperature = YAXArray((common_axis, time_axis), rand(-40:40, (100, 36)))
+
+ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
+Shared Axes: 
+(↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
+Variables: 
+longitudes
+latitudes
+temperature
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
Select all points between 20ºN and 85ºN, and 0ºE to 180ºE
julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+                             ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+()
+Variables: 
+longitudes
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
+latitudes
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
+temperature
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
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
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+()
+Variables: 
+longitudes
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
+latitudes
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points)
+temperature
+  (↓ points Sampled{Int64} [6, 8, …, 89, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
╭──────────────────────────────╮
+│ 20×10×15 YAXArray{Float64,3} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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 ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘
To add some complexity, we will multiply each value for π and then divided for the sum of each time step. We will use the ds1 cube for this purpose.
julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
+           (xin * π) ./ maximum(skipmissing(xin))
+       end
╭──────────────────────────────────────────────╮
+│ 10×15×20 YAXArray{Union{Missing, Float64},3} │
+├──────────────────────────────────────────────┴───────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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 ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
+GLMakie.activate!()
+# This is how our classification map looks like
+fig, ax, obj = heatmap(classes;
+    colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
+cbar = Colorbar(fig[1,2], obj)
+fig
Now we define the input cubes that will be considered for the iterable table
julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+using OnlineStats
+## visualization of the CubeTable
+c_tbl = DataFrame(t[1])
+first(c_tbl, 5)
In this line we calculate the Mean for each class
julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
╭───────────────────────────────────────────────╮
+│ 3-element YAXArray{Union{Missing, Float64},1} │
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file 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 ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
+  → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
How do I assing 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)
+Variables:
+a
Multiple variable names ​
julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+a
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+b
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+c
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
`,113)]))}const o=i(h,[["render",l]]);export{E as __pageData,o as default};
diff --git a/dev/assets/UserGuide_faq.md.CfSW0c59.lean.js b/dev/assets/UserGuide_faq.md.CfSW0c59.lean.js
deleted file mode 100644
index 7748cf14..00000000
--- a/dev/assets/UserGuide_faq.md.CfSW0c59.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const t="/YAXArrays.jl/dev/assets/tlkgzfl.DA3FSb99.jpeg",c=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),e={name:"UserGuide/faq.md"},h=n("",113),l=[h];function p(k,d,r,g,y,E){return a(),i("div",null,l)}const u=s(e,[["render",p]]);export{c as __pageData,u as default};
diff --git a/dev/assets/UserGuide_group.md.C3qv_f3u.lean.js b/dev/assets/UserGuide_group.md.C3qv_f3u.lean.js
deleted file mode 100644
index c7d9d858..00000000
--- a/dev/assets/UserGuide_group.md.C3qv_f3u.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const e="/YAXArrays.jl/dev/assets/mzrclpj.CBBZcGwj.png",E=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),h={name:"UserGuide/group.md"},t=n("",62),l=[t];function p(k,d,r,g,o,N){return a(),i("div",null,l)}const c=s(h,[["render",p]]);export{E as __pageData,c as default};
diff --git a/dev/assets/UserGuide_group.md.C3qv_f3u.js b/dev/assets/UserGuide_group.md.De_4u3Mq.js
similarity index 98%
rename from dev/assets/UserGuide_group.md.C3qv_f3u.js
rename to dev/assets/UserGuide_group.md.De_4u3Mq.js
index 3a5d8887..cf18ce21 100644
--- a/dev/assets/UserGuide_group.md.C3qv_f3u.js
+++ b/dev/assets/UserGuide_group.md.De_4u3Mq.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const e="/YAXArrays.jl/dev/assets/mzrclpj.CBBZcGwj.png",E=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),h={name:"UserGuide/group.md"},t=n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const h="/YAXArrays.jl/dev/assets/zbtetzz.CBBZcGwj.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),t={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using NetCDF
 using Downloads
 using Dates
@@ -187,4 +187,4 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const e
     colgap!(fig.layout, 5)
     rowgap!(fig.layout, 5)
     fig
-end
which shows a good agreement with the results first published by Joe Hamman.
',62),l=[t];function p(k,d,r,g,o,N){return a(),i("div",null,l)}const c=s(h,[["render",p]]);export{E as __pageData,c as default};
+end
which shows a good agreement with the results first published by Joe Hamman.
',62)]))}const N=i(t,[["render",l]]);export{y as __pageData,N as default};
diff --git a/dev/assets/UserGuide_group.md.De_4u3Mq.lean.js b/dev/assets/UserGuide_group.md.De_4u3Mq.lean.js
new file mode 100644
index 00000000..cf18ce21
--- /dev/null
+++ b/dev/assets/UserGuide_group.md.De_4u3Mq.lean.js
@@ -0,0 +1,190 @@
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.js";const h="/YAXArrays.jl/dev/assets/zbtetzz.CBBZcGwj.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),t={name:"UserGuide/group.md"};function l(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+using NetCDF
+using Downloads
+using Dates
+using Statistics
Seasonal Averages from Time Series of Monthly Means ​
The following reproduces the example in xarray by Joe Hamman.
Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.
Download the data ​
julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+filename = Downloads.download(url_path, "rasm.nc")
+ds_o = Cube(filename)
WARNING
The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642
julia
axs = dims(ds_o) # get the dimensions
+data = ds_o.data[:,:,:] # read the data
+_FillValue = ds_o.properties["_FillValue"]
+data = replace(data, _FillValue => NaN)
+# create new YAXArray
+ds = YAXArray(axs, data)
GroupBy: seasons ​
function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :Ti)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, Ti => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:Ti)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, Ti => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:Ti)
+    mean_g = dropdims.(mean_g, dims=:Ti)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :Ti);
+    weighted_g = dropdims.(weighted_g, dims=:Ti)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :Ti)
+    return mean_g, weighted_g, diff_g, seasons_g
+end
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, Ti => seasons(; start=December))
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray
And the mean per season is calculated as follows
julia
julia> mean_g = mean.(g_ds, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{CFTime.DateTimeNoLeap, CFTime.DateTimeNoLeap}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]
dropdims ​
Note that now the time dimension has length one, we can use dropdims to remove it
julia
julia> mean_g = dropdims.(mean_g, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404]
seasons ​
Due to the groupby function we will obtain new grouping names, in this case in the time dimension:
julia
seasons_g = lookup(mean_g, :Ti)
Categorical{Symbol} Unordered
+wrapping: 4-element Vector{Symbol}:
+ :Dec_Jan_Feb
+ :Mar_Apr_May
+ :Jun_Jul_Aug
+ :Sep_Oct_Nov
Next, we will weight this grouping by days/month in each group.
GroupBy: weight ​
Create a YAXArray for the month length
julia
tempo = dims(ds, :Ti)
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
╭──────────────────────────────╮
+│ 36-element YAXArray{Int64,1} │
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ 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
+└──────────────────────────────────────────────────────────────────────────────┘
Now group it by season
julia
julia> g_tempo = groupby(month_length, Ti => seasons(; start=December))
╭────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Int64,0},1} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray
Get the number of days per season
julia
julia> sum_days = sum.(g_tempo, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Int64, 1, Vector{Int64}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{CFTime.DateTimeNoLeap, CFTime.DateTimeNoLeap}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  [270]
+ :Mar_Apr_May  [276]
+ :Jun_Jul_Aug  [276]
+ :Sep_Oct_Nov  [273]
weights ​
Weight the seasonal groups by sum_days
julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, SubArray{CFTime.DateTimeNoLeap, 1, Vector{CFTime.DateTimeNoLeap}, Tuple{Vector{Int64}}, false}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{CFTime.DateTimeNoLeap, CFTime.DateTimeNoLeap}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} groupby │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
+ :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
+ :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]
Verify that the sum per season is 1
julia
julia> sum.(weights)
╭───────────────────────────────╮
+│ 4-element DimArray{Float64,1} │
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1.0
+ :Mar_Apr_May  1.0
+ :Jun_Jul_Aug  1.0
+ :Sep_Oct_Nov  1.0
weighted seasons ​
Now, let's weight the seasons
julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, SubArray{CFTime.DateTimeNoLeap, 1, Vector{CFTime.DateTimeNoLeap}, Tuple{Vector{Int64}}, false}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{CFTime.DateTimeNoLeap, CFTime.DateTimeNoLeap}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.32149 1.33565; NaN NaN … 1.29564 1.32555; … ; NaN NaN … 1.3188 1.3169; NaN NaN … 1.17863 1.18434;;; NaN NaN … 1.29816 1.34218; NaN NaN … 1.30113 1.35483; … ; NaN NaN … 1.30142 1.31753; NaN NaN … 1.16258 1.17647;;; NaN NaN … 1.34549 1.37878; NaN NaN … 1.36836 1.41634; … ; NaN NaN … 1.34832 1.38364; NaN NaN … 1.17852 1.16713]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.87705 1.90365; NaN NaN … 2.30018 2.35432; … ; NaN NaN … 2.41049 2.43254; NaN NaN … 2.65105 2.69085;;; NaN NaN … 1.86457 1.90712; NaN NaN … 2.2894 2.34818; … ; NaN NaN … 2.3866 2.41241; NaN NaN … 2.61197 2.64976;;; NaN NaN … 1.89237 1.8984; NaN NaN … 2.29473 2.312; … ; NaN NaN … 2.36142 2.36126; NaN NaN … 2.56632 2.59085]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]
apply a sum over the time dimension and drop it
julia
julia> weighted_g = sum.(g_dsW, dims = :Ti);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]
Calculate the differences
julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────────────────────────────────╮
+│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.019016 -0.0115514; NaN NaN … -0.0183003 -0.00990356]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.0121037 -0.0123091; NaN NaN … -0.0127077 -0.0117519]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.00709111 -0.00693713; NaN NaN … -0.00684233 -0.00722034]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 0.0180572 0.0182373; NaN NaN … 0.0178074 0.018571]
All the previous steps are equivalent to calling the function defined at the top:
julia
mean_g, weighted_g, diff_g, seasons_g = weighted_seasons(ds)
Once all calculations are done we can plot the results with Makie.jl as follows:
julia
using CairoMakie
+# define plot arguments/attributes
+colorrange = (-30,30)
+colormap = Reverse(:Spectral)
+highclip = :red
+lowclip = :grey15
+cb_label =  ds_o.properties["long_name"]
"Surface air temperature"
julia
with_theme(theme_ggplot2()) do
+    hm_o, hm_d, hm_w = nothing, nothing, nothing
+    # the figure
+    fig = Figure(; size = (850,500))
+    axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
+    for (j, s) in enumerate(seasons_g)
+        hm_o = heatmap!(axs[1,j], mean_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_w = heatmap!(axs[2,j], weighted_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_d = heatmap!(axs[3,j], diff_g[Ti=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
+            colormap=:diverging_bwr_20_95_c54_n256)
+    end
+    Colorbar(fig[1:2,5], hm_o, label=cb_label)
+    Colorbar(fig[3,5], hm_d, label="Tair")
+    hidedecorations!.(axs, grid=false, ticks=false, label=false)
+    # some labels
+    [axs[1,j].title = string.(s) for (j,s) in enumerate(seasons_g)]
+    Label(fig[0,1:5], "Seasonal Surface Air Temperature", fontsize=18, font=:bold)
+    axs[1,1].ylabel = "Unweighted"
+    axs[2,1].ylabel = "Weighted"
+    axs[3,1].ylabel = "Difference"
+    colgap!(fig.layout, 5)
+    rowgap!(fig.layout, 5)
+    fig
+end
which shows a good agreement with the results first published by Joe Hamman.
',62)]))}const N=i(t,[["render",l]]);export{y as __pageData,N as default};
diff --git a/dev/assets/UserGuide_read.md.i_gnCx2-.js b/dev/assets/UserGuide_read.md.BCQMfZ77.js
similarity index 91%
rename from dev/assets/UserGuide_read.md.i_gnCx2-.js
rename to dev/assets/UserGuide_read.md.BCQMfZ77.js
index f7e956e8..c727b9ca 100644
--- a/dev/assets/UserGuide_read.md.i_gnCx2-.js
+++ b/dev/assets/UserGuide_read.md.BCQMfZ77.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework._h4tgM90.js";const g=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),i={name:"UserGuide/read.md"},e=t(`
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 t,o as i}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return i(),n("div",null,s[0]||(s[0]=[t(`
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)
@@ -8,9 +8,9 @@ import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework._h4tgM90.js";const g
 Variables: 
 height
 tas
-  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
 
 Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
╭────────────────────────────────────╮
 │ 384×192×251288 YAXArray{Float32,3} │
@@ -39,9 +39,9 @@ import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework._h4tgM90.js";const g
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
@@ -71,4 +71,4 @@ import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework._h4tgM90.js";const g
 
 path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
 # ds = open_dataset(path) # this is broken
-nothing
`,20),p=[e];function l(o,r,d,h,u,c){return n(),a("div",null,p)}const q=s(i,[["render",l]]);export{g as __pageData,q as default};
+nothing
`,20)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default};
diff --git a/dev/assets/UserGuide_read.md.BCQMfZ77.lean.js b/dev/assets/UserGuide_read.md.BCQMfZ77.lean.js
new file mode 100644
index 00000000..c727b9ca
--- /dev/null
+++ b/dev/assets/UserGuide_read.md.BCQMfZ77.lean.js
@@ -0,0 +1,74 @@
+import{_ as a,c as n,a2 as t,o as i}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),e={name:"UserGuide/read.md"};function p(l,s,o,r,d,h){return i(),n("div",null,s[0]||(s[0]=[t(`
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)
+ds = open_dataset(store)
YAXArray Dataset
+Shared Axes: 
+()
+Variables: 
+height
+tas
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
We can set path to a URL, a local directory, or in this case to a cloud object storage path.
A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tas
╭────────────────────────────────────╮
+│ 384×192×251288 YAXArray{Float32,3} │
+├────────────────────────────────────┴─────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"         => "K"
+  "history"       => "2019-07-21T06:26:13Z altered by CMOR: Treated scalar dime…
+  "name"          => "tas"
+  "cell_methods"  => "area: mean time: point"
+  "cell_measures" => "area: areacella"
+  "long_name"     => "Near-Surface Air Temperature"
+  "coordinates"   => "height"
+  "standard_name" => "air_temperature"
+  "_FillValue"    => 1.0f20
+  "comment"       => "near-surface (usually, 2 meter) air temperature"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 69.02 GB
+└──────────────────────────────────────────────────────────────────────────────┘
Read NetCDF ​
Open a NetCDF file as a Dataset:
julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+→ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:
julia
ds.tos
╭────────────────────────────────────────────────╮
+│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘
Read GDAL (GeoTIFF, GeoJSON) ​
All GDAL compatible files can be read as a YAXArrays.Dataset after loading ArchGDAL:
julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+# ds = open_dataset(path) # this is broken
+nothing
`,20)]))}const k=a(e,[["render",p]]);export{c as __pageData,k as default};
diff --git a/dev/assets/UserGuide_read.md.i_gnCx2-.lean.js b/dev/assets/UserGuide_read.md.i_gnCx2-.lean.js
deleted file mode 100644
index 9c728119..00000000
--- a/dev/assets/UserGuide_read.md.i_gnCx2-.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework._h4tgM90.js";const g=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),i={name:"UserGuide/read.md"},e=t("",20),p=[e];function l(o,r,d,h,u,c){return n(),a("div",null,p)}const q=s(i,[["render",l]]);export{g as __pageData,q as default};
diff --git a/dev/assets/UserGuide_select.md.BWVJxXiv.lean.js b/dev/assets/UserGuide_select.md.BWVJxXiv.lean.js
deleted file mode 100644
index cebb4058..00000000
--- a/dev/assets/UserGuide_select.md.BWVJxXiv.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),e={name:"UserGuide/select.md"},t=n("",56),p=[t];function l(h,o,k,d,r,u){return i(),a("div",null,p)}const y=s(e,[["render",l]]);export{c as __pageData,y as default};
diff --git a/dev/assets/UserGuide_select.md.BWVJxXiv.js b/dev/assets/UserGuide_select.md.MJRJAz_y.js
similarity index 98%
rename from dev/assets/UserGuide_select.md.BWVJxXiv.js
rename to dev/assets/UserGuide_select.md.MJRJAz_y.js
index a78bf5b7..be8de214 100644
--- a/dev/assets/UserGuide_select.md.BWVJxXiv.js
+++ b/dev/assets/UserGuide_select.md.MJRJAz_y.js
@@ -1,13 +1,13 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),e={name:"UserGuide/select.md"},t=n(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.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(),i("div",null,s[0]||(s[0]=[n(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
@@ -290,4 +290,4 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c
   88.5
   89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
which is equivalent to:
julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
-wrapping: 1.0:2.0:359.0
`,56),p=[t];function l(h,o,k,d,r,u){return i(),a("div",null,p)}const y=s(e,[["render",l]]);export{c as __pageData,y as default};
+wrapping: 1.0:2.0:359.0
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default};
diff --git a/dev/assets/UserGuide_select.md.MJRJAz_y.lean.js b/dev/assets/UserGuide_select.md.MJRJAz_y.lean.js
new file mode 100644
index 00000000..be8de214
--- /dev/null
+++ b/dev/assets/UserGuide_select.md.MJRJAz_y.lean.js
@@ -0,0 +1,293 @@
+import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.Bwkmg46C.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(),i("div",null,s[0]||(s[0]=[n(`
Select YAXArrays and Datasets ​
The dimensions or axes of an YAXArray are named making it easier to subset or query certain ranges of an array. Let's open an example Dataset used to select certain elements:
julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+→ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Select a YAXArray ​
Get the sea surface temperature of the Dataset:
julia
tos = ds.tos
╭────────────────────────────────────────────────╮
+│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘
which is the same as:
julia
tos = ds.cubes[:tos]
╭────────────────────────────────────────────────╮
+│ 180×170×24 YAXArray{Union{Missing, Float32},3} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘
Select elements ​
Using positional integer indexing:
julia
tos[lon = 1, lat = 1]
╭────────────────────────────────────────────────╮
+│ 24-element YAXArray{Union{Missing, Float32},1} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Ti Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ Ti Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
Using special types:
julia
using CFTime
+time1 = DateTime360Day(2001,01,16)
+tos[time = At(time1)]
╭─────────────────────────────────────────────╮
+│ 180×170 YAXArray{Union{Missing, Float32},2} │
+├─────────────────────────────────────────────┴───────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────────────────┴ metadata ┐
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├───────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ lon Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
+  → lat Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ lon Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ lon Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file 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 ┐
+  ↓ lon Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
╭───────────────────────────────────────────────╮
+│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
╭───────────────────────────────────────────────╮
+│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
╭───────────────────────────────────────────────╮
+│ 45×170×24 YAXArray{Union{Missing, Float32},3} │
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
See tutorials for use cases.
Get a dimension ​
Get values, .e.g., axis tick labels, of a dimension that can be used for subseting:
julia
collect(tos.lat)
170-element Vector{Float64}:
+ -79.5
+ -78.5
+ -77.5
+ -76.5
+ -75.5
+ -74.5
+ -73.5
+ -72.5
+ -71.5
+ -70.5
+   ⋮
+  81.5
+  82.5
+  83.5
+  84.5
+  85.5
+  86.5
+  87.5
+  88.5
+  89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0
which is equivalent to:
julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default};
diff --git a/dev/assets/UserGuide_types.md.B2iGHzIZ.js b/dev/assets/UserGuide_types.md.B2iGHzIZ.js
deleted file mode 100644
index 66dd9bf7..00000000
--- a/dev/assets/UserGuide_types.md.B2iGHzIZ.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as e,c as a,o as t,a4 as r}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),s={name:"UserGuide/types.md"},o=r('
Types ​
This section describes the data structures used to work with n-dimensional arrays in YAXArrays.
YAXArray ​
An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.
A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.
A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.
Dataset ​
A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.
(Data) Cube ​
A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020
Dimension ​
A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.
',12),n=[o];function i(d,l,c,h,m,u){return t(),a("div",null,n)}const f=e(s,[["render",i]]);export{y as __pageData,f as default};
diff --git a/dev/assets/UserGuide_types.md.B2iGHzIZ.lean.js b/dev/assets/UserGuide_types.md.B2iGHzIZ.lean.js
deleted file mode 100644
index a20571bf..00000000
--- a/dev/assets/UserGuide_types.md.B2iGHzIZ.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as e,c as a,o as t,a4 as r}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),s={name:"UserGuide/types.md"},o=r("",12),n=[o];function i(d,l,c,h,m,u){return t(),a("div",null,n)}const f=e(s,[["render",i]]);export{y as __pageData,f as default};
diff --git a/dev/assets/UserGuide_types.md.DAaXu8Uc.js b/dev/assets/UserGuide_types.md.DAaXu8Uc.js
new file mode 100644
index 00000000..cdedef41
--- /dev/null
+++ b/dev/assets/UserGuide_types.md.DAaXu8Uc.js
@@ -0,0 +1 @@
+import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.Bwkmg46C.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
Types ​
This section describes the data structures used to work with n-dimensional arrays in YAXArrays.
YAXArray ​
An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.
A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.
A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.
Dataset ​
A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.
(Data) Cube ​
A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020
Dimension ​
A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
diff --git a/dev/assets/UserGuide_types.md.DAaXu8Uc.lean.js b/dev/assets/UserGuide_types.md.DAaXu8Uc.lean.js
new file mode 100644
index 00000000..cdedef41
--- /dev/null
+++ b/dev/assets/UserGuide_types.md.DAaXu8Uc.lean.js
@@ -0,0 +1 @@
+import{_ as a,c as t,a2 as r,o as s}from"./chunks/framework.Bwkmg46C.js";const u=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),o={name:"UserGuide/types.md"};function n(i,e,d,l,c,h){return s(),t("div",null,e[0]||(e[0]=[r('
Types ​
This section describes the data structures used to work with n-dimensional arrays in YAXArrays.
YAXArray ​
An Array stores a sequence of ordered elements of the same type usually across multiple dimensions or axes. For example, one can measure temperature across all time points of the time dimension or brightness values of a picture across X and Y dimensions. A one dimensional array is called Vector and a two dimensional array is called a Matrix. In many Machine Learning libraries, arrays are also called tensors. Arrays are designed to store dense spatial-temporal data stored in a grid, whereas a collection of sparse points is usually stored in data frames or relational databases.
A DimArray as defined by DimensionalData.jl adds names to the dimensions and their axes ticks for a given Array. These names can be used to access the data, e.g., by date instead of just by integer position.
A YAXArray is a subtype of a AbstractDimArray and adds functions to load and process the named arrays. For example, it can also handle very large arrays stored on disk that are too big to fit in memory. In addition, it provides functions for parallel computation.
Dataset ​
A Dataset is an ordered dictionary of YAXArrays that usually share dimensions. For example, it can bundle arrays storing temperature and precipitation that are measured at the same time points and the same locations. One also can store a picture in a Dataset with three arrays containing brightness values for red green and blue, respectively. Internally, those arrays are still separated allowing to chose different element types for each array. Analog to the (NetCDF Data Model)[https://docs.unidata.ucar.edu/netcdf-c/current/netcdf_data_model.html], a Dataset usually represents variables belonging to the same group.
(Data) Cube ​
A (Data) Cube is just a YAXArray in which arrays from a dataset are combined together by introducing a new dimension containing labels of which array the corresponding element came from. Unlike a Dataset, all arrays must have the same element type to be converted into a cube. This data structure is useful when we want to use all variables at once. For example, the arrays temperature and precipitation which are measured at the same locations and dates can be combined into a single cube. A more formal definition of Data Cubes are given in Mahecha et al. 2020
Dimension ​
A Dimension or axis as defined by DimensionalData.jl adds tick labels, e.g., to each row or column of an array. It's name is used to access particular subsets of that array.
',12)]))}const p=a(o,[["render",n]]);export{u as __pageData,p as default};
diff --git a/dev/assets/UserGuide_write.md.C3OHkkA6.js b/dev/assets/UserGuide_write.md.BtDfDOfT.js
similarity index 81%
rename from dev/assets/UserGuide_write.md.C3OHkkA6.js
rename to dev/assets/UserGuide_write.md.BtDfDOfT.js
index 406a5bee..d3d669dd 100644
--- a/dev/assets/UserGuide_write.md.C3OHkkA6.js
+++ b/dev/assets/UserGuide_write.md.BtDfDOfT.js
@@ -1,39 +1,39 @@
-import{_ as s,c as i,o as a,a4 as t}from"./chunks/framework._h4tgM90.js";const u=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),e={name:"UserGuide/write.md"},n=t(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
+import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),n={name:"UserGuide/write.md"};function l(h,s,p,k,r,d){return t(),a("div",null,s[0]||(s[0]=[e(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
 using NetCDF
 using Downloads: download
 
 path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
 ds = open_dataset(path)
YAXArray Dataset
 Shared Axes: 
-↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
 → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 Variables: 
 tos
 
 Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Write Zarr ​
Save a single YAXArray to a directory:
julia
using Zarr
 savecube(ds.tos, "tos.zarr", driver=:zarr)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.zarr", driver=:zarr)
Write NetCDF ​
Save a single YAXArray to a directory:
julia
using NetCDF
-savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
# YAXArrays.Datasets.savedataset — Function. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
# YAXArrays.Datasets.savedataset — Function. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 ()
 Variables:
 tos
-  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
   → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
-  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
 z
-  ↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
-  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
 
 Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Int32}, 10, 20))
╭─────────────────────────────────────────╮
 │ 10×20 YAXArray{Union{Missing, Int32},2} │
-├─────────────────────────────────────────┴────────────────────── dims ┐
+├─────────────────────────────────────────┴────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
   → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
-├──────────────────────────────────────────────────────────── metadata ┤
+├──────────────────────────────────────────────────────────────────── metadata ┤
   Dict{String, Any}()
-├─────────────────────────────────────────────────────────── file size ┤
+├─────────────────────────────────────────────────────────────────── file size ┤
   file size: 800.0 bytes
-└──────────────────────────────────────────────────────────────────────┘
and save them as
julia
r = savecube(a, "skeleton.zarr", driver=:zarr, skeleton=true)
and 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.
`,39),l=[n];function h(p,k,r,d,o,g){return a(),i("div",null,l)}const y=s(e,[["render",h]]);export{u as __pageData,y as default};
+└──────────────────────────────────────────────────────────────────────────────┘
and save them as
julia
r = savecube(a, "skeleton.zarr", driver=:zarr, skeleton=true)
and 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.
`,39)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default};
diff --git a/dev/assets/UserGuide_write.md.BtDfDOfT.lean.js b/dev/assets/UserGuide_write.md.BtDfDOfT.lean.js
new file mode 100644
index 00000000..d3d669dd
--- /dev/null
+++ b/dev/assets/UserGuide_write.md.BtDfDOfT.lean.js
@@ -0,0 +1,39 @@
+import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),n={name:"UserGuide/write.md"};function l(h,s,p,k,r,d){return t(),a("div",null,s[0]||(s[0]=[e(`
Write YAXArrays and Datasets ​
Create an example Dataset:
julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+(↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+→ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Write Zarr ​
Save a single YAXArray to a directory:
julia
using Zarr
+savecube(ds.tos, "tos.zarr", driver=:zarr)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.zarr", driver=:zarr)
Write NetCDF ​
Save a single YAXArray to a directory:
julia
using NetCDF
+savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
# YAXArrays.Datasets.savedataset — Function. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+tos
+  (↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+z
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)
Save Skeleton ​
Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:
julia
using YAXArrays, Zarr, FillArrays
create the Zeros array
julia
julia> a = YAXArray(Zeros(Union{Missing, Int32}, 10, 20))
╭─────────────────────────────────────────╮
+│ 10×20 YAXArray{Union{Missing, Int32},2} │
+├─────────────────────────────────────────┴────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
and save them as
julia
r = savecube(a, "skeleton.zarr", driver=:zarr, skeleton=true)
and 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.
`,39)]))}const c=i(n,[["render",l]]);export{g as __pageData,c as default};
diff --git a/dev/assets/UserGuide_write.md.C3OHkkA6.lean.js b/dev/assets/UserGuide_write.md.C3OHkkA6.lean.js
deleted file mode 100644
index 5ed4a372..00000000
--- a/dev/assets/UserGuide_write.md.C3OHkkA6.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as t}from"./chunks/framework._h4tgM90.js";const u=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),e={name:"UserGuide/write.md"},n=t("",39),l=[n];function h(p,k,r,d,o,g){return a(),i("div",null,l)}const y=s(e,[["render",h]]);export{u as __pageData,y as default};
diff --git a/dev/assets/api.md.DU-CSbKR.js b/dev/assets/api.md.DSVzzvyi.js
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-import{_ as e,c as a,o as s,a4 as i}from"./chunks/framework._h4tgM90.js";const y=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"},r=i(`
API Reference ​
This section describes all available functions of this package.
Public API ​
# YAXArrays.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# YAXArrays.DAT.mapCube — Method. 
julia
mapCube(fun, cube, addargs...;kwargs...)
+import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.Bwkmg46C.js";const b=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"};function o(l,e,n,d,p,c){return t(),s("div",null,e[0]||(e[0]=[i(`
API Reference ​
This section describes all available functions of this package.
Public API ​
# YAXArrays.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="Variable")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source

# YAXArrays.Datasets.open_dataset — Method. 
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source

# YAXArrays.Datasets.savecube — Method. 
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source

# YAXArrays.Datasets.savedataset — Method. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

# YAXArrays.Datasets.to_dataset — Method. 
to_dataset(c;datasetaxis = "Variable", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source

Internal API ​
# YAXArrays.YAXDefaults — Constant. 
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source

# YAXArrays.findAxis — Method. 
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

# YAXArrays.getOutAxis — Method. 
julia
getOutAxis
source

# YAXArrays.get_descriptor — Method. 
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source

# YAXArrays.match_axis — Method. 
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+A dictionary can hold cubes with differing axes. But it will share the common axes between the subcubes.
source

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

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="Variable")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source

# YAXArrays.Datasets.open_dataset — Method. 
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source

# YAXArrays.Datasets.savecube — Method. 
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source

# YAXArrays.Datasets.savedataset — Method. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

# YAXArrays.Datasets.to_dataset — Method. 
to_dataset(c;datasetaxis = "Variable", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source

Internal API ​
# YAXArrays.YAXDefaults — Constant. 
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source

# YAXArrays.findAxis — Method. 
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

# YAXArrays.getOutAxis — Method. 
julia
getOutAxis
source

# YAXArrays.get_descriptor — Method. 
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source

# YAXArrays.match_axis — Method. 
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source

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

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

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

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

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::Tuple{Vararg{YAXArrays.DAT.InputCube, NIN}} where NIN: The input data cubes
  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source

# YAXArrays.DAT.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source

# YAXArrays.Datasets.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

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

`,100),o=[r];function l(d,n,p,c,h,u){return s(),a("div",null,o)}const A=e(t,[["render",l]]);export{y as __pageData,A as default};
+For example to disregard differences of captialisation.
source

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

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

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

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

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::Tuple{Vararg{YAXArrays.DAT.InputCube, NIN}} where NIN: The input data cubes
  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source

# YAXArrays.DAT.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source

# YAXArrays.Datasets.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

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

`,100)]))}const u=a(r,[["render",o]]);export{b as __pageData,u as default};
diff --git a/dev/assets/api.md.DSVzzvyi.lean.js b/dev/assets/api.md.DSVzzvyi.lean.js
new file mode 100644
index 00000000..0f71642d
--- /dev/null
+++ b/dev/assets/api.md.DSVzzvyi.lean.js
@@ -0,0 +1,13 @@
+import{_ as a,c as s,a2 as i,o as t}from"./chunks/framework.Bwkmg46C.js";const b=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),r={name:"api.md"};function o(l,e,n,d,p,c){return t(),s("div",null,e[0]||(e[0]=[i(`
API Reference ​
This section describes all available functions of this package.
Public API ​
# YAXArrays.getAxis — Method. 
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.Cube — Method. 
julia
Cube(ds::Dataset; joinname="Variable")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source

# YAXArrays.Datasets.open_dataset — Method. 
open_dataset(g; driver=:all)
Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.
source

# YAXArrays.Datasets.savecube — Method. 
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source

# YAXArrays.Datasets.savedataset — Method. 
savedataset(ds::Dataset; path = "", persist = nothing, overwrite = false, append = false, skeleton=false, backend = :all, driver = backend, max_cache = 5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite = true, deletes ALL your data and it will create a new file.
source

# YAXArrays.Datasets.to_dataset — Method. 
to_dataset(c;datasetaxis = "Variable", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername
source

Internal API ​
# YAXArrays.YAXDefaults — Constant. 
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source

# YAXArrays.findAxis — Method. 
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source

# YAXArrays.getOutAxis — Method. 
julia
getOutAxis
source

# YAXArrays.get_descriptor — Method. 
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source

# YAXArrays.match_axis — Method. 
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+This is used to find different axes and to make certain axis description the same.
+For example to disregard differences of captialisation.
source

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

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

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

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

# YAXArrays.DAT.DATConfig — Type. 
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::Tuple{Vararg{YAXArrays.DAT.InputCube, NIN}} where NIN: The input data cubes
  • outcubes::Tuple{Vararg{YAXArrays.DAT.OutputCube, NOUT}} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source

# YAXArrays.DAT.InputCube — Type. 
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source

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

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

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

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

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

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

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

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

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

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

# YAXArrays.Datasets.createdataset — Method. 
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variable" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source

# YAXArrays.Datasets.getarrayinfo — Method. 
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source

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

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t=this.opt.caseSensitive?"":"i",s=this.opt.caseSensitive?["aàáảãạăằắẳẵặâầấẩẫậäåāą","AÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬÄÅĀĄ","cçćč","CÇĆČ","dđď","DĐĎ","eèéẻẽẹêềếểễệëěēę","EÈÉẺẼẸÊỀẾỂỄỆËĚĒĘ","iìíỉĩịîïī","IÌÍỈĨỊÎÏĪ","lł","LŁ","nñňń","NÑŇŃ","oòóỏõọôồốổỗộơởỡớờợöøō","OÒÓỎÕỌÔỒỐỔỖỘƠỞỠỚỜỢÖØŌ","rř","RŘ","sšśșş","SŠŚȘŞ","tťțţ","TŤȚŢ","uùúủũụưừứửữựûüůū","UÙÚỦŨỤƯỪỨỬỮỰÛÜŮŪ","yýỳỷỹỵÿ","YÝỲỶỸỴŸ","zžżź","ZŽŻŹ"]:["aàáảãạăằắẳẵặâầấẩẫậäåāąAÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬÄÅĀĄ","cçćčCÇĆČ","dđďDĐĎ","eèéẻẽẹêềếểễệëěēęEÈÉẺẼẸÊỀẾỂỄỆËĚĒĘ","iìíỉĩịîïīIÌÍỈĨỊÎÏĪ","lłLŁ","nñňńNÑŇŃ","oòóỏõọôồốổỗộơởỡớờợöøōOÒÓỎÕỌÔỒỐỔỖỘƠỞỠỚỜỢÖØŌ","rřRŘ","sšśșşSŠŚȘŞ","tťțţTŤȚŢ","uùúủũụưừứửữựûüůūUÙÚỦŨỤƯỪỨỬỮỰÛÜŮŪ","yýỳỷỹỵÿYÝỲỶỸỴŸ","zžżźZŽŻŹ"];let n=[];return e.split("").forEach(r=>{s.every(i=>{if(i.indexOf(r)!==-1){if(n.indexOf(i)>-1)return!1;e=e.replace(new RegExp(`[${i}]`,`gm${t}`),`[${i}]`),n.push(i)}return!0})}),e}createMergedBlanksRegExp(e){return e.replace(/[\s]+/gmi,"[\\s]+")}createAccuracyRegExp(e){const t="!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~¡¿";let 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e.sort((n,r)=>n.start-r.start).forEach(n=>{let{start:r,end:i,valid:o}=this.callNoMatchOnInvalidRanges(n,s);o&&(n.start=r,n.length=i-r,t.push(n),s=i)}),t}callNoMatchOnInvalidRanges(e,t){let s,n,r=!1;return e&&typeof e.start<"u"?(s=parseInt(e.start,10),n=s+parseInt(e.length,10),this.isNumeric(e.start)&&this.isNumeric(e.length)&&n-t>0&&n-s>0?r=!0:(this.log(`Ignoring invalid or overlapping range: ${JSON.stringify(e)}`),this.opt.noMatch(e))):(this.log(`Ignoring invalid range: ${JSON.stringify(e)}`),this.opt.noMatch(e)),{start:s,end:n,valid:r}}checkWhitespaceRanges(e,t,s){let n,r=!0,i=s.length,o=t-i,c=parseInt(e.start,10)-o;return c=c>i?i:c,n=c+parseInt(e.length,10),n>i&&(n=i,this.log(`End range automatically set to the max value of ${i}`)),c<0||n-c<0||c>i||n>i?(r=!1,this.log(`Invalid range: ${JSON.stringify(e)}`),this.opt.noMatch(e)):s.substring(c,n).replace(/\s+/g,"")===""&&(r=!1,this.log("Skipping whitespace only range: 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s=document.createDocumentFragment();for(;e.firstChild;)s.appendChild(e.removeChild(e.firstChild));t.replaceChild(s,e),this.ie?this.normalizeTextNode(t):t.normalize()}normalizeTextNode(e){if(e){if(e.nodeType===3)for(;e.nextSibling&&e.nextSibling.nodeType===3;)e.nodeValue+=e.nextSibling.nodeValue,e.parentNode.removeChild(e.nextSibling);else this.normalizeTextNode(e.firstChild);this.normalizeTextNode(e.nextSibling)}}markRegExp(e,t){this.opt=t,this.log(`Searching with expression "${e}"`);let s=0,n="wrapMatches";const r=i=>{s++,this.opt.each(i)};this.opt.acrossElements&&(n="wrapMatchesAcrossElements"),this[n](e,this.opt.ignoreGroups,(i,o)=>this.opt.filter(o,i,s),r,()=>{s===0&&this.opt.noMatch(e),this.opt.done(s)})}mark(e,t){this.opt=t;let s=0,n="wrapMatches";const{keywords:r,length:i}=this.getSeparatedKeywords(typeof e=="string"?[e]:e),o=this.opt.caseSensitive?"":"i",c=l=>{let h=new RegExp(this.createRegExp(l),`gm${o}`),v=0;this.log(`Searching with expression 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l=0;for(const[h,v]of s){const f=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield Ee(y))}++l%1e3===0&&(yield kt(0)),c._index.set(h,f)}return c})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:c}=e;if(c!==1&&c!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const l=new le(t);return l._documentCount=s,l._nextId=n,l._idToShortId=new Map,l._fieldIds=r,l._avgFieldLength=i,l._dirtCount=o||0,l._index=new X,l}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return 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s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,c,l=new Map){if(r==null)return l;for(const h of Object.keys(i)){const v=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const _=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const C=b.get(w),J=this._fieldLength.get(w)[f],H=Bs(C,y,this._documentCount,J,_,c),W=s*n*v*R*H,j=l.get(w);if(j){j.score+=W,Js(j.terms,e);const $=ze(j.match,t);$?$.push(h):j.match[t]=[h]}else l.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return 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diff --git a/dev/assets/chunks/VPLocalSearchBox.tGRBlptZ.js b/dev/assets/chunks/VPLocalSearchBox.tGRBlptZ.js
new file mode 100644
index 00000000..69c2625f
--- /dev/null
+++ b/dev/assets/chunks/VPLocalSearchBox.tGRBlptZ.js
@@ -0,0 +1,7 @@
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if(e.size===1){const[s,n]=e.entries().next().value;s!==z&&Tt(a.slice(0,-1),s,n)}},Tt=(a,e,t)=>{if(a.length===0)return;const[s,n]=Ke(a);s.set(n+e,t),s.delete(n)},Ke=a=>a[a.length-1],Je="or",It="and",Ds="and_not";class le{constructor(e){if((e==null?void 0:e.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const t=e.autoVacuum==null||e.autoVacuum===!0?Pe:e.autoVacuum;this._options=Object.assign(Object.assign(Object.assign({},ze),e),{autoVacuum:t,searchOptions:Object.assign(Object.assign({},dt),e.searchOptions||{}),autoSuggestOptions:Object.assign(Object.assign({},$s),e.autoSuggestOptions||{})}),this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=We,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const m=s(h.toString(),c),f=this._fieldIds[c],b=new Set(m).size;this.addFieldLength(l,f,this._documentCount-1,b);for(const y of m){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(f,l,w);else x&&this.addTerm(f,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new 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Omit the argument to remove all documents.");this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(e){const t=this._idToShortId.get(e);if(t==null)throw new Error(`MiniSearch: cannot discard document with ID ${e}: it is not in the index`);this._idToShortId.delete(e),this._documentIds.delete(t),this._storedFields.delete(t),(this._fieldLength.get(t)||[]).forEach((s,n)=>{this.removeFieldLength(t,n,this._documentCount,s)}),this._fieldLength.delete(t),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:e,minDirtCount:t,batchSize:s,batchWait:n}=this._options.autoVacuum;this.conditionalVacuum({batchSize:s,batchWait:n},{minDirtCount:t,minDirtFactor:e})}discardAll(e){const t=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const s of e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=We,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Ee(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Be.batchSize,r=e.batchWait||Be.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[m]of h)this._documentIds.has(m)||(h.size<=1?l.delete(c):h.delete(m));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Pe.minDirtCount,s=s||Pe.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===le.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Ee(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(e),t)})}static getDefault(e){if(ze.hasOwnProperty(e))return De(ze,e);throw new Error(`MiniSearch: unknown option "${e}"`)}static loadJS(e,t){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=Se(n),l._fieldLength=Se(r),l._storedFields=Se(i);for(const[c,h]of l._documentIds)l._idToShortId.set(h,c);for(const[c,h]of s){const m=new Map;for(const f of Object.keys(h)){let b=h[f];o===1&&(b=b.ds),m.set(parseInt(f,10),Se(b))}l._index.set(c,m)}return l}static loadJSAsync(e,t){return Ee(this,void 0,void 0,function*(){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=yield _e(n),l._fieldLength=yield _e(r),l._storedFields=yield _e(i);for(const[h,m]of l._documentIds)l._idToShortId.set(m,h);let c=0;for(const[h,m]of s){const f=new Map;for(const b of Object.keys(m)){let y=m[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield _e(y))}++c%1e3===0&&(yield Nt(0)),l._index.set(h,f)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new le(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return this.combineResults(b,f.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,m=o(e).flatMap(f=>l(f)).filter(f=>!!f).map(js(i)).map(f=>this.executeQuerySpec(f,i));return this.combineResults(m,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:De(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),m=this._index.get(e.term),f=this.termResults(e.term,e.term,1,e.termBoost,m,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const R=x.length-e.term.length;if(!R)continue;y==null||y.delete(x);const C=h*x.length/(x.length+.3*R);this.termResults(e.term,x,C,e.termBoost,w,n,r,l,f)}if(y)for(const x of y.keys()){const[w,R]=y.get(x);if(!R)continue;const C=c*x.length/(x.length+R);this.termResults(e.term,x,C,e.termBoost,w,n,r,l,f)}return f}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Je){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const m=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const C=b.get(w),K=this._fieldLength.get(w)[f],H=Vs(C,y,this._documentCount,K,x,l),W=s*n*m*R*H,j=c.get(w);if(j){j.score+=W,Bs(j.terms,e);const $=De(j.match,t);$?$.push(h):j.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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a.fuzzy=="function"?a.fuzzy(e,t,s):a.fuzzy||!1,r=typeof a.prefix=="function"?a.prefix(e,t,s):a.prefix===!0,i=typeof a.boostTerm=="function"?a.boostTerm(e,t,s):1;return{term:e,fuzzy:n,prefix:r,termBoost:i}},ze={idField:"id",extractField:(a,e)=>a[e],tokenize:a=>a.split(Ws),processTerm:a=>a.toLowerCase(),fields:void 0,searchOptions:void 0,storeFields:[],logger:(a,e)=>{typeof(console==null?void 0:console[a])=="function"&&console[a](e)},autoVacuum:!0},dt={combineWith:Je,prefix:!1,fuzzy:!1,maxFuzzy:6,boost:{},weights:{fuzzy:.45,prefix:.375},bm25:Ps},$s={combineWith:It,prefix:(a,e,t)=>e===t.length-1},Be={batchSize:1e3,batchWait:10},We={minDirtFactor:.1,minDirtCount:20},Pe=Object.assign(Object.assign({},Be),We),Bs=(a,e)=>{a.includes(e)||a.push(e)},ht=(a,e)=>{for(const t of e)a.includes(t)||a.push(t)},ft=({score:a},{score:e})=>e-a,pt=()=>new Map,Se=a=>{const e=new Map;for(const t of Object.keys(a))e.set(parseInt(t,10),a[t]);return e},_e=a=>Ee(void 0,void 0,void 0,function*(){const e=new Map;let 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diff --git a/dev/assets/chunks/framework.Bwkmg46C.js b/dev/assets/chunks/framework.Bwkmg46C.js
new file mode 100644
index 00000000..824c9532
--- /dev/null
+++ b/dev/assets/chunks/framework.Bwkmg46C.js
@@ -0,0 +1,17 @@
+/**
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diff --git a/dev/assets/chunks/theme.o12TANz8.js b/dev/assets/chunks/theme.o12TANz8.js
new file mode 100644
index 00000000..1c532229
--- /dev/null
+++ b/dev/assets/chunks/theme.o12TANz8.js
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diff --git a/dev/assets/development_contribute.md.BjKHzE54.js b/dev/assets/development_contribute.md.BjKHzE54.js
deleted file mode 100644
index 09b7f25a..00000000
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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:
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Build docs locally ​
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Install the dependencies in your system, locate yourself at the docs level folder, then do
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npm i
Then simply go to your docs env and activate it, i.e.
sh
docs> julia
-julia> ]
-pkg> activate .
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sh
include("make.jl")
Now go to your terminal in the same path docs> and run:
sh
npm run docs:dev
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deleted file mode 100644
index f77c2d56..00000000
--- a/dev/assets/development_contribute.md.BjKHzE54.lean.js
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new file mode 100644
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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.
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new file mode 100644
index 00000000..ec875108
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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:
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Build docs locally ​
If you want to take a look at the docs locally before doing a PR follow the next steps:
Install the dependencies in your system, locate yourself at the docs level folder, then do
sh
npm i
Then simply go to your docs env and activate it, i.e.
sh
docs> julia
+julia> ]
+pkg> activate .
Next, run the scripts. Generate files and build docs by running:
sh
include("make.jl")
Now go to your terminal in the same path docs> and run:
sh
npm run docs:dev
This should ouput http://localhost:5173/YAXArrays.jl/, copy/paste this into your browser and you are all set.
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new file mode 100644
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zXV@DmE9MPT&MdR?6PImnswu&6>r_6vQ+PQRw(u{l)xWm!|NHl!ru~0Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 
 a = YAXArray(rand(2,3))
╭─────────────────────────╮
 │ 2×3 YAXArray{Float64,2} │
@@ -44,13 +44,13 @@ import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 46.88 KB
 └──────────────────────────────────────────────────────────────────────────────┘
Get the temperature map at the first point in time:
julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
- 0.212529   0.687095  0.329389   0.322666  …  0.943401  0.0794068  0.124242
- 0.148317   0.255777  0.0451893  0.90528      0.932458  0.743448   0.50247
- 0.345306   0.817761  0.371764   0.77842      0.460725  0.346683   0.997411
- 0.11084    0.112418  0.595773   0.859691     0.85927   0.663178   0.979973
- 0.0714621  0.249736  0.759224   0.667162     0.259272  0.180714   0.550461
- 0.158962   0.709966  0.763967   0.343173  …  0.260239  0.410862   0.245069
- 0.287222   0.653128  0.0305119  0.354183     0.716081  0.965427   0.688638
- 0.821934   0.89118   0.885742   0.594211     0.768702  0.061001   0.953996
- 0.256631   0.996559  0.2551     0.763404     0.391745  0.835697   0.0833944
- 0.650262   0.579623  0.147435   0.494427     0.57022   0.344799   0.915389
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.
`,21),e=[p];function l(h,k,r,d,o,g){return i(),a("div",null,e)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
+ 0.917506  0.818381  0.568985  0.864111  …  0.831293   0.430697    0.914342
+ 0.170553  0.416222  0.222088  0.231967     0.680628   0.535731    0.617046
+ 0.997315  0.307643  0.695412  0.233097     0.173082   0.895866    0.997734
+ 0.140123  0.558322  0.753357  0.437726     0.654983   0.154468    0.676268
+ 0.460593  0.342506  0.228113  0.197427     0.448462   0.00520612  0.292358
+ 0.417004  0.720753  0.398057  0.740896  …  0.209609   0.75727     0.209998
+ 0.65235   0.948571  0.27538   0.830771     0.0164863  0.76584     0.552837
+ 0.174313  0.984091  0.466392  0.744666     0.302653   0.26802     0.596946
+ 0.982001  0.361453  0.560824  0.70521      0.140938   0.883224    0.599861
+ 0.526898  0.691289  0.804233  0.127126     0.266098   0.644498    0.685735
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.
`,21)]))}const E=a(p,[["render",l]]);export{g as __pageData,E as default};
diff --git a/dev/assets/get_started.md.2M_EpbbP.lean.js b/dev/assets/get_started.md.2M_EpbbP.lean.js
new file mode 100644
index 00000000..25a16505
--- /dev/null
+++ b/dev/assets/get_started.md.2M_EpbbP.lean.js
@@ -0,0 +1,56 @@
+import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.Bwkmg46C.js";const g=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
+
+a = YAXArray(rand(2,3))
╭─────────────────────────╮
+│ 2×3 YAXArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────────── file size ┤ 
+  file 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
+axlist = (
+    Dim{:time}(range(1, 20, length=20)),
+    X(range(1, 10, length=10)),
+    Y(range(1, 5, length=15)),
+    Dim{:variable}(["temperature", "precipitation"])
+)
+
+# the actual data matching the dimensions defined in axlist
+data = rand(20, 10, 15, 2)
+
+# metadata about the array
+props = Dict(
+    "origin" => "YAXArrays.jl example",
+    "x" => "longitude",
+    "y" => "latitude",
+);
+
+a2 = YAXArray(axlist, data, props)
╭────────────────────────────────╮
+│ 20×10×15×2 YAXArray{Float64,4} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ time     Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → X        Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ Y        Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ variable Categorical{String} ["temperature", "precipitation"] ReverseOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 3 entries:
+  "y"      => "latitude"
+  "x"      => "longitude"
+  "origin" => "YAXArrays.jl example"
+├─────────────────────────────────────────────────────────────────── file size ┤ 
+  file size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘
Get the temperature map at the first point in time:
julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
+ 0.917506  0.818381  0.568985  0.864111  …  0.831293   0.430697    0.914342
+ 0.170553  0.416222  0.222088  0.231967     0.680628   0.535731    0.617046
+ 0.997315  0.307643  0.695412  0.233097     0.173082   0.895866    0.997734
+ 0.140123  0.558322  0.753357  0.437726     0.654983   0.154468    0.676268
+ 0.460593  0.342506  0.228113  0.197427     0.448462   0.00520612  0.292358
+ 0.417004  0.720753  0.398057  0.740896  …  0.209609   0.75727     0.209998
+ 0.65235   0.948571  0.27538   0.830771     0.0164863  0.76584     0.552837
+ 0.174313  0.984091  0.466392  0.744666     0.302653   0.26802     0.596946
+ 0.982001  0.361453  0.560824  0.70521      0.140938   0.883224    0.599861
+ 0.526898  0.691289  0.804233  0.127126     0.266098   0.644498    0.685735
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.
`,21)]))}const E=a(p,[["render",l]]);export{g as __pageData,E as default};
diff --git a/dev/assets/get_started.md.EFW7umcj.lean.js b/dev/assets/get_started.md.EFW7umcj.lean.js
deleted file mode 100644
index 50ea4609..00000000
--- a/dev/assets/get_started.md.EFW7umcj.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework._h4tgM90.js";const c=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),t={name:"get_started.md"},p=n("",21),e=[p];function l(h,k,r,d,o,g){return i(),a("div",null,e)}const y=s(t,[["render",l]]);export{c as __pageData,y as default};
diff --git a/dev/assets/gazrluv.xrZxBsPv.jpeg b/dev/assets/hkbjqfv.xrZxBsPv.jpeg
similarity index 100%
rename from dev/assets/gazrluv.xrZxBsPv.jpeg
rename to dev/assets/hkbjqfv.xrZxBsPv.jpeg
diff --git a/dev/assets/index.md.D-zmVfSn.js b/dev/assets/index.md.DU-mPNT1.js
similarity index 96%
rename from dev/assets/index.md.D-zmVfSn.js
rename to dev/assets/index.md.DU-mPNT1.js
index db8e4046..d8b44121 100644
--- a/dev/assets/index.md.D-zmVfSn.js
+++ b/dev/assets/index.md.DU-mPNT1.js
@@ -1 +1 @@
-import{_ as e,c as t,o as a}from"./chunks/framework._h4tgM90.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"alt","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/openZarr"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group_by"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
+import{_ as e,c as t,o as a}from"./chunks/framework.Bwkmg46C.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"alt","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/openZarr"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group_by"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
diff --git a/dev/assets/index.md.D-zmVfSn.lean.js b/dev/assets/index.md.DU-mPNT1.lean.js
similarity index 96%
rename from dev/assets/index.md.D-zmVfSn.lean.js
rename to dev/assets/index.md.DU-mPNT1.lean.js
index db8e4046..d8b44121 100644
--- a/dev/assets/index.md.D-zmVfSn.lean.js
+++ b/dev/assets/index.md.DU-mPNT1.lean.js
@@ -1 +1 @@
-import{_ as e,c as t,o as a}from"./chunks/framework._h4tgM90.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"alt","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/openZarr"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group_by"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
+import{_ as e,c as t,o as a}from"./chunks/framework.Bwkmg46C.js";const f=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"alt","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/openZarr"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported."},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group_by"},{"title":"Efficiency","details":"Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets)."}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),o={name:"index.md"};function i(r,n,l,s,d,c){return a(),t("div")}const m=e(o,[["render",i]]);export{f as __pageData,m as default};
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diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.lean.js
deleted file mode 100644
index e011d22c..00000000
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const h="/YAXArrays.jl/dev/assets/qrtcgza.B1F72mIS.png",l="/YAXArrays.jl/dev/assets/yrexdyf.Dk-HtvWS.png",o=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),k={name:"tutorials/mean_seasonal_cycle.md"},p=n("",21),t=[p];function e(E,d,r,g,y,c){return a(),i("div",null,t)}const C=s(k,[["render",e]]);export{o as __pageData,C as default};
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.js b/dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.js
similarity index 81%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.js
index 69bc185b..42c12f99 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.BCu3jBkO.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const h="/YAXArrays.jl/dev/assets/qrtcgza.B1F72mIS.png",l="/YAXArrays.jl/dev/assets/yrexdyf.Dk-HtvWS.png",o=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),k={name:"tutorials/mean_seasonal_cycle.md"},p=n(`
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.Bwkmg46C.js";const l="/YAXArrays.jl/dev/assets/ulsahek.w0JksFmk.png",k="/YAXArrays.jl/dev/assets/jthhtav.BmXbFoQB.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
 CairoMakie.activate!()
 using Dates
 using Statistics
We define the data span. For simplicity, three non-leap years were selected.
julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
@@ -9,9 +9,9 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const h
     )
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
-fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
+fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
 
-julia> axes = (Dim{:Time}(t),)
↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31")
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
+julia> axes = (Dim{:Time}(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
 │ 1095-element YAXArray{Float64,1} │
 ├──────────────────────────────────┴───────────────────────────────────── dims ┐
   ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
@@ -37,26 +37,26 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const h
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.13291526713087612
- -0.05460756353473867
-  0.07204935956348457
- -0.05326101988982565
-  0.15868041606243408
-  0.11439100033904083
-  0.13930646027762436
-  0.08757811935252037
-  0.05341039407809093
-  0.18867824088012022
+ -0.02831455834460911
+  0.04453476627315919
+  0.035686570524083555
+  0.11722933412557059
+  0.036946732134505465
+  0.19277839017642331
+  0.07495148408717336
+  0.023101933734291022
+  0.2784376308132314
+  0.17321929531017177
   ⋮
- -0.17597786204903687
- -0.0803349435034534
- -0.11344554908666375
- -0.005146734261364652
- -0.054866954001569136
- -0.07512042630397335
- -0.06048141295750401
-  0.048565067687498124
-  0.04428068807380173
TODO: Apply the new groupby funtion from DD
Plot results: mean seasonal cycle ​
julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+ -0.14858712445280844
+ -0.1058503173631838
+ -0.11471619358216052
+ -0.09915529827483704
+ -0.1388557751513135
+ -0.000924951522542868
+ -0.034309078782668714
+  0.03329805652756149
+ -0.050857935248364274
TODO: Apply the new groupby funtion from DD
Plot results: mean seasonal cycle ​
julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
@@ -69,4 +69,4 @@ import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework._h4tgM90.js";const h
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
 fig
-current_figure()
',21),t=[p];function e(E,d,r,g,y,c){return a(),i("div",null,t)}const C=s(k,[["render",e]]);export{o as __pageData,C as default};
+current_figure()
',21)]))}const F=i(p,[["render",t]]);export{c as __pageData,F as default};
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.lean.js
new file mode 100644
index 00000000..42c12f99
--- /dev/null
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.DnEN9eOf.lean.js
@@ -0,0 +1,72 @@
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.Bwkmg46C.js";const l="/YAXArrays.jl/dev/assets/ulsahek.w0JksFmk.png",k="/YAXArrays.jl/dev/assets/jthhtav.BmXbFoQB.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
+CairoMakie.activate!()
+using Dates
+using Statistics
We define the data span. For simplicity, three non-leap years were selected.
julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
+NpY = 3
3
and create some seasonal dummy data
julia
x = repeat(range(0, 2π, length=365), NpY)
+var = @. sin(x) + 0.1 * randn()
julia
fig, ax, obj = lines(t, var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; size = (600,400))
+    )
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
+
+julia> axes = (Dim{:Time}(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
+│ 1095-element YAXArray{Float64,1} │
+├──────────────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ 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
+└──────────────────────────────────────────────────────────────────────────────┘
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))
+    datesid = unique(monthday)
+    ## number of years
+    NpY = Int(size(monthday,1)/ndays)
+    idx = Int.(zeros(ndays, NpY))
+    ## get the day-month indices for data subsetting
+    for i in 1:ndays
+        idx[i,:] = Int.(findall(x-> x == datesid[i], monthday))
+    end
+    ## compute the mean seasonal cycle
+    mscarray = map(x->var[x], idx)
+    msc = mapslices(mean, mscarray, dims=2)
+    return msc
+end
+
+msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
+ -0.02831455834460911
+  0.04453476627315919
+  0.035686570524083555
+  0.11722933412557059
+  0.036946732134505465
+  0.19277839017642331
+  0.07495148408717336
+  0.023101933734291022
+  0.2784376308132314
+  0.17321929531017177
+  ⋮
+ -0.14858712445280844
+ -0.1058503173631838
+ -0.11471619358216052
+ -0.09915529827483704
+ -0.1388557751513135
+ -0.000924951522542868
+ -0.034309078782668714
+  0.03329805652756149
+ -0.050857935248364274
TODO: Apply the new groupby funtion from DD
Plot results: mean seasonal cycle ​
julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+    linewidth=2.0, linestyle=:dot,
+    axis = (;  xlabel="Day of Year", ylabel="Variable"),
+    figure=(; size = (600,400))
+    )
+lines!(1:365, var[366:730], label="2022", color=:brown,
+    linewidth=1.5, linestyle=:dash
+    )
+lines!(1:365, msc[:,1]; label="MSC", color=:dodgerblue, linewidth=2.5)
+axislegend()
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
+current_figure()
',21)]))}const F=i(p,[["render",t]]);export{c as __pageData,F as default};
diff --git a/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.js b/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.js
new file mode 100644
index 00000000..29143d3d
--- /dev/null
+++ b/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.js
@@ -0,0 +1 @@
+import{_ as t,c as a,a2 as r,o}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
Other tutorials ​
If you are interested in learning how to work with YAXArrays for different use cases you can follow along one of the following tutorials.
  • Currently the overview tutorial is located at ESDLTutorials Repository
  • You can find further tutorial videos at the EO College. Beware that the syntax in the video tutorials might be slightly changed.
  • the other tutorials are still work in progress.
General overview of the functionality of YAXArrays ​
This tutorial provides a broad overview about the features of YAXArrays.
Table-style iteration over YAXArrays ​
Work in progress
Sometimes you want to combine the data that is represented in the data cube with other datasets, which are best described as a data frame. In this tutorial you will learn how to use the Tables.jl interface to iterate over the data in the YAXArray.
Combining multiple tiff files into a zarr based datacube ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
diff --git a/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.lean.js b/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.lean.js
new file mode 100644
index 00000000..29143d3d
--- /dev/null
+++ b/dev/assets/tutorials_other_tutorials.md.DMk6XJBb.lean.js
@@ -0,0 +1 @@
+import{_ as t,c as a,a2 as r,o}from"./chunks/framework.Bwkmg46C.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('
Other tutorials ​
If you are interested in learning how to work with YAXArrays for different use cases you can follow along one of the following tutorials.
  • Currently the overview tutorial is located at ESDLTutorials Repository
  • You can find further tutorial videos at the EO College. Beware that the syntax in the video tutorials might be slightly changed.
  • the other tutorials are still work in progress.
General overview of the functionality of YAXArrays ​
This tutorial provides a broad overview about the features of YAXArrays.
Table-style iteration over YAXArrays ​
Work in progress
Sometimes you want to combine the data that is represented in the data cube with other datasets, which are best described as a data frame. In this tutorial you will learn how to use the Tables.jl interface to iterate over the data in the YAXArray.
Combining multiple tiff files into a zarr based datacube ​
',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default};
diff --git a/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.js b/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.js
deleted file mode 100644
index 301df584..00000000
--- a/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as e,c as t,o as a,a4 as r}from"./chunks/framework._h4tgM90.js";const p=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),o={name:"tutorials/other_tutorials.md"},i=r('
Other tutorials ​
If you are interested in learning how to work with YAXArrays for different use cases you can follow along one of the following tutorials.
  • Currently the overview tutorial is located at ESDLTutorials Repository
  • You can find further tutorial videos at the EO College. Beware that the syntax in the video tutorials might be slightly changed.
  • the other tutorials are still work in progress.
General overview of the functionality of YAXArrays ​
This tutorial provides a broad overview about the features of YAXArrays.
Table-style iteration over YAXArrays ​
Work in progress
Sometimes you want to combine the data that is represented in the data cube with other datasets, which are best described as a data frame. In this tutorial you will learn how to use the Tables.jl interface to iterate over the data in the YAXArray.
Combining multiple tiff files into a zarr based datacube ​
',9),l=[i];function s(n,h,u,f,d,c){return a(),t("div",null,l)}const _=e(o,[["render",s]]);export{p as __pageData,_ as default};
diff --git a/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.lean.js b/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.lean.js
deleted file mode 100644
index 1eec65b3..00000000
--- a/dev/assets/tutorials_other_tutorials.md.DofrLRlZ.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as e,c as t,o as a,a4 as r}from"./chunks/framework._h4tgM90.js";const p=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),o={name:"tutorials/other_tutorials.md"},i=r("",9),l=[i];function s(n,h,u,f,d,c){return a(),t("div",null,l)}const _=e(o,[["render",s]]);export{p as __pageData,_ as default};
diff --git a/dev/assets/tutorials_plottingmaps.md._uyphNwM.js b/dev/assets/tutorials_plottingmaps.md.C_xuWMeL.js
similarity index 93%
rename from dev/assets/tutorials_plottingmaps.md._uyphNwM.js
rename to dev/assets/tutorials_plottingmaps.md.C_xuWMeL.js
index 8326a8d8..6d340652 100644
--- a/dev/assets/tutorials_plottingmaps.md._uyphNwM.js
+++ b/dev/assets/tutorials_plottingmaps.md.C_xuWMeL.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework._h4tgM90.js";const n="/YAXArrays.jl/dev/assets/gazrluv.xrZxBsPv.jpeg",t="/YAXArrays.jl/dev/assets/ngaxsqo.B7KFIfDV.jpeg",k="/YAXArrays.jl/dev/assets/pcibivl.96k_BqPR.jpeg",C=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),l={name:"tutorials/plottingmaps.md"},p=h(`
Plotting maps ​
As test data we use the CMIP6 Scenarios.
julia
using Zarr, YAXArrays, Dates
+import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.Bwkmg46C.js";const t="/YAXArrays.jl/dev/assets/hkbjqfv.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/cgfvxvy.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/vafryxt.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`
Plotting maps ​
As test data we use the CMIP6 Scenarios.
julia
using Zarr, YAXArrays, Dates
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
@@ -9,9 +9,9 @@ import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework._h4tgM90.js";const n
 Variables:
 height
 tas
-  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
 
 Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[Ti = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon = lookup(ct1_slice, :lon)
 lat = lookup(ct1_slice, :lat)
@@ -20,19 +20,19 @@ import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework._h4tgM90.js";const n
 fig, ax, plt = heatmap(ct1_slice; colormap = :seaborn_icefire_gradient,
     axis = (; aspect=DataAspect()),
     figure = (; size = (1200,600), fontsize=24))
-fig
Wintri Projection ​
Some transformations
julia
δlon = (lon[2]-lon[1])/2
+fig
Wintri Projection ​
Some transformations
julia
δlon = (lon[2]-lon[1])/2
 nlon = lon .- 180 .+ δlon
 ndata = circshift(data, (192,1))
and add Coastlines with GeoMakie.coastlines(),
julia
fig = Figure(;size=(1200,600))
 ax = GeoAxis(fig[1,1])
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig
Moll projection ​
julia
fig = Figure(; size=(1200,600))
+fig
Moll projection ​
julia
fig = Figure(; size=(1200,600))
 ax = GeoAxis(fig[1,1]; dest = "+proj=moll")
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig
3D sphere plot ​
julia
using Bonito, WGLMakie
+fig
3D sphere plot ​
julia
using Bonito, WGLMakie
 Page(exportable=true, offline=true)
 
 WGLMakie.activate!()
@@ -47,4 +47,4 @@ import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework._h4tgM90.js";const n
     colormap = :seaborn_icefire_gradient)
 zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
 rotate!(ax.scene, 2.5)
-fig
`,25),e=[p];function E(r,d,g,y,o,F){return a(),i("div",null,e)}const u=s(l,[["render",E]]);export{C as __pageData,u as default};
+fig
`,25)]))}const c=i(p,[["render",e]]);export{F as __pageData,c as default};
diff --git a/dev/assets/tutorials_plottingmaps.md.C_xuWMeL.lean.js b/dev/assets/tutorials_plottingmaps.md.C_xuWMeL.lean.js
new file mode 100644
index 00000000..6d340652
--- /dev/null
+++ b/dev/assets/tutorials_plottingmaps.md.C_xuWMeL.lean.js
@@ -0,0 +1,50 @@
+import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.Bwkmg46C.js";const t="/YAXArrays.jl/dev/assets/hkbjqfv.xrZxBsPv.jpeg",k="/YAXArrays.jl/dev/assets/cgfvxvy.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/vafryxt.96k_BqPR.jpeg",F=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),p={name:"tutorials/plottingmaps.md"};function e(E,s,r,d,g,y){return n(),a("div",null,s[0]||(s[0]=[h(`
Plotting maps ​
As test data we use the CMIP6 Scenarios.
julia
using Zarr, YAXArrays, Dates
+using DimensionalData
+using GLMakie, GeoMakie
+using GLMakie.GeometryBasics
+
+store ="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
"gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
julia
julia> g = open_dataset(zopen(store, consolidated=true))
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+height
+tas
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[Ti = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon = lookup(ct1_slice, :lon)
+lat = lookup(ct1_slice, :lat)
+data = ct1_slice.data[:,:];
Heatmap plot ​
julia
GLMakie.activate!()
+
+fig, ax, plt = heatmap(ct1_slice; colormap = :seaborn_icefire_gradient,
+    axis = (; aspect=DataAspect()),
+    figure = (; size = (1200,600), fontsize=24))
+fig
Wintri Projection ​
Some transformations
julia
δlon = (lon[2]-lon[1])/2
+nlon = lon .- 180 .+ δlon
+ndata = circshift(data, (192,1))
and add Coastlines with GeoMakie.coastlines(),
julia
fig = Figure(;size=(1200,600))
+ax = GeoAxis(fig[1,1])
+surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
+cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
+translate!(cl, 0, 0, 1000)
+fig
Moll projection ​
julia
fig = Figure(; size=(1200,600))
+ax = GeoAxis(fig[1,1]; dest = "+proj=moll")
+surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
+cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
+translate!(cl, 0, 0, 1000)
+fig
3D sphere plot ​
julia
using Bonito, WGLMakie
+Page(exportable=true, offline=true)
+
+WGLMakie.activate!()
+Makie.inline!(true) # Make sure to inline plots into Documenter output!
+
+ds = replace(ndata, missing =>NaN)
+sphere = uv_normal_mesh(Tesselation(Sphere(Point3f(0), 1), 128))
+
+fig = Figure(backgroundcolor=:grey25, size=(500,500))
+ax = LScene(fig[1,1], show_axis=false)
+mesh!(ax, sphere; color = ds'[end:-1:1,:], shading=false,
+    colormap = :seaborn_icefire_gradient)
+zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
+rotate!(ax.scene, 2.5)
+fig
`,25)]))}const c=i(p,[["render",e]]);export{F as __pageData,c as default};
diff --git a/dev/assets/tutorials_plottingmaps.md._uyphNwM.lean.js b/dev/assets/tutorials_plottingmaps.md._uyphNwM.lean.js
deleted file mode 100644
index a619c42e..00000000
--- a/dev/assets/tutorials_plottingmaps.md._uyphNwM.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework._h4tgM90.js";const n="/YAXArrays.jl/dev/assets/gazrluv.xrZxBsPv.jpeg",t="/YAXArrays.jl/dev/assets/ngaxsqo.B7KFIfDV.jpeg",k="/YAXArrays.jl/dev/assets/pcibivl.96k_BqPR.jpeg",C=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),l={name:"tutorials/plottingmaps.md"},p=h("",25),e=[p];function E(r,d,g,y,o,F){return a(),i("div",null,e)}const u=s(l,[["render",E]]);export{C as __pageData,u as default};
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diff --git a/dev/assets/mzrclpj.CBBZcGwj.png b/dev/assets/zbtetzz.CBBZcGwj.png
similarity index 100%
rename from dev/assets/mzrclpj.CBBZcGwj.png
rename to dev/assets/zbtetzz.CBBZcGwj.png
diff --git a/dev/development/contribute.html b/dev/development/contribute.html
index a07e05df..6eeef24b 100644
--- a/dev/development/contribute.html
+++ b/dev/development/contribute.html
@@ -8,19 +8,19 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

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

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

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diff --git a/dev/development/contributors.html b/dev/development/contributors.html
index 8bfd01b0..eb1b667a 100644
--- a/dev/development/contributors.html
+++ b/dev/development/contributors.html
@@ -8,17 +8,17 @@
     
     
     
-    
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-    
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YAXArrays.jl
Appearance
Contributors
Current core contributors  
 They have taking the lead for the ongoing organizational maintenance and technical direction of ,  and . 
Fabian Gans
Fabian Gans
Geoscientific Programmer
Felix Cremer
Felix Cremer
PhD Candidate in Remote Sensing
Rafael Schouten
Rafael Schouten
Spatial/ecological modelling
Lazaro Alonso
Lazaro Alonso
Scientist. Data Visualization
Our valuable contributors
 We appreciate all contributions from the Julia community so that this ecosystem can thrive.

-    
+    
YAXArrays.jl
Appearance
Contributors
Current core contributors  
 They have taking the lead for the ongoing organizational maintenance and technical direction of ,  and . 
Fabian Gans
Fabian Gans
Geoscientific Programmer
Felix Cremer
Felix Cremer
PhD Candidate in Remote Sensing
Rafael Schouten
Rafael Schouten
Spatial/ecological modelling
Lazaro Alonso
Lazaro Alonso
Scientist. Data Visualization
Our valuable contributors
 We appreciate all contributions from the Julia community so that this ecosystem can thrive.

+    
     
   
 
\ No newline at end of file
diff --git a/dev/get_started.html b/dev/get_started.html
index 40a07e49..912172de 100644
--- a/dev/get_started.html
+++ b/dev/get_started.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 
 a = YAXArray(rand(2,3))
╭─────────────────────────╮
 │ 2×3 YAXArray{Float64,2} │
@@ -63,17 +63,17 @@
 ├─────────────────────────────────────────────────────────────────── file size ┤ 
   file size: 46.88 KB
 └──────────────────────────────────────────────────────────────────────────────┘
Get the temperature map at the first point in time:
julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
- 0.212529   0.687095  0.329389   0.322666  …  0.943401  0.0794068  0.124242
- 0.148317   0.255777  0.0451893  0.90528      0.932458  0.743448   0.50247
- 0.345306   0.817761  0.371764   0.77842      0.460725  0.346683   0.997411
- 0.11084    0.112418  0.595773   0.859691     0.85927   0.663178   0.979973
- 0.0714621  0.249736  0.759224   0.667162     0.259272  0.180714   0.550461
- 0.158962   0.709966  0.763967   0.343173  …  0.260239  0.410862   0.245069
- 0.287222   0.653128  0.0305119  0.354183     0.716081  0.965427   0.688638
- 0.821934   0.89118   0.885742   0.594211     0.768702  0.061001   0.953996
- 0.256631   0.996559  0.2551     0.763404     0.391745  0.835697   0.0833944
- 0.650262   0.579623  0.147435   0.494427     0.57022   0.344799   0.915389
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.

-    
+ 0.917506  0.818381  0.568985  0.864111  …  0.831293   0.430697    0.914342
+ 0.170553  0.416222  0.222088  0.231967     0.680628   0.535731    0.617046
+ 0.997315  0.307643  0.695412  0.233097     0.173082   0.895866    0.997734
+ 0.140123  0.558322  0.753357  0.437726     0.654983   0.154468    0.676268
+ 0.460593  0.342506  0.228113  0.197427     0.448462   0.00520612  0.292358
+ 0.417004  0.720753  0.398057  0.740896  …  0.209609   0.75727     0.209998
+ 0.65235   0.948571  0.27538   0.830771     0.0164863  0.76584     0.552837
+ 0.174313  0.984091  0.466392  0.744666     0.302653   0.26802     0.596946
+ 0.982001  0.361453  0.560824  0.70521      0.140938   0.883224    0.599861
+ 0.526898  0.691289  0.804233  0.127126     0.266098   0.644498    0.685735
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 7746a0e6..5afb1188 100644
--- a/dev/hashmap.json
+++ b/dev/hashmap.json
@@ -1 +1 @@
-{"api.md":"DU-CSbKR","development_contribute.md":"BjKHzE54","development_contributors.md":"Ce2z0IUh","get_started.md":"EFW7umcj","index.md":"D-zmVfSn","tutorials_mean_seasonal_cycle.md":"BCu3jBkO","tutorials_other_tutorials.md":"DofrLRlZ","tutorials_plottingmaps.md":"_uyphNwM","userguide_cache.md":"aYjxevW4","userguide_chunk.md":"DZgkcenL","userguide_combine.md":"CgXxcV_r","userguide_compute.md":"CLq4k1Y9","userguide_convert.md":"CSe_wnNP","userguide_create.md":"JoOy7H4V","userguide_faq.md":"CfSW0c59","userguide_group.md":"C3qv_f3u","userguide_read.md":"i_gnCx2-","userguide_select.md":"BWVJxXiv","userguide_types.md":"B2iGHzIZ","userguide_write.md":"C3OHkkA6"}
+{"api.md":"DSVzzvyi","development_contribute.md":"CPZprWRQ","development_contributors.md":"BTH2E-fs","get_started.md":"2M_EpbbP","index.md":"DU-mPNT1","tutorials_mean_seasonal_cycle.md":"DnEN9eOf","tutorials_other_tutorials.md":"DMk6XJBb","tutorials_plottingmaps.md":"C_xuWMeL","userguide_cache.md":"BxJ3IRnF","userguide_chunk.md":"DQmK3Tmu","userguide_combine.md":"Bs3BBbp0","userguide_compute.md":"BPtHMXa2","userguide_convert.md":"D6pPrrfh","userguide_create.md":"D-fbNPyl","userguide_faq.md":"BZaTxhVy","userguide_group.md":"De_4u3Mq","userguide_read.md":"BCQMfZ77","userguide_select.md":"MJRJAz_y","userguide_types.md":"DAaXu8Uc","userguide_write.md":"BtDfDOfT"}
diff --git a/dev/index.html b/dev/index.html
index 86e19d9c..386bb400 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -8,17 +8,17 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
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YAXArrays.jl
Appearance
YAXArrays.jl
Yet another xarray-like Julia package
A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.
Get Started
View on Github
API reference
VitePress
Flexible I/O capabilities
Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.
Interoperability
Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.
Named dimensions and GroupBy(in memory)
Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.
Efficiency
Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).

-    
+    
YAXArrays.jl
Appearance
YAXArrays.jl
Yet another xarray-like Julia package
A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.
Get Started
View on Github
API reference
VitePress
Flexible I/O capabilities
Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.
Interoperability
Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.
Named dimensions and GroupBy(in memory)
Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.
Efficiency
Efficient mapslices(x)  and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/mean_seasonal_cycle.html b/dev/tutorials/mean_seasonal_cycle.html
index 08255d83..0c1fe005 100644
--- a/dev/tutorials/mean_seasonal_cycle.html
+++ b/dev/tutorials/mean_seasonal_cycle.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
 CairoMakie.activate!()
 using Dates
 using Statistics
We define the data span. For simplicity, three non-leap years were selected.
julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
@@ -28,9 +28,9 @@
     )
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
-fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
+fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
 
-julia> axes = (Dim{:Time}(t),)
↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31")
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
+julia> axes = (Dim{:Time}(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
 │ 1095-element YAXArray{Float64,1} │
 ├──────────────────────────────────┴───────────────────────────────────── dims ┐
   ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
@@ -56,26 +56,26 @@
 end
 
 msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
- -0.13291526713087612
- -0.05460756353473867
-  0.07204935956348457
- -0.05326101988982565
-  0.15868041606243408
-  0.11439100033904083
-  0.13930646027762436
-  0.08757811935252037
-  0.05341039407809093
-  0.18867824088012022
+ -0.02831455834460911
+  0.04453476627315919
+  0.035686570524083555
+  0.11722933412557059
+  0.036946732134505465
+  0.19277839017642331
+  0.07495148408717336
+  0.023101933734291022
+  0.2784376308132314
+  0.17321929531017177
   ⋮
- -0.17597786204903687
- -0.0803349435034534
- -0.11344554908666375
- -0.005146734261364652
- -0.054866954001569136
- -0.07512042630397335
- -0.06048141295750401
-  0.048565067687498124
-  0.04428068807380173
TODO: Apply the new groupby funtion from DD
Plot results: mean seasonal cycle ​
julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+ -0.14858712445280844
+ -0.1058503173631838
+ -0.11471619358216052
+ -0.09915529827483704
+ -0.1388557751513135
+ -0.000924951522542868
+ -0.034309078782668714
+  0.03329805652756149
+ -0.050857935248364274
TODO: Apply the new groupby funtion from DD
Plot results: mean seasonal cycle ​
julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
     linewidth=2.0, linestyle=:dot,
     axis = (;  xlabel="Day of Year", ylabel="Variable"),
     figure=(; size = (600,400))
@@ -88,8 +88,8 @@
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
 fig
-current_figure()

-    
+current_figure()

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/other_tutorials.html b/dev/tutorials/other_tutorials.html
index 710182f0..aa0f0719 100644
--- a/dev/tutorials/other_tutorials.html
+++ b/dev/tutorials/other_tutorials.html
@@ -8,17 +8,17 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

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

-    
+    
YAXArrays.jl
Appearance

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

+    
     
   
 
\ No newline at end of file
diff --git a/dev/tutorials/plottingmaps.html b/dev/tutorials/plottingmaps.html
index 52ce97d3..34ddf172 100644
--- a/dev/tutorials/plottingmaps.html
+++ b/dev/tutorials/plottingmaps.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Plotting maps ​
As test data we use the CMIP6 Scenarios.
julia
using Zarr, YAXArrays, Dates
+    
YAXArrays.jl
Appearance

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Plotting maps ​
As test data we use the CMIP6 Scenarios.
julia
using Zarr, YAXArrays, Dates
 using DimensionalData
 using GLMakie, GeoMakie
 using GLMakie.GeometryBasics
@@ -28,9 +28,9 @@
 Variables:
 height
 tas
-  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  (↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
   → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
 
 Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[Ti = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon = lookup(ct1_slice, :lon)
 lat = lookup(ct1_slice, :lat)
@@ -39,19 +39,19 @@
 fig, ax, plt = heatmap(ct1_slice; colormap = :seaborn_icefire_gradient,
     axis = (; aspect=DataAspect()),
     figure = (; size = (1200,600), fontsize=24))
-fig
Wintri Projection ​
Some transformations
julia
δlon = (lon[2]-lon[1])/2
+fig
Wintri Projection ​
Some transformations
julia
δlon = (lon[2]-lon[1])/2
 nlon = lon .- 180 .+ δlon
 ndata = circshift(data, (192,1))
and add Coastlines with GeoMakie.coastlines(),
julia
fig = Figure(;size=(1200,600))
 ax = GeoAxis(fig[1,1])
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig
Moll projection ​
julia
fig = Figure(; size=(1200,600))
+fig
Moll projection ​
julia
fig = Figure(; size=(1200,600))
 ax = GeoAxis(fig[1,1]; dest = "+proj=moll")
 surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
 cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
 translate!(cl, 0, 0, 1000)
-fig
3D sphere plot ​
julia
using Bonito, WGLMakie
+fig
3D sphere plot ​
julia
using Bonito, WGLMakie
 Page(exportable=true, offline=true)
 
 WGLMakie.activate!()
@@ -67,7 +67,7 @@
 zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
 rotate!(ax.scene, 2.5)
 fig

-    
+    
     
   
 
\ No newline at end of file

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