diff --git a/dev/404.html b/dev/404.html index af063c96..18c7d0e1 100644 --- a/dev/404.html +++ b/dev/404.html @@ -6,10 +6,10 @@ 404 | YAXArrays.jl - + - + @@ -19,7 +19,7 @@
- + \ No newline at end of file diff --git a/dev/UserGuide/cache.html b/dev/UserGuide/cache.html index bd411286..65e5858f 100644 --- a/dev/UserGuide/cache.html +++ b/dev/UserGuide/cache.html @@ -6,12 +6,12 @@ Caching YAXArrays | YAXArrays.jl - + - + - + @@ -21,12 +21,12 @@ -
YAXArrays.jl

Appearance

0.1k

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
0.1k

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)

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/chunk.html b/dev/UserGuide/chunk.html
index 94f9d7b1..f1bf4c82 100644
--- a/dev/UserGuide/chunk.html
+++ b/dev/UserGuide/chunk.html
@@ -6,12 +6,12 @@
     Chunk YAXArrays | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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}}:
@@ -119,7 +119,7 @@
 
 Variables: 
 x, y, z
Suggestions on how to improve or add to these examples is welcome.

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\ No newline at end of file
diff --git a/dev/UserGuide/combine.html b/dev/UserGuide/combine.html
index a32521c3..23de40a2 100644
--- a/dev/UserGuide/combine.html
+++ b/dev/UserGuide/combine.html
@@ -6,12 +6,12 @@
     Combine YAXArrays | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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
 using YAXArrays: YAXArrays as YAX
 
 first_half = YAXArray((YAX.time(1:6),), rand(6))
@@ -49,7 +49,7 @@
 ├────────────────────────────────────────────────── loaded lazily ┤
   data size: 96.0 bytes
 └─────────────────────────────────────────────────────────────────┘

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/compute.html b/dev/UserGuide/compute.html
index 572eaf04..f09e11e8 100644
--- a/dev/UserGuide/compute.html
+++ b/dev/UserGuide/compute.html
@@ -6,14 +6,14 @@
     Compute YAXArrays | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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 YAXArrays: YAXArrays as YAX
 using Dates
 
@@ -42,7 +42,7 @@
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.44064638619459007
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} ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.2567290116753578
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,
@@ -413,7 +413,7 @@
 mapCube(mymean, a, indims=InDims("time"), outdims=OutDims())
In the last example, mapCube was used to map the mymean function. mapslices is a convenient function that can replace mapCube, where you can omit defining an extra function with the output argument as an input (e.g. mymean). It is possible to simply use mapslice
julia
mapslices(mean ∘ skipmissing, a, dims="time")
It is also possible to distribute easily the workload on a cluster, with little modification to the code. To do so, we use the ClusterManagers package.
julia
using Distributed
 using ClusterManagers
 addprocs(SlurmManager(10))

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/convert.html b/dev/UserGuide/convert.html
index bb27f318..cd34efa6 100644
--- a/dev/UserGuide/convert.html
+++ b/dev/UserGuide/convert.html
@@ -6,12 +6,12 @@
     Convert YAXArrays | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Convert YAXArrays ​
This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.
WARNING
YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.
Convert Base.Array ​
Convert Base.Array to YAXArray:
julia
using YAXArrays
 
 m = rand(5,10)
 a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
@@ -67,7 +67,7 @@
  3     0.492817   0.718667    0.0702532   0.926096   0.225542   0.100622
  4     0.268675   0.0566881   0.916686    0.973332   0.744521   0.052264
  5     0.540514   0.215973    0.617023    0.796375   0.13205    0.366625
INFO
At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.

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+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/create.html b/dev/UserGuide/create.html
index bbaab3be..f4cecccd 100644
--- a/dev/UserGuide/create.html
+++ b/dev/UserGuide/create.html
@@ -6,12 +6,12 @@
     Create YAXArrays and Datasets | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Create YAXArrays and Datasets ​
This section describes how to create arrays and datasets by filling values directly.
Create a YAXArray ​
We can create a new YAXArray by filling the values directly:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
 a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
@@ -69,7 +69,7 @@
 a2, a3
 
 Properties: Dict(:origin => "user guide")

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/faq.html b/dev/UserGuide/faq.html
index fad470cd..8f78273f 100644
--- a/dev/UserGuide/faq.html
+++ b/dev/UserGuide/faq.html
@@ -6,14 +6,14 @@
     Frequently Asked Questions (FAQ) | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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 ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -210,7 +210,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -221,7 +221,7 @@
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -236,15 +236,15 @@
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  latitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  latitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+  temperature
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,
@@ -287,7 +287,7 @@
 fig, ax, obj = heatmap(classes;
     colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
 cbar = Colorbar(fig[1,2], obj)
-fig
Now we define the input cubes that will be considered for the iterable table
julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+fig
Now we define the input cubes that will be considered for the iterable table
julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
 using OnlineStats
 ## visualization of the CubeTable
 c_tbl = DataFrame(t[1])
@@ -348,19 +348,19 @@
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock2
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1
+  Stock3
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock2
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
@@ -385,7 +385,7 @@
 ├─────────────────────────────────────────────────── loaded in memory ┤
   summarysize: 172.0 bytes
 └─────────────────────────────────────────────────────────────────────┘
WARNING
Note that although their creation is allowed, it is not possible to save these types into Zarr or NetCDF.

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/group.html b/dev/UserGuide/group.html
index 93ec23f2..d68aab31 100644
--- a/dev/UserGuide/group.html
+++ b/dev/UserGuide/group.html
@@ -6,14 +6,14 @@
     Group YAXArrays and Datasets | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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 YAXArrays: YAXArrays as YAX
 using NetCDF
 using Downloads
@@ -80,7 +80,7 @@
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end
In what follows, note how we are referencing the _time_ dimension via _YAX.time_. This approach is used to avoid name clashes with _time_ (_Time_) from **Base** (**Dates**). For convenience, we have defined the **Dimensions** _time_ and _Time_ in **YAXArrays.jl**, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
+end
INFO
In what follows, note how we are referencing the time dimension via YAX.time. This approach is used to avoid name clashes with time (Time) from Base (Dates). For convenience, we have defined the Dimensions time and Time in YAXArrays.jl, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
@@ -228,8 +228,8 @@
     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 dc8804d5..d5d1a4ec 100644
--- a/dev/UserGuide/read.html
+++ b/dev/UserGuide/read.html
@@ -6,14 +6,14 @@
     Read YAXArrays and Datasets | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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.
open_dataset ​
The usual method for reading any format is using this function. See its docstring for more information.
YAXArrays.Datasets.open_dataset Function
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
Now, let's explore different examples.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
+    
YAXArrays.jl
Appearance
0.1k

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.
open_dataset ​
The usual method for reading any format is using this function. See its docstring for more information.
YAXArrays.Datasets.open_dataset Function
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
Now, let's explore different examples.
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)
@@ -211,7 +211,7 @@
  2020-01-05T00:00:00
  2020-01-06T00:00:00
  2020-01-07T00:00:00
providing us with a wide range of options to work with.

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/select.html b/dev/UserGuide/select.html
index e1ba8177..a50f585e 100644
--- a/dev/UserGuide/select.html
+++ b/dev/UserGuide/select.html
@@ -6,12 +6,12 @@
     Select YAXArrays and Datasets | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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
 
@@ -297,7 +297,7 @@
   89.5   89.5
These values are defined as lookups in the package DimensionalData:
julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0
which is equivalent to:
julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
 wrapping: 1.0:2.0:359.0

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/types.html b/dev/UserGuide/types.html
index 3329e0db..a04e1d25 100644
--- a/dev/UserGuide/types.html
+++ b/dev/UserGuide/types.html
@@ -6,12 +6,12 @@
     Types | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,9 +21,9 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
Dimensions ​
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.
Lon, Lat, time ​
For convenience, several Dimensions have been defined in YAXArrays.jl, but only a few have been exported. The remaining dimensions can be used by calling them explicitly. See the next table for an overview
Dimensionexportedusage: using YAXArrays: YAXArrays as YAX
lon✔lon or YAX.lon
Lon✔Lon or YAX.Lon
longitude✔longitude or YAX.longitude
Longitude✔Longitude or YAX.Longitude
lat✔lat or YAX.lat
Lat✔Lat or YAX.Lat
latitude✔latitude or YAX.latitude
Latitude✔Latitude or YAX.Latitude
time✘YAX.time
Time✘YAX.Time
rlat✘YAX.rlat
rlon✘YAX.rlon
lat_c✘YAX.lat_c
lon_c✘YAX.lon_c
height✘YAX.height
depth✘YAX.depth
Variables✔Variables or YAX.Variables
INFO
If the dimension you are looking for is not in that table, you can define your own by doing
julia
using DimensionalData: @dim, XDim # If you want it to be a subtype of XDim
+    
YAXArrays.jl
Appearance
0.1k

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
Dimensions ​
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.
Lon, Lat, time ​
For convenience, several Dimensions have been defined in YAXArrays.jl, but only a few have been exported. The remaining dimensions can be used by calling them explicitly. See the next table for an overview
Dimensionexportedusage: using YAXArrays: YAXArrays as YAX
lon✔lon or YAX.lon
Lon✔Lon or YAX.Lon
longitude✔longitude or YAX.longitude
Longitude✔Longitude or YAX.Longitude
lat✔lat or YAX.lat
Lat✔Lat or YAX.Lat
latitude✔latitude or YAX.latitude
Latitude✔Latitude or YAX.Latitude
time✘YAX.time
Time✘YAX.Time
rlat✘YAX.rlat
rlon✘YAX.rlon
lat_c✘YAX.lat_c
lon_c✘YAX.lon_c
height✘YAX.height
depth✘YAX.depth
Variables✔Variables or YAX.Variables
INFO
If the dimension you are looking for is not in that table, you can define your own by doing
julia
using DimensionalData: @dim, XDim # If you want it to be a subtype of XDim
 @dim newDim XDim "Your newDim label"
Sometimes, when you want to operate on a specific dimension in your dataset (for example, a dimension named date), then doing
julia
groupby(ds, Dim{:date} => seasons())
should do the job.

-    
+    
     
   
 
\ No newline at end of file
diff --git a/dev/UserGuide/write.html b/dev/UserGuide/write.html
index 642fa767..b4a53e64 100644
--- a/dev/UserGuide/write.html
+++ b/dev/UserGuide/write.html
@@ -6,14 +6,14 @@
     Write YAXArrays and Datasets | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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
 
@@ -43,7 +43,7 @@
 savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
netcdf compression ​
Save a dataset to NetCDF format with compression:
julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)
Comparing it to the default saved file
julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
YAXArrays.Datasets.savedataset Function
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
YAXArrays.Datasets.savedataset Function
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
@@ -92,7 +92,7 @@
  0.716952  0.95965   0.599987   0.337016
  0.506947  0.923876  0.0940127  0.54942
  0.847062  0.298617  0.182371   0.921
indeed, those entries had been updated.

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@@ -6,14 +6,14 @@
     API Reference | YAXArrays.jl
     
     
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YAXArrays.jl
Appearance
0.1k

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" or max_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
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
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
YAXArrays.Datasets.Cube Method
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
YAXArrays.Datasets.open_dataset Method
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
YAXArrays.Datasets.open_mfdataset Method
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+    
YAXArrays.jl
Appearance
0.1k

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" or max_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
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
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
YAXArrays.Datasets.Cube Method
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
YAXArrays.Datasets.open_dataset Method
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
YAXArrays.Datasets.open_mfdataset Method
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
 open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
YAXArrays.Datasets.savecube Method
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
YAXArrays.Datasets.savedataset Method
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
YAXArrays.Datasets.to_dataset Method
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
Internal API ​
YAXArrays.YAXDefaults Constant
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
YAXArrays.findAxis Method
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
YAXArrays.getOutAxis Method
julia
getOutAxis
source
YAXArrays.get_descriptor Method
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
YAXArrays.match_axis Method
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
YAXArrays.Datasets.savecube Method
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
YAXArrays.Datasets.savedataset Method
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
YAXArrays.Datasets.to_dataset Method
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
Internal API ​
YAXArrays.YAXDefaults Constant
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
YAXArrays.findAxis Method
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
YAXArrays.getOutAxis Method
julia
getOutAxis
source
YAXArrays.get_descriptor Method
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
YAXArrays.match_axis Method
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
YAXArrays.Cubes.CleanMe Type
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
YAXArrays.Cubes.clean Method
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
YAXArrays.Cubes.copydata Method
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
YAXArrays.Cubes.optifunc Method
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
YAXArrays.DAT.DATConfig Type
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
YAXArrays.DAT.InputCube Type
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
YAXArrays.DAT.OutputCube Type
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
YAXArrays.DAT.YAXColumn Type
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
YAXArrays.DAT.cmpcachmisses Method
Function that compares two cache miss specifiers by their importance
source
YAXArrays.DAT.getFrontPerm Method
Calculate an axis permutation that brings the wanted dimensions to the front
source
YAXArrays.DAT.getLoopCacheSize Method
Calculate optimal Cache size to DAT operation
source
YAXArrays.DAT.getOuttype Method
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
YAXArrays.DAT.getloopchunks Method
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
-This computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
YAXArrays.Cubes.setchunks Method
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
YAXArrays.Datasets.collectfromhandle Method
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source
YAXArrays.Datasets.createdataset Method
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
YAXArrays.Datasets.getarrayinfo Method
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source
YAXArrays.Datasets.testrange Method
Test if data in x can be approximated by a step range
source

-    
+For example to disregard differences of captialisation.
source
YAXArrays.Cubes.CleanMe Type
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
YAXArrays.Cubes.clean Method
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
YAXArrays.Cubes.copydata Method
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
YAXArrays.Cubes.optifunc Method
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
YAXArrays.DAT.DATConfig Type
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
YAXArrays.DAT.InputCube Type
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
YAXArrays.DAT.OutputCube Type
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
YAXArrays.DAT.YAXColumn Type
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
YAXArrays.DAT.cmpcachmisses Method
Function that compares two cache miss specifiers by their importance
source
YAXArrays.DAT.getFrontPerm Method
Calculate an axis permutation that brings the wanted dimensions to the front
source
YAXArrays.DAT.getLoopCacheSize Method
Calculate optimal Cache size to DAT operation
source
YAXArrays.DAT.getOuttype Method
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
YAXArrays.DAT.getloopchunks Method
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.
+This computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
YAXArrays.DAT.permuteloopaxes Method
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
YAXArrays.Cubes.setchunks Method
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
YAXArrays.Datasets.collectfromhandle Method
Extracts a YAXArray from a dataset handle that was just created from a arrayinfo
source
YAXArrays.Datasets.createdataset Method
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
YAXArrays.Datasets.getarrayinfo Method
Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair
source
YAXArrays.Datasets.testrange Method
Test if data in x can be approximated by a step range
source

+    
     
   
 
\ No newline at end of file
diff --git a/dev/assets/UserGuide_compute.md.40iuroaW.js b/dev/assets/UserGuide_compute.md.DZWxbglL.js
similarity index 97%
rename from dev/assets/UserGuide_compute.md.40iuroaW.js
rename to dev/assets/UserGuide_compute.md.DZWxbglL.js
index b7696bfa..94142896 100644
--- a/dev/assets/UserGuide_compute.md.40iuroaW.js
+++ b/dev/assets/UserGuide_compute.md.DZWxbglL.js
@@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.swGcc4Ky.js";const g
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.44064638619459007
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} ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.2567290116753578
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,
diff --git a/dev/assets/UserGuide_compute.md.40iuroaW.lean.js b/dev/assets/UserGuide_compute.md.DZWxbglL.lean.js
similarity index 97%
rename from dev/assets/UserGuide_compute.md.40iuroaW.lean.js
rename to dev/assets/UserGuide_compute.md.DZWxbglL.lean.js
index b7696bfa..94142896 100644
--- a/dev/assets/UserGuide_compute.md.40iuroaW.lean.js
+++ b/dev/assets/UserGuide_compute.md.DZWxbglL.lean.js
@@ -19,7 +19,7 @@ import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.swGcc4Ky.js";const g
   :origin => "user guide"
 ├──────────────────────────────────────────────────────────── loaded in memory ┤
   data size: 35.16 KB
-└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.44064638619459007
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} ┐
+└──────────────────────────────────────────────────────────────────────────────┘
Modify elements of a YAXArray ​
julia
a[1,2,3]
0.2567290116753578
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,
diff --git a/dev/assets/UserGuide_faq.md.1WiBDJb9.js b/dev/assets/UserGuide_faq.md.C332mxDc.js
similarity index 98%
rename from dev/assets/UserGuide_faq.md.1WiBDJb9.js
rename to dev/assets/UserGuide_faq.md.C332mxDc.js
index a4829235..72e0fd33 100644
--- a/dev/assets/UserGuide_faq.md.1WiBDJb9.js
+++ b/dev/assets/UserGuide_faq.md.C332mxDc.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/zvezrog.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/lnenoem.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
 using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -187,7 +187,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -198,7 +198,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -213,15 +213,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  latitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  latitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+  temperature
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,
@@ -325,19 +325,19 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock2
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1
+  Stock3
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock2
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
diff --git a/dev/assets/UserGuide_faq.md.1WiBDJb9.lean.js b/dev/assets/UserGuide_faq.md.C332mxDc.lean.js
similarity index 98%
rename from dev/assets/UserGuide_faq.md.1WiBDJb9.lean.js
rename to dev/assets/UserGuide_faq.md.C332mxDc.lean.js
index a4829235..72e0fd33 100644
--- a/dev/assets/UserGuide_faq.md.1WiBDJb9.lean.js
+++ b/dev/assets/UserGuide_faq.md.C332mxDc.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/zvezrog.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/lnenoem.DldUI1n7.jpeg",o=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),l={name:"UserGuide/faq.md"};function h(p,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Frequently Asked Questions (FAQ) ​
The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.
Extract the axes names from a Cube ​
julia
using YAXArrays
 using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
 ├──────────────────────────────┴───────────────────────────────────────── dims ┐
   ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
@@ -187,7 +187,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  longitudes
+  latitudes
 
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
@@ -198,7 +198,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   Additional Axes: 
   (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  latitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+  longitudes
If your dataset has been read from a file with Cube it is not loaded into memory, and you have to load the latitudes and longitudes YAXArrays into memory:
julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
 longitudes_yasxa = readcubedata(ds["longitudes"])
 ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
                              longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
@@ -213,15 +213,15 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   longitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
-  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
   Variables: 
-  temperature
+  latitudes
 
   Additional Axes: 
-  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points,
+  → Time   Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
   Variables: 
-  latitudes
How do I apply map algebra? ​
Our next step is map algebra computations. This can be done effectively using the 'map' function. For example:
Multiplying cubes with only spatio-temporal dimensions
julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+  temperature
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,
@@ -325,19 +325,19 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock2
+  Stock1
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock1
+  Stock3
 
   Additional Axes: 
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
   Variables: 
-  Stock3
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+  Stock2
and, it looks like there some small differences in the axes, they are being printed independently although they should be the same. Well, they are at least at the == level but not at ===. We could use the axes from one YAXArray as reference and rebuild all the others
julia
yax_list = [rebuild(yax_list[1], values(stocks[k])) for k in d_keys];
and voilà
julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
 Shared Axes:
   (↓ time      Sampled{DateTime} [2020-01-01T00:00:00, …, 2020-01-21T19:00:00] ForwardOrdered Irregular Points,
   → Variables Categorical{Symbol} [:Open, :High, :Low, :Close, :Volume] Unordered)
diff --git a/dev/assets/UserGuide_group.md.BuMj7-Wl.js b/dev/assets/UserGuide_group.md.BS0wle11.js
similarity index 97%
rename from dev/assets/UserGuide_group.md.BuMj7-Wl.js
rename to dev/assets/UserGuide_group.md.BS0wle11.js
index a3b507ae..d17eac6f 100644
--- a/dev/assets/UserGuide_group.md.BuMj7-Wl.js
+++ b/dev/assets/UserGuide_group.md.BS0wle11.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/oowdcxc.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/mmzaksu.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using YAXArrays: YAXArrays as YAX
 using NetCDF
 using Downloads
@@ -57,7 +57,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end
In what follows, note how we are referencing the _time_ dimension via _YAX.time_. This approach is used to avoid name clashes with _time_ (_Time_) from **Base** (**Dates**). For convenience, we have defined the **Dimensions** _time_ and _Time_ in **YAXArrays.jl**, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
+end
INFO
In what follows, note how we are referencing the time dimension via YAX.time. This approach is used to avoid name clashes with time (Time) from Base (Dates). For convenience, we have defined the Dimensions time and Time in YAXArrays.jl, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
diff --git a/dev/assets/UserGuide_group.md.BuMj7-Wl.lean.js b/dev/assets/UserGuide_group.md.BS0wle11.lean.js
similarity index 97%
rename from dev/assets/UserGuide_group.md.BuMj7-Wl.lean.js
rename to dev/assets/UserGuide_group.md.BS0wle11.lean.js
index a3b507ae..d17eac6f 100644
--- a/dev/assets/UserGuide_group.md.BuMj7-Wl.lean.js
+++ b/dev/assets/UserGuide_group.md.BS0wle11.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/oowdcxc.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
+import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/mmzaksu.BJNzQY3Z.png",y=JSON.parse('{"title":"Group YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/group.md","filePath":"UserGuide/group.md","lastUpdated":null}'),l={name:"UserGuide/group.md"};function p(h,s,k,d,r,g){return e(),a("div",null,s[0]||(s[0]=[n(`
Group YAXArrays and Datasets ​
The following examples will use the groupby function to calculate temporal and spatial averages.
julia
using YAXArrays, DimensionalData
 using YAXArrays: YAXArrays as YAX
 using NetCDF
 using Downloads
@@ -57,7 +57,7 @@ import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.swGcc4Ky.js";const t
     diff_g = map(.-, weighted_g, mean_g)
     seasons_g = lookup(mean_g, :time)
     return mean_g, weighted_g, diff_g, seasons_g
-end
In what follows, note how we are referencing the _time_ dimension via _YAX.time_. This approach is used to avoid name clashes with _time_ (_Time_) from **Base** (**Dates**). For convenience, we have defined the **Dimensions** _time_ and _Time_ in **YAXArrays.jl**, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
+end
INFO
In what follows, note how we are referencing the time dimension via YAX.time. This approach is used to avoid name clashes with time (Time) from Base (Dates). For convenience, we have defined the Dimensions time and Time in YAXArrays.jl, which are only accessible when explicitly called.
Now, we continue with the groupby operations as usual
julia
julia> g_ds = groupby(ds, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Float64,2},1} ┐
 ├──────────────────────────────────────────────────┴───────────────────── dims ┐
   ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
 ├──────────────────────────────────────────────────────────────────── metadata ┤
diff --git a/dev/assets/UserGuide_read.md.Cw-3n0BR.js b/dev/assets/UserGuide_read.md.Dpjhykk1.js
similarity index 99%
rename from dev/assets/UserGuide_read.md.Cw-3n0BR.js
rename to dev/assets/UserGuide_read.md.Dpjhykk1.js
index b2330e2f..03af5cf3 100644
--- a/dev/assets/UserGuide_read.md.Cw-3n0BR.js
+++ b/dev/assets/UserGuide_read.md.Dpjhykk1.js
@@ -1,4 +1,4 @@
-import{_ as d,c as r,j as s,a,G as n,a2 as l,w as e,B as p,o}from"./chunks/framework.swGcc4Ky.js";const b=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),g={name:"UserGuide/read.md"},E={class:"jldocstring custom-block",open:""};function c(u,i,y,F,m,C){const h=p("Badge"),t=p("PluginTabsTab"),k=p("PluginTabs");return o(),r("div",null,[i[6]||(i[6]=s("h1",{id:"Read-YAXArrays-and-Datasets",tabindex:"-1"},[a("Read YAXArrays and Datasets "),s("a",{class:"header-anchor",href:"#Read-YAXArrays-and-Datasets","aria-label":'Permalink to "Read YAXArrays and Datasets {#Read-YAXArrays-and-Datasets}"'},"​")],-1)),i[7]||(i[7]=s("p",null,"This section describes how to read files, URLs, and directories into YAXArrays and datasets.",-1)),i[8]||(i[8]=s("h2",{id:"open-dataset",tabindex:"-1"},[a("open_dataset "),s("a",{class:"header-anchor",href:"#open-dataset","aria-label":'Permalink to "open_dataset"'},"​")],-1)),i[9]||(i[9]=s("p",null,[a("The usual method for reading any format is using this function. See its "),s("code",null,"docstring"),a(" for more information.")],-1)),s("details",E,[s("summary",null,[i[0]||(i[0]=s("a",{id:"YAXArrays.Datasets.open_dataset",href:"#YAXArrays.Datasets.open_dataset"},[s("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),i[1]||(i[1]=a()),n(h,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),i[2]||(i[2]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),i[10]||(i[10]=l(`
Now, let's explore different examples.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
+import{_ as d,c as r,j as s,a,G as n,a2 as l,w as e,B as p,o}from"./chunks/framework.swGcc4Ky.js";const b=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),g={name:"UserGuide/read.md"},E={class:"jldocstring custom-block",open:""};function c(u,i,y,F,m,C){const h=p("Badge"),t=p("PluginTabsTab"),k=p("PluginTabs");return o(),r("div",null,[i[6]||(i[6]=s("h1",{id:"Read-YAXArrays-and-Datasets",tabindex:"-1"},[a("Read YAXArrays and Datasets "),s("a",{class:"header-anchor",href:"#Read-YAXArrays-and-Datasets","aria-label":'Permalink to "Read YAXArrays and Datasets {#Read-YAXArrays-and-Datasets}"'},"​")],-1)),i[7]||(i[7]=s("p",null,"This section describes how to read files, URLs, and directories into YAXArrays and datasets.",-1)),i[8]||(i[8]=s("h2",{id:"open-dataset",tabindex:"-1"},[a("open_dataset "),s("a",{class:"header-anchor",href:"#open-dataset","aria-label":'Permalink to "open_dataset"'},"​")],-1)),i[9]||(i[9]=s("p",null,[a("The usual method for reading any format is using this function. See its "),s("code",null,"docstring"),a(" for more information.")],-1)),s("details",E,[s("summary",null,[i[0]||(i[0]=s("a",{id:"YAXArrays.Datasets.open_dataset",href:"#YAXArrays.Datasets.open_dataset"},[s("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),i[1]||(i[1]=a()),n(h,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),i[2]||(i[2]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),i[10]||(i[10]=l(`
Now, let's explore different examples.
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)
diff --git a/dev/assets/UserGuide_read.md.Cw-3n0BR.lean.js b/dev/assets/UserGuide_read.md.Dpjhykk1.lean.js
similarity index 99%
rename from dev/assets/UserGuide_read.md.Cw-3n0BR.lean.js
rename to dev/assets/UserGuide_read.md.Dpjhykk1.lean.js
index b2330e2f..03af5cf3 100644
--- a/dev/assets/UserGuide_read.md.Cw-3n0BR.lean.js
+++ b/dev/assets/UserGuide_read.md.Dpjhykk1.lean.js
@@ -1,4 +1,4 @@
-import{_ as d,c as r,j as s,a,G as n,a2 as l,w as e,B as p,o}from"./chunks/framework.swGcc4Ky.js";const b=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),g={name:"UserGuide/read.md"},E={class:"jldocstring custom-block",open:""};function c(u,i,y,F,m,C){const h=p("Badge"),t=p("PluginTabsTab"),k=p("PluginTabs");return o(),r("div",null,[i[6]||(i[6]=s("h1",{id:"Read-YAXArrays-and-Datasets",tabindex:"-1"},[a("Read YAXArrays and Datasets "),s("a",{class:"header-anchor",href:"#Read-YAXArrays-and-Datasets","aria-label":'Permalink to "Read YAXArrays and Datasets {#Read-YAXArrays-and-Datasets}"'},"​")],-1)),i[7]||(i[7]=s("p",null,"This section describes how to read files, URLs, and directories into YAXArrays and datasets.",-1)),i[8]||(i[8]=s("h2",{id:"open-dataset",tabindex:"-1"},[a("open_dataset "),s("a",{class:"header-anchor",href:"#open-dataset","aria-label":'Permalink to "open_dataset"'},"​")],-1)),i[9]||(i[9]=s("p",null,[a("The usual method for reading any format is using this function. See its "),s("code",null,"docstring"),a(" for more information.")],-1)),s("details",E,[s("summary",null,[i[0]||(i[0]=s("a",{id:"YAXArrays.Datasets.open_dataset",href:"#YAXArrays.Datasets.open_dataset"},[s("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),i[1]||(i[1]=a()),n(h,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),i[2]||(i[2]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),i[10]||(i[10]=l(`
Now, let's explore different examples.
Read Zarr ​
Open a Zarr store as a Dataset:
julia
using YAXArrays
+import{_ as d,c as r,j as s,a,G as n,a2 as l,w as e,B as p,o}from"./chunks/framework.swGcc4Ky.js";const b=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),g={name:"UserGuide/read.md"},E={class:"jldocstring custom-block",open:""};function c(u,i,y,F,m,C){const h=p("Badge"),t=p("PluginTabsTab"),k=p("PluginTabs");return o(),r("div",null,[i[6]||(i[6]=s("h1",{id:"Read-YAXArrays-and-Datasets",tabindex:"-1"},[a("Read YAXArrays and Datasets "),s("a",{class:"header-anchor",href:"#Read-YAXArrays-and-Datasets","aria-label":'Permalink to "Read YAXArrays and Datasets {#Read-YAXArrays-and-Datasets}"'},"​")],-1)),i[7]||(i[7]=s("p",null,"This section describes how to read files, URLs, and directories into YAXArrays and datasets.",-1)),i[8]||(i[8]=s("h2",{id:"open-dataset",tabindex:"-1"},[a("open_dataset "),s("a",{class:"header-anchor",href:"#open-dataset","aria-label":'Permalink to "open_dataset"'},"​")],-1)),i[9]||(i[9]=s("p",null,[a("The usual method for reading any format is using this function. See its "),s("code",null,"docstring"),a(" for more information.")],-1)),s("details",E,[s("summary",null,[i[0]||(i[0]=s("a",{id:"YAXArrays.Datasets.open_dataset",href:"#YAXArrays.Datasets.open_dataset"},[s("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),i[1]||(i[1]=a()),n(h,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),i[2]||(i[2]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),i[10]||(i[10]=l(`
Now, let's explore different examples.
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)
diff --git a/dev/assets/UserGuide_write.md.0oH3mU4C.js b/dev/assets/UserGuide_write.md.DWXQN9K3.js
similarity index 99%
rename from dev/assets/UserGuide_write.md.0oH3mU4C.js
rename to dev/assets/UserGuide_write.md.DWXQN9K3.js
index 459bd479..8ede74a4 100644
--- a/dev/assets/UserGuide_write.md.0oH3mU4C.js
+++ b/dev/assets/UserGuide_write.md.DWXQN9K3.js
@@ -20,7 +20,7 @@ import{_ as e,c as n,a2 as a,j as i,a as l,G as p,B as h,o as k}from"./chunks/fr
 savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
netcdf compression ​
Save a dataset to NetCDF format with compression:
julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)
Comparing it to the default saved file
julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
diff --git a/dev/assets/UserGuide_write.md.0oH3mU4C.lean.js b/dev/assets/UserGuide_write.md.DWXQN9K3.lean.js
similarity index 99%
rename from dev/assets/UserGuide_write.md.0oH3mU4C.lean.js
rename to dev/assets/UserGuide_write.md.DWXQN9K3.lean.js
index 459bd479..8ede74a4 100644
--- a/dev/assets/UserGuide_write.md.0oH3mU4C.lean.js
+++ b/dev/assets/UserGuide_write.md.DWXQN9K3.lean.js
@@ -20,7 +20,7 @@ import{_ as e,c as n,a2 as a,j as i,a as l,G as p,B as h,o as k}from"./chunks/fr
 savecube(ds.tos, "tos.nc", driver=:netcdf)
Save an entire Dataset to a directory:
julia
savedataset(ds, path="ds.nc", driver=:netcdf)
netcdf compression ​
Save a dataset to NetCDF format with compression:
julia
n = 7 # compression level, number between 0 (no compression) and 9 (max compression)
 savedataset(ds, path="ds_c.nc", driver=:netcdf, compress=n)
Comparing it to the default saved file
julia
ds_info = stat("ds.nc")
 ds_c_info = stat("ds_c.nc")
-println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+println("File size: ", "default: ", ds_info.size, " bytes", ", compress: ", ds_c_info.size, " bytes")
File size: default: 2963860 bytes, compress: 1159916 bytes
Overwrite a Dataset ​
If a path already exists, an error will be thrown. Set overwrite=true to delete the existing dataset
julia
savedataset(ds, path="ds.zarr", driver=:zarr, overwrite=true)
DANGER
Again, setting overwrite will delete all your previous saved data.
Look at the doc string for more information
`,29)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"YAXArrays.Datasets.savedataset",href:"#YAXArrays.Datasets.savedataset"},[i("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[1]||(s[1]=l()),p(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[2]||(s[2]=a('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),s[4]||(s[4]=a(`
Append to a Dataset ​
New variables can be added to an existing dataset using the append=true keyword.
julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
 savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
 Shared Axes:
 None
diff --git a/dev/assets/api.md.D0CdfjsI.js b/dev/assets/api.md.BRif97cy.js
similarity index 92%
rename from dev/assets/api.md.D0CdfjsI.js
rename to dev/assets/api.md.BRif97cy.js
index 3a0da1fb..1669e1f8 100644
--- a/dev/assets/api.md.D0CdfjsI.js
+++ b/dev/assets/api.md.BRif97cy.js
@@ -1,5 +1,5 @@
-import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.swGcc4Ky.js";const us=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[157]||(s[157]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[158]||(s[158]=e("p",null,"This section describes all available functions of this package.",-1)),s[159]||(s[159]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",u,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",T,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",F,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" or max_cache=1GB defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",X,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=e("p",null,[a("Dataset object which stores an "),e("code",null,"OrderedDict"),a(" 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.")],-1)),s[57]||(s[57]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L18-L22",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Y,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[59]||(s[59]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[60]||(s[60]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[61]||(s[61]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[62]||(s[62]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[63]||(s[63]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[64]||(s[64]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[65]||(s[65]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[66]||(s[66]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),e("details",L,[e("summary",null,[s[67]||(s[67]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[68]||(s[68]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[69]||(s[69]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.swGcc4Ky.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[157]||(s[157]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[158]||(s[158]=e("p",null,"This section describes all available functions of this package.",-1)),s[159]||(s[159]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",u,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",T,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",F,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" or max_cache=1GB defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",X,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=e("p",null,[a("Dataset object which stores an "),e("code",null,"OrderedDict"),a(" 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.")],-1)),s[57]||(s[57]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L18-L22",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Y,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[59]||(s[59]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[60]||(s[60]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[61]||(s[61]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[62]||(s[62]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[63]||(s[63]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[64]||(s[64]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[65]||(s[65]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[66]||(s[66]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),e("details",L,[e("summary",null,[s[67]||(s[67]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[68]||(s[68]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[69]||(s[69]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
 open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
',3))]),s[160]||(s[160]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[81]||(s[81]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",q,[e("summary",null,[s[85]||(s[85]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[86]||(s[86]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=l('
julia
getOutAxis
source
',2))]),e("details",P,[e("summary",null,[s[88]||(s[88]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[89]||(s[89]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[90]||(s[90]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[91]||(s[91]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[92]||(s[92]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[93]||(s[93]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
',3))]),s[160]||(s[160]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[81]||(s[81]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",q,[e("summary",null,[s[85]||(s[85]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[86]||(s[86]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=l('
julia
getOutAxis
source
',2))]),e("details",P,[e("summary",null,[s[88]||(s[88]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[89]||(s[89]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[90]||(s[90]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[91]||(s[91]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[92]||(s[92]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[93]||(s[93]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[94]||(s[94]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[95]||(s[95]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[96]||(s[96]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[97]||(s[97]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[98]||(s[98]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[99]||(s[99]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",V,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[101]||(s[101]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[102]||(s[102]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",R,[e("summary",null,[s[103]||(s[103]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[104]||(s[104]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[105]||(s[105]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[106]||(s[106]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[107]||(s[107]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[108]||(s[108]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[109]||(s[109]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[110]||(s[110]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[111]||(s[111]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[113]||(s[113]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[114]||(s[114]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",K,[e("summary",null,[s[115]||(s[115]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[116]||(s[116]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[117]||(s[117]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",$,[e("summary",null,[s[118]||(s[118]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[119]||(s[119]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[120]||(s[120]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[121]||(s[121]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[122]||(s[122]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[123]||(s[123]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[124]||(s[124]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[125]||(s[125]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[133]||(s[133]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[134]||(s[134]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[135]||(s[135]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[136]||(s[136]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[137]||(s[137]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[138]||(s[138]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[139]||(s[139]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[140]||(s[140]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[143]||(s[143]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[144]||(s[144]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[145]||(s[145]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L561-L563",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[146]||(s[146]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[147]||(s[147]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[148]||(s[148]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[149]||(s[149]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[150]||(s[150]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[152]||(s[152]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L530-L532",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[153]||(s[153]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[154]||(s[154]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[155]||(s[155]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[156]||(s[156]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{us as __pageData,bs as default};
+For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[94]||(s[94]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[95]||(s[95]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[96]||(s[96]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[97]||(s[97]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[98]||(s[98]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[99]||(s[99]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",V,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[101]||(s[101]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[102]||(s[102]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",R,[e("summary",null,[s[103]||(s[103]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[104]||(s[104]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[105]||(s[105]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[106]||(s[106]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[107]||(s[107]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[108]||(s[108]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[109]||(s[109]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[110]||(s[110]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[111]||(s[111]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[113]||(s[113]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[114]||(s[114]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",K,[e("summary",null,[s[115]||(s[115]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[116]||(s[116]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[117]||(s[117]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",$,[e("summary",null,[s[118]||(s[118]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[119]||(s[119]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[120]||(s[120]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[121]||(s[121]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[122]||(s[122]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[123]||(s[123]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[124]||(s[124]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[125]||(s[125]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[133]||(s[133]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[134]||(s[134]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[135]||(s[135]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[136]||(s[136]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[137]||(s[137]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[138]||(s[138]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[139]||(s[139]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[140]||(s[140]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[143]||(s[143]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[144]||(s[144]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[145]||(s[145]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L561-L563",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[146]||(s[146]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[147]||(s[147]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[148]||(s[148]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[149]||(s[149]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[150]||(s[150]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[152]||(s[152]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L530-L532",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[153]||(s[153]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[154]||(s[154]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[155]||(s[155]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[156]||(s[156]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{cs as __pageData,bs as default};
diff --git a/dev/assets/api.md.D0CdfjsI.lean.js b/dev/assets/api.md.BRif97cy.lean.js
similarity index 92%
rename from dev/assets/api.md.D0CdfjsI.lean.js
rename to dev/assets/api.md.BRif97cy.lean.js
index 3a0da1fb..1669e1f8 100644
--- a/dev/assets/api.md.D0CdfjsI.lean.js
+++ b/dev/assets/api.md.BRif97cy.lean.js
@@ -1,5 +1,5 @@
-import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.swGcc4Ky.js";const us=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[157]||(s[157]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[158]||(s[158]=e("p",null,"This section describes all available functions of this package.",-1)),s[159]||(s[159]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",c,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",u,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
',5))]),e("details",T,[e("summary",null,[s[48]||(s[48]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Dataset, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[49]||(s[49]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[50]||(s[50]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",F,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" or max_cache=1GB defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",X,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=e("p",null,[a("Dataset object which stores an "),e("code",null,"OrderedDict"),a(" 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.")],-1)),s[57]||(s[57]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L18-L22",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Y,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[59]||(s[59]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[60]||(s[60]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[61]||(s[61]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[62]||(s[62]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[63]||(s[63]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[64]||(s[64]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[65]||(s[65]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[66]||(s[66]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),e("details",L,[e("summary",null,[s[67]||(s[67]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[68]||(s[68]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[69]||(s[69]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
+import{_ as n,c as o,j as e,a,G as i,a2 as l,B as r,o as p}from"./chunks/framework.swGcc4Ky.js";const cs=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},X={class:"jldocstring custom-block",open:""},Y={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},L={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},O={class:"jldocstring custom-block",open:""},I={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},V={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},G={class:"jldocstring custom-block",open:""},W={class:"jldocstring custom-block",open:""},U={class:"jldocstring custom-block",open:""},K={class:"jldocstring custom-block",open:""},$={class:"jldocstring custom-block",open:""},H={class:"jldocstring custom-block",open:""},Z={class:"jldocstring custom-block",open:""},Q={class:"jldocstring custom-block",open:""},_={class:"jldocstring custom-block",open:""},ss={class:"jldocstring custom-block",open:""},es={class:"jldocstring custom-block",open:""},as={class:"jldocstring custom-block",open:""},ts={class:"jldocstring custom-block",open:""},is={class:"jldocstring custom-block",open:""},ls={class:"jldocstring custom-block",open:""};function ns(os,s,rs,ps,ds,hs){const t=r("Badge");return p(),o("div",null,[s[157]||(s[157]=e("h1",{id:"API-Reference",tabindex:"-1"},[a("API Reference "),e("a",{class:"header-anchor",href:"#API-Reference","aria-label":'Permalink to "API Reference {#API-Reference}"'},"​")],-1)),s[158]||(s[158]=e("p",null,"This section describes all available functions of this package.",-1)),s[159]||(s[159]=e("h2",{id:"Public-API",tabindex:"-1"},[a("Public API "),e("a",{class:"header-anchor",href:"#Public-API","aria-label":'Permalink to "Public API {#Public-API}"'},"​")],-1)),e("details",h,[e("summary",null,[s[0]||(s[0]=e("a",{id:"YAXArrays.getAxis-Tuple{Any, Any}",href:"#YAXArrays.getAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getAxis")],-1)),s[1]||(s[1]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[2]||(s[2]=l('
julia
getAxis(desc, c)
Given an Axis description and a cube, returns the corresponding axis of the cube. The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',4))]),e("details",u,[e("summary",null,[s[3]||(s[3]=e("a",{id:"YAXArrays.Cubes",href:"#YAXArrays.Cubes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes")],-1)),s[4]||(s[4]=a()),i(t,{type:"info",class:"jlObjectType jlModule",text:"Module"})]),s[5]||(s[5]=e("p",null,"The functions provided by YAXArrays are supposed to work on different types of cubes. This module defines the interface for all Data types that",-1)),s[6]||(s[6]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L1-L4",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[s[7]||(s[7]=e("a",{id:"YAXArrays.Cubes.YAXArray",href:"#YAXArrays.Cubes.YAXArray"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.YAXArray")],-1)),s[8]||(s[8]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[9]||(s[9]=l('
julia
YAXArray{T,N}
An array labelled with named axes that have values associated with them. It can wrap normal arrays or, more typically DiskArrays.
Fields
  • axes: Tuple of Dimensions containing the Axes of the Cube
  • data: length(axes)-dimensional array which holds the data, this can be a lazy DiskArray
  • properties: Metadata properties describing the content of the data
  • chunks: Representation of the chunking of the data
  • cleaner: Cleaner objects to track which objects to tidy up when the YAXArray goes out of scope
source
',5))]),e("details",b,[e("summary",null,[s[10]||(s[10]=e("a",{id:"YAXArrays.Cubes.caxes",href:"#YAXArrays.Cubes.caxes"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[11]||(s[11]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[12]||(s[12]=e("p",null,"Returns the axes of a Cube",-1)),s[13]||(s[13]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[s[14]||(s[14]=e("a",{id:"YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.caxes-Tuple{DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.caxes")],-1)),s[15]||(s[15]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[16]||(s[16]=l('
julia
caxes
Embeds Cube inside a new Cube
source
',3))]),e("details",y,[e("summary",null,[s[17]||(s[17]=e("a",{id:"YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}",href:"#YAXArrays.Cubes.concatenatecubes-Tuple{Any, DimensionalData.Dimensions.Dimension}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.concatenatecubes")],-1)),s[18]||(s[18]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[19]||(s[19]=l('
julia
function concatenateCubes(cubelist, cataxis::CategoricalAxis)
Concatenates a vector of datacubes that have identical axes to a new single cube along the new axis cataxis
source
',3))]),e("details",g,[e("summary",null,[s[20]||(s[20]=e("a",{id:"YAXArrays.Cubes.readcubedata-Tuple{Any}",href:"#YAXArrays.Cubes.readcubedata-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.readcubedata")],-1)),s[21]||(s[21]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[22]||(s[22]=l('
julia
readcubedata(cube)
Given any array implementing the YAXArray interface it returns an in-memory YAXArray from it.
source
',3))]),e("details",A,[e("summary",null,[s[23]||(s[23]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{YAXArray, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[24]||(s[24]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[25]||(s[25]=l('
julia
setchunks(c::YAXArray,chunks)
Resets the chunks of a YAXArray and returns a new YAXArray. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savecube on the resulting array. The chunks argument can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',4))]),e("details",f,[e("summary",null,[s[26]||(s[26]=e("a",{id:"YAXArrays.Cubes.subsetcube",href:"#YAXArrays.Cubes.subsetcube"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.subsetcube")],-1)),s[27]||(s[27]=a()),i(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[28]||(s[28]=e("p",null,"This function calculates a subset of a cube's data",-1)),s[29]||(s[29]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/Cubes/Cubes.jl#L22-L24",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",m,[e("summary",null,[s[30]||(s[30]=e("a",{id:"YAXArrays.DAT.InDims",href:"#YAXArrays.DAT.InDims"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InDims")],-1)),s[31]||(s[31]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[32]||(s[32]=l('
julia
InDims(axisdesc...;...)
Creates a description of an Input Data Cube for cube operations. Takes a single or multiple axis descriptions as first arguments. Alternatively a MovingWindow(@ref) struct can be passed to include neighbour slices of one or more axes in the computation. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
Keyword arguments
  • artype how shall the array be represented in the inner function. Defaults to Array, alternatives are DataFrame or AsAxisArray
  • filter define some filter to skip the computation, e.g. when all values are missing. Defaults to AllMissing(), possible values are AnyMissing(), AnyOcean(), StdZero(), NValid(n) (for at least n non-missing elements). It is also possible to provide a custom one-argument function that takes the array and returns true if the compuation shall be skipped and false otherwise.
  • window_oob_value if one of the input dimensions is a MowingWindow, this value will be used to fill out-of-bounds areas
source
',5))]),e("details",E,[e("summary",null,[s[33]||(s[33]=e("a",{id:"YAXArrays.DAT.MovingWindow",href:"#YAXArrays.DAT.MovingWindow"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.MovingWindow")],-1)),s[34]||(s[34]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[35]||(s[35]=l('
julia
MovingWindow(desc, pre, after)
Constructs a MovingWindow object to be passed to an InDims constructor to define that the axis in desc shall participate in the inner function (i.e. shall be looped over), but inside the inner function pre values before and after values after the center value will be passed as well.
For example passing MovingWindow("Time", 2, 0) will loop over the time axis and always pass the current time step plus the 2 previous steps. So in the inner function the array will have an additional dimension of size 3.
source
',4))]),e("details",j,[e("summary",null,[s[36]||(s[36]=e("a",{id:"YAXArrays.DAT.OutDims-Tuple",href:"#YAXArrays.DAT.OutDims-Tuple"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutDims")],-1)),s[37]||(s[37]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[38]||(s[38]=l('
julia
OutDims(axisdesc;...)
Creates a description of an Output Data Cube for cube operations. Takes a single or a Vector/Tuple of axes as first argument. Axes can be specified by their name (String), through an Axis type, or by passing a concrete axis.
  • axisdesc: List of input axis names
  • backend : specifies the dataset backend to write data to, must be either :auto or a key in YAXArrayBase.backendlist
  • update : specifies wether the function operates inplace or if an output is returned
  • artype : specifies the Array type inside the inner function that is mapped over
  • chunksize: A Dict specifying the chunksizes for the output dimensions of the cube, or :input to copy chunksizes from input cube axes or :max to not chunk the inner dimensions
  • outtype: force the output type to a specific type, defaults to Any which means that the element type of the first input cube is used
source
',4))]),e("details",C,[e("summary",null,[s[39]||(s[39]=e("a",{id:"YAXArrays.DAT.CubeTable-Tuple{}",href:"#YAXArrays.DAT.CubeTable-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.CubeTable")],-1)),s[40]||(s[40]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[41]||(s[41]=l('
julia
CubeTable()
Function to turn a DataCube object into an iterable table. Takes a list of as arguments, specified as a name=cube expression. For example CubeTable(data=cube1,country=cube2) would generate a Table with the entries data and country, where data contains the values of cube1 and country the values of cube2. The cubes are matched and broadcasted along their axes like in mapCube.
source
',3))]),e("details",D,[e("summary",null,[s[42]||(s[42]=e("a",{id:"YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}",href:"#YAXArrays.DAT.cubefittable-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cubefittable")],-1)),s[43]||(s[43]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[44]||(s[44]=l('
julia
cubefittable(tab,o,fitsym;post=getpostfunction(o),kwargs...)
Executes fittable on the CubeTable tab with the (Weighted-)OnlineStat o, looping through the values specified by fitsym. Finally, writes the results from the TableAggregator to an output data cube.
source
',3))]),e("details",v,[e("summary",null,[s[45]||(s[45]=e("a",{id:"YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}",href:"#YAXArrays.DAT.fittable-Tuple{YAXArrays.DAT.CubeIterator, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.fittable")],-1)),s[46]||(s[46]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[47]||(s[47]=l('
julia
fittable(tab,o,fitsym;by=(),weight=nothing)
Loops through an iterable table tab and thereby fitting an OnlineStat o with the values specified through fitsym. Optionally one can specify a field (or tuple) to group by. Any groupby specifier can either be a symbol denoting the entry to group by or an anynymous function calculating the group from a table row.
For example the following would caluclate a weighted mean over a cube weighted by grid cell area and grouped by country and month:
julia
fittable(iter,WeightedMean,:tair,weight=(i->abs(cosd(i.lat))),by=(i->month(i.time),:country))
source
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julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of all cubes of the dataset ds. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
For Datasets, only one output cube can be specified. In contrast to the mapCube function for cubes, additional arguments for the inner function should be set as keyword arguments.
For the specific keyword arguments see the docstring of the mapCube function for cubes.
source
',5))]),e("details",F,[e("summary",null,[s[51]||(s[51]=e("a",{id:"YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}",href:"#YAXArrays.DAT.mapCube-Tuple{Function, Tuple, Vararg{Any}}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.mapCube")],-1)),s[52]||(s[52]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[53]||(s[53]=l('
julia
mapCube(fun, cube, addargs...;kwargs...)
Map a given function fun over slices of the data cube cube. The additional arguments addargs will be forwarded to the inner function fun. Use InDims to discribe the input dimensions and OutDims to describe the output dimensions of the function.
Keyword arguments
  • max_cache=YAXDefaults.max_cache Float64 maximum size of blocks that are read into memory in bits e.g. max_cache=5.0e8. Or String. e.g. max_cache="10MB" or max_cache=1GB defaults to approx 10Mb.
  • indims::InDims List of input cube descriptors of type InDims for each input data cube.
  • outdims::OutDims List of output cube descriptors of type OutDims for each output cube.
  • inplace does the function write to an output array inplace or return a single value> defaults to true
  • ispar boolean to determine if parallelisation should be applied, defaults to true if workers are available.
  • showprog boolean indicating if a ProgressMeter shall be shown
  • include_loopvars boolean to indicate if the varoables looped over should be added as function arguments
  • nthreads number of threads for the computation, defaults to Threads.nthreads for every worker.
  • loopchunksize determines the chunk sizes of variables which are looped over, a dict
  • kwargs additional keyword arguments are passed to the inner function
The first argument is always the function to be applied, the second is the input cube or a tuple of input cubes if needed.
source
',6))]),e("details",X,[e("summary",null,[s[54]||(s[54]=e("a",{id:"YAXArrays.Datasets.Dataset",href:"#YAXArrays.Datasets.Dataset"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[55]||(s[55]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[56]||(s[56]=e("p",null,[a("Dataset object which stores an "),e("code",null,"OrderedDict"),a(" 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.")],-1)),s[57]||(s[57]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L18-L22",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Y,[e("summary",null,[s[58]||(s[58]=e("a",{id:"YAXArrays.Datasets.Dataset-Tuple{}",href:"#YAXArrays.Datasets.Dataset-Tuple{}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Dataset")],-1)),s[59]||(s[59]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[60]||(s[60]=l('
julia
Dataset(; properties = Dict{String,Any}, cubes...)
Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...
source
',3))]),e("details",x,[e("summary",null,[s[61]||(s[61]=e("a",{id:"YAXArrays.Datasets.Cube-Tuple{Dataset}",href:"#YAXArrays.Datasets.Cube-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.Cube")],-1)),s[62]||(s[62]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[63]||(s[63]=l('
julia
Cube(ds::Dataset; joinname="Variables")
Construct a single YAXArray from the dataset ds by concatenating the cubes in the datset on the joinname dimension.
source
',3))]),e("details",w,[e("summary",null,[s[64]||(s[64]=e("a",{id:"YAXArrays.Datasets.open_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.open_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_dataset")],-1)),s[65]||(s[65]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[66]||(s[66]=l('
julia
open_dataset(g; skip_keys=(), 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.
Keyword arguments
  • skip_keys are passed as symbols, i.e., skip_keys = (:a, :b)
  • driver=:all, common options are :netcdf or :zarr.
Example:
julia
ds = open_dataset(f, driver=:zarr, skip_keys = (:c,))
source
',7))]),e("details",L,[e("summary",null,[s[67]||(s[67]=e("a",{id:'YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}',href:'#YAXArrays.Datasets.open_mfdataset-Tuple{DimensionalData.DimVector{var"#s34", D, R, A} where {var"#s34"<:AbstractString, D<:Tuple, R<:Tuple, A<:AbstractVector{var"#s34"}}}'},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.open_mfdataset")],-1)),s[68]||(s[68]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[69]||(s[69]=l(`
julia
open_mfdataset(files::DD.DimVector{<:AbstractString}; kwargs...)
Opens and concatenates a list of dataset paths along the dimension specified in files. This method can be used when the generic glob-based version of open_mfdataset fails or is too slow. For example, to concatenate a list of annual NetCDF files along the time dimension, one can use:
julia
files = ["1990.nc","1991.nc","1992.nc"]
 open_mfdataset(DD.DimArray(files, YAX.time()))
alternatively, if the dimension to concatenate along does not exist yet, the dimension provided in the input arg is used:
julia
files = ["a.nc", "b.nc", "c.nc"]
-open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
',3))]),s[160]||(s[160]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[81]||(s[81]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",q,[e("summary",null,[s[85]||(s[85]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[86]||(s[86]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=l('
julia
getOutAxis
source
',2))]),e("details",P,[e("summary",null,[s[88]||(s[88]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[89]||(s[89]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[90]||(s[90]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[91]||(s[91]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[92]||(s[92]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[93]||(s[93]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
+open_mfdataset(DD.DimArray(files, DD.Dim{:NewDim}(["a","b","c"])))
source
`,6))]),e("details",M,[e("summary",null,[s[70]||(s[70]=e("a",{id:"YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}",href:"#YAXArrays.Datasets.savecube-Tuple{Any, AbstractString}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savecube")],-1)),s[71]||(s[71]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[72]||(s[72]=l('
julia
savecube(cube,name::String)
Save a YAXArray to the path.
Extended Help
The keyword arguments are:
  • name:
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • max_cache: The number of bits that are used as cache for the data handling.
  • backend: The backend, that is used to save the data. Falls back to searching the backend according to the extension of the path.
  • driver: The same setting as backend.
  • overwrite::Bool=false overwrite cube if it already exists
source
',6))]),e("details",B,[e("summary",null,[s[73]||(s[73]=e("a",{id:"YAXArrays.Datasets.savedataset-Tuple{Dataset}",href:"#YAXArrays.Datasets.savedataset-Tuple{Dataset}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.savedataset")],-1)),s[74]||(s[74]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[75]||(s[75]=l('
julia
savedataset(ds::Dataset; path= "", persist=nothing, overwrite=false, append=false, skeleton=false, backend=:all, driver=backend, max_cache=5e8, writefac=4.0)
Saves a Dataset into a file at path with the format given by driver, i.e., driver=:netcdf or driver=:zarr.
Warning
overwrite=true, deletes ALL your data and it will create a new file.
source
',4))]),e("details",O,[e("summary",null,[s[76]||(s[76]=e("a",{id:"YAXArrays.Datasets.to_dataset-Tuple{Any}",href:"#YAXArrays.Datasets.to_dataset-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.to_dataset")],-1)),s[77]||(s[77]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[78]||(s[78]=l('
julia
to_dataset(c;datasetaxis = "Variables", layername = "layer")
Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a datasetaxis i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername.
source
',3))]),s[160]||(s[160]=e("h2",{id:"Internal-API",tabindex:"-1"},[a("Internal API "),e("a",{class:"header-anchor",href:"#Internal-API","aria-label":'Permalink to "Internal API {#Internal-API}"'},"​")],-1)),e("details",I,[e("summary",null,[s[79]||(s[79]=e("a",{id:"YAXArrays.YAXDefaults",href:"#YAXArrays.YAXDefaults"},[e("span",{class:"jlbinding"},"YAXArrays.YAXDefaults")],-1)),s[80]||(s[80]=a()),i(t,{type:"info",class:"jlObjectType jlConstant",text:"Constant"})]),s[81]||(s[81]=l('
Default configuration for YAXArrays, has the following fields:
  • workdir[]::String = "./" The default location for temporary cubes.
  • recal[]::Bool = false set to true if you want @loadOrGenerate to always recalculate the results.
  • chunksize[]::Any = :input Set the default output chunksize.
  • max_cache[]::Float64 = 1e8 The maximum cache used by mapCube.
  • cubedir[]::"" the default location for Cube() without an argument.
  • subsetextensions::Array{Any} = [] List of registered functions, that convert subsetting input into dimension boundaries.
source
',3))]),e("details",J,[e("summary",null,[s[82]||(s[82]=e("a",{id:"YAXArrays.findAxis-Tuple{Any, Any}",href:"#YAXArrays.findAxis-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.findAxis")],-1)),s[83]||(s[83]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[84]||(s[84]=l('
julia
findAxis(desc, c)
Internal function
Extended Help
Given an Axis description and a cube return the index of the Axis.
The Axis description can be:
  • the name as a string or symbol.
  • an Axis object
source
',7))]),e("details",q,[e("summary",null,[s[85]||(s[85]=e("a",{id:"YAXArrays.getOutAxis-NTuple{5, Any}",href:"#YAXArrays.getOutAxis-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.getOutAxis")],-1)),s[86]||(s[86]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[87]||(s[87]=l('
julia
getOutAxis
source
',2))]),e("details",P,[e("summary",null,[s[88]||(s[88]=e("a",{id:"YAXArrays.get_descriptor-Tuple{String}",href:"#YAXArrays.get_descriptor-Tuple{String}"},[e("span",{class:"jlbinding"},"YAXArrays.get_descriptor")],-1)),s[89]||(s[89]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[90]||(s[90]=l('
julia
get_descriptor(a)
Get the descriptor of an Axis. This is used to dispatch on the descriptor.
source
',3))]),e("details",z,[e("summary",null,[s[91]||(s[91]=e("a",{id:"YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}",href:"#YAXArrays.match_axis-Tuple{YAXArrays.ByName, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.match_axis")],-1)),s[92]||(s[92]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[93]||(s[93]=l(`
julia
match_axis
Internal function
Extended Help
Match the Axis based on the AxisDescriptor.
 This is used to find different axes and to make certain axis description the same.
-For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[94]||(s[94]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[95]||(s[95]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[96]||(s[96]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[97]||(s[97]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[98]||(s[98]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[99]||(s[99]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",V,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[101]||(s[101]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[102]||(s[102]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",R,[e("summary",null,[s[103]||(s[103]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[104]||(s[104]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[105]||(s[105]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[106]||(s[106]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[107]||(s[107]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[108]||(s[108]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[109]||(s[109]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[110]||(s[110]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[111]||(s[111]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[113]||(s[113]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[114]||(s[114]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",K,[e("summary",null,[s[115]||(s[115]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[116]||(s[116]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[117]||(s[117]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",$,[e("summary",null,[s[118]||(s[118]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[119]||(s[119]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[120]||(s[120]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[121]||(s[121]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[122]||(s[122]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[123]||(s[123]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[124]||(s[124]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[125]||(s[125]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[133]||(s[133]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[134]||(s[134]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[135]||(s[135]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[136]||(s[136]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[137]||(s[137]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[138]||(s[138]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[139]||(s[139]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[140]||(s[140]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[143]||(s[143]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[144]||(s[144]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[145]||(s[145]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L561-L563",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[146]||(s[146]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[147]||(s[147]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[148]||(s[148]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[149]||(s[149]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[150]||(s[150]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[152]||(s[152]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L530-L532",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[153]||(s[153]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[154]||(s[154]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[155]||(s[155]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[156]||(s[156]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/fa39711c45bcc3886cd6278b5e877fc839dda4db/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{us as __pageData,bs as default};
+For example to disregard differences of captialisation.
source
`,5))]),e("details",N,[e("summary",null,[s[94]||(s[94]=e("a",{id:"YAXArrays.Cubes.CleanMe",href:"#YAXArrays.Cubes.CleanMe"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.CleanMe")],-1)),s[95]||(s[95]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[96]||(s[96]=l('
julia
mutable struct CleanMe
Struct which describes data paths and their persistency. Non-persistend paths/files are removed at finalize step
source
',3))]),e("details",S,[e("summary",null,[s[97]||(s[97]=e("a",{id:"YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}",href:"#YAXArrays.Cubes.clean-Tuple{YAXArrays.Cubes.CleanMe}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.clean")],-1)),s[98]||(s[98]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[99]||(s[99]=l('
julia
clean(c::CleanMe)
finalizer function for CleanMe struct. The main process removes all directories/files which are not persistent.
source
',3))]),e("details",V,[e("summary",null,[s[100]||(s[100]=e("a",{id:"YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}",href:"#YAXArrays.Cubes.copydata-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.copydata")],-1)),s[101]||(s[101]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[102]||(s[102]=l('
julia
copydata(outar, inar, copybuf)
Internal function which copies the data from the input inar into the output outar at the copybuf positions.
source
',3))]),e("details",R,[e("summary",null,[s[103]||(s[103]=e("a",{id:"YAXArrays.Cubes.optifunc-NTuple{7, Any}",href:"#YAXArrays.Cubes.optifunc-NTuple{7, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.optifunc")],-1)),s[104]||(s[104]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[105]||(s[105]=l('
julia
optifunc(s, maxbuf, incs, outcs, insize, outsize, writefac)
Internal
This function is going to be minimized to detect the best possible chunk setting for the rechunking of the data.
source
',4))]),e("details",G,[e("summary",null,[s[106]||(s[106]=e("a",{id:"YAXArrays.DAT.DATConfig",href:"#YAXArrays.DAT.DATConfig"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.DATConfig")],-1)),s[107]||(s[107]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[108]||(s[108]=l('
Configuration object of a DAT process. This holds all necessary information to perform the calculations. It contains the following fields:
  • incubes::NTuple{NIN, YAXArrays.DAT.InputCube} where NIN: The input data cubes
  • outcubes::NTuple{NOUT, YAXArrays.DAT.OutputCube} where NOUT: The output data cubes
  • allInAxes::Vector: List of all axes of the input cubes
  • LoopAxes::Vector: List of axes that are looped through
  • ispar::Bool: Flag whether the computation is parallelized
  • loopcachesize::Vector{Int64}:
  • allow_irregular_chunks::Bool:
  • max_cache::Any: Maximal size of the in memory cache
  • fu::Any: Inner function which is computed
  • inplace::Bool: Flag whether the computation happens in place
  • include_loopvars::Bool:
  • ntr::Any:
  • do_gc::Bool: Flag if GC should be called explicitly. Probably necessary for many runs in Julia 1.9
  • addargs::Any: Additional arguments for the inner function
  • kwargs::Any: Additional keyword arguments for the inner function
source
',3))]),e("details",W,[e("summary",null,[s[109]||(s[109]=e("a",{id:"YAXArrays.DAT.InputCube",href:"#YAXArrays.DAT.InputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.InputCube")],-1)),s[110]||(s[110]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[111]||(s[111]=l('
Internal representation of an input cube for DAT operations
  • cube: The input data
  • desc: The input description given by the user/registration
  • axesSmall: List of axes that were actually selected through the description
  • icolon
  • colonperm
  • loopinds: Indices of loop axes that this cube does not contain, i.e. broadcasts
  • cachesize: Number of elements to keep in cache along each axis
  • window
  • iwindow
  • windowloopinds
  • iall
source
',3))]),e("details",U,[e("summary",null,[s[112]||(s[112]=e("a",{id:"YAXArrays.DAT.OutputCube",href:"#YAXArrays.DAT.OutputCube"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.OutputCube")],-1)),s[113]||(s[113]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[114]||(s[114]=l('
Internal representation of an output cube for DAT operations
Fields
  • cube: The actual outcube cube, once it is generated
  • cube_unpermuted: The unpermuted output cube
  • desc: The description of the output axes as given by users or registration
  • axesSmall: The list of output axes determined through the description
  • allAxes: List of all the axes of the cube
  • loopinds: Index of the loop axes that are broadcasted for this output cube
  • innerchunks
  • outtype: Elementtype of the outputcube
source
',4))]),e("details",K,[e("summary",null,[s[115]||(s[115]=e("a",{id:"YAXArrays.DAT.YAXColumn",href:"#YAXArrays.DAT.YAXColumn"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.YAXColumn")],-1)),s[116]||(s[116]=a()),i(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[117]||(s[117]=l('
julia
YAXColumn
A struct representing a single column of a YAXArray partitioned Table # Fields
  • inarBC
  • inds
source
',4))]),e("details",$,[e("summary",null,[s[118]||(s[118]=e("a",{id:"YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}",href:"#YAXArrays.DAT.cmpcachmisses-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.cmpcachmisses")],-1)),s[119]||(s[119]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[120]||(s[120]=e("p",null,"Function that compares two cache miss specifiers by their importance",-1)),s[121]||(s[121]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L958-L960",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",H,[e("summary",null,[s[122]||(s[122]=e("a",{id:"YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}",href:"#YAXArrays.DAT.getFrontPerm-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getFrontPerm")],-1)),s[123]||(s[123]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[124]||(s[124]=e("p",null,"Calculate an axis permutation that brings the wanted dimensions to the front",-1)),s[125]||(s[125]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L1203",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Z,[e("summary",null,[s[126]||(s[126]=e("a",{id:"YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}",href:"#YAXArrays.DAT.getLoopCacheSize-NTuple{5, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getLoopCacheSize")],-1)),s[127]||(s[127]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[128]||(s[128]=e("p",null,"Calculate optimal Cache size to DAT operation",-1)),s[129]||(s[129]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DAT/DAT.jl#L1057",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",Q,[e("summary",null,[s[130]||(s[130]=e("a",{id:"YAXArrays.DAT.getOuttype-Tuple{Int64, Any}",href:"#YAXArrays.DAT.getOuttype-Tuple{Int64, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getOuttype")],-1)),s[131]||(s[131]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[132]||(s[132]=l('
julia
getOuttype(outtype, cdata)
Internal function
Get the element type for the output cube
source
',4))]),e("details",_,[e("summary",null,[s[133]||(s[133]=e("a",{id:"YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}",href:"#YAXArrays.DAT.getloopchunks-Tuple{YAXArrays.DAT.DATConfig}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.getloopchunks")],-1)),s[134]||(s[134]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[135]||(s[135]=l('
julia
getloopchunks(dc::DATConfig)
Internal function
Returns the chunks that can be looped over toghether for all dimensions.\nThis computation of the size of the chunks is handled by [`DiskArrays.approx_chunksize`](@ref)
source
',4))]),e("details",ss,[e("summary",null,[s[136]||(s[136]=e("a",{id:"YAXArrays.DAT.permuteloopaxes-Tuple{Any}",href:"#YAXArrays.DAT.permuteloopaxes-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.DAT.permuteloopaxes")],-1)),s[137]||(s[137]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[138]||(s[138]=l('
julia
permuteloopaxes(dc)
Internal function
Permute the dimensions of the cube, so that the axes that are looped through are in the first positions. This is necessary for a faster looping through the data.
source
',4))]),e("details",es,[e("summary",null,[s[139]||(s[139]=e("a",{id:"YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}",href:"#YAXArrays.Cubes.setchunks-Tuple{Dataset, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Cubes.setchunks")],-1)),s[140]||(s[140]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[141]||(s[141]=l('
julia
setchunks(c::Dataset,chunks)
Resets the chunks of all or a subset YAXArrays in the dataset and returns a new Dataset. Note that this will not change the chunking of the underlying data itself, it will just make the data "look" like it had a different chunking. If you need a persistent on-disk representation of this chunking, use savedataset on the resulting array. The chunks argument can take one of the following forms:
  • a NamedTuple or AbstractDict mapping from variable name to a description of the desired variable chunks
  • a NamedTuple or AbstractDict mapping from dimension name to a description of the desired variable chunks
  • a description of the desired variable chunks applied to all members of the Dataset
where a description of the desired variable chunks can take one of the following forms:
  • a DiskArrays.GridChunks object
  • a tuple specifying the chunk size along each dimension
  • an AbstractDict or NamedTuple mapping one or more axis names to chunk sizes
source
',6))]),e("details",as,[e("summary",null,[s[142]||(s[142]=e("a",{id:"YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}",href:"#YAXArrays.Datasets.collectfromhandle-Tuple{Any, Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.collectfromhandle")],-1)),s[143]||(s[143]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[144]||(s[144]=e("p",null,"Extracts a YAXArray from a dataset handle that was just created from a arrayinfo",-1)),s[145]||(s[145]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L561-L563",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ts,[e("summary",null,[s[146]||(s[146]=e("a",{id:"YAXArrays.Datasets.createdataset-Tuple{Any, Any}",href:"#YAXArrays.Datasets.createdataset-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.createdataset")],-1)),s[147]||(s[147]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[148]||(s[148]=l('
julia
function createdataset(DS::Type,axlist; kwargs...)
Creates a new dataset with axes specified in axlist. Each axis must be a subtype of CubeAxis. A new empty Zarr array will be created and can serve as a sink for mapCube operations.
Keyword arguments
  • path="" location where the new cube is stored
  • T=Union{Float32,Missing} data type of the target cube
  • chunksize = ntuple(i->length(axlist[i]),length(axlist)) chunk sizes of the array
  • chunkoffset = ntuple(i->0,length(axlist)) offsets of the chunks
  • persist::Bool=true shall the disk data be garbage-collected when the cube goes out of scope?
  • overwrite::Bool=false overwrite cube if it already exists
  • properties=Dict{String,Any}() additional cube properties
  • globalproperties=Dict{String,Any} global attributes to be added to the dataset
  • fillvalue= T>:Missing ? defaultfillval(Base.nonmissingtype(T)) : nothing fill value
  • datasetaxis="Variables" special treatment of a categorical axis that gets written into separate zarr arrays
  • layername="layer" Fallback name of the variable stored in the dataset if no datasetaxis is found
source
',5))]),e("details",is,[e("summary",null,[s[149]||(s[149]=e("a",{id:"YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}",href:"#YAXArrays.Datasets.getarrayinfo-Tuple{Any, Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.getarrayinfo")],-1)),s[150]||(s[150]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[151]||(s[151]=e("p",null,"Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair",-1)),s[152]||(s[152]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L530-L532",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",ls,[e("summary",null,[s[153]||(s[153]=e("a",{id:"YAXArrays.Datasets.testrange-Tuple{Any}",href:"#YAXArrays.Datasets.testrange-Tuple{Any}"},[e("span",{class:"jlbinding"},"YAXArrays.Datasets.testrange")],-1)),s[154]||(s[154]=a()),i(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[155]||(s[155]=e("p",null,"Test if data in x can be approximated by a step range",-1)),s[156]||(s[156]=e("p",null,[e("a",{href:"https://github.com/JuliaDataCubes/YAXArrays.jl/blob/348f26b5b14e4b2c11f3705612425ef7377edce5/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const bs=n(d,[["render",ns]]);export{cs as __pageData,bs as default};
diff --git a/dev/assets/app.Z09qLQeP.js b/dev/assets/app.-6rU1gpv.js
similarity index 95%
rename from dev/assets/app.Z09qLQeP.js
rename to dev/assets/app.-6rU1gpv.js
index 946702bc..3e1f0e8c 100644
--- a/dev/assets/app.Z09qLQeP.js
+++ b/dev/assets/app.-6rU1gpv.js
@@ -1 +1 @@
-import{R as p}from"./chunks/theme.BUJdPzA-.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.swGcc4Ky.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp};
+import{R as p}from"./chunks/theme.jGI52cm3.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a5 as S}from"./chunks/framework.swGcc4Ky.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp};
diff --git a/dev/assets/chunks/@localSearchIndexroot.CqDnS4DG.js b/dev/assets/chunks/@localSearchIndexroot.CqDnS4DG.js
new file mode 100644
index 00000000..1ec5daaa
--- /dev/null
+++ b/dev/assets/chunks/@localSearchIndexroot.CqDnS4DG.js
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similarity index 94%
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index 0fe07242..cd5a4899 100644
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-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
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similarity index 94%
rename from dev/assets/index.md.CHMH1Jjf.lean.js
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index 0fe07242..cd5a4899 100644
--- a/dev/assets/index.md.CHMH1Jjf.lean.js
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 # or
 julia> ] # ']' should be pressed
-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
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similarity index 60%
rename from dev/assets/style.Cp9CzvzH.css
rename to dev/assets/style.9k8cB8eK.css
index 1119ec91..91d605fa 100644
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diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.lean.js b/dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.js
similarity index 99%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.lean.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.js
index 5868d149..6f61910e 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.lean.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.swGcc4Ky.js";const l="/YAXArrays.jl/dev/assets/dzarsbx.C-WFBMfk.png",k="/YAXArrays.jl/dev/assets/frwqpez.D9rvatPm.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.swGcc4Ky.js";const l="/YAXArrays.jl/dev/assets/jkytbph.C-WFBMfk.png",k="/YAXArrays.jl/dev/assets/obatlyf.D9rvatPm.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")
diff --git a/dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.js b/dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.lean.js
similarity index 99%
rename from dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.js
rename to dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.lean.js
index 5868d149..6f61910e 100644
--- a/dev/assets/tutorials_mean_seasonal_cycle.md.Z_LYzz3j.js
+++ b/dev/assets/tutorials_mean_seasonal_cycle.md.NEdSTLYW.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.swGcc4Ky.js";const l="/YAXArrays.jl/dev/assets/dzarsbx.C-WFBMfk.png",k="/YAXArrays.jl/dev/assets/frwqpez.D9rvatPm.png",c=JSON.parse('{"title":"Mean Seasonal Cycle for a single pixel","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/mean_seasonal_cycle.md","filePath":"tutorials/mean_seasonal_cycle.md","lastUpdated":null}'),p={name:"tutorials/mean_seasonal_cycle.md"};function t(e,s,E,d,r,g){return h(),a("div",null,s[0]||(s[0]=[n(`
Mean Seasonal Cycle for a single pixel ​
julia
using CairoMakie
+import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.swGcc4Ky.js";const l="/YAXArrays.jl/dev/assets/jkytbph.C-WFBMfk.png",k="/YAXArrays.jl/dev/assets/obatlyf.D9rvatPm.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")
diff --git a/dev/assets/tutorials_plottingmaps.md.Dt607_PJ.js b/dev/assets/tutorials_plottingmaps.md.Dt607_PJ.js
deleted file mode 100644
index 952c540a..00000000
--- a/dev/assets/tutorials_plottingmaps.md.Dt607_PJ.js
+++ /dev/null
@@ -1,54 +0,0 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/taklqov.CQ9uchq9.jpeg",k="/YAXArrays.jl/dev/assets/cyhvrkj.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/mbalbzx.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:
-None
-Variables: 
-height
-
-Variables with additional axes:
-  Additional Axes: 
-  (↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
-  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
-  Variables: 
-  tas
-
-Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[time = 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.Dt607_PJ.lean.js b/dev/assets/tutorials_plottingmaps.md.Dt607_PJ.lean.js
deleted file mode 100644
index 952c540a..00000000
--- a/dev/assets/tutorials_plottingmaps.md.Dt607_PJ.lean.js
+++ /dev/null
@@ -1,54 +0,0 @@
-import{_ as i,c as a,a2 as h,o as n}from"./chunks/framework.swGcc4Ky.js";const t="/YAXArrays.jl/dev/assets/taklqov.CQ9uchq9.jpeg",k="/YAXArrays.jl/dev/assets/cyhvrkj.B7KFIfDV.jpeg",l="/YAXArrays.jl/dev/assets/mbalbzx.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:
-None
-Variables: 
-height
-
-Variables with additional axes:
-  Additional Axes: 
-  (↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
-  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
-  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
-  Variables: 
-  tas
-
-Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[time = 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.PIUGqyW7.js b/dev/assets/tutorials_plottingmaps.md.PIUGqyW7.js
new file mode 100644
index 00000000..850d3718
--- /dev/null
+++ b/dev/assets/tutorials_plottingmaps.md.PIUGqyW7.js
@@ -0,0 +1,110 @@
+import{_ as i,c as a,a2 as t,o as n}from"./chunks/framework.swGcc4Ky.js";const h="/YAXArrays.jl/dev/assets/taklqov.CQ9uchq9.jpeg",l="/YAXArrays.jl/dev/assets/cyhvrkj.B7KFIfDV.jpeg",p="/YAXArrays.jl/dev/assets/mbalbzx.96k_BqPR.jpeg",k="/YAXArrays.jl/dev/assets/oowdcxc.B7b9FwLj.jpeg",e="/YAXArrays.jl/dev/assets/zvezrog.B074eX2X.jpeg",r="/YAXArrays.jl/dev/assets/dzarsbx.C5U_qDue.jpeg",d="/YAXArrays.jl/dev/assets/frwqpez.DX1O6I5P.jpeg",E="/YAXArrays.jl/dev/assets/idgplot.Blc9BtwN.jpeg",g="/YAXArrays.jl/dev/assets/weuosxb.Bcyn0CpL.jpeg",y="/YAXArrays.jl/dev/assets/xzwnmje.-RBZ8LkA.jpeg",o="/YAXArrays.jl/dev/assets/ssvwqbb.Cj8iZQLt.jpeg",F="/YAXArrays.jl/dev/assets/pdifvqr.Dwd2F2F-.jpeg",v=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),c={name:"tutorials/plottingmaps.md"};function C(u,s,m,A,B,b){return n(),a("div",null,s[0]||(s[0]=[t(`
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:
+None
+Variables: 
+height
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
+  Variables: 
+  tas
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[time = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon_d = lookup(ct1_slice, :lon)
+lat_d = lookup(ct1_slice, :lat)
+data_d = 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_d[2] - lon_d[1])/2
+nlon = lon_d .- 180 .+ δlon
+ndata = circshift(data_d, (192,1))
and add Coastlines with GeoMakie.coastlines(),
julia
fig = Figure(;size=(1200,600))
+ax = GeoAxis(fig[1,1])
+surface!(ax, nlon, lat_d, 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_d, 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 GLMakie
+using GLMakie.GeometryBasics
+GLMakie.activate!()
+
+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
AlgebraOfGraphics.jl ​
INFO
From DimensionalData docs :
AlgebraOfGraphics.jl is a high-level plotting library built on top of Makie.jl that provides a declarative algebra for creating complex visualizations, similar to ggplot2's "grammar of graphics" in R. It allows you to construct plots using algebraic operations like * and +, making it easy to create sophisticated graphics with minimal code.
julia
using YAXArrays, Zarr, Dates
+using GLMakie
+using AlgebraOfGraphics
+using GLMakie.GeometryBasics
+GLMakie.activate!()
let's continue using the cmip6 dataset
julia
store ="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
+g = open_dataset(zopen(store, consolidated=true))
+c = g["tas"];
and let's focus on the first time step:
julia
dim_data = readcubedata(c[time=1]); # read into memory first!
and now plot
julia
data(dim_data) * mapping(:lon, :lat; color=:value) * visual(Scatter) |> draw
WARNING
Note that we are using a Scatter type per point and not the Heatmap one. There are workarounds for this, albeit cumbersome, so for now, let's keep this simpler syntax in mind along with the current approach being used.
set other attributes
julia
plt = data(dim_data) * mapping(:lon, :lat; color=:value)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :plasma));
+    axis = (width = 600, height = 400, limits=(0, 360, -90, 90)))
Faceting ​
For this let's consider more time steps from our dataset:
julia
using Dates
+dim_time = c[time=DateTime("2015-01-01") .. DateTime("2015-01-01T21:00:00")] # subset 7 t steps
┌ 384×192×7 YAXArray{Float32, 3} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-01T21:00:00] 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"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 1.97 MB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
dim_time = readcubedata(dim_time); # read into memory first!
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect))
again, let's add some additional attributes
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :magma));
+    axis = (; limits=(0, 360, -90, 90)),
+    figure=(; size=(900,600)))
most Makie plot functions should work. See lines for example
julia
plt = data(dim_data[lon=50..100]) * mapping(:lat, :value => "tas"; color=:value => "tas")
+draw(plt * visual(Lines); figure=(; size=(650,400)))
or faceting them
julia
plt = data(dim_data[lon=50..59]) * mapping(:lat, :value => "tas"; color=:value => "tas",
+    layout = :lon => nonnumeric)
+draw(plt * visual(Lines); figure=(; size=(650,400)))
Time series ​
For this, let's load a little bit more of time steps
julia
dim_series = c[time=DateTime("2015-01-01") .. DateTime("2015-01-04"), lon = 150 .. 157, lat = 0..1] |> readcubedata
┌ 8×1×24 YAXArray{Float32, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 150.0:0.9375:156.5625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [0.4675308904227747] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-04T00:00:00] 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"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 768.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
and plot
julia
plt = data(dim_series) * mapping(:time, :value => "tas"; color=:lon => nonnumeric)
+draw(plt * visual(ScatterLines), scales(Color = (; palette = :tableau_colorblind));
+    figure=(; size=(800,400)))
Analysis ​
Basic statistical analysis can also be done, for example:
julia
specs = data(dim_data[lat=50..55]) * mapping(:lon, :value => "tas"; color=:lat => nonnumeric)
+specs *= (smooth() + visual(Scatter))
+draw(specs;  figure=(; size=(700,400)))
For more, visit AlgebraOfGraphics.jl.
',68)]))}const q=i(c,[["render",C]]);export{v as __pageData,q as default};
diff --git a/dev/assets/tutorials_plottingmaps.md.PIUGqyW7.lean.js b/dev/assets/tutorials_plottingmaps.md.PIUGqyW7.lean.js
new file mode 100644
index 00000000..850d3718
--- /dev/null
+++ b/dev/assets/tutorials_plottingmaps.md.PIUGqyW7.lean.js
@@ -0,0 +1,110 @@
+import{_ as i,c as a,a2 as t,o as n}from"./chunks/framework.swGcc4Ky.js";const h="/YAXArrays.jl/dev/assets/taklqov.CQ9uchq9.jpeg",l="/YAXArrays.jl/dev/assets/cyhvrkj.B7KFIfDV.jpeg",p="/YAXArrays.jl/dev/assets/mbalbzx.96k_BqPR.jpeg",k="/YAXArrays.jl/dev/assets/oowdcxc.B7b9FwLj.jpeg",e="/YAXArrays.jl/dev/assets/zvezrog.B074eX2X.jpeg",r="/YAXArrays.jl/dev/assets/dzarsbx.C5U_qDue.jpeg",d="/YAXArrays.jl/dev/assets/frwqpez.DX1O6I5P.jpeg",E="/YAXArrays.jl/dev/assets/idgplot.Blc9BtwN.jpeg",g="/YAXArrays.jl/dev/assets/weuosxb.Bcyn0CpL.jpeg",y="/YAXArrays.jl/dev/assets/xzwnmje.-RBZ8LkA.jpeg",o="/YAXArrays.jl/dev/assets/ssvwqbb.Cj8iZQLt.jpeg",F="/YAXArrays.jl/dev/assets/pdifvqr.Dwd2F2F-.jpeg",v=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),c={name:"tutorials/plottingmaps.md"};function C(u,s,m,A,B,b){return n(),a("div",null,s[0]||(s[0]=[t(`
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:
+None
+Variables: 
+height
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points)
+  Variables: 
+  tas
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[time = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon_d = lookup(ct1_slice, :lon)
+lat_d = lookup(ct1_slice, :lat)
+data_d = 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_d[2] - lon_d[1])/2
+nlon = lon_d .- 180 .+ δlon
+ndata = circshift(data_d, (192,1))
and add Coastlines with GeoMakie.coastlines(),
julia
fig = Figure(;size=(1200,600))
+ax = GeoAxis(fig[1,1])
+surface!(ax, nlon, lat_d, 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_d, 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 GLMakie
+using GLMakie.GeometryBasics
+GLMakie.activate!()
+
+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
AlgebraOfGraphics.jl ​
INFO
From DimensionalData docs :
AlgebraOfGraphics.jl is a high-level plotting library built on top of Makie.jl that provides a declarative algebra for creating complex visualizations, similar to ggplot2's "grammar of graphics" in R. It allows you to construct plots using algebraic operations like * and +, making it easy to create sophisticated graphics with minimal code.
julia
using YAXArrays, Zarr, Dates
+using GLMakie
+using AlgebraOfGraphics
+using GLMakie.GeometryBasics
+GLMakie.activate!()
let's continue using the cmip6 dataset
julia
store ="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
+g = open_dataset(zopen(store, consolidated=true))
+c = g["tas"];
and let's focus on the first time step:
julia
dim_data = readcubedata(c[time=1]); # read into memory first!
and now plot
julia
data(dim_data) * mapping(:lon, :lat; color=:value) * visual(Scatter) |> draw
WARNING
Note that we are using a Scatter type per point and not the Heatmap one. There are workarounds for this, albeit cumbersome, so for now, let's keep this simpler syntax in mind along with the current approach being used.
set other attributes
julia
plt = data(dim_data) * mapping(:lon, :lat; color=:value)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :plasma));
+    axis = (width = 600, height = 400, limits=(0, 360, -90, 90)))
Faceting ​
For this let's consider more time steps from our dataset:
julia
using Dates
+dim_time = c[time=DateTime("2015-01-01") .. DateTime("2015-01-01T21:00:00")] # subset 7 t steps
┌ 384×192×7 YAXArray{Float32, 3} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-01T21:00:00] 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"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 1.97 MB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
dim_time = readcubedata(dim_time); # read into memory first!
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect))
again, let's add some additional attributes
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :magma));
+    axis = (; limits=(0, 360, -90, 90)),
+    figure=(; size=(900,600)))
most Makie plot functions should work. See lines for example
julia
plt = data(dim_data[lon=50..100]) * mapping(:lat, :value => "tas"; color=:value => "tas")
+draw(plt * visual(Lines); figure=(; size=(650,400)))
or faceting them
julia
plt = data(dim_data[lon=50..59]) * mapping(:lat, :value => "tas"; color=:value => "tas",
+    layout = :lon => nonnumeric)
+draw(plt * visual(Lines); figure=(; size=(650,400)))
Time series ​
For this, let's load a little bit more of time steps
julia
dim_series = c[time=DateTime("2015-01-01") .. DateTime("2015-01-04"), lon = 150 .. 157, lat = 0..1] |> readcubedata
┌ 8×1×24 YAXArray{Float32, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 150.0:0.9375:156.5625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [0.4675308904227747] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-04T00:00:00] 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"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 768.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
and plot
julia
plt = data(dim_series) * mapping(:time, :value => "tas"; color=:lon => nonnumeric)
+draw(plt * visual(ScatterLines), scales(Color = (; palette = :tableau_colorblind));
+    figure=(; size=(800,400)))
Analysis ​
Basic statistical analysis can also be done, for example:
julia
specs = data(dim_data[lat=50..55]) * mapping(:lon, :value => "tas"; color=:lat => nonnumeric)
+specs *= (smooth() + visual(Scatter))
+draw(specs;  figure=(; size=(700,400)))
For more, visit AlgebraOfGraphics.jl.
',68)]))}const q=i(c,[["render",C]]);export{v as __pageData,q as default};
diff --git a/dev/assets/weuosxb.Bcyn0CpL.jpeg b/dev/assets/weuosxb.Bcyn0CpL.jpeg
new file mode 100644
index 00000000..3dd7cbb7
Binary files /dev/null and b/dev/assets/weuosxb.Bcyn0CpL.jpeg differ
diff --git a/dev/assets/xzwnmje.-RBZ8LkA.jpeg b/dev/assets/xzwnmje.-RBZ8LkA.jpeg
new file mode 100644
index 00000000..c09363b8
Binary files /dev/null and b/dev/assets/xzwnmje.-RBZ8LkA.jpeg differ
diff --git a/dev/assets/zvezrog.B074eX2X.jpeg b/dev/assets/zvezrog.B074eX2X.jpeg
new file mode 100644
index 00000000..6636367d
Binary files /dev/null and b/dev/assets/zvezrog.B074eX2X.jpeg differ
diff --git a/dev/development/contribute.html b/dev/development/contribute.html
index f3cf9d56..47bf7d87 100644
--- a/dev/development/contribute.html
+++ b/dev/development/contribute.html
@@ -6,12 +6,12 @@
     Contribute to YAXArrays.jl | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,10 +21,10 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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 b8d9b4e1..68ff4b12 100644
--- a/dev/development/contributors.html
+++ b/dev/development/contributors.html
@@ -6,14 +6,14 @@
     YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,8 +21,8 @@
     
   
   
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YAXArrays.jl
Appearance
0.1k
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
0.1k
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 a9f4d442..7855af73 100644
--- a/dev/get_started.html
+++ b/dev/get_started.html
@@ -6,12 +6,12 @@
     Getting Started | YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

Read n-d array like-data
DiskArrays.jl
Get your chunks!
Named Dimensions
DimensionalData.jl
Select & Index!
Out of memory data
Zarr.jl
Chunkerd, compressed !
Rasterized spatial data
Rasters.jl
Read and manipulate !
Array-oriented data
NetCDF.jl
Scientific binary data.
 Raster and vector data
ArchGDAL.jl
GDAL in Julia.
An interface for
GeoInterface.jl
geospatial data in Julia.
A higher level interface
GRIBDatasets.jl
for reading GRIB files.
Array-oriented data
NCDatasets.jl
Scientific binary data.
Getting Started ​
Installation ​
Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing
julia
pkg> add YAXArrays
Alternatively, you can also do
julia
import Pkg; Pkg.add("YAXArrays")
Quickstart ​
Create a simple array from random numbers given the size of each dimension or axis:
julia
using YAXArrays
 using YAXArrays: YAXArrays as YAX
 
 a = YAXArray(rand(2,3))
┌ 2×3 YAXArray{Float64, 2} ┐
@@ -74,7 +74,7 @@
  0.683599  0.598769   0.0571978  0.155874     0.623962  0.959705  0.957463
  0.179919  0.551487   0.783779   0.828388     0.303359  0.542756  0.903079
  0.487093  0.78064    0.191898   0.908084     0.60764   0.833498  0.198806
Updates ​
TIP
The Julia Compiler is always improving. As such, we recommend using the latest stable version of Julia.
You may check the installed version with:
julia
pkg> st YAXArrays
INFO
With YAXArrays.jl 0.5 we switched the underlying data type to be a subtype of the DimensionalData.jl types. Therefore the indexing with named dimensions changed to the DimensionalData syntax. See the DimensionalData.jl docs.

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diff --git a/dev/hashmap.json b/dev/hashmap.json
index d7a5479e..1616fa67 100644
--- a/dev/hashmap.json
+++ b/dev/hashmap.json
@@ -1 +1 @@
-{"api.md":"D0CdfjsI","development_contribute.md":"BnCGLWxW","development_contributors.md":"Dzp2_bAK","get_started.md":"DSAvkcBC","index.md":"CHMH1Jjf","tutorials_mean_seasonal_cycle.md":"Z_LYzz3j","tutorials_other_tutorials.md":"Fy42Y-_T","tutorials_plottingmaps.md":"Dt607_PJ","userguide_cache.md":"QKgZScmZ","userguide_chunk.md":"BUA8jTbX","userguide_combine.md":"DAjEWyko","userguide_compute.md":"40iuroaW","userguide_convert.md":"mNrn75us","userguide_create.md":"NfaKG9hj","userguide_faq.md":"1WiBDJb9","userguide_group.md":"BuMj7-Wl","userguide_read.md":"Cw-3n0BR","userguide_select.md":"DTBr1pV5","userguide_types.md":"CFLbNjhn","userguide_write.md":"0oH3mU4C"}
+{"api.md":"BRif97cy","development_contribute.md":"BnCGLWxW","development_contributors.md":"RriTQphZ","get_started.md":"DSAvkcBC","index.md":"DQl4mNds","tutorials_mean_seasonal_cycle.md":"NEdSTLYW","tutorials_other_tutorials.md":"Fy42Y-_T","tutorials_plottingmaps.md":"PIUGqyW7","userguide_cache.md":"QKgZScmZ","userguide_chunk.md":"BUA8jTbX","userguide_combine.md":"DAjEWyko","userguide_compute.md":"DZWxbglL","userguide_convert.md":"mNrn75us","userguide_create.md":"NfaKG9hj","userguide_faq.md":"C332mxDc","userguide_group.md":"BS0wle11","userguide_read.md":"Dpjhykk1","userguide_select.md":"DTBr1pV5","userguide_types.md":"CFLbNjhn","userguide_write.md":"DWXQN9K3"}
diff --git a/dev/index.html b/dev/index.html
index 492fe741..3e110990 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -6,14 +6,14 @@
     YAXArrays.jl
     
     
-    
+    
     
     
-    
+    
     
-    
+    
     
-    
+    
     
     
     
@@ -21,16 +21,16 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k
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).
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
+    
YAXArrays.jl
Appearance
0.1k
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).
How to Install YAXArrays.jl? ​
Since YAXArrays.jl is registered in the Julia General registry, you can simply run the following command in the Julia REPL:
julia
julia> using Pkg
 julia> Pkg.add("YAXArrays.jl")
 # or
 julia> ] # ']' should be pressed
-pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
+pkg> add YAXArrays
If you want to use the latest unreleased version, you can run the following command:
julia
pkg> add YAXArrays#master
Want interoperability? ​
Install the following package(s) for:
julia
using Pkg
 Pkg.add("ArchGDAL")
julia
using Pkg
 Pkg.add("NetCDF")
julia
using Pkg
 Pkg.add("Zarr")
julia
using Pkg
 Pkg.add(["GLMakie", "GeoMakie", "AlgebraOfGraphics", "DimensionalData"])

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diff --git a/dev/tutorials/mean_seasonal_cycle.html b/dev/tutorials/mean_seasonal_cycle.html
index 8cb93ad3..fa6665b9 100644
--- a/dev/tutorials/mean_seasonal_cycle.html
+++ b/dev/tutorials/mean_seasonal_cycle.html
@@ -6,14 +6,14 @@
     Mean Seasonal Cycle for a single pixel | YAXArrays.jl
     
     
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+    
     
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@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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")
@@ -32,7 +32,7 @@
     )
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
-fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
+fig
Define the cube ​
julia
julia> using YAXArrays, DimensionalData
 
 julia> using YAXArrays: YAXArrays as YAX
 
@@ -93,8 +93,8 @@
 ax.xticklabelrotation = π / 4
 ax.xticklabelalign = (:right, :center)
 fig
-current_figure()

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diff --git a/dev/tutorials/other_tutorials.html b/dev/tutorials/other_tutorials.html
index 974b1fbf..a94650d2 100644
--- a/dev/tutorials/other_tutorials.html
+++ b/dev/tutorials/other_tutorials.html
@@ -6,12 +6,12 @@
     Other tutorials | YAXArrays.jl
     
     
-    
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-    
+    
     
-    
+    
     
     
     
@@ -21,8 +21,8 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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

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diff --git a/dev/tutorials/plottingmaps.html b/dev/tutorials/plottingmaps.html
index 403a65cf..253c4cda 100644
--- a/dev/tutorials/plottingmaps.html
+++ b/dev/tutorials/plottingmaps.html
@@ -6,14 +6,14 @@
     Plotting maps | YAXArrays.jl
     
     
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@@ -21,7 +21,7 @@
     
   
   
-    
YAXArrays.jl
Appearance
0.1k

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
0.1k

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
@@ -40,30 +40,28 @@
   Variables: 
   tas
 
-Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];
Subset, first time step
julia
julia> ct1_slice = c[time = 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!()
+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[time = Near(Date("2015-01-01"))];
use lookup to get axis values
julia
lon_d = lookup(ct1_slice, :lon)
+lat_d = lookup(ct1_slice, :lat)
+data_d = 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))
+fig
Wintri Projection ​
Some transformations
julia
δlon = (lon_d[2] - lon_d[1])/2
+nlon = lon_d .- 180 .+ δlon
+ndata = circshift(data_d, (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)
+surface!(ax, nlon, lat_d, 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)
+surface!(ax, nlon, lat_d, 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!
+fig
3D sphere plot ​
julia
using GLMakie
+using GLMakie.GeometryBasics
+GLMakie.activate!()
 
 ds = replace(ndata, missing =>NaN)
 sphere = uv_normal_mesh(Tesselation(Sphere(Point3f(0), 1), 128))
@@ -74,8 +72,66 @@
     colormap = :seaborn_icefire_gradient)
 zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
 rotate!(ax.scene, 2.5)
-fig

-    
+fig
AlgebraOfGraphics.jl ​
INFO
From DimensionalData docs :
AlgebraOfGraphics.jl is a high-level plotting library built on top of Makie.jl that provides a declarative algebra for creating complex visualizations, similar to ggplot2's "grammar of graphics" in R. It allows you to construct plots using algebraic operations like * and +, making it easy to create sophisticated graphics with minimal code.
julia
using YAXArrays, Zarr, Dates
+using GLMakie
+using AlgebraOfGraphics
+using GLMakie.GeometryBasics
+GLMakie.activate!()
let's continue using the cmip6 dataset
julia
store ="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
+g = open_dataset(zopen(store, consolidated=true))
+c = g["tas"];
and let's focus on the first time step:
julia
dim_data = readcubedata(c[time=1]); # read into memory first!
and now plot
julia
data(dim_data) * mapping(:lon, :lat; color=:value) * visual(Scatter) |> draw
WARNING
Note that we are using a Scatter type per point and not the Heatmap one. There are workarounds for this, albeit cumbersome, so for now, let's keep this simpler syntax in mind along with the current approach being used.
set other attributes
julia
plt = data(dim_data) * mapping(:lon, :lat; color=:value)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :plasma));
+    axis = (width = 600, height = 400, limits=(0, 360, -90, 90)))
Faceting ​
For this let's consider more time steps from our dataset:
julia
using Dates
+dim_time = c[time=DateTime("2015-01-01") .. DateTime("2015-01-01T21:00:00")] # subset 7 t steps
┌ 384×192×7 YAXArray{Float32, 3} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-01T21:00:00] 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"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 1.97 MB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
dim_time = readcubedata(dim_time); # read into memory first!
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect))
again, let's add some additional attributes
julia
plt = data(dim_time) * mapping(:lon, :lat; color = :value, layout = :time => nonnumeric)
+draw(plt * visual(Scatter, marker=:rect), scales(Color = (; colormap = :magma));
+    axis = (; limits=(0, 360, -90, 90)),
+    figure=(; size=(900,600)))
most Makie plot functions should work. See lines for example
julia
plt = data(dim_data[lon=50..100]) * mapping(:lat, :value => "tas"; color=:value => "tas")
+draw(plt * visual(Lines); figure=(; size=(650,400)))
or faceting them
julia
plt = data(dim_data[lon=50..59]) * mapping(:lat, :value => "tas"; color=:value => "tas",
+    layout = :lon => nonnumeric)
+draw(plt * visual(Lines); figure=(; size=(650,400)))
Time series ​
For this, let's load a little bit more of time steps
julia
dim_series = c[time=DateTime("2015-01-01") .. DateTime("2015-01-04"), lon = 150 .. 157, lat = 0..1] |> readcubedata
┌ 8×1×24 YAXArray{Float32, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 150.0:0.9375:156.5625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [0.4675308904227747] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2015-01-04T00:00:00] 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"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 768.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
and plot
julia
plt = data(dim_series) * mapping(:time, :value => "tas"; color=:lon => nonnumeric)
+draw(plt * visual(ScatterLines), scales(Color = (; palette = :tableau_colorblind));
+    figure=(; size=(800,400)))
Analysis ​
Basic statistical analysis can also be done, for example:
julia
specs = data(dim_data[lat=50..55]) * mapping(:lon, :value => "tas"; color=:lat => nonnumeric)
+specs *= (smooth() + visual(Scatter))
+draw(specs;  figure=(; size=(700,400)))
For more, visit AlgebraOfGraphics.jl.

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diff --git a/dev/vp-icons.css b/dev/vp-icons.css
index 9863249e..e3160864 100644
--- a/dev/vp-icons.css
+++ b/dev/vp-icons.css
@@ -1 +1 @@
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