diff --git a/previews/PR486/404.html b/previews/PR486/404.html new file mode 100644 index 00000000..8405ed28 --- /dev/null +++ b/previews/PR486/404.html @@ -0,0 +1,25 @@ + + + + + + 404 | YAXArrays.jl + + + + + + + + + + + + + + +
+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/cache.html b/previews/PR486/UserGuide/cache.html new file mode 100644 index 00000000..c2b42a0f --- /dev/null +++ b/previews/PR486/UserGuide/cache.html @@ -0,0 +1,32 @@ + + + + + + Caching YAXArrays | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Rasters.jl

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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/previews/PR486/UserGuide/chunk.html b/previews/PR486/UserGuide/chunk.html new file mode 100644 index 00000000..9338d963 --- /dev/null +++ b/previews/PR486/UserGuide/chunk.html @@ -0,0 +1,125 @@ + + + + + + Chunk YAXArrays | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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DimensionalData.jl

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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}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

And the saved file is also splitted into Chunks.

julia
f = tempname()
+savecube(a_chunked, f, backend=:zarr)
+Cube(f).chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Alternatively chunk sizes can be given by dimension name, so the following results in the same chunks:

julia
a_chunked = setchunks(a, (Dim_2=10, Dim_1=5))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Chunking Datasets ​

Setchunks can also be applied to a Dataset.

Set Chunks by Axis ​

Set chunk size for each axis occuring in a Dataset. This will be applied to all variables in the dataset:

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds, Dict("Dim_1"=>5, "Dim_2"=>10, "Dim_3"=>2))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

Saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking by Variable ​

The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking for all variables ​

The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
+dschunked = setchunks(ds,(5,10))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:1)   (1:5, 11:20, 1:1)
+ (6:10, 1:10, 1:1)  (6:10, 11:20, 1:1)
+
+[:, :, 2] =
+ (1:5, 1:10, 2:2)   (1:5, 11:20, 2:2)
+ (6:10, 1:10, 2:2)  (6:10, 11:20, 2:2)
+
+[:, :, 3] =
+ (1:5, 1:10, 3:3)   (1:5, 11:20, 3:3)
+ (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+
+Variables: 
+x, y, z

Suggestions on how to improve or add to these examples is welcome.

+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/combine.html b/previews/PR486/UserGuide/combine.html new file mode 100644 index 00000000..000c251a --- /dev/null +++ b/previews/PR486/UserGuide/combine.html @@ -0,0 +1,55 @@ + + + + + + Combine YAXArrays | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Rasters.jl

Read and manipulate !

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GeoInterface.jl

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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))
+second_half = YAXArray((YAX.time(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── loaded in memory ┤
+  data size: 96.0 bytes
+└───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+temperature = YAXArray((YAX.time(1:6),), rand(6))
+precipitation = YAXArray((YAX.time(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Variables(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
+├─────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└─────────────────────────────────────────────────────────────────┘
+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/compute.html b/previews/PR486/UserGuide/compute.html new file mode 100644 index 00000000..0ba9c9f6 --- /dev/null +++ b/previews/PR486/UserGuide/compute.html @@ -0,0 +1,419 @@ + + + + + + Compute YAXArrays | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Rasters.jl

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GeoInterface.jl

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GRIBDatasets.jl

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NCDatasets.jl

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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
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.40878677319295353
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
+map(a) do x
+    (x + offset) / 2 * 3
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices ​

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

julia
import Statistics: mean
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapCube ​

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

Operations over several YAXArrays ​

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

julia
using YAXArrays, Zarr
+using YAXArrays: YAXArrays as YAX
+using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
+    xout .= f.(lo, la, t)
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims(), InDims(), InDims("time")),
+           outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

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

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

but, we can generate a another cube with a different output order as follows

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

which outputs the same as the gen_cube.data[1, :, :] called above.

OutDims and YAXArray Properties ​

Here, we will consider different scenarios, namely how we deal with different input cubes and how to specify the output ones. We will illustrate this with the following test example and the subsequent function definitions.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Zarr
+using Random
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(range(1, 4, length=4)),
+    lat(range(1, 3, length=3)),
+    Variables(["a", "b"])
+)
+
+Random.seed!(123)
+data = rand(1:5, 5, 4, 3, 2)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims ​

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

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2.(xin_one)
+    xout_flat .= sum(xin_one)
+    return nothing
+end
+
+f1(xin) = xin + 1
+f2(xin) = xin + 2
f2 (generic function with 1 method)

now, we define InDims and OutDims:

julia
indims_one   = InDims("Time")
+# outputs dimension
+properties_one = Dict{String, Any}("name" => "plus_one")
+properties_two = Dict{String, Any}("name" => "plus_two")
+
+outdims_one = OutDims("Time"; properties=properties_one)
+outdims_two = OutDims("Time"; properties=properties_two)
+outdims_flat = OutDims(;) # it will get the default `layer` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
+    indims = indims_one,
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "name" => "plus_two"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├─────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "2d dimensional test cube"
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
+└─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2mix.(xin_one, xin_two)
+    xout_flat .= sum(xin_drei) # this will reduce the time dimension if we set outdims = OutDims()
+    return nothing
+end
+f2mix(xin_xyt, xin_xy) = xin_xyt - xin_xy
f2mix (generic function with 1 method)

Specify path in OutDims ​

julia
indims_one   = InDims("Time")
+indims_2d   = InDims() # ? it matches only to the other 2 dimensions and uses the same values for each time step
+properties = Dict{String, Any}("name"=> "many_to_many_two")
+outdims_one = OutDims("Time")
+outdims_two = OutDims("Time"; path = "test_mm.zarr", properties)
+outdims_flat = OutDims()
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(many_to_many, (yax_test, yax_2d, yax_test),
+    indims = (indims_one, indims_2d, indims_one),
+    outdims = (outdims_one, outdims_two, outdims_flat));

And we can open the one that was saved directly to disk.

julia
ds_mm = open_dataset("test_mm.zarr")
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{DateTime} [2022-01-01T00:00:00, …, 2022-01-05T00:00:00] ForwardOrdered Irregular Points,
+  → lon  Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points)
+
+Variables: 
+a, b

Different InDims names ​

Here, the goal is to operate at the pixel level (longitude, latitude), and then apply the corresponding function to the extracted values. Consider the following toy cubes:

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
+├───────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
+└───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(1:4), lat(1:3),)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
+    s = sum(abs.(xin_xyz))
+    return xin_xyt.^2 .+ s
+end
+
+function time_depth(xout, xin_one, xin_two)
+    xout .= mix_time_depth(xin_one, xin_two)
+    # Note also that there is no dot anymore in the function application!
+    return nothing
+end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
+    indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
  • TODO:

    • Example passing additional arguments to function.

    • MovingWindow

    • Multiple variables outputs, OutDims, in the same YAXArray

Creating a vector array ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
+    1 <= lon < 10 && 1 <= lat < 5 && return "A"
+    1 <= lon < 10 && 5 <= lat < 10 && return "B"
+    10 <= lon < 15 && 1 <= lat < 5 && return "C"
+    return "D"
+end
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0   1.28571  1.57143  1.85714  …  4.14286  4.42857  4.71429  5.0
+  1.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  2.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  3.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  4.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  5.0   "A"   "A"      "A"      "A"     …   "A"      "A"      "A"      "B"
+  6.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571   1.57143    …  4.42857   4.71429   5.0
+  1.0  0.17593   0.417937  0.0723492     0.178603  0.781773  0.875658
+  2.0  0.701332  0.15394   0.685454      0.372761  0.984803  0.472308
+  3.0  0.120997  0.829062  0.684389      0.463503  0.840389  0.536399
+  ⋮                                   ⋱                      ⋮
+  8.0  0.145747  0.432286  0.465103      0.889583  0.514979  0.671662
+  9.0  0.538981  0.497189  0.167676      0.595405  0.752417  0.93986
+ 10.0  0.824354  0.376135  0.551732   …  0.101524  0.121947  0.508557

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

julia
regions = ["A", "B", "C", "D"]
+points_of_regions = map(enumerate(regions)) do (i,region)
+    region => findall(isequal(region), regions_mat)
+end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
+  "A" => [CartesianIndex(1, 1), CartesianIndex(2, 1), CartesianIndex(3, 1), Car…
+  "B" => [CartesianIndex(1, 15), CartesianIndex(2, 15), CartesianIndex(3, 15), …
+  "C" => [CartesianIndex(10, 1), CartesianIndex(10, 2), CartesianIndex(10, 3), …
+  "D" => [CartesianIndex(10, 15)]

Finally, we can transform the entire raster array:

julia
vector_array = mapCube(
+    raster_arr,
+    indims=InDims("lon", "lat"),
+    outdims=OutDims(Dim{:region}(regions))
+) do xout, xin
+    for (region_pos, points) in enumerate(points_of_regions.vals)
+        # aggregate values of points in the current region at the current date
+        xout[region_pos] = sum(view(xin, points))
+    end
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
+  → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Distributed Computation ​

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

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Distributed
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
+
+addprocs(2)
+
+@everywhere begin
+  using YAXArrays
+  using Zarr
+  using Statistics
+end
+
+@everywhere function mymean(output, pixel)
+  @show "doing a mean"
+     output[:] .= mean(pixel)
+end
+
+mapCube(mymean, a, indims=InDims("time"), outdims=OutDims())

In the last example, mapCube was used to map the mymean function. mapslices is a convenient function that can replace mapCube, where you can omit defining an extra function with the output argument as an input (e.g. mymean). It is possible to simply use mapslice

julia
mapslices(mean ∘ skipmissing, a, dims="time")

It is also possible to distribute easily the workload on a cluster, with little modification to the code. To do so, we use the ClusterManagers package.

julia
using Distributed
+using ClusterManagers
+addprocs(SlurmManager(10))
+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/convert.html b/previews/PR486/UserGuide/convert.html new file mode 100644 index 00000000..0a2ce3e4 --- /dev/null +++ b/previews/PR486/UserGuide/convert.html @@ -0,0 +1,73 @@ + + + + + + Convert YAXArrays | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
+ 0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
+ 0.83556   0.456765  0.197238  0.645758     0.74732   0.652339   0.935631
+ 0.337926  0.151146  0.673373  0.169284     0.75269   0.166212   0.0358348
+ 0.594514  0.364288  0.78467   0.830391     0.128204  0.174934   0.0210077

Convert Raster ​

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

julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
julia
ras2 = Raster(a)

Convert DimArray ​

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

Convert DimArray to YAXArray:

julia
using DimensionalData
+using YAXArrayBase
+
+dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0       11.0        12.0        13.0       14.0       15.0
+ 1     0.862644   0.872575    0.0620649   0.193109   0.475725   0.953391
+ 2     0.203714   0.770949    0.731779    0.71314    0.687891   0.435994
+ 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.

+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/create.html b/previews/PR486/UserGuide/create.html new file mode 100644 index 00000000..c72b467a --- /dev/null +++ b/previews/PR486/UserGuide/create.html @@ -0,0 +1,75 @@ + + + + + + Create YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

Appearance

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

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} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 7.81 KB
+└──────────────────────────────────────────────────────────────────────┘

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

julia
using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data2 = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+→ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)

Create a Dataset ​

julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
+
+Variables: 
+a2, a3
+
+Properties: Dict(:origin => "user guide")
+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/faq.html b/previews/PR486/UserGuide/faq.html new file mode 100644 index 00000000..355f3dc7 --- /dev/null +++ b/previews/PR486/UserGuide/faq.html @@ -0,0 +1,391 @@ + + + + + + Frequently Asked Questions (FAQ) | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

Appearance

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Frequently Asked Questions (FAQ) ​

The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.

Extract the axes names from a Cube ​

julia
using YAXArrays
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 3.91 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

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

julia
julia> dims(c)
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

INFO

Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.

rebuild ​

As an example let's consider the following

julia
using YAXArrays
+using DimensionalData
+
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube ​

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

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
+collect(c.axes[1].val)
10-element Vector{Int64}:
+  1
+  2
+  3
+  4
+  5
+  6
+  7
+  8
+  9
+ 10

to collect data from a cube works exactly the same as doing it from an array

julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes ​

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

Let's create two dummy cubes

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15))
+    )
+
+data1 = rand(20, 10, 15)
+ds1 = YAXArray(axlist, data1)
+
+data2 = rand(20, 10, 15)
+ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["var1", "var2"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Subsetting a YAXArray ​

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

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

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
+→ Lat  1:10,
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

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

Subset YAXArray by a specific date

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

Subset YAXArray by a date range

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

Subset YAXArray by longitude and latitude

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

Subsetting a Dataset ​

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

Subsetting a Dataset whose variables share all their dimensions ​

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

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" option for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
+
+var1 = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
+var2 = YAXArray(axes, reshape((1:3600)*5, (10, 10, 36)))
+
+ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2

Subsetting a Dataset whose variables share some but not all of their dimensions ​

In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.

But we can also subset a variable by the values of another variable with which it shares some dimensions.

Warning

If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.

Let's make an example.

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" selector for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+common_axis = Dim{:points}(1:100)
+time_axis =   YAX.Time(t)
+
+# Note that longitudes and latitudes are not dimensions, but YAXArrays
+longitudes = YAXArray((common_axis,), rand(1:369, 100)) # 100 random values taken from 1 to 359
+latitudes  = YAXArray((common_axis,), rand(0:90, 100))  # 100 random values taken from 0 to 90
+temperature = YAXArray((common_axis, time_axis), rand(-40:40, (100, 36)))
+
+ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
+Shared Axes: 
+  (↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
+
+Variables: 
+latitudes, longitudes
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  Variables: 
+  temperature

Select all points between 20ºN and 85ºN, and 0ºE to 180ºE

julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+                             ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes
+
+  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)
+  Variables: 
+  temperature

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+longitudes_yasxa = readcubedata(ds["longitudes"])
+ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
+                             longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  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)
+  Variables: 
+  temperature
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
+           (xin * π) ./ maximum(skipmissing(xin))
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function? ​

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
+GLMakie.activate!()
+# This is how our classification map looks like
+fig, ax, obj = heatmap(classes;
+    colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
+cbar = Colorbar(fig[1,2], obj)
+fig

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

julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+using OnlineStats
+## visualization of the CubeTable
+c_tbl = DataFrame(t[1])
+first(c_tbl, 5)

In this line we calculate the Mean for each class

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
+  → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset ​

One variable name ​

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
+Shared Axes:
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+a

Multiple variable names ​

julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  b
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  c

WARNING

You will not be able to save this dataset, first you will need to rename those dimensions with the same name but different values.

Ho do I construct a Dataset from a TimeArray ​

In this section we will use MarketData.jl and TimeSeries.jl to simulate some stocks.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using MarketData, TimeSeries
+
+stocks = Dict(:Stock1 => random_ohlcv(), :Stock2 => random_ohlcv(), :Stock3 => random_ohlcv())
+d_keys = keys(stocks)
KeySet for a Dict{Symbol, TimeSeries.TimeArray{Float64, 2, DateTime, Matrix{Float64}}} with 3 entries. Keys:
+  :Stock3
+  :Stock1
+  :Stock2

currently there is not direct support to obtain dims from a TimeArray, but we can code a function for it

julia
getTArrayAxes(ta::TimeArray) = (YAX.time(timestamp(ta)), Variables(colnames(ta)), );

then, we create the YAXArrays as

julia
yax_list = [YAXArray(getTArrayAxes(stocks[k]), values(stocks[k])) for k in d_keys];

and a Dataset with all stocks names

julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  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
+
+  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: 
+  Stock2
+
+  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
+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)
+
+Variables: 
+Stock1, Stock2, Stock3

now they are printed together, showing that is exactly the same axis structure for all variables.

Create a YAXArray with unions containing Strings ​

julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  summarysize: 121.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── 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/previews/PR486/UserGuide/group.html b/previews/PR486/UserGuide/group.html new file mode 100644 index 00000000..c995379c --- /dev/null +++ b/previews/PR486/UserGuide/group.html @@ -0,0 +1,235 @@ + + + + + + Group YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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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
+using Dates
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+filename = Downloads.download(url_path, "rasm.nc")
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

WARNING

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

julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :time)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:time)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, YAX.time => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:time)
+    mean_g = dropdims.(mean_g, dims=:time)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :time);
+    weighted_g = dropdims.(weighted_g, dims=:time)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :time)
+    return mean_g, weighted_g, diff_g, seasons_g
+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 ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404]

seasons ​

Due to the groupby function we will obtain new grouping names, in this case in the time dimension:

julia
seasons_g = lookup(mean_g, :time)
Categorical{Symbol} Unordered
+wrapping: 4-element Vector{Symbol}:
+ :Dec_Jan_Feb
+ :Mar_Apr_May
+ :Jun_Jul_Aug
+ :Sep_Oct_Nov

Next, we will weight this grouping by days/month in each group.

GroupBy: weight ​

Create a YAXArray for the month length

julia
tempo = dims(ds, :time)
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 288.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  [270]
+ :Mar_Apr_May  [276]
+ :Jun_Jul_Aug  [276]
+ :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
+ :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
+ :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1.0
+ :Mar_Apr_May  1.0
+ :Jun_Jul_Aug  1.0
+ :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.32149 1.33565; NaN NaN … 1.29564 1.32555; … ; NaN NaN … 1.3188 1.3169; NaN NaN … 1.17863 1.18434;;; NaN NaN … 1.29816 1.34218; NaN NaN … 1.30113 1.35483; … ; NaN NaN … 1.30142 1.31753; NaN NaN … 1.16258 1.17647;;; NaN NaN … 1.34549 1.37878; NaN NaN … 1.36836 1.41634; … ; NaN NaN … 1.34832 1.38364; NaN NaN … 1.17852 1.16713]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.87705 1.90365; NaN NaN … 2.30018 2.35432; … ; NaN NaN … 2.41049 2.43254; NaN NaN … 2.65105 2.69085;;; NaN NaN … 1.86457 1.90712; NaN NaN … 2.2894 2.34818; … ; NaN NaN … 2.3866 2.41241; NaN NaN … 2.61197 2.64976;;; NaN NaN … 1.89237 1.8984; NaN NaN … 2.29473 2.312; … ; NaN NaN … 2.36142 2.36126; NaN NaN … 2.56632 2.59085]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :time);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.019016 -0.0115514; NaN NaN … -0.0183003 -0.00990356]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.0121037 -0.0123091; NaN NaN … -0.0127077 -0.0117519]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.00709111 -0.00693713; NaN NaN … -0.00684233 -0.00722034]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 0.0180572 0.0182373; NaN NaN … 0.0178074 0.018571]

All the previous steps are equivalent to calling the function defined at the top:

julia
mean_g, weighted_g, diff_g, seasons_g = weighted_seasons(ds)

Once all calculations are done we can plot the results with Makie.jl as follows:

julia
using CairoMakie
+# define plot arguments/attributes
+colorrange = (-30,30)
+colormap = Reverse(:Spectral)
+highclip = :red
+lowclip = :grey15
+cb_label =  ds_o.properties["long_name"]
"Surface air temperature"
julia
with_theme(theme_ggplot2()) do
+    hm_o, hm_d, hm_w = nothing, nothing, nothing
+    # the figure
+    fig = Figure(; size = (850,500))
+    axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
+    for (j, s) in enumerate(seasons_g)
+        hm_o = heatmap!(axs[1,j], mean_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_w = heatmap!(axs[2,j], weighted_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_d = heatmap!(axs[3,j], diff_g[time=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
+            colormap=:diverging_bwr_20_95_c54_n256)
+    end
+    Colorbar(fig[1:2,5], hm_o, label=cb_label)
+    Colorbar(fig[3,5], hm_d, label="Tair")
+    hidedecorations!.(axs, grid=false, ticks=false, label=false)
+    # some labels
+    [axs[1,j].title = string.(s) for (j,s) in enumerate(seasons_g)]
+    Label(fig[0,1:5], "Seasonal Surface Air Temperature", fontsize=18, font=:bold)
+    axs[1,1].ylabel = "Unweighted"
+    axs[2,1].ylabel = "Weighted"
+    axs[3,1].ylabel = "Difference"
+    colgap!(fig.layout, 5)
+    rowgap!(fig.layout, 5)
+    fig
+end

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

+ + + + \ No newline at end of file diff --git a/previews/PR486/UserGuide/read.html b/previews/PR486/UserGuide/read.html new file mode 100644 index 00000000..c5eb4f90 --- /dev/null +++ b/previews/PR486/UserGuide/read.html @@ -0,0 +1,217 @@ + + + + + + Read YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

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)
+ds = open_dataset(store)
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"…)

We can set path to a URL, a local directory, or in this case to a cloud object storage path.

A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"         => "K"
+  "history"       => "2019-07-21T06:26:13Z altered by CMOR: Treated scalar dime…
+  "name"          => "tas"
+  "cell_methods"  => "area: mean time: point"
+  "cell_measures" => "area: areacella"
+  "long_name"     => "Near-Surface Air Temperature"
+  "coordinates"   => "height"
+  "standard_name" => "air_temperature"
+  "_FillValue"    => 1.0f20
+  "comment"       => "near-surface (usually, 2 meter) air temperature"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 69.02 GB
+└──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF ​

Open a NetCDF file as a Dataset:

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
my_lock = ReentrantLock()
+Threads.@threads for i in 1:10
+    @lock my_lock @info ds.tos[1, 1, 1]
+end
[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing

This code will ensure that the data is only accessed by one thread at a time, i.e. making it actual single-threaded but thread-safe.

Read GDAL (GeoTIFF, GeoJSON) ​

All GDAL compatible files can be read as a YAXArrays.Dataset after loading ArchGDAL:

julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ X Sampled{Float64} -180.0:0.17578125:179.82421875 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 90.0:-0.17578125:-89.82421875 ReverseOrdered Regular Points)
+
+Variables: 
+Blue, Green, Red
+
+Properties: Dict{String, Any}("projection" => "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")

Load data into memory ​

For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.

readcubedata ​

julia
readcubedata(ds.tos)
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

Note how the loading status changes from loaded lazily to loaded in memory.

open_mfdataset ​

There are situations when we would like to open and concatenate a list of dataset paths along a certain dimension. For example, to concatenate a list of NetCDF files along a new time dimension, one can use:

creation of NetCDF files
julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+
+dates_1 = [Date(2020, 1, 1) + Dates.Day(i) for i in 1:3]
+dates_2 = [Date(2020, 1, 4) + Dates.Day(i) for i in 1:3]
+
+a1 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+a2 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+
+a3 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_1)), rand(5, 7, 3))
+a4 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_2)), rand(5, 7, 3))
+
+savecube(a1, "a1.nc")
+savecube(a2, "a2.nc")
+savecube(a3, "a3.nc")
+savecube(a4, "a4.nc")
┌ 5×7×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [2020-01-05, …, 2020-01-07] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 840.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

along a new dimension ​

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a1.nc", "a2.nc"]
+
+dates_read = [Date(2024, 1, 1) + Dates.Day(i) for i in 1:2]
+ds = open_mfdataset(DD.DimArray(files, YAX.time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

and even opening files along a new Time dimension that already have a time dimension

julia
files = ["a3.nc", "a4.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.Time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-04T00:00:00] ForwardOrdered Irregular Points,
+  ⬔ Time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

Note that opening along a new dimension name without specifying values also works; however, it defaults to 1:length(files) for the dimension values.

julia
files = ["a1.nc", "a2.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.time))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Int64} 1:2 ForwardOrdered Regular Points)
+
+Variables: 
+layer

along a existing dimension ​

Another use case is when we want to open files along an existing dimension. In this case, open_mfdataset will concatenate the paths along the specified dimension

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a3.nc", "a4.nc"]
+
+ds = open_mfdataset(DD.DimArray(files, YAX.time()))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-07T00:00:00] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

where the contents of the time dimension are the merged values from both files

julia
julia> ds["time"]
time Sampled{DateTime} ForwardOrdered Irregular DimensionalData.Dimensions.Lookups.Points
+wrapping: 6-element Vector{DateTime}:
+ 2020-01-02T00:00:00
+ 2020-01-03T00:00:00
+ 2020-01-04T00:00:00
+ 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/previews/PR486/UserGuide/select.html b/previews/PR486/UserGuide/select.html new file mode 100644 index 00000000..91146935 --- /dev/null +++ b/previews/PR486/UserGuide/select.html @@ -0,0 +1,303 @@ + + + + + + Select YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Select YAXArrays and Datasets ​

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

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

Select elements ​

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
+time1 = DateTime360Day(2001,01,16)
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────────────────┴ metadata ┐
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
+└────────────────────────────────────────────────────────────────────────────────┘

Select ranges ​

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

julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals ​

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

julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension ​

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

julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  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/previews/PR486/UserGuide/types.html b/previews/PR486/UserGuide/types.html new file mode 100644 index 00000000..edab0beb --- /dev/null +++ b/previews/PR486/UserGuide/types.html @@ -0,0 +1,29 @@ + + + + + + Types | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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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/previews/PR486/UserGuide/write.html b/previews/PR486/UserGuide/write.html new file mode 100644 index 00000000..03992ee8 --- /dev/null +++ b/previews/PR486/UserGuide/write.html @@ -0,0 +1,98 @@ + + + + + + Write YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Write Zarr ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

zarr compression ​

Save a dataset to Zarr format with compression:

julia
n = 9 # compression level, number between 0 (no compression) and 9 (max compression)
+compression = Zarr.BloscCompressor(; clevel=n)
+
+savedataset(ds; path="ds_c.zarr", driver=:zarr, compressor=compression)

More on Zarr Compressors. Also, if you use this option and don't notice a significant improvement, please feel free to open an issue or start a discussion.

Write NetCDF ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

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)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+  Variables: 
+  tos
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
+  Variables: 
+  z
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

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

WARNING

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

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

Now, we check that all the values are missing

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

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

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+
+Variables: 
+skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset ​

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

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

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 0.716952  0.95965   0.599987   0.337016
+ 0.506947  0.923876  0.0940127  0.54942
+ 0.847062  0.298617  0.182371   0.921

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

julia
ds_open = open_dataset("skeleton.zarr")
+ds_array = ds_open["skeleton"]
+ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 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.

+ + + + \ No newline at end of file diff --git a/previews/PR486/api.html b/previews/PR486/api.html new file mode 100644 index 00000000..16a936d3 --- /dev/null +++ b/previews/PR486/api.html @@ -0,0 +1,33 @@ + + + + + + API Reference | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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.
+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

+ + + + \ No newline at end of file diff --git a/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.js b/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.js new file mode 100644 index 00000000..56ea08af --- /dev/null +++ b/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.js @@ -0,0 +1,5 @@ +import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.piKCME0r.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`

Caching YAXArrays ​

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

julia
using YAXArrays, Zarr
+ds = open_dataset("path/to/source")
+cachesize = 500 #MB
+cache(ds,maxsize = cachesize)

The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays

julia
yax = ds.avariable
+cache(yax,maxsize = 1000)
`,5)]))}const g=a(n,[["render",h]]);export{o as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.lean.js b/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.lean.js new file mode 100644 index 00000000..56ea08af --- /dev/null +++ b/previews/PR486/assets/UserGuide_cache.md.tsnWjcXo.lean.js @@ -0,0 +1,5 @@ +import{_ as a,c as i,a2 as e,o as t}from"./chunks/framework.piKCME0r.js";const o=JSON.parse('{"title":"Caching YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/cache.md","filePath":"UserGuide/cache.md","lastUpdated":null}'),n={name:"UserGuide/cache.md"};function h(l,s,p,r,c,d){return t(),i("div",null,s[0]||(s[0]=[e(`

Caching YAXArrays ​

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

julia
using YAXArrays, Zarr
+ds = open_dataset("path/to/source")
+cachesize = 500 #MB
+cache(ds,maxsize = cachesize)

The above will wrap every array in the dataset into its own cache, where the 500MB are distributed equally across datasets. Alternatively individual caches can be applied to single YAXArrays

julia
yax = ds.avariable
+cache(yax,maxsize = 1000)
`,5)]))}const g=a(n,[["render",h]]);export{o as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.js b/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.js new file mode 100644 index 00000000..814e2c67 --- /dev/null +++ b/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.js @@ -0,0 +1,98 @@ +import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`

Chunk YAXArrays ​

Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.

To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.

Chunking YAXArrays ​

julia
using YAXArrays, Zarr
+a = YAXArray(rand(10,20))
+a_chunked = setchunks(a, (5,10))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

And the saved file is also splitted into Chunks.

julia
f = tempname()
+savecube(a_chunked, f, backend=:zarr)
+Cube(f).chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Alternatively chunk sizes can be given by dimension name, so the following results in the same chunks:

julia
a_chunked = setchunks(a, (Dim_2=10, Dim_1=5))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Chunking Datasets ​

Setchunks can also be applied to a Dataset.

Set Chunks by Axis ​

Set chunk size for each axis occuring in a Dataset. This will be applied to all variables in the dataset:

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds, Dict("Dim_1"=>5, "Dim_2"=>10, "Dim_3"=>2))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

Saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking by Variable ​

The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking for all variables ​

The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
+dschunked = setchunks(ds,(5,10))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:1)   (1:5, 11:20, 1:1)
+ (6:10, 1:10, 1:1)  (6:10, 11:20, 1:1)
+
+[:, :, 2] =
+ (1:5, 1:10, 2:2)   (1:5, 11:20, 2:2)
+ (6:10, 1:10, 2:2)  (6:10, 11:20, 2:2)
+
+[:, :, 3] =
+ (1:5, 1:10, 3:3)   (1:5, 11:20, 3:3)
+ (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+
+Variables: 
+x, y, z

Suggestions on how to improve or add to these examples is welcome.

`,36)]))}const c=a(l,[["render",h]]);export{g as __pageData,c as default}; diff --git a/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.lean.js b/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.lean.js new file mode 100644 index 00000000..814e2c67 --- /dev/null +++ b/previews/PR486/assets/UserGuide_chunk.md.DKasdhoL.lean.js @@ -0,0 +1,98 @@ +import{_ as a,c as i,a2 as n,o as p}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),l={name:"UserGuide/chunk.md"};function h(e,s,t,k,r,d){return p(),i("div",null,s[0]||(s[0]=[n(`

Chunk YAXArrays ​

Thinking about chunking is important when it comes to analyzing your data, because in most situations this will not fit into memory, hence having the fastest read access to it is crucial for your workflows. For example, for geo-spatial data do you want fast access on time or space, or... think about it.

To determine the chunk size of the array representation on disk, call the setchunks function prior to saving.

Chunking YAXArrays ​

julia
using YAXArrays, Zarr
+a = YAXArray(rand(10,20))
+a_chunked = setchunks(a, (5,10))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

And the saved file is also splitted into Chunks.

julia
f = tempname()
+savecube(a_chunked, f, backend=:zarr)
+Cube(f).chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Alternatively chunk sizes can be given by dimension name, so the following results in the same chunks:

julia
a_chunked = setchunks(a, (Dim_2=10, Dim_1=5))
+a_chunked.chunks
2×2 DiskArrays.GridChunks{2, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+ (1:5, 1:10)   (1:5, 11:20)
+ (6:10, 1:10)  (6:10, 11:20)

Chunking Datasets ​

Setchunks can also be applied to a Dataset.

Set Chunks by Axis ​

Set chunk size for each axis occuring in a Dataset. This will be applied to all variables in the dataset:

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds, Dict("Dim_1"=>5, "Dim_2"=>10, "Dim_3"=>2))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

Saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking by Variable ​

The following will set the chunk size for each Variable separately and results in exactly the same chunking as the example above

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10)), z = YAXArray(rand(10,20,5)))
+dschunked = setchunks(ds,(x = (5,10), y = Dict("Dim_1"=>5), z = (Dim_1 = 5, Dim_2 = 10, Dim_3 = 2)))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:2)   (1:5, 11:20, 1:2)
+ (6:10, 1:10, 1:2)  (6:10, 11:20, 1:2)
+
+[:, :, 2] =
+ (1:5, 1:10, 3:4)   (1:5, 11:20, 3:4)
+ (6:10, 1:10, 3:4)  (6:10, 11:20, 3:4)
+
+[:, :, 3] =
+ (1:5, 1:10, 5:5)   (1:5, 11:20, 5:5)
+ (6:10, 1:10, 5:5)  (6:10, 11:20, 5:5)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+y
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+  Variables: 
+  x
+
+  Additional Axes: 
+  (↓ Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  → Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  z

Set chunking for all variables ​

The following code snippet only works when all member variables of the dataset have the same shape and sets the output chunks for all arrays.

julia
using YAXArrays, Zarr
+ds = Dataset(x = YAXArray(rand(10,20)), y = YAXArray(rand(10,20)), z = YAXArray(rand(10,20)))
+dschunked = setchunks(ds,(5,10))
+Cube(dschunked).chunks
2×2×3 DiskArrays.GridChunks{3, Tuple{DiskArrays.RegularChunks, DiskArrays.RegularChunks, DiskArrays.RegularChunks}}:
+[:, :, 1] =
+ (1:5, 1:10, 1:1)   (1:5, 11:20, 1:1)
+ (6:10, 1:10, 1:1)  (6:10, 11:20, 1:1)
+
+[:, :, 2] =
+ (1:5, 1:10, 2:2)   (1:5, 11:20, 2:2)
+ (6:10, 1:10, 2:2)  (6:10, 11:20, 2:2)
+
+[:, :, 3] =
+ (1:5, 1:10, 3:3)   (1:5, 11:20, 3:3)
+ (6:10, 1:10, 3:3)  (6:10, 11:20, 3:3)

saving...

julia
f = tempname()
+savedataset(dschunked, path=f, driver=:zarr)
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points)
+
+Variables: 
+x, y, z

Suggestions on how to improve or add to these examples is welcome.

`,36)]))}const c=a(l,[["render",h]]);export{g as __pageData,c as default}; diff --git a/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.js b/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.js new file mode 100644 index 00000000..4b64d7ef --- /dev/null +++ b/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.js @@ -0,0 +1,28 @@ +import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function h(l,s,p,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`

Combine YAXArrays ​

Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.

cat along an existing dimension ​

Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+first_half = YAXArray((YAX.time(1:6),), rand(6))
+second_half = YAXArray((YAX.time(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── loaded in memory ┤
+  data size: 96.0 bytes
+└───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+temperature = YAXArray((YAX.time(1:6),), rand(6))
+precipitation = YAXArray((YAX.time(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Variables(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
+├─────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└─────────────────────────────────────────────────────────────────┘
`,10)]))}const g=i(t,[["render",h]]);export{E as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.lean.js b/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.lean.js new file mode 100644 index 00000000..4b64d7ef --- /dev/null +++ b/previews/PR486/assets/UserGuide_combine.md.DX6-a-cs.lean.js @@ -0,0 +1,28 @@ +import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const E=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),t={name:"UserGuide/combine.md"};function h(l,s,p,k,r,d){return e(),a("div",null,s[0]||(s[0]=[n(`

Combine YAXArrays ​

Data is often scattered across multiple files and corresponding arrays, e.g. one file per time step. This section describes methods on how to combine them into a single YAXArray.

cat along an existing dimension ​

Here we use cat to combine two arrays consisting of data from the first and the second half of a year into one single array containing the whole year. We glue the arrays along the first dimension using dims = 1: The resulting array whole_year still has one dimension, i.e. time, but with 12 instead of 6 elements.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+first_half = YAXArray((YAX.time(1:6),), rand(6))
+second_half = YAXArray((YAX.time(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
┌ 12-element YAXArray{Float64, 1} ┐
+├─────────────────────────────────┴─────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── loaded in memory ┤
+  data size: 96.0 bytes
+└───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+temperature = YAXArray((YAX.time(1:6),), rand(6))
+precipitation = YAXArray((YAX.time(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Variables(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
┌ 6×2 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time      Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → Variables Categorical{String} ["temp", "prep"] ReverseOrdered
+├─────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└─────────────────────────────────────────────────────────────────┘
`,10)]))}const g=i(t,[["render",h]]);export{E as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.js b/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.js new file mode 100644 index 00000000..f18148ad --- /dev/null +++ b/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.js @@ -0,0 +1,392 @@ +import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function l(e,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`

Compute YAXArrays ​

This section describes how to create new YAXArrays by performing operations on them.

Let's start by creating an example dataset:

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.40878677319295353
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
+map(a) do x
+    (x + offset) / 2 * 3
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices ​

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

julia
import Statistics: mean
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapCube ​

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

Operations over several YAXArrays ​

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

julia
using YAXArrays, Zarr
+using YAXArrays: YAXArrays as YAX
+using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
+    xout .= f.(lo, la, t)
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims(), InDims(), InDims("time")),
+           outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

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

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

but, we can generate a another cube with a different output order as follows

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

which outputs the same as the gen_cube.data[1, :, :] called above.

OutDims and YAXArray Properties ​

Here, we will consider different scenarios, namely how we deal with different input cubes and how to specify the output ones. We will illustrate this with the following test example and the subsequent function definitions.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Zarr
+using Random
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(range(1, 4, length=4)),
+    lat(range(1, 3, length=3)),
+    Variables(["a", "b"])
+)
+
+Random.seed!(123)
+data = rand(1:5, 5, 4, 3, 2)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims ​

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

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2.(xin_one)
+    xout_flat .= sum(xin_one)
+    return nothing
+end
+
+f1(xin) = xin + 1
+f2(xin) = xin + 2
f2 (generic function with 1 method)

now, we define InDims and OutDims:

julia
indims_one   = InDims("Time")
+# outputs dimension
+properties_one = Dict{String, Any}("name" => "plus_one")
+properties_two = Dict{String, Any}("name" => "plus_two")
+
+outdims_one = OutDims("Time"; properties=properties_one)
+outdims_two = OutDims("Time"; properties=properties_two)
+outdims_flat = OutDims(;) # it will get the default \`layer\` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
+    indims = indims_one,
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "name" => "plus_two"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├─────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "2d dimensional test cube"
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
+└─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2mix.(xin_one, xin_two)
+    xout_flat .= sum(xin_drei) # this will reduce the time dimension if we set outdims = OutDims()
+    return nothing
+end
+f2mix(xin_xyt, xin_xy) = xin_xyt - xin_xy
f2mix (generic function with 1 method)

Specify path in OutDims ​

julia
indims_one   = InDims("Time")
+indims_2d   = InDims() # ? it matches only to the other 2 dimensions and uses the same values for each time step
+properties = Dict{String, Any}("name"=> "many_to_many_two")
+outdims_one = OutDims("Time")
+outdims_two = OutDims("Time"; path = "test_mm.zarr", properties)
+outdims_flat = OutDims()
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(many_to_many, (yax_test, yax_2d, yax_test),
+    indims = (indims_one, indims_2d, indims_one),
+    outdims = (outdims_one, outdims_two, outdims_flat));

And we can open the one that was saved directly to disk.

julia
ds_mm = open_dataset("test_mm.zarr")
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{DateTime} [2022-01-01T00:00:00, …, 2022-01-05T00:00:00] ForwardOrdered Irregular Points,
+  → lon  Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points)
+
+Variables: 
+a, b

Different InDims names ​

Here, the goal is to operate at the pixel level (longitude, latitude), and then apply the corresponding function to the extracted values. Consider the following toy cubes:

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
+├───────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
+└───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(1:4), lat(1:3),)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
+    s = sum(abs.(xin_xyz))
+    return xin_xyt.^2 .+ s
+end
+
+function time_depth(xout, xin_one, xin_two)
+    xout .= mix_time_depth(xin_one, xin_two)
+    # Note also that there is no dot anymore in the function application!
+    return nothing
+end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
+    indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Creating a vector array ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
+    1 <= lon < 10 && 1 <= lat < 5 && return "A"
+    1 <= lon < 10 && 5 <= lat < 10 && return "B"
+    10 <= lon < 15 && 1 <= lat < 5 && return "C"
+    return "D"
+end
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0   1.28571  1.57143  1.85714  …  4.14286  4.42857  4.71429  5.0
+  1.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  2.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  3.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  4.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  5.0   "A"   "A"      "A"      "A"     …   "A"      "A"      "A"      "B"
+  6.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571   1.57143    …  4.42857   4.71429   5.0
+  1.0  0.17593   0.417937  0.0723492     0.178603  0.781773  0.875658
+  2.0  0.701332  0.15394   0.685454      0.372761  0.984803  0.472308
+  3.0  0.120997  0.829062  0.684389      0.463503  0.840389  0.536399
+  ⋮                                   ⋱                      ⋮
+  8.0  0.145747  0.432286  0.465103      0.889583  0.514979  0.671662
+  9.0  0.538981  0.497189  0.167676      0.595405  0.752417  0.93986
+ 10.0  0.824354  0.376135  0.551732   …  0.101524  0.121947  0.508557

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

julia
regions = ["A", "B", "C", "D"]
+points_of_regions = map(enumerate(regions)) do (i,region)
+    region => findall(isequal(region), regions_mat)
+end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
+  "A" => [CartesianIndex(1, 1), CartesianIndex(2, 1), CartesianIndex(3, 1), Car…
+  "B" => [CartesianIndex(1, 15), CartesianIndex(2, 15), CartesianIndex(3, 15), …
+  "C" => [CartesianIndex(10, 1), CartesianIndex(10, 2), CartesianIndex(10, 3), …
+  "D" => [CartesianIndex(10, 15)]

Finally, we can transform the entire raster array:

julia
vector_array = mapCube(
+    raster_arr,
+    indims=InDims("lon", "lat"),
+    outdims=OutDims(Dim{:region}(regions))
+) do xout, xin
+    for (region_pos, points) in enumerate(points_of_regions.vals)
+        # aggregate values of points in the current region at the current date
+        xout[region_pos] = sum(view(xin, points))
+    end
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
+  → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Distributed Computation ​

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

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Distributed
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
+
+addprocs(2)
+
+@everywhere begin
+  using YAXArrays
+  using Zarr
+  using Statistics
+end
+
+@everywhere function mymean(output, pixel)
+  @show "doing a mean"
+     output[:] .= mean(pixel)
+end
+
+mapCube(mymean, a, indims=InDims("time"), outdims=OutDims())

In the last example, mapCube was used to map the mymean function. mapslices is a convenient function that can replace mapCube, where you can omit defining an extra function with the output argument as an input (e.g. mymean). It is possible to simply use mapslice

julia
mapslices(mean ∘ skipmissing, a, dims="time")

It is also possible to distribute easily the workload on a cluster, with little modification to the code. To do so, we use the ClusterManagers package.

julia
using Distributed
+using ClusterManagers
+addprocs(SlurmManager(10))
`,138)]))}const o=i(p,[["render",l]]);export{g as __pageData,o as default}; diff --git a/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.lean.js b/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.lean.js new file mode 100644 index 00000000..f18148ad --- /dev/null +++ b/previews/PR486/assets/UserGuide_compute.md.CUq5TZYp.lean.js @@ -0,0 +1,392 @@ +import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),p={name:"UserGuide/compute.md"};function l(e,s,h,k,d,r){return t(),a("div",null,s[0]||(s[0]=[n(`

Compute YAXArrays ​

This section describes how to create new YAXArrays by performing operations on them.

Let's start by creating an example dataset:

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.40878677319295353
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

Some arrays, e.g. those saved in a cloud object storage are immutable making any modification of the data impossible.

Arithmetics ​

Add a value to all elements of an array and save it as a new array:

julia
a2 = a .+ 5
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2[1,2,3] == a[1,2,3] + 5
true

map ​

Apply a function on every element of an array individually:

julia
offset = 5
+map(a) do x
+    (x + offset) / 2 * 3
+end
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapslices ​

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

julia
import Statistics: mean
+mapslices(mean, a, dims="Time")
┌ 10×15 YAXArray{Union{Missing, Float64}, 2} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

mapCube ​

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

Operations over several YAXArrays ​

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

julia
using YAXArrays, Zarr
+using YAXArrays: YAXArrays as YAX
+using Dates

Define function in space and time

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

now, mapCube requires this function to be wrapped as follows

julia
function g(xout, lo, la, t)
+    xout .= f.(lo, la, t)
+end
g (generic function with 1 method)

INFO

Note the . after f, this is because we will slice across time, namely, the function is broadcasted along this dimension.

Here, we do create YAXArrays only with the desired dimensions as

julia
julia> lon_yax = YAXArray(lon(range(1, 15)))
┌ 15-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Int64} 1:15 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 120.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> lat_yax = YAXArray(lat(range(1, 10)))
┌ 10-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 80.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

And a time Cube's Axis

julia
tspan = Date("2022-01-01"):Day(1):Date("2022-01-30")
+time_yax = YAXArray(YAX.time(tspan))
┌ 30-element YAXArray{Date, 1} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims(), InDims(), InDims("time")),
+           outdims = OutDims("time", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 30×15×10 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

"time axis goes first"

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

Check that it is working

julia
julia> gen_cube.data[1, :, :]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

but, we can generate a another cube with a different output order as follows

julia
julia> gen_cube = mapCube(g, (lon_yax, lat_yax, time_yax);
+           indims = (InDims("lon"), InDims(), InDims()),
+           outdims = OutDims("lon", overwrite=true, path="my_gen_cube.zarr", backend=:zarr,
+           outtype = Float32)
+           # max_cache=1e9
+       )
┌ 15×10×30 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:15 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "missing_value" => 1.0f32
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 17.58 KB
+└──────────────────────────────────────────────────────────────────────────────┘

INFO

Note that now the broadcasted dimension is lon.

we can see this by slicing on the last dimension now

julia
gen_cube.data[:, :, 1]
15×10 Matrix{Union{Missing, Float32}}:
+  3.0   4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0
+  4.0   5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0
+  5.0   6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0
+  6.0   7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0
+  7.0   8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0
+  8.0   9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0
+  9.0  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0
+ 10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0
+ 11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0
+ 12.0  13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0
+ 13.0  14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0
+ 14.0  15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0
+ 15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0
+ 16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0
+ 17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  25.0  26.0

which outputs the same as the gen_cube.data[1, :, :] called above.

OutDims and YAXArray Properties ​

Here, we will consider different scenarios, namely how we deal with different input cubes and how to specify the output ones. We will illustrate this with the following test example and the subsequent function definitions.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Zarr
+using Random
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(range(1, 4, length=4)),
+    lat(range(1, 3, length=3)),
+    Variables(["a", "b"])
+)
+
+Random.seed!(123)
+data = rand(1:5, 5, 4, 3, 2)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3×2 YAXArray{Int64, 4} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

One InDims to many OutDims ​

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

julia
function one_to_many(xout_one, xout_two, xout_flat, xin_one)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2.(xin_one)
+    xout_flat .= sum(xin_one)
+    return nothing
+end
+
+f1(xin) = xin + 1
+f2(xin) = xin + 2
f2 (generic function with 1 method)

now, we define InDims and OutDims:

julia
indims_one   = InDims("Time")
+# outputs dimension
+properties_one = Dict{String, Any}("name" => "plus_one")
+properties_two = Dict{String, Any}("name" => "plus_two")
+
+outdims_one = OutDims("Time"; properties=properties_one)
+outdims_two = OutDims("Time"; properties=properties_two)
+outdims_flat = OutDims(;) # it will get the default \`layer\` name if open as dataset
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(one_to_many, yax_test,
+    indims = indims_one,
+    outdims = (outdims_one, outdims_two, outdims_flat));

let's see the second output

julia
ds[2]
┌ 5×4×3×2 YAXArray{Union{Missing, Int64}, 4} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time      Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 1 entry:
+  "name" => "plus_two"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Many InDims to many OutDims ​

Let's consider a second test set

julia
properties_2d = Dict("description" => "2d dimensional test cube")
+yax_2d = YAXArray(axlist[2:end], rand(-1:1, 4, 3, 2), properties_2d)
┌ 4×3×2 YAXArray{Int64, 3} ┐
+├──────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ lon       Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  → lat       Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points,
+  ↗ Variables Categorical{String} ["a", "b"] ForwardOrdered
+├─────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "2d dimensional test cube"
+├─────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 192.0 bytes
+└─────────────────────────────────────────────────────────────────────────┘

The function definitions operating in this case are as follows

julia
function many_to_many(xout_one, xout_two, xout_flat, xin_one, xin_two, xin_drei)
+    xout_one .= f1.(xin_one)
+    xout_two .= f2mix.(xin_one, xin_two)
+    xout_flat .= sum(xin_drei) # this will reduce the time dimension if we set outdims = OutDims()
+    return nothing
+end
+f2mix(xin_xyt, xin_xy) = xin_xyt - xin_xy
f2mix (generic function with 1 method)

Specify path in OutDims ​

julia
indims_one   = InDims("Time")
+indims_2d   = InDims() # ? it matches only to the other 2 dimensions and uses the same values for each time step
+properties = Dict{String, Any}("name"=> "many_to_many_two")
+outdims_one = OutDims("Time")
+outdims_two = OutDims("Time"; path = "test_mm.zarr", properties)
+outdims_flat = OutDims()
OutDims((), :auto, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}(), false, Array, :input, 1)
julia
ds = mapCube(many_to_many, (yax_test, yax_2d, yax_test),
+    indims = (indims_one, indims_2d, indims_one),
+    outdims = (outdims_one, outdims_two, outdims_flat));

And we can open the one that was saved directly to disk.

julia
ds_mm = open_dataset("test_mm.zarr")
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{DateTime} [2022-01-01T00:00:00, …, 2022-01-05T00:00:00] ForwardOrdered Irregular Points,
+  → lon  Sampled{Float64} 1.0:1.0:4.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:1.0:3.0 ForwardOrdered Regular Points)
+
+Variables: 
+a, b

Different InDims names ​

Here, the goal is to operate at the pixel level (longitude, latitude), and then apply the corresponding function to the extracted values. Consider the following toy cubes:

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (lon(1:4), lat(1:3), Dim{:depth}(1:7),)
+yax_2d = YAXArray(axlist, rand(-3.0:0.0, 4, 3, 7))
┌ 4×3×7 YAXArray{Float64, 3} ┐
+├────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon   Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  → lat   Sampled{Int64} 1:3 ForwardOrdered Regular Points,
+  ↗ depth Sampled{Int64} 1:7 ForwardOrdered Regular Points
+├───────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────── loaded in memory ┤
+  data size: 672.0 bytes
+└───────────────────────────────────────────────────────────┘

and

julia
Random.seed!(123)
+data = rand(3.0:5.0, 5, 4, 3)
+
+axlist = (YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-05")),
+    lon(1:4), lat(1:3),)
+
+properties = Dict("description" => "multi dimensional test cube")
+yax_test = YAXArray(axlist, data, properties)
┌ 5×4×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 1 entry:
+  "description" => "multi dimensional test cube"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

and the corresponding functions

julia
function mix_time_depth(xin_xyt, xin_xyz)
+    s = sum(abs.(xin_xyz))
+    return xin_xyt.^2 .+ s
+end
+
+function time_depth(xout, xin_one, xin_two)
+    xout .= mix_time_depth(xin_one, xin_two)
+    # Note also that there is no dot anymore in the function application!
+    return nothing
+end
time_depth (generic function with 1 method)

with the final mapCube operation as follows

julia
ds = mapCube(time_depth, (yax_test, yax_2d),
+    indims = (InDims("Time"), InDims("depth")), # ? anchor dimensions and then map over the others.
+    outdims = OutDims("Time"))
┌ 5×4×3 YAXArray{Union{Missing, Float64}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-05") ForwardOrdered Regular Points,
+  → lon  Sampled{Int64} 1:4 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Int64} 1:3 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Creating a vector array ​

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+raster_arr = YAXArray(axlist, data)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
regions_mat = map(Iterators.product(raster_arr.lon, raster_arr.lat)) do (lon, lat)
+    1 <= lon < 10 && 1 <= lat < 5 && return "A"
+    1 <= lon < 10 && 5 <= lat < 10 && return "B"
+    10 <= lon < 15 && 1 <= lat < 5 && return "C"
+    return "D"
+end
+regions_mat = DimArray(regions_mat, (raster_arr.lon, raster_arr.lat))
┌ 10×15 DimArray{String, 2} ┐
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0   1.28571  1.57143  1.85714  …  4.14286  4.42857  4.71429  5.0
+  1.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  2.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  3.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  4.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  5.0   "A"   "A"      "A"      "A"     …   "A"      "A"      "A"      "B"
+  6.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  7.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  8.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+  9.0   "A"   "A"      "A"      "A"         "A"      "A"      "A"      "B"
+ 10.0   "C"   "C"      "C"      "C"     …   "C"      "C"      "C"      "D"

which has the same spatial dimensions as the raster array at any given point in time:

julia
DimArray(raster_arr[time = 1])
┌ 10×15 DimArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571   1.57143    …  4.42857   4.71429   5.0
+  1.0  0.17593   0.417937  0.0723492     0.178603  0.781773  0.875658
+  2.0  0.701332  0.15394   0.685454      0.372761  0.984803  0.472308
+  3.0  0.120997  0.829062  0.684389      0.463503  0.840389  0.536399
+  ⋮                                   ⋱                      ⋮
+  8.0  0.145747  0.432286  0.465103      0.889583  0.514979  0.671662
+  9.0  0.538981  0.497189  0.167676      0.595405  0.752417  0.93986
+ 10.0  0.824354  0.376135  0.551732   …  0.101524  0.121947  0.508557

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

julia
regions = ["A", "B", "C", "D"]
+points_of_regions = map(enumerate(regions)) do (i,region)
+    region => findall(isequal(region), regions_mat)
+end |> Dict |> sort
OrderedCollections.OrderedDict{String, Vector{CartesianIndex{2}}} with 4 entries:
+  "A" => [CartesianIndex(1, 1), CartesianIndex(2, 1), CartesianIndex(3, 1), Car…
+  "B" => [CartesianIndex(1, 15), CartesianIndex(2, 15), CartesianIndex(3, 15), …
+  "C" => [CartesianIndex(10, 1), CartesianIndex(10, 2), CartesianIndex(10, 3), …
+  "D" => [CartesianIndex(10, 15)]

Finally, we can transform the entire raster array:

julia
vector_array = mapCube(
+    raster_arr,
+    indims=InDims("lon", "lat"),
+    outdims=OutDims(Dim{:region}(regions))
+) do xout, xin
+    for (region_pos, points) in enumerate(points_of_regions.vals)
+        # aggregate values of points in the current region at the current date
+        xout[region_pos] = sum(view(xin, points))
+    end
+end
┌ 4×30 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ region Categorical{String} ["A", "B", "C", "D"] ForwardOrdered,
+  → time   Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 960.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Distributed Computation ​

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

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

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using Dates
+using Distributed
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a = YAXArray(axlist, data, properties)
+
+addprocs(2)
+
+@everywhere begin
+  using YAXArrays
+  using Zarr
+  using Statistics
+end
+
+@everywhere function mymean(output, pixel)
+  @show "doing a mean"
+     output[:] .= mean(pixel)
+end
+
+mapCube(mymean, a, indims=InDims("time"), outdims=OutDims())

In the last example, mapCube was used to map the mymean function. mapslices is a convenient function that can replace mapCube, where you can omit defining an extra function with the output argument as an input (e.g. mymean). It is possible to simply use mapslice

julia
mapslices(mean ∘ skipmissing, a, dims="time")

It is also possible to distribute easily the workload on a cluster, with little modification to the code. To do so, we use the ClusterManagers package.

julia
using Distributed
+using ClusterManagers
+addprocs(SlurmManager(10))
`,138)]))}const o=i(p,[["render",l]]);export{g as __pageData,o as default}; diff --git a/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.js b/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.js new file mode 100644 index 00000000..05e8a388 --- /dev/null +++ b/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.js @@ -0,0 +1,46 @@ +import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),t={name:"UserGuide/convert.md"};function p(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`

Convert YAXArrays ​

This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.

WARNING

YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.

Convert Base.Array ​

Convert Base.Array to YAXArray:

julia
using YAXArrays
+
+m = rand(5,10)
+a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
+ 0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
+ 0.83556   0.456765  0.197238  0.645758     0.74732   0.652339   0.935631
+ 0.337926  0.151146  0.673373  0.169284     0.75269   0.166212   0.0358348
+ 0.594514  0.364288  0.78467   0.830391     0.128204  0.174934   0.0210077

Convert Raster ​

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

julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
julia
ras2 = Raster(a)

Convert DimArray ​

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

Convert DimArray to YAXArray:

julia
using DimensionalData
+using YAXArrayBase
+
+dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0       11.0        12.0        13.0       14.0       15.0
+ 1     0.862644   0.872575    0.0620649   0.193109   0.475725   0.953391
+ 2     0.203714   0.770949    0.731779    0.71314    0.687891   0.435994
+ 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.

`,23)]))}const g=a(t,[["render",p]]);export{c as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.lean.js b/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.lean.js new file mode 100644 index 00000000..05e8a388 --- /dev/null +++ b/previews/PR486/assets/UserGuide_convert.md.CkB9umGg.lean.js @@ -0,0 +1,46 @@ +import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const c=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),t={name:"UserGuide/convert.md"};function p(l,s,h,r,k,d){return e(),i("div",null,s[0]||(s[0]=[n(`

Convert YAXArrays ​

This section describes how to convert variables from types of other Julia packages into YAXArrays and vice versa.

WARNING

YAXArrays is designed to work with large datasets that are way larger than the memory. However, most types are designed to work in memory. Those conversions are only possible if the entire dataset fits into memory. In addition, metadata might be lost during conversion.

Convert Base.Array ​

Convert Base.Array to YAXArray:

julia
using YAXArrays
+
+m = rand(5,10)
+a = YAXArray(m)
┌ 5×10 YAXArray{Float64, 2} ┐
+├───────────────────────────┴─────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.265797  0.789891  0.611084  0.845983  …  0.918555  0.870826   0.348362
+ 0.665723  0.241882  0.426519  0.581312     0.949935  0.0214057  0.152534
+ 0.83556   0.456765  0.197238  0.645758     0.74732   0.652339   0.935631
+ 0.337926  0.151146  0.673373  0.169284     0.75269   0.166212   0.0358348
+ 0.594514  0.364288  0.78467   0.830391     0.128204  0.174934   0.0210077

Convert Raster ​

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

julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
julia
ras2 = Raster(a)

Convert DimArray ​

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

Convert DimArray to YAXArray:

julia
using DimensionalData
+using YAXArrayBase
+
+dim_arr = rand(X(1:5), Y(10.0:15.0), metadata = Dict{String, Any}())
+a = yaxconvert(YAXArray, dim_arr)
┌ 5×6 YAXArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────── loaded in memory ┤
+  data size: 240.0 bytes
+└──────────────────────────────────────────────────────────────────┘

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
┌ 5×6 DimArray{Float64, 2} ┐
+├──────────────────────────┴───────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0       11.0        12.0        13.0       14.0       15.0
+ 1     0.862644   0.872575    0.0620649   0.193109   0.475725   0.953391
+ 2     0.203714   0.770949    0.731779    0.71314    0.687891   0.435994
+ 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.

`,23)]))}const g=a(t,[["render",p]]);export{c as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_create.md.Bweykjuq.js b/previews/PR486/assets/UserGuide_create.md.Bweykjuq.js new file mode 100644 index 00000000..e6cb8731 --- /dev/null +++ b/previews/PR486/assets/UserGuide_create.md.Bweykjuq.js @@ -0,0 +1,48 @@ +import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`

Create YAXArrays and Datasets ​

This section describes how to create arrays and datasets by filling values directly.

Create a YAXArray ​

We can create a new YAXArray by filling the values directly:

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 7.81 KB
+└──────────────────────────────────────────────────────────────────────┘

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

julia
using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data2 = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+→ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)

Create a Dataset ​

julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
+
+Variables: 
+a2, a3
+
+Properties: Dict(:origin => "user guide")
`,16)]))}const o=a(e,[["render",p]]);export{g as __pageData,o as default}; diff --git a/previews/PR486/assets/UserGuide_create.md.Bweykjuq.lean.js b/previews/PR486/assets/UserGuide_create.md.Bweykjuq.lean.js new file mode 100644 index 00000000..e6cb8731 --- /dev/null +++ b/previews/PR486/assets/UserGuide_create.md.Bweykjuq.lean.js @@ -0,0 +1,48 @@ +import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.piKCME0r.js";const g=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),e={name:"UserGuide/create.md"};function p(l,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`

Create YAXArrays and Datasets ​

This section describes how to create arrays and datasets by filling values directly.

Create a YAXArray ​

We can create a new YAXArray by filling the values directly:

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+a1 = YAXArray(rand(10, 20, 5))
┌ 10×20×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 7.81 KB
+└──────────────────────────────────────────────────────────────────────┘

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

julia
using Dates
+
+axlist = (
+    YAX.time(Date("2022-01-01"):Day(1):Date("2022-01-30")),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+)
+data2 = rand(30, 10, 15)
+properties = Dict(:origin => "user guide")
+a2 = YAXArray(axlist, data2, properties)
┌ 30×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 35.16 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
a2.properties
Dict{Symbol, String} with 1 entry:
+  :origin => "user guide"
julia
a2.axes
(↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+→ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)

Create a Dataset ​

julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared Axes: 
+  (↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points)
+
+Variables: 
+a2, a3
+
+Properties: Dict(:origin => "user guide")
`,16)]))}const o=a(e,[["render",p]]);export{g as __pageData,o as default}; diff --git a/previews/PR486/assets/UserGuide_faq.md.uhp-zjxe.js b/previews/PR486/assets/UserGuide_faq.md.uhp-zjxe.js new file mode 100644 index 00000000..f9181e0b --- /dev/null +++ b/previews/PR486/assets/UserGuide_faq.md.uhp-zjxe.js @@ -0,0 +1,364 @@ +import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const t="/YAXArrays.jl/previews/PR486/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,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 3.91 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

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

julia
julia> dims(c)
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

INFO

Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.

rebuild ​

As an example let's consider the following

julia
using YAXArrays
+using DimensionalData
+
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube ​

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

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
+collect(c.axes[1].val)
10-element Vector{Int64}:
+  1
+  2
+  3
+  4
+  5
+  6
+  7
+  8
+  9
+ 10

to collect data from a cube works exactly the same as doing it from an array

julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes ​

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

Let's create two dummy cubes

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15))
+    )
+
+data1 = rand(20, 10, 15)
+ds1 = YAXArray(axlist, data1)
+
+data2 = rand(20, 10, 15)
+ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["var1", "var2"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Subsetting a YAXArray ​

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

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

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
+→ Lat  1:10,
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

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

Subset YAXArray by a specific date

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

Subset YAXArray by a date range

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

Subset YAXArray by longitude and latitude

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

Subsetting a Dataset ​

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

Subsetting a Dataset whose variables share all their dimensions ​

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

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" option for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
+
+var1 = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
+var2 = YAXArray(axes, reshape((1:3600)*5, (10, 10, 36)))
+
+ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2

Subsetting a Dataset whose variables share some but not all of their dimensions ​

In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.

But we can also subset a variable by the values of another variable with which it shares some dimensions.

Warning

If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.

Let's make an example.

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" selector for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+common_axis = Dim{:points}(1:100)
+time_axis =   YAX.Time(t)
+
+# Note that longitudes and latitudes are not dimensions, but YAXArrays
+longitudes = YAXArray((common_axis,), rand(1:369, 100)) # 100 random values taken from 1 to 359
+latitudes  = YAXArray((common_axis,), rand(0:90, 100))  # 100 random values taken from 0 to 90
+temperature = YAXArray((common_axis, time_axis), rand(-40:40, (100, 36)))
+
+ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
+Shared Axes: 
+  (↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
+
+Variables: 
+latitudes, longitudes
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  Variables: 
+  temperature

Select all points between 20ºN and 85ºN, and 0ºE to 180ºE

julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+                             ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes
+
+  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)
+  Variables: 
+  temperature

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+longitudes_yasxa = readcubedata(ds["longitudes"])
+ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
+                             longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  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)
+  Variables: 
+  temperature
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
+           (xin * π) ./ maximum(skipmissing(xin))
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function? ​

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
+GLMakie.activate!()
+# This is how our classification map looks like
+fig, ax, obj = heatmap(classes;
+    colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
+cbar = Colorbar(fig[1,2], obj)
+fig

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

julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+using OnlineStats
+## visualization of the CubeTable
+c_tbl = DataFrame(t[1])
+first(c_tbl, 5)

In this line we calculate the Mean for each class

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
+  → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset ​

One variable name ​

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
+Shared Axes:
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+a

Multiple variable names ​

julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  b
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  c

WARNING

You will not be able to save this dataset, first you will need to rename those dimensions with the same name but different values.

Ho do I construct a Dataset from a TimeArray ​

In this section we will use MarketData.jl and TimeSeries.jl to simulate some stocks.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using MarketData, TimeSeries
+
+stocks = Dict(:Stock1 => random_ohlcv(), :Stock2 => random_ohlcv(), :Stock3 => random_ohlcv())
+d_keys = keys(stocks)
KeySet for a Dict{Symbol, TimeSeries.TimeArray{Float64, 2, DateTime, Matrix{Float64}}} with 3 entries. Keys:
+  :Stock3
+  :Stock1
+  :Stock2

currently there is not direct support to obtain dims from a TimeArray, but we can code a function for it

julia
getTArrayAxes(ta::TimeArray) = (YAX.time(timestamp(ta)), Variables(colnames(ta)), );

then, we create the YAXArrays as

julia
yax_list = [YAXArray(getTArrayAxes(stocks[k]), values(stocks[k])) for k in d_keys];

and a Dataset with all stocks names

julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  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
+
+  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: 
+  Stock2
+
+  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
+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)
+
+Variables: 
+Stock1, Stock2, Stock3

now they are printed together, showing that is exactly the same axis structure for all variables.

Create a YAXArray with unions containing Strings ​

julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  summarysize: 121.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── 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.

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Frequently Asked Questions (FAQ) ​

The purpose of this section is to do a collection of small convinient pieces of code on how to do simple things.

Extract the axes names from a Cube ​

julia
using YAXArrays
+using DimensionalData
julia
julia> c = YAXArray(rand(10, 10, 5))
┌ 10×10×5 YAXArray{Float64, 3} ┐
+├──────────────────────────────┴───────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 3.91 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> caxes(c) # former way of doing it
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

WARNING

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

julia
julia> dims(c)
(↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+→ Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)

INFO

Also, use DD.rebuild(c, values) to copy axes from c and build a new cube but with different values.

rebuild ​

As an example let's consider the following

julia
using YAXArrays
+using DimensionalData
+
+c = YAXArray(ones(Int, 10,10))
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────┘

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

julia
julia> new_c = rebuild(c, rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> c_c = YAXArray(dims(c), rand(10,10))
┌ 10×10 YAXArray{Float64, 2} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

which achieves the same goal as rebuild.

Obtain values from axes and data from the cube ​

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

These two examples bring the same result

julia
collect(getAxis("Dim_1", c).val)
+collect(c.axes[1].val)
10-element Vector{Int64}:
+  1
+  2
+  3
+  4
+  5
+  6
+  7
+  8
+  9
+ 10

to collect data from a cube works exactly the same as doing it from an array

julia
julia> c[:, :, 1]
┌ 10×10 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I concatenate cubes ​

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

Let's create two dummy cubes

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15))
+    )
+
+data1 = rand(20, 10, 15)
+ds1 = YAXArray(axlist, data1)
+
+data2 = rand(20, 10, 15)
+ds2 = YAXArray(axlist, data2)

Now we can concatenate ds1 and ds2:

julia
julia> dsfinal = concatenatecubes([ds1, ds2], Dim{:Variables}(["var1", "var2"]))
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["var1", "var2"] ForwardOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

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

Subsetting a YAXArray ​

Let's start by creating a dummy YAXArray.

Firstly, load the required libraries

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

Define the time span of the YAXArray

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

create YAXArray axes

julia
axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
(↓ Lon  1:10,
+→ Lat  1:10,
+↗ Time Date("2020-01-01"):Dates.Month(1):Date("2022-12-01"))

create the YAXArray

julia
y = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
┌ 10×10×36 YAXArray{Int64, 3} ┐
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 28.12 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Now we subset the YAXArray by any dimension.

Subset YAXArray by years

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

Subset YAXArray by a specific date

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

Subset YAXArray by a date range

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

Subset YAXArray by longitude and latitude

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

Subsetting a Dataset ​

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

Subsetting a Dataset whose variables share all their dimensions ​

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

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" option for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+axes = (Lon(1:10), Lat(1:10), YAX.Time(t))
+
+var1 = YAXArray(axes, reshape(1:3600, (10, 10, 36)))
+var2 = YAXArray(axes, reshape((1:3600)*5, (10, 10, 36)))
+
+ds = Dataset(; var1=var1, var2=var2)
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2
julia
ds_lonlat = ds[Lon=1 .. 5, Lat=5 .. 10]
YAXArray Dataset
+Shared Axes: 
+  (↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+
+Variables: 
+var1, var2

Subsetting a Dataset whose variables share some but not all of their dimensions ​

In this case, if we subset by the common dimension/s, this works the same as for YAXArrays, Cubes, and datasets that share all their dimensions.

But we can also subset a variable by the values of another variable with which it shares some dimensions.

Warning

If your data is not loaded into memory, the selection will be too slow. So, you have load into memory, at least, the variable with which you make the selection.

Let's make an example.

julia
using YAXArrays
+using Dates # To generate the dates of the time axis
+using DimensionalData # To use the "Between" selector for selecting data
+
+t = Date("2020-01-01"):Month(1):Date("2022-12-31")
+common_axis = Dim{:points}(1:100)
+time_axis =   YAX.Time(t)
+
+# Note that longitudes and latitudes are not dimensions, but YAXArrays
+longitudes = YAXArray((common_axis,), rand(1:369, 100)) # 100 random values taken from 1 to 359
+latitudes  = YAXArray((common_axis,), rand(0:90, 100))  # 100 random values taken from 0 to 90
+temperature = YAXArray((common_axis, time_axis), rand(-40:40, (100, 36)))
+
+ds = Dataset(; longitudes=longitudes, latitudes=latitudes, temperature=temperature)
YAXArray Dataset
+Shared Axes: 
+  (↓ points Sampled{Int64} 1:100 ForwardOrdered Regular Points)
+
+Variables: 
+latitudes, longitudes
+
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points)
+  Variables: 
+  temperature

Select all points between 20ºN and 85ºN, and 0ºE to 180ºE

julia
ds_subset = ds[points = Where(p-> ds["latitudes"][p]  >= 20 && ds["latitudes"][p]  <= 80 &&
+                             ds["longitudes"][p] >= 0  && ds["longitudes"][p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  longitudes
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes
+
+  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)
+  Variables: 
+  temperature

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

julia
latitudes_yasxa  = readcubedata(ds["latitudes"])
+longitudes_yasxa = readcubedata(ds["longitudes"])
+ds_subset = ds[points = Where(p-> latitudes_yasxa[p]  >= 20 && latitudes_yasxa[p]  <= 80 &&
+                             longitudes_yasxa[p] >= 0  && longitudes_yasxa[p] <= 180
+                             ) # Where
+              ] # ds
YAXArray Dataset
+Shared Axes: 
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  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)
+  Variables: 
+  temperature
+
+  Additional Axes: 
+  (↓ points Sampled{Int64} [2, 4, …, 96, 98] ForwardOrdered Irregular Points)
+  Variables: 
+  latitudes

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

julia
julia> map((x, y) -> x * y, ds1, ds2)
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Cubes with more than 3 dimensions

julia
julia> map((x, y) -> x * y, dsfinal[Variables=At("var1")], dsfinal[Variables=At("var2")])
┌ 20×10×15 YAXArray{Float64, 3} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> mapslices(ds1, dims=("Lon", "Lat")) do xin
+           (xin * π) ./ maximum(skipmissing(xin))
+       end
┌ 10×15×20 YAXArray{Union{Missing, Float64}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ↗ time Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 23.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

How do I use the CubeTable function? ​

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

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

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

julia
julia> classes = YAXArray((getAxis("lon", dsfinal), getAxis("lat", dsfinal)), rand(1:3, 10, 15))
┌ 10×15 YAXArray{Int64, 2} ┐
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 1.17 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
using GLMakie
+GLMakie.activate!()
+# This is how our classification map looks like
+fig, ax, obj = heatmap(classes;
+    colormap=Makie.Categorical(cgrad([:grey15, :orangered, :snow3])))
+cbar = Colorbar(fig[1,2], obj)
+fig

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

julia
t = CubeTable(values=ds1, classes=classes)
Datacube iterator with 1 subtables with fields: (:values, :classes, :time, :lon, :lat)
julia
using DataFrames
+using OnlineStats
+## visualization of the CubeTable
+c_tbl = DataFrame(t[1])
+first(c_tbl, 5)

In this line we calculate the Mean for each class

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes))
┌ 3-element YAXArray{Union{Missing, Float64}, 1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 24.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
julia> fitcube = cubefittable(t, Mean, :values, by=(:classes, :time))
┌ 3×20 YAXArray{Union{Missing, Float64}, 2} ┐
+├───────────────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ classes Sampled{Int64} [1, 2, 3] ForwardOrdered Irregular Points,
+  → time    Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 480.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

How do I assign variable names to YAXArrays in a Dataset ​

One variable name ​

julia
julia> ds = YAXArrays.Dataset(; (:a => YAXArray(rand(10)),)...)
YAXArray Dataset
+Shared Axes:
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+
+Variables: 
+a

Multiple variable names ​

julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  b
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points)
+  Variables: 
+  a
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+  Variables: 
+  c

WARNING

You will not be able to save this dataset, first you will need to rename those dimensions with the same name but different values.

Ho do I construct a Dataset from a TimeArray ​

In this section we will use MarketData.jl and TimeSeries.jl to simulate some stocks.

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+using DimensionalData
+using MarketData, TimeSeries
+
+stocks = Dict(:Stock1 => random_ohlcv(), :Stock2 => random_ohlcv(), :Stock3 => random_ohlcv())
+d_keys = keys(stocks)
KeySet for a Dict{Symbol, TimeSeries.TimeArray{Float64, 2, DateTime, Matrix{Float64}}} with 3 entries. Keys:
+  :Stock3
+  :Stock1
+  :Stock2

currently there is not direct support to obtain dims from a TimeArray, but we can code a function for it

julia
getTArrayAxes(ta::TimeArray) = (YAX.time(timestamp(ta)), Variables(colnames(ta)), );

then, we create the YAXArrays as

julia
yax_list = [YAXArray(getTArrayAxes(stocks[k]), values(stocks[k])) for k in d_keys];

and a Dataset with all stocks names

julia
julia> ds = Dataset(; (d_keys .=> yax_list)...)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  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
+
+  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: 
+  Stock2
+
+  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
+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)
+
+Variables: 
+Stock1, Stock2, Stock3

now they are printed together, showing that is exactly the same axis structure for all variables.

Create a YAXArray with unions containing Strings ​

julia
test_x = stack(Vector{Union{Int,String}}[[1, "Test"], [2, "Test2"]])
+yax_string = YAXArray(test_x)
┌ 2×2 YAXArray{Union{Int64, String}, 2} ┐
+├───────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  summarysize: 121.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

or simply with an Any type

julia
test_bool = ["Test1" 1 false; 2 "Test2" true; 1 2f0 1f2]
+yax_bool = YAXArray(test_bool)
┌ 3×3 YAXArray{Any, 2} ┐
+├──────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  summarysize: 172.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

WARNING

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

`,149)]))}const E=i(l,[["render",h]]);export{o as __pageData,E as default}; diff --git a/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.js b/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.js new file mode 100644 index 00000000..461e1d7a --- /dev/null +++ b/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.js @@ -0,0 +1,208 @@ +import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const t="/YAXArrays.jl/previews/PR486/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
+using Dates
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+filename = Downloads.download(url_path, "rasm.nc")
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

WARNING

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

julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :time)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:time)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, YAX.time => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:time)
+    mean_g = dropdims.(mean_g, dims=:time)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :time);
+    weighted_g = dropdims.(weighted_g, dims=:time)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :time)
+    return mean_g, weighted_g, diff_g, seasons_g
+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 ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404]

seasons ​

Due to the groupby function we will obtain new grouping names, in this case in the time dimension:

julia
seasons_g = lookup(mean_g, :time)
Categorical{Symbol} Unordered
+wrapping: 4-element Vector{Symbol}:
+ :Dec_Jan_Feb
+ :Mar_Apr_May
+ :Jun_Jul_Aug
+ :Sep_Oct_Nov

Next, we will weight this grouping by days/month in each group.

GroupBy: weight ​

Create a YAXArray for the month length

julia
tempo = dims(ds, :time)
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 288.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  [270]
+ :Mar_Apr_May  [276]
+ :Jun_Jul_Aug  [276]
+ :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
+ :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
+ :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1.0
+ :Mar_Apr_May  1.0
+ :Jun_Jul_Aug  1.0
+ :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.32149 1.33565; NaN NaN … 1.29564 1.32555; … ; NaN NaN … 1.3188 1.3169; NaN NaN … 1.17863 1.18434;;; NaN NaN … 1.29816 1.34218; NaN NaN … 1.30113 1.35483; … ; NaN NaN … 1.30142 1.31753; NaN NaN … 1.16258 1.17647;;; NaN NaN … 1.34549 1.37878; NaN NaN … 1.36836 1.41634; … ; NaN NaN … 1.34832 1.38364; NaN NaN … 1.17852 1.16713]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.87705 1.90365; NaN NaN … 2.30018 2.35432; … ; NaN NaN … 2.41049 2.43254; NaN NaN … 2.65105 2.69085;;; NaN NaN … 1.86457 1.90712; NaN NaN … 2.2894 2.34818; … ; NaN NaN … 2.3866 2.41241; NaN NaN … 2.61197 2.64976;;; NaN NaN … 1.89237 1.8984; NaN NaN … 2.29473 2.312; … ; NaN NaN … 2.36142 2.36126; NaN NaN … 2.56632 2.59085]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :time);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.019016 -0.0115514; NaN NaN … -0.0183003 -0.00990356]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.0121037 -0.0123091; NaN NaN … -0.0127077 -0.0117519]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.00709111 -0.00693713; NaN NaN … -0.00684233 -0.00722034]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 0.0180572 0.0182373; NaN NaN … 0.0178074 0.018571]

All the previous steps are equivalent to calling the function defined at the top:

julia
mean_g, weighted_g, diff_g, seasons_g = weighted_seasons(ds)

Once all calculations are done we can plot the results with Makie.jl as follows:

julia
using CairoMakie
+# define plot arguments/attributes
+colorrange = (-30,30)
+colormap = Reverse(:Spectral)
+highclip = :red
+lowclip = :grey15
+cb_label =  ds_o.properties["long_name"]
"Surface air temperature"
julia
with_theme(theme_ggplot2()) do
+    hm_o, hm_d, hm_w = nothing, nothing, nothing
+    # the figure
+    fig = Figure(; size = (850,500))
+    axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
+    for (j, s) in enumerate(seasons_g)
+        hm_o = heatmap!(axs[1,j], mean_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_w = heatmap!(axs[2,j], weighted_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_d = heatmap!(axs[3,j], diff_g[time=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
+            colormap=:diverging_bwr_20_95_c54_n256)
+    end
+    Colorbar(fig[1:2,5], hm_o, label=cb_label)
+    Colorbar(fig[3,5], hm_d, label="Tair")
+    hidedecorations!.(axs, grid=false, ticks=false, label=false)
+    # some labels
+    [axs[1,j].title = string.(s) for (j,s) in enumerate(seasons_g)]
+    Label(fig[0,1:5], "Seasonal Surface Air Temperature", fontsize=18, font=:bold)
+    axs[1,1].ylabel = "Unweighted"
+    axs[2,1].ylabel = "Weighted"
+    axs[3,1].ylabel = "Difference"
+    colgap!(fig.layout, 5)
+    rowgap!(fig.layout, 5)
+    fig
+end

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

',66)]))}const N=i(l,[["render",p]]);export{y as __pageData,N as default}; diff --git a/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.lean.js b/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.lean.js new file mode 100644 index 00000000..461e1d7a --- /dev/null +++ b/previews/PR486/assets/UserGuide_group.md.B_BCz8Qu.lean.js @@ -0,0 +1,208 @@ +import{_ as i,c as a,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const t="/YAXArrays.jl/previews/PR486/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
+using Dates
+using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
+filename = Downloads.download(url_path, "rasm.nc")
+ds_o = Cube(filename)
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

WARNING

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

julia
_FillValue = ds_o.properties["_FillValue"]
+ds = replace(ds_o[:,:,:], _FillValue => NaN) # load into memory and replace _FillValue by NaN
┌ 275×205×36 YAXArray{Float64, 3} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ x    Sampled{Int64} 1:275 ForwardOrdered Regular Points,
+  → y    Sampled{Int64} 1:205 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 7 entries:
+  "units"          => "C"
+  "coordinates"    => "yc xc"
+  "name"           => "Tair"
+  "long_name"      => "Surface air temperature"
+  "type_preferred" => "double"
+  "_FillValue"     => 9.96921e36
+  "time_rep"       => "instantaneous"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 15.48 MB
+└──────────────────────────────────────────────────────────────────────────────┘

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :time)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:time)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, YAX.time => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:time)
+    mean_g = dropdims.(mean_g, dims=:time)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :time);
+    weighted_g = dropdims.(weighted_g, dims=:time)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :time)
+    return mean_g, weighted_g, diff_g, seasons_g
+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 ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404]

seasons ​

Due to the groupby function we will obtain new grouping names, in this case in the time dimension:

julia
seasons_g = lookup(mean_g, :time)
Categorical{Symbol} Unordered
+wrapping: 4-element Vector{Symbol}:
+ :Dec_Jan_Feb
+ :Mar_Apr_May
+ :Jun_Jul_Aug
+ :Sep_Oct_Nov

Next, we will weight this grouping by days/month in each group.

GroupBy: weight ​

Create a YAXArray for the month length

julia
tempo = dims(ds, :time)
+month_length = YAXArray((tempo,), daysinmonth.(tempo))
┌ 36-element YAXArray{Int64, 1} ┐
+├───────────────────────────────┴──────────────────────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 288.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, YAX.time => seasons(; start=December))
┌ 4-element DimGroupByArray{YAXArray{Int64,0},1} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ time
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:time)
┌ 4-element DimArray{YAXArray{Int64, 1, DimensionalData.DimVector{Int64, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Tuple{}, Vector{Int64}, Symbol, DimensionalData.Dimensions.Lookups.NoMetadata}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  [270]
+ :Mar_Apr_May  [276]
+ :Jun_Jul_Aug  [276]
+ :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
┌ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}}, 1} groupby ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
+ :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
+ :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
+ :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
┌ 4-element DimArray{Float64, 1} ┐
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1.0
+ :Mar_Apr_May  1.0
+ :Jun_Jul_Aug  1.0
+ :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
┌ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{YAXArrays.time{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.32149 1.33565; NaN NaN … 1.29564 1.32555; … ; NaN NaN … 1.3188 1.3169; NaN NaN … 1.17863 1.18434;;; NaN NaN … 1.29816 1.34218; NaN NaN … 1.30113 1.35483; … ; NaN NaN … 1.30142 1.31753; NaN NaN … 1.16258 1.17647;;; NaN NaN … 1.34549 1.37878; NaN NaN … 1.36836 1.41634; … ; NaN NaN … 1.34832 1.38364; NaN NaN … 1.17852 1.16713]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 1.87705 1.90365; NaN NaN … 2.30018 2.35432; … ; NaN NaN … 2.41049 2.43254; NaN NaN … 2.65105 2.69085;;; NaN NaN … 1.86457 1.90712; NaN NaN … 2.2894 2.34818; … ; NaN NaN … 2.3866 2.41241; NaN NaN … 2.61197 2.64976;;; NaN NaN … 1.89237 1.8984; NaN NaN … 2.29473 2.312; … ; NaN NaN … 2.36142 2.36126; NaN NaN … 2.56632 2.59085]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN … NaN NaN;;; … ;;; NaN NaN … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :time);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:time)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
┌ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata}, 1} ┐
+├──────────────────────────────────────────────────────────────────────── dims ┤
+  ↓ time Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :time=>CyclicBins(month; cycle=12, step=3, start=12)…
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.019016 -0.0115514; NaN NaN … -0.0183003 -0.00990356]
+ :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.0121037 -0.0123091; NaN NaN … -0.0127077 -0.0117519]
+ :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.00709111 -0.00693713; NaN NaN … -0.00684233 -0.00722034]
+ :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 0.0180572 0.0182373; NaN NaN … 0.0178074 0.018571]

All the previous steps are equivalent to calling the function defined at the top:

julia
mean_g, weighted_g, diff_g, seasons_g = weighted_seasons(ds)

Once all calculations are done we can plot the results with Makie.jl as follows:

julia
using CairoMakie
+# define plot arguments/attributes
+colorrange = (-30,30)
+colormap = Reverse(:Spectral)
+highclip = :red
+lowclip = :grey15
+cb_label =  ds_o.properties["long_name"]
"Surface air temperature"
julia
with_theme(theme_ggplot2()) do
+    hm_o, hm_d, hm_w = nothing, nothing, nothing
+    # the figure
+    fig = Figure(; size = (850,500))
+    axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
+    for (j, s) in enumerate(seasons_g)
+        hm_o = heatmap!(axs[1,j], mean_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_w = heatmap!(axs[2,j], weighted_g[time=At(s)]; colorrange, lowclip, highclip, colormap)
+        hm_d = heatmap!(axs[3,j], diff_g[time=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
+            colormap=:diverging_bwr_20_95_c54_n256)
+    end
+    Colorbar(fig[1:2,5], hm_o, label=cb_label)
+    Colorbar(fig[3,5], hm_d, label="Tair")
+    hidedecorations!.(axs, grid=false, ticks=false, label=false)
+    # some labels
+    [axs[1,j].title = string.(s) for (j,s) in enumerate(seasons_g)]
+    Label(fig[0,1:5], "Seasonal Surface Air Temperature", fontsize=18, font=:bold)
+    axs[1,1].ylabel = "Unweighted"
+    axs[2,1].ylabel = "Weighted"
+    axs[3,1].ylabel = "Difference"
+    colgap!(fig.layout, 5)
+    rowgap!(fig.layout, 5)
+    fig
+end

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

',66)]))}const N=i(l,[["render",p]]);export{y as __pageData,N as default}; diff --git a/previews/PR486/assets/UserGuide_read.md.CncWl83I.js b/previews/PR486/assets/UserGuide_read.md.CncWl83I.js new file mode 100644 index 00000000..78048f36 --- /dev/null +++ b/previews/PR486/assets/UserGuide_read.md.CncWl83I.js @@ -0,0 +1,229 @@ +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.piKCME0r.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

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)
+ds = open_dataset(store)
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"…)

We can set path to a URL, a local directory, or in this case to a cloud object storage path.

A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"         => "K"
+  "history"       => "2019-07-21T06:26:13Z altered by CMOR: Treated scalar dime…
+  "name"          => "tas"
+  "cell_methods"  => "area: mean time: point"
+  "cell_measures" => "area: areacella"
+  "long_name"     => "Near-Surface Air Temperature"
+  "coordinates"   => "height"
+  "standard_name" => "air_temperature"
+  "_FillValue"    => 1.0f20
+  "comment"       => "near-surface (usually, 2 meter) air temperature"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 69.02 GB
+└──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF ​

Open a NetCDF file as a Dataset:

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
my_lock = ReentrantLock()
+Threads.@threads for i in 1:10
+    @lock my_lock @info ds.tos[1, 1, 1]
+end
[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing

This code will ensure that the data is only accessed by one thread at a time, i.e. making it actual single-threaded but thread-safe.

Read GDAL (GeoTIFF, GeoJSON) ​

All GDAL compatible files can be read as a YAXArrays.Dataset after loading ArchGDAL:

julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ X Sampled{Float64} -180.0:0.17578125:179.82421875 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 90.0:-0.17578125:-89.82421875 ReverseOrdered Regular Points)
+
+Variables: 
+Blue, Green, Red
+
+Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")

Load data into memory ​

For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.

readcubedata ​

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+`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(` +`),s("span",{class:"line"},[s("span",null," ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────────────── metadata ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," Dict{String, Any} with 10 entries:")]),a(` +`),s("span",{class:"line"},[s("span",null,' "units" => "K"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "missing_value" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "history" => " At 16:37:23 on 01/11/2005: CMOR altered the data in t…')]),a(` +`),s("span",{class:"line"},[s("span",null,' "cell_methods" => "time: mean (interval: 30 minutes)"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "name" => "tos"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "long_name" => "Sea Surface Temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_units" => "degC"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "standard_name" => "sea_surface_temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "_FillValue" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_name" => "sosstsst"')]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," data size: 2.8 MB")]),a(` +`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1}),n(t,{label:"with the `:` operator"},{default:e(()=>i[4]||(i[4]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds"),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"tos[:, :, :]")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(` +`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(` +`),s("span",{class:"line"},[s("span",null," ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────────────── metadata ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," Dict{String, Any} with 10 entries:")]),a(` +`),s("span",{class:"line"},[s("span",null,' "units" => "K"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "missing_value" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "history" => " At 16:37:23 on 01/11/2005: CMOR altered the data in t…')]),a(` +`),s("span",{class:"line"},[s("span",null,' "cell_methods" => "time: mean (interval: 30 minutes)"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "name" => "tos"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "long_name" => "Sea Surface Temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_units" => "degC"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "standard_name" => "sea_surface_temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "_FillValue" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_name" => "sosstsst"')]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," data size: 2.8 MB")]),a(` +`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1),s("p",null,"In this case, you should know in advance how many dimensions there are and how long they are, which shouldn't be hard to determine since this information is already displayed when querying such variables.",-1)])),_:1}),n(t,{label:"Complete Dataset"},{default:e(()=>i[5]||(i[5]=[s("div",{class:"language-julia vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"},"julia"),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded "),s("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),s("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," readcubedata"),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(ds)")]),a(` +`),s("span",{class:"line"},[s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"ds_loaded["),s("span",{style:{"--shiki-light":"#032F62","--shiki-dark":"#9ECBFF"}},'"tos"'),s("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"] "),s("span",{style:{"--shiki-light":"#6A737D","--shiki-dark":"#6A737D"}},"# Load the variable of interest; the loaded status is shown for each variable.")])])])],-1),s("div",{class:"language- vp-adaptive-theme"},[s("button",{title:"Copy Code",class:"copy"}),s("span",{class:"lang"}),s("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[s("code",null,[s("span",{class:"line"},[s("span",null,"┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐")]),a(` +`),s("span",{class:"line"},[s("span",null,"├─────────────────────────────────────────────────┴────────────────────── dims ┐")]),a(` +`),s("span",{class:"line"},[s("span",null," ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,")]),a(` +`),s("span",{class:"line"},[s("span",null," ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points")]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────────────── metadata ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," Dict{String, Any} with 10 entries:")]),a(` +`),s("span",{class:"line"},[s("span",null,' "units" => "K"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "missing_value" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "history" => " At 16:37:23 on 01/11/2005: CMOR altered the data in t…')]),a(` +`),s("span",{class:"line"},[s("span",null,' "cell_methods" => "time: mean (interval: 30 minutes)"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "name" => "tos"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "long_name" => "Sea Surface Temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_units" => "degC"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "standard_name" => "sea_surface_temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "_FillValue" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_name" => "sosstsst"')]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," data size: 2.8 MB")]),a(` +`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),i[11]||(i[11]=l(`

Note how the loading status changes from loaded lazily to loaded in memory.

open_mfdataset ​

There are situations when we would like to open and concatenate a list of dataset paths along a certain dimension. For example, to concatenate a list of NetCDF files along a new time dimension, one can use:

creation of NetCDF files
julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+
+dates_1 = [Date(2020, 1, 1) + Dates.Day(i) for i in 1:3]
+dates_2 = [Date(2020, 1, 4) + Dates.Day(i) for i in 1:3]
+
+a1 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+a2 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+
+a3 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_1)), rand(5, 7, 3))
+a4 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_2)), rand(5, 7, 3))
+
+savecube(a1, "a1.nc")
+savecube(a2, "a2.nc")
+savecube(a3, "a3.nc")
+savecube(a4, "a4.nc")
┌ 5×7×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [2020-01-05, …, 2020-01-07] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 840.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

along a new dimension ​

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a1.nc", "a2.nc"]
+
+dates_read = [Date(2024, 1, 1) + Dates.Day(i) for i in 1:2]
+ds = open_mfdataset(DD.DimArray(files, YAX.time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

and even opening files along a new Time dimension that already have a time dimension

julia
files = ["a3.nc", "a4.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.Time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-04T00:00:00] ForwardOrdered Irregular Points,
+  ⬔ Time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

Note that opening along a new dimension name without specifying values also works; however, it defaults to 1:length(files) for the dimension values.

julia
files = ["a1.nc", "a2.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.time))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Int64} 1:2 ForwardOrdered Regular Points)
+
+Variables: 
+layer

along a existing dimension ​

Another use case is when we want to open files along an existing dimension. In this case, open_mfdataset will concatenate the paths along the specified dimension

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a3.nc", "a4.nc"]
+
+ds = open_mfdataset(DD.DimArray(files, YAX.time()))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-07T00:00:00] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

where the contents of the time dimension are the merged values from both files

julia
julia> ds["time"]
time Sampled{DateTime} ForwardOrdered Irregular DimensionalData.Dimensions.Lookups.Points
+wrapping: 6-element Vector{DateTime}:
+ 2020-01-02T00:00:00
+ 2020-01-03T00:00:00
+ 2020-01-04T00:00:00
+ 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.

`,21))])}const D=d(g,[["render",c]]);export{b as __pageData,D as default}; diff --git a/previews/PR486/assets/UserGuide_read.md.CncWl83I.lean.js b/previews/PR486/assets/UserGuide_read.md.CncWl83I.lean.js new file mode 100644 index 00000000..78048f36 --- /dev/null +++ b/previews/PR486/assets/UserGuide_read.md.CncWl83I.lean.js @@ -0,0 +1,229 @@ +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.piKCME0r.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

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)
+ds = open_dataset(store)
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"…)

We can set path to a URL, a local directory, or in this case to a cloud object storage path.

A zarr store may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tas
┌ 384×192×251288 YAXArray{Float32, 3} ┐
+├─────────────────────────────────────┴────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ time Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"         => "K"
+  "history"       => "2019-07-21T06:26:13Z altered by CMOR: Treated scalar dime…
+  "name"          => "tas"
+  "cell_methods"  => "area: mean time: point"
+  "cell_measures" => "area: areacella"
+  "long_name"     => "Near-Surface Air Temperature"
+  "coordinates"   => "height"
+  "standard_name" => "air_temperature"
+  "_FillValue"    => 1.0f20
+  "comment"       => "near-surface (usually, 2 meter) air temperature"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 69.02 GB
+└──────────────────────────────────────────────────────────────────────────────┘

Read NetCDF ​

Open a NetCDF file as a Dataset:

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

A NetCDF file may contain multiple arrays. Individual arrays can be accessed using subsetting:

julia
ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
my_lock = ReentrantLock()
+Threads.@threads for i in 1:10
+    @lock my_lock @info ds.tos[1, 1, 1]
+end
[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing
+[ Info: missing

This code will ensure that the data is only accessed by one thread at a time, i.e. making it actual single-threaded but thread-safe.

Read GDAL (GeoTIFF, GeoJSON) ​

All GDAL compatible files can be read as a YAXArrays.Dataset after loading ArchGDAL:

julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ X Sampled{Float64} -180.0:0.17578125:179.82421875 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 90.0:-0.17578125:-89.82421875 ReverseOrdered Regular Points)
+
+Variables: 
+Blue, Green, Red
+
+Properties: Dict{String, Any}("projection" => "GEOGCS[\\"WGS 84\\",DATUM[\\"WGS_1984\\",SPHEROID[\\"WGS 84\\",6378137,298.257223563,AUTHORITY[\\"EPSG\\",\\"7030\\"]],AUTHORITY[\\"EPSG\\",\\"6326\\"]],PRIMEM[\\"Greenwich\\",0,AUTHORITY[\\"EPSG\\",\\"8901\\"]],UNIT[\\"degree\\",0.0174532925199433,AUTHORITY[\\"EPSG\\",\\"9122\\"]],AXIS[\\"Latitude\\",NORTH],AXIS[\\"Longitude\\",EAST],AUTHORITY[\\"EPSG\\",\\"4326\\"]]")

Load data into memory ​

For datasets or variables that could fit in RAM, you might want to load them completely into memory. This can be done using the readcubedata function. As an example, let's use the NetCDF workflow; the same should be true for other cases.

readcubedata ​

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"cell_methods" => "time: mean (interval: 30 minutes)"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "name" => "tos"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "long_name" => "Sea Surface Temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_units" => "degC"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "standard_name" => "sea_surface_temperature"')]),a(` +`),s("span",{class:"line"},[s("span",null,' "_FillValue" => 1.0f20')]),a(` +`),s("span",{class:"line"},[s("span",null,' "original_name" => "sosstsst"')]),a(` +`),s("span",{class:"line"},[s("span",null,"├──────────────────────────────────────────────────────────── loaded in memory ┤")]),a(` +`),s("span",{class:"line"},[s("span",null," data size: 2.8 MB")]),a(` +`),s("span",{class:"line"},[s("span",null,"└──────────────────────────────────────────────────────────────────────────────┘")])])])],-1)])),_:1})]),_:1}),i[11]||(i[11]=l(`

Note how the loading status changes from loaded lazily to loaded in memory.

open_mfdataset ​

There are situations when we would like to open and concatenate a list of dataset paths along a certain dimension. For example, to concatenate a list of NetCDF files along a new time dimension, one can use:

creation of NetCDF files
julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+
+dates_1 = [Date(2020, 1, 1) + Dates.Day(i) for i in 1:3]
+dates_2 = [Date(2020, 1, 4) + Dates.Day(i) for i in 1:3]
+
+a1 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+a2 = YAXArray((lon(1:5), lat(1:7)), rand(5, 7))
+
+a3 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_1)), rand(5, 7, 3))
+a4 = YAXArray((lon(1:5), lat(1:7), YAX.time(dates_2)), rand(5, 7, 3))
+
+savecube(a1, "a1.nc")
+savecube(a2, "a2.nc")
+savecube(a3, "a3.nc")
+savecube(a4, "a4.nc")
┌ 5×7×3 YAXArray{Float64, 3} ┐
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [2020-01-05, …, 2020-01-07] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 840.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

along a new dimension ​

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a1.nc", "a2.nc"]
+
+dates_read = [Date(2024, 1, 1) + Dates.Day(i) for i in 1:2]
+ds = open_mfdataset(DD.DimArray(files, YAX.time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

and even opening files along a new Time dimension that already have a time dimension

julia
files = ["a3.nc", "a4.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.Time(dates_read)))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-04T00:00:00] ForwardOrdered Irregular Points,
+  ⬔ Time Sampled{Date} [Date("2024-01-02"), Date("2024-01-03")] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

Note that opening along a new dimension name without specifying values also works; however, it defaults to 1:length(files) for the dimension values.

julia
files = ["a1.nc", "a2.nc"]
+ds = open_mfdataset(DD.DimArray(files, YAX.time))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{Int64} 1:2 ForwardOrdered Regular Points)
+
+Variables: 
+layer

along a existing dimension ​

Another use case is when we want to open files along an existing dimension. In this case, open_mfdataset will concatenate the paths along the specified dimension

julia
using YAXArrays, NetCDF, Dates
+using YAXArrays: YAXArrays as YAX
+import DimensionalData as DD
+
+files = ["a3.nc", "a4.nc"]
+
+ds = open_mfdataset(DD.DimArray(files, YAX.time()))
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Int64} 1:1:5 ForwardOrdered Regular Points,
+  → lat  Sampled{Int64} 1:1:7 ForwardOrdered Regular Points,
+  ↗ time Sampled{DateTime} [2020-01-02T00:00:00, …, 2020-01-07T00:00:00] ForwardOrdered Irregular Points)
+
+Variables: 
+layer

where the contents of the time dimension are the merged values from both files

julia
julia> ds["time"]
time Sampled{DateTime} ForwardOrdered Irregular DimensionalData.Dimensions.Lookups.Points
+wrapping: 6-element Vector{DateTime}:
+ 2020-01-02T00:00:00
+ 2020-01-03T00:00:00
+ 2020-01-04T00:00:00
+ 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.

`,21))])}const D=d(g,[["render",c]]);export{b as __pageData,D as default}; diff --git a/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.js b/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.js new file mode 100644 index 00000000..48dea775 --- /dev/null +++ b/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.js @@ -0,0 +1,276 @@ +import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),i("div",null,s[0]||(s[0]=[n(`

Select YAXArrays and Datasets ​

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

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

Select elements ​

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
+time1 = DateTime360Day(2001,01,16)
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────────────────┴ metadata ┐
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
+└────────────────────────────────────────────────────────────────────────────────┘

Select ranges ​

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

julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals ​

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

julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension ​

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

julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  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
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.lean.js b/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.lean.js new file mode 100644 index 00000000..48dea775 --- /dev/null +++ b/previews/PR486/assets/UserGuide_select.md.B1gCBPvb.lean.js @@ -0,0 +1,276 @@ +import{_ as a,c as i,a2 as n,o as e}from"./chunks/framework.piKCME0r.js";const u=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),t={name:"UserGuide/select.md"};function p(l,s,h,o,d,k){return e(),i("div",null,s[0]||(s[0]=[n(`

Select YAXArrays and Datasets ​

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

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

julia
tos = ds.tos
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

which is the same as:

julia
tos = ds.cubes[:tos]
┌ 180×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 2.8 MB
+└──────────────────────────────────────────────────────────────────────────────┘

Select elements ​

Using positional integer indexing:

julia
tos[lon = 1, lat = 1]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1), lat = At(-79.5)]
┌ 24-element YAXArray{Union{Missing, Float32}, 1} ┐
+├─────────────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 96.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Using special types:

julia
using CFTime
+time1 = DateTime360Day(2001,01,16)
+tos[time = At(time1)]
┌ 180×170 YAXArray{Union{Missing, Float32}, 2} ┐
+├──────────────────────────────────────────────┴──────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────────────────┴ metadata ┐
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├───────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 119.53 KB
+└────────────────────────────────────────────────────────────────────────────────┘

Select ranges ​

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

julia
tos[lon = 1:10, lat = 1:10]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 1.0:2.0:19.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:-70.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Same but using named indexing:

julia
tos[lon = At(1.0:2:19), lat = At(-79.5:1:-70.5)]
┌ 10×10×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├───────────────────────────────────────────────┴──────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 3.0, …, 17.0, 19.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} [-79.5, -78.5, …, -71.5, -70.5] ForwardOrdered Irregular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
tos[lon = At(1:10; atol = 1)]
┌ 10×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} [1.0, 1.0, …, 9.0, 9.0] ForwardOrdered Irregular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 159.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Closed and open intervals ​

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

julia
tos[lon = 90 .. 180]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

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

julia
using IntervalSets
julia
julia> tos[lon = OpenInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon = ClosedInterval(90, 180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:open,:closed}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘
julia
julia> tos[lon =Interval{:closed,:open}(90,180)]
┌ 45×170×24 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ lon  Sampled{Float64} 91.0:2.0:179.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any} with 10 entries:
+  "units"          => "K"
+  "missing_value"  => 1.0f20
+  "history"        => " At   16:37:23 on 01/11/2005: CMOR altered the data in t…
+  "cell_methods"   => "time: mean (interval: 30 minutes)"
+  "name"           => "tos"
+  "long_name"      => "Sea Surface Temperature"
+  "original_units" => "degC"
+  "standard_name"  => "sea_surface_temperature"
+  "_FillValue"     => 1.0f20
+  "original_name"  => "sosstsst"
+├─────────────────────────────────────────────────────────────── loaded lazily ┤
+  data size: 717.19 KB
+└──────────────────────────────────────────────────────────────────────────────┘

See tutorials for use cases.

Get a dimension ​

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

julia
collect(tos.lat)
┌ 170-element DimArray{Float64, 1} ┐
+├──────────────────────────────────┴──────────────────────────── dims ┐
+  ↓ lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points
+└─────────────────────────────────────────────────────────────────────┘
+ -79.5  -79.5
+ -78.5  -78.5
+ -77.5  -77.5
+ -76.5  -76.5
+ -75.5  -75.5
+ -74.5  -74.5
+   ⋮    
+  85.5   85.5
+  86.5   86.5
+  87.5   87.5
+  88.5   88.5
+  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
`,56)]))}const g=a(t,[["render",p]]);export{u as __pageData,g as default}; diff --git a/previews/PR486/assets/UserGuide_types.md.DuodkEtM.js b/previews/PR486/assets/UserGuide_types.md.DuodkEtM.js new file mode 100644 index 00000000..897b4c09 --- /dev/null +++ b/previews/PR486/assets/UserGuide_types.md.DuodkEtM.js @@ -0,0 +1,2 @@ +import{_ as e,c as a,a2 as i,o as s}from"./chunks/framework.piKCME0r.js";const p=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),d={name:"UserGuide/types.md"};function o(l,t,n,r,c,h){return s(),a("div",null,t[0]||(t[0]=[i(`

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.

`,16)]))}const y=e(d,[["render",o]]);export{p as __pageData,y as default}; diff --git a/previews/PR486/assets/UserGuide_types.md.DuodkEtM.lean.js b/previews/PR486/assets/UserGuide_types.md.DuodkEtM.lean.js new file mode 100644 index 00000000..897b4c09 --- /dev/null +++ b/previews/PR486/assets/UserGuide_types.md.DuodkEtM.lean.js @@ -0,0 +1,2 @@ +import{_ as e,c as a,a2 as i,o as s}from"./chunks/framework.piKCME0r.js";const p=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),d={name:"UserGuide/types.md"};function o(l,t,n,r,c,h){return s(),a("div",null,t[0]||(t[0]=[i(`

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.

`,16)]))}const y=e(d,[["render",o]]);export{p as __pageData,y as default}; diff --git a/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.js b/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.js new file mode 100644 index 00000000..52e07ad5 --- /dev/null +++ b/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.js @@ -0,0 +1,71 @@ +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/framework.piKCME0r.js";const C=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,E,y,u){const t=h("Badge");return k(),n("div",null,[s[3]||(s[3]=a(`

Write YAXArrays and Datasets ​

Create an example Dataset:

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Write Zarr ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

zarr compression ​

Save a dataset to Zarr format with compression:

julia
n = 9 # compression level, number between 0 (no compression) and 9 (max compression)
+compression = Zarr.BloscCompressor(; clevel=n)
+
+savedataset(ds; path="ds_c.zarr", driver=:zarr, compressor=compression)

More on Zarr Compressors. Also, if you use this option and don't notice a significant improvement, please feel free to open an issue or start a discussion.

Write NetCDF ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

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)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+  Variables: 
+  tos
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
+  Variables: 
+  z
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

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

WARNING

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

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

Now, we check that all the values are missing

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

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

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+
+Variables: 
+skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset ​

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

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

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 0.716952  0.95965   0.599987   0.337016
+ 0.506947  0.923876  0.0940127  0.54942
+ 0.847062  0.298617  0.182371   0.921

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

julia
ds_open = open_dataset("skeleton.zarr")
+ds_array = ds_open["skeleton"]
+ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 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.

`,35))])}const v=e(d,[["render",o]]);export{C as __pageData,v as default}; diff --git a/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.lean.js b/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.lean.js new file mode 100644 index 00000000..52e07ad5 --- /dev/null +++ b/previews/PR486/assets/UserGuide_write.md.Dt-jU2T_.lean.js @@ -0,0 +1,71 @@ +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/framework.piKCME0r.js";const C=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),d={name:"UserGuide/write.md"},r={class:"jldocstring custom-block",open:""};function o(g,s,c,E,y,u){const t=h("Badge");return k(),n("div",null,[s[3]||(s[3]=a(`

Write YAXArrays and Datasets ​

Create an example Dataset:

julia
using YAXArrays
+using NetCDF
+using Downloads: download
+
+path = download("https://www.unidata.ucar.edu/software/netcdf/examples/tos_O1_2001-2002.nc", "example.nc")
+ds = open_dataset(path)
YAXArray Dataset
+Shared Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+
+Variables: 
+tos
+
+Properties: Dict{String, Any}("cmor_version" => 0.96f0, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "Conventions" => "CF-1.0", "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Write Zarr ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

zarr compression ​

Save a dataset to Zarr format with compression:

julia
n = 9 # compression level, number between 0 (no compression) and 9 (max compression)
+compression = Zarr.BloscCompressor(; clevel=n)
+
+savedataset(ds; path="ds_c.zarr", driver=:zarr, compressor=compression)

More on Zarr Compressors. Also, if you use this option and don't notice a significant improvement, please feel free to open an issue or start a discussion.

Write NetCDF ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

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)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+None
+Variables with additional axes:
+  Additional Axes: 
+  (↓ lon  Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat  Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ time Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points)
+  Variables: 
+  tos
+
+  Additional Axes: 
+  (↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points)
+  Variables: 
+  z
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

Sometimes one merely wants to create a datacube "Skeleton" on disk and gradually fill it with data. Here we make use of FillArrays to create a YAXArray and write only the axis data and array metadata to disk, while no actual array data is copied:

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Float32},  5, 4, 5))
┌ 5×4×5 YAXArray{Union{Missing, Float32}, 3} ┐
+├────────────────────────────────────────────┴─────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 400.0 bytes
+└──────────────────────────────────────────────────────────────────────────────┘

Now, save to disk with

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

WARNING

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

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

Now, we check that all the values are missing

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

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

INFO

The skeleton argument is also available for savedataset.

Using the toy array defined above we can do

julia
ds = Dataset(skeleton=a) # skeleton will the variable name
YAXArray Dataset
+Shared Axes: 
+  (↓ Dim_1 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(4) ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points)
+
+Variables: 
+skeleton
julia
ds_s = savedataset(ds, path="skeleton.zarr", driver=:zarr, skeleton=true, overwrite=true)

Update values of dataset ​

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

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

and then we simply update values by indexing them where necessary

julia
ds_array[:,:,1] = rand(Float32, 5, 4) # this will update values directly into disk!
5×4 Matrix{Float32}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 0.716952  0.95965   0.599987   0.337016
+ 0.506947  0.923876  0.0940127  0.54942
+ 0.847062  0.298617  0.182371   0.921

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

julia
ds_open = open_dataset("skeleton.zarr")
+ds_array = ds_open["skeleton"]
+ds_array.data[:,:,1]
5×4 Matrix{Union{Missing, Float32}}:
+ 0.746259  0.259455  0.831968   0.275265
+ 0.547738  0.35762   0.158741   0.991508
+ 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.

`,35))])}const v=e(d,[["render",o]]);export{C as __pageData,v as default}; diff --git a/previews/PR486/assets/api.md.CRtEnxW2.js b/previews/PR486/assets/api.md.CRtEnxW2.js new file mode 100644 index 00000000..b4fa9c86 --- /dev/null +++ b/previews/PR486/assets/api.md.CRtEnxW2.js @@ -0,0 +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.piKCME0r.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:""},k={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},A={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:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={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:

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

source

',5))]),e("details",k,[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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",b,[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",f,[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:

source

',4))]),e("details",A,[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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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.

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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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:

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:

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:

source

',7))]),e("details",P,[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",q,[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",R,[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",V,[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:

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

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

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

source

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julia
getOuttype(outtype, cdata)

Internal function

Get the element type for the output cube

source

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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:

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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const ks=n(d,[["render",ns]]);export{us as __pageData,ks as default}; diff --git a/previews/PR486/assets/api.md.CRtEnxW2.lean.js b/previews/PR486/assets/api.md.CRtEnxW2.lean.js new file mode 100644 index 00000000..b4fa9c86 --- /dev/null +++ b/previews/PR486/assets/api.md.CRtEnxW2.lean.js @@ -0,0 +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.piKCME0r.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:""},k={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},A={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:""},P={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},z={class:"jldocstring custom-block",open:""},N={class:"jldocstring custom-block",open:""},S={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},V={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:

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

source

',5))]),e("details",k,[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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/src/Cubes/Cubes.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",b,[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",f,[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:

source

',4))]),e("details",A,[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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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.

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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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:

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:

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:

source

',7))]),e("details",P,[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",q,[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",R,[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",V,[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:

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

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

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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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:

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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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

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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/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/dc38fbff028e43cf42daaf13dd96f95cfec895e6/src/DatasetAPI/Datasets.jl#L312",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const ks=n(d,[["render",ns]]);export{us as __pageData,ks as default}; diff --git a/previews/PR486/assets/app.sPp5m4iP.js b/previews/PR486/assets/app.sPp5m4iP.js new file mode 100644 index 00000000..d40e0028 --- /dev/null +++ b/previews/PR486/assets/app.sPp5m4iP.js @@ -0,0 +1 @@ +import{R as p}from"./chunks/theme.ChQEK1xa.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.piKCME0r.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 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this.mark=(t,s)=>(e.mark(t,s),this),this.markRegExp=(t,s)=>(e.markRegExp(t,s),this),this.markRanges=(t,s)=>(e.markRanges(t,s),this),this.unmark=t=>(e.unmark(t),this),this}function ke(a,e,t,s){function n(r){return r instanceof t?r:new t(function(i){i(r)})}return new(t||(t=Promise))(function(r,i){function o(h){try{c(s.next(h))}catch(m){i(m)}}function l(h){try{c(s.throw(h))}catch(m){i(m)}}function c(h){h.done?r(h.value):n(h.value).then(o,l)}c((s=s.apply(a,[])).next())})}const js="ENTRIES",_t="KEYS",St="VALUES",D="";class De{constructor(e,t){const s=e._tree,n=Array.from(s.keys());this.set=e,this._type=t,this._path=n.length>0?[{node:s,keys:n}]:[]}next(){const e=this.dive();return this.backtrack(),e}dive(){if(this._path.length===0)return{done:!0,value:void 0};const{node:e,keys:t}=le(this._path);if(le(t)===D)return{done:!1,value:this.result()};const s=e.get(le(t));return this._path.push({node:s,keys:Array.from(s.keys())}),this.dive()}backtrack(){if(this._path.length===0)return;const 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Map;return o.set(i.slice(r.length),n.get(i)),new X(o,e)}}return new X(t,e)}clear(){this._size=void 0,this._tree.clear()}delete(e){return this._size=void 0,$s(this._tree,e)}entries(){return new De(this,js)}forEach(e){for(const[t,s]of this)e(t,s,this)}fuzzyGet(e,t){return Vs(this._tree,e,t)}get(e){const t=Ke(this._tree,e);return t!==void 0?t.get(D):void 0}has(e){const t=Ke(this._tree,e);return t!==void 0&&t.has(D)}keys(){return new De(this,_t)}set(e,t){if(typeof e!="string")throw new Error("key must be a string");return this._size=void 0,ze(this._tree,e).set(D,t),this}get size(){if(this._size)return this._size;this._size=0;const e=this.entries();for(;!e.next().done;)this._size+=1;return this._size}update(e,t){if(typeof e!="string")throw new Error("key must be a string");this._size=void 0;const s=ze(this._tree,e);return s.set(D,t(s.get(D))),this}fetch(e,t){if(typeof e!="string")throw new Error("key must be a string");this._size=void 0;const s=ze(this._tree,e);let n=s.get(D);return n===void 0&&s.set(D,n=t()),n}values(){return new De(this,St)}[Symbol.iterator](){return this.entries()}static from(e){const t=new X;for(const[s,n]of e)t.set(s,n);return t}static fromObject(e){return X.from(Object.entries(e))}}const Re=(a,e,t=[])=>{if(e.length===0||a==null)return[a,t];for(const s of a.keys())if(s!==D&&e.startsWith(s))return t.push([a,s]),Re(a.get(s),e.slice(s.length),t);return t.push([a,e]),Re(void 0,"",t)},Ke=(a,e)=>{if(e.length===0||a==null)return a;for(const t of a.keys())if(t!==D&&e.startsWith(t))return Ke(a.get(t),e.slice(t.length))},ze=(a,e)=>{const t=e.length;e:for(let s=0;a&&s{const[t,s]=Re(a,e);if(t!==void 0){if(t.delete(D),t.size===0)Tt(s);else if(t.size===1){const[n,r]=t.entries().next().value;It(s,n,r)}}},Tt=a=>{if(a.length===0)return;const[e,t]=qe(a);if(e.delete(t),e.size===0)Tt(a.slice(0,-1));else if(e.size===1){const[s,n]=e.entries().next().value;s!==D&&It(a.slice(0,-1),s,n)}},It=(a,e,t)=>{if(a.length===0)return;const[s,n]=qe(a);s.set(n+e,t),s.delete(n)},qe=a=>a[a.length-1],Ge="or",kt="and",Bs="and_not";class ue{constructor(e){if((e==null?void 0:e.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const t=e.autoVacuum==null||e.autoVacuum===!0?Ve:e.autoVacuum;this._options=Object.assign(Object.assign(Object.assign({},je),e),{autoVacuum:t,searchOptions:Object.assign(Object.assign({},ht),e.searchOptions||{}),autoSuggestOptions:Object.assign(Object.assign({},qs),e.autoSuggestOptions||{})}),this._index=new X,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ue,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const m=s(h.toString(),c),f=this._fieldIds[c],b=new Set(m).size;this.addFieldLength(l,f,this._documentCount-1,b);for(const y of m){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(f,l,w);else x&&this.addTerm(f,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new Promise(m=>setTimeout(m,0))).then(()=>this.addAll(o))}:{chunk:o,promise:l}),n);return i.then(()=>this.addAll(r))}remove(e){const{tokenize:t,processTerm:s,extractField:n,fields:r,idField:i}=this._options,o=n(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);const l=this._idToShortId.get(o);if(l==null)throw new Error(`MiniSearch: cannot remove document with ID ${o}: it is not in the index`);for(const c of r){const h=n(e,c);if(h==null)continue;const m=t(h.toString(),c),f=this._fieldIds[c],b=new Set(m).size;this.removeFieldLength(l,f,this._documentCount,b);for(const y of m){const x=s(y,c);if(Array.isArray(x))for(const w of x)this.removeTerm(f,l,w);else x&&this.removeTerm(f,l,x)}}this._storedFields.delete(l),this._documentIds.delete(l),this._idToShortId.delete(o),this._fieldLength.delete(l),this._documentCount-=1}removeAll(e){if(e)for(const t of e)this.remove(t);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new X,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(e){const t=this._idToShortId.get(e);if(t==null)throw new Error(`MiniSearch: cannot discard document with ID ${e}: it is not in the index`);this._idToShortId.delete(e),this._documentIds.delete(t),this._storedFields.delete(t),(this._fieldLength.get(t)||[]).forEach((s,n)=>{this.removeFieldLength(t,n,this._documentCount,s)}),this._fieldLength.delete(t),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:e,minDirtCount:t,batchSize:s,batchWait:n}=this._options.autoVacuum;this.conditionalVacuum({batchSize:s,batchWait:n},{minDirtCount:t,minDirtFactor:e})}discardAll(e){const t=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const s of e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Ue,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return ke(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Je.batchSize,r=e.batchWait||Je.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[m]of h)this._documentIds.has(m)||(h.size<=1?l.delete(c):h.delete(m));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Ve.minDirtCount,s=s||Ve.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const{searchOptions:s}=this._options,n=Object.assign(Object.assign({},s),t),r=this.executeQuery(e,t),i=[];for(const[o,{score:l,terms:c,match:h}]of r){const m=c.length||1,f={id:this._documentIds.get(o),score:l*m,terms:Object.keys(h),queryTerms:c,match:h};Object.assign(f,this._storedFields.get(o)),(n.filter==null||n.filter(f))&&i.push(f)}return e===ue.wildcard&&n.boostDocument==null||i.sort(pt),i}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(pt),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return ke(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(e),t)})}static getDefault(e){if(je.hasOwnProperty(e))return Pe(je,e);throw new Error(`MiniSearch: unknown option "${e}"`)}static loadJS(e,t){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=Te(n),l._fieldLength=Te(r),l._storedFields=Te(i);for(const[c,h]of l._documentIds)l._idToShortId.set(h,c);for(const[c,h]of s){const m=new Map;for(const f of Object.keys(h)){let b=h[f];o===1&&(b=b.ds),m.set(parseInt(f,10),Te(b))}l._index.set(c,m)}return l}static loadJSAsync(e,t){return ke(this,void 0,void 0,function*(){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=yield Ie(n),l._fieldLength=yield Ie(r),l._storedFields=yield Ie(i);for(const[h,m]of l._documentIds)l._idToShortId.set(m,h);let c=0;for(const[h,m]of s){const f=new Map;for(const b of Object.keys(m)){let y=m[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield Ie(y))}++c%1e3===0&&(yield Nt(0)),l._index.set(h,f)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ue(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new X,c}executeQuery(e,t={}){if(e===ue.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return this.combineResults(b,f.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,m=o(e).flatMap(f=>l(f)).filter(f=>!!f).map(Us(i)).map(f=>this.executeQuerySpec(f,i));return this.combineResults(m,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:Pe(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},ht.weights),i),m=this._index.get(e.term),f=this.termResults(e.term,e.term,1,e.termBoost,m,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const R=x.length-e.term.length;if(!R)continue;y==null||y.delete(x);const A=h*x.length/(x.length+.3*R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}if(y)for(const x of y.keys()){const[w,R]=y.get(x);if(!R)continue;const A=c*x.length/(x.length+R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}return f}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Ge){if(e.length===0)return new Map;const s=t.toLowerCase(),n=Ws[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const m=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const A=b.get(w),J=this._fieldLength.get(w)[f],Q=Js(A,y,this._documentCount,J,x,l),W=s*n*m*R*Q,V=c.get(w);if(V){V.score+=W,Gs(V.terms,e);const $=Pe(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,vt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,vt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(e){const t=this._nextId;return this._idToShortId.set(e,t),this._documentIds.set(t,e),this._documentCount+=1,this._nextId+=1,t}addFields(e){for(let t=0;tObject.prototype.hasOwnProperty.call(a,e)?a[e]:void 0,Ws={[Ge]:(a,e)=>{for(const t of e.keys()){const s=a.get(t);if(s==null)a.set(t,e.get(t));else{const{score:n,terms:r,match:i}=e.get(t);s.score=s.score+n,s.match=Object.assign(s.match,i),ft(s.terms,r)}}return a},[kt]:(a,e)=>{const t=new Map;for(const s of e.keys()){const n=a.get(s);if(n==null)continue;const{score:r,terms:i,match:o}=e.get(s);ft(n.terms,i),t.set(s,{score:n.score+r,terms:n.terms,match:Object.assign(n.match,o)})}return t},[Bs]:(a,e)=>{for(const t of e.keys())a.delete(t);return a}},Ks={k:1.2,b:.7,d:.5},Js=(a,e,t,s,n,r)=>{const{k:i,b:o,d:l}=r;return Math.log(1+(t-e+.5)/(e+.5))*(l+a*(i+1)/(a+i*(1-o+o*s/n)))},Us=a=>(e,t,s)=>{const n=typeof a.fuzzy=="function"?a.fuzzy(e,t,s):a.fuzzy||!1,r=typeof a.prefix=="function"?a.prefix(e,t,s):a.prefix===!0,i=typeof a.boostTerm=="function"?a.boostTerm(e,t,s):1;return{term:e,fuzzy:n,prefix:r,termBoost:i}},je={idField:"id",extractField:(a,e)=>a[e],tokenize:a=>a.split(Hs),processTerm:a=>a.toLowerCase(),fields:void 0,searchOptions:void 0,storeFields:[],logger:(a,e)=>{typeof(console==null?void 0:console[a])=="function"&&console[a](e)},autoVacuum:!0},ht={combineWith:Ge,prefix:!1,fuzzy:!1,maxFuzzy:6,boost:{},weights:{fuzzy:.45,prefix:.375},bm25:Ks},qs={combineWith:kt,prefix:(a,e,t)=>e===t.length-1},Je={batchSize:1e3,batchWait:10},Ue={minDirtFactor:.1,minDirtCount:20},Ve=Object.assign(Object.assign({},Je),Ue),Gs=(a,e)=>{a.includes(e)||a.push(e)},ft=(a,e)=>{for(const t of e)a.includes(t)||a.push(t)},pt=({score:a},{score:e})=>e-a,vt=()=>new Map,Te=a=>{const e=new Map;for(const t of Object.keys(a))e.set(parseInt(t,10),a[t]);return e},Ie=a=>ke(void 0,void 0,void 0,function*(){const e=new Map;let 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Get your chunks!

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geospatial data in Julia.

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Contribute to YAXArrays.jl ​

Pull requests and bug reports are always welcome at the YAXArrays.jl GitHub repository.

Contribute to Documentation ​

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npm i

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include("make.jl")

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Contribute to YAXArrays.jl ​

Pull requests and bug reports are always welcome at the YAXArrays.jl GitHub repository.

Contribute to Documentation ​

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npm i

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docs> julia
+julia> ]
+pkg> activate .

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sh
include("make.jl")

Now go to your terminal in the same path docs> and run:

sh
npm run docs:dev

This should ouput http://localhost:5173/YAXArrays.jl/, copy/paste this into your browser and you are all set.

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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} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 48.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
# axes or dimensions with name and tick values
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+    Variables(["temperature", "precipitation"])
+)
+
+# the actual data matching the dimensions defined in axlist
+data = rand(20, 10, 15, 2)
+
+# metadata about the array
+props = Dict(
+    "origin" => "YAXArrays.jl example",
+    "x" => "longitude",
+    "y" => "latitude",
+);
+
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["temperature", "precipitation"] ReverseOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 3 entries:
+  "y"      => "latitude"
+  "x"      => "longitude"
+  "origin" => "YAXArrays.jl example"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Get the temperature map at the first point in time:

julia
a2[Variables=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
+ 0.320052  0.500909   0.827727   0.415137  …  0.825496  0.487315  0.0962708
+ 0.209357  0.0833026  0.207967   0.224959     0.309666  0.211397  0.552582
+ 0.507229  0.757781   0.514759   0.973442     0.111379  0.787476  0.0247931
+ 0.879677  0.374138   0.97643    0.748725     0.665274  0.776172  0.666534
+ 0.79472   0.958973   0.0114824  0.274902     0.499743  0.645512  0.622774
+ 0.320869  0.952795   0.547668   0.982108  …  0.80871   0.253383  0.743343
+ 0.496429  0.0436     0.790617   0.233118     0.137114  0.55245   0.716721
+ 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.

`,21)]))}const g=a(p,[["render",l]]);export{E as __pageData,g as default}; diff --git a/previews/PR486/assets/get_started.md.CdXe2EOO.lean.js b/previews/PR486/assets/get_started.md.CdXe2EOO.lean.js new file mode 100644 index 00000000..6e526b28 --- /dev/null +++ b/previews/PR486/assets/get_started.md.CdXe2EOO.lean.js @@ -0,0 +1,53 @@ +import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.piKCME0r.js";const E=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),p={name:"get_started.md"};function l(e,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`

Getting Started ​

Installation ​

Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing

julia
pkg> add YAXArrays

Alternatively, you can also do

julia
import Pkg; Pkg.add("YAXArrays")

Quickstart ​

Create a simple array from random numbers given the size of each dimension or axis:

julia
using YAXArrays
+using YAXArrays: YAXArrays as YAX
+
+a = YAXArray(rand(2,3))
┌ 2×3 YAXArray{Float64, 2} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 48.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
# axes or dimensions with name and tick values
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+    Variables(["temperature", "precipitation"])
+)
+
+# the actual data matching the dimensions defined in axlist
+data = rand(20, 10, 15, 2)
+
+# metadata about the array
+props = Dict(
+    "origin" => "YAXArrays.jl example",
+    "x" => "longitude",
+    "y" => "latitude",
+);
+
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["temperature", "precipitation"] ReverseOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 3 entries:
+  "y"      => "latitude"
+  "x"      => "longitude"
+  "origin" => "YAXArrays.jl example"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Get the temperature map at the first point in time:

julia
a2[Variables=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
+ 0.320052  0.500909   0.827727   0.415137  …  0.825496  0.487315  0.0962708
+ 0.209357  0.0833026  0.207967   0.224959     0.309666  0.211397  0.552582
+ 0.507229  0.757781   0.514759   0.973442     0.111379  0.787476  0.0247931
+ 0.879677  0.374138   0.97643    0.748725     0.665274  0.776172  0.666534
+ 0.79472   0.958973   0.0114824  0.274902     0.499743  0.645512  0.622774
+ 0.320869  0.952795   0.547668   0.982108  …  0.80871   0.253383  0.743343
+ 0.496429  0.0436     0.790617   0.233118     0.137114  0.55245   0.716721
+ 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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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("ArchGDAL")
julia
using Pkg
+Pkg.add("NetCDF")
julia
using Pkg
+Pkg.add("Zarr")
julia
using Pkg
+Pkg.add(["GLMakie", "GeoMakie", "AlgebraOfGraphics", "DimensionalData"])
`,8)]))}const E=s(l,[["render",n]]);export{g as __pageData,E as default}; diff --git a/previews/PR486/assets/index.md.N2EASpe3.lean.js b/previews/PR486/assets/index.md.N2EASpe3.lean.js new file mode 100644 index 00000000..252a4700 --- /dev/null +++ b/previews/PR486/assets/index.md.N2EASpe3.lean.js @@ -0,0 +1,9 @@ +import{_ as s,c as a,a2 as t,o as e}from"./chunks/framework.piKCME0r.js";const g=JSON.parse(`{"title":"","description":"","frontmatter":{"layout":"home","hero":{"name":"YAXArrays.jl","text":"Yet another xarray-like Julia package","tagline":"A package for operating on out-of-core labeled arrays, based on stores like NetCDF, Zarr or GDAL.","image":{"src":"/logo.png","alt":"VitePress"},"actions":[{"theme":"brand","text":"Get Started","link":"/get_started"},{"theme":"alt","text":"View on Github","link":"https://github.com/JuliaDataCubes/YAXArrays.jl"},{"theme":"alt","text":"API reference","link":"/api"}]},"features":[{"title":"Flexible I/O capabilities","details":"Open and operate on NetCDF and Zarr datasets directly. Or bring in data from other sources with ArchGDAL.jl, GRIBDatasets.jl, GeoJSON.jl, HDF5.jl, Shapefile.jl, GeoParquet.jl, etc.","link":"/UserGuide/read"},{"title":"Interoperability","details":"Well integrated with Julia's ecosystem, i.e., distributed operations are native. And plotting with Makie.jl is well supported.","link":"/tutorials/plottingmaps"},{"title":"Named dimensions and GroupBy(in memory)","details":"Apply operations over named dimensions, select values by labels and integers as well as efficient split-apply-combine operations with groupby via DimensionalData.jl.","link":"/UserGuide/group"},{"title":"Efficiency","details":"Efficient mapslices(x) and mapCube operations on huge multiple arrays, optimized for high-latency data access (object storage, compressed datasets).","link":"/UserGuide/compute"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}`),l={name:"index.md"};function n(p,i,h,r,k,o){return e(),a("div",null,i[0]||(i[0]=[t(`

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("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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var(--vp-c-red-soft)}:root{--vp-font-family-base: "Inter", ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--vp-font-family-mono: ui-monospace, "Menlo", "Monaco", "Consolas", "Liberation Mono", "Courier New", monospace;font-optical-sizing:auto}:root:where(:lang(zh)){--vp-font-family-base: "Punctuation SC", "Inter", ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"}:root{--vp-shadow-1: 0 1px 2px rgba(0, 0, 0, .04), 0 1px 2px rgba(0, 0, 0, .06);--vp-shadow-2: 0 3px 12px rgba(0, 0, 0, .07), 0 1px 4px rgba(0, 0, 0, .07);--vp-shadow-3: 0 12px 32px rgba(0, 0, 0, .1), 0 2px 6px rgba(0, 0, 0, .08);--vp-shadow-4: 0 14px 44px rgba(0, 0, 0, .12), 0 3px 9px rgba(0, 0, 0, .12);--vp-shadow-5: 0 18px 56px rgba(0, 0, 0, .16), 0 4px 12px rgba(0, 0, 0, .16)}:root{--vp-z-index-footer: 10;--vp-z-index-local-nav: 20;--vp-z-index-nav: 30;--vp-z-index-layout-top: 40;--vp-z-index-backdrop: 50;--vp-z-index-sidebar: 60}@media (min-width: 960px){:root{--vp-z-index-sidebar: 25}}:root{--vp-layout-max-width: 1440px}:root{--vp-header-anchor-symbol: "#"}:root{--vp-code-line-height: 1.7;--vp-code-font-size: .875em;--vp-code-color: var(--vp-c-brand-1);--vp-code-link-color: var(--vp-c-brand-1);--vp-code-link-hover-color: var(--vp-c-brand-2);--vp-code-bg: var(--vp-c-default-soft);--vp-code-block-color: var(--vp-c-text-2);--vp-code-block-bg: var(--vp-c-bg-alt);--vp-code-block-divider-color: var(--vp-c-gutter);--vp-code-lang-color: var(--vp-c-text-3);--vp-code-line-highlight-color: var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: transparent;--vp-button-sponsor-active-border: var(--vp-c-sponsor);--vp-button-sponsor-active-text: var(--vp-c-sponsor);--vp-button-sponsor-active-bg: transparent}:root{--vp-custom-block-font-size: 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var(--vp-c-warning-soft);--vp-custom-block-warning-code-bg: var(--vp-c-warning-soft);--vp-custom-block-danger-border: transparent;--vp-custom-block-danger-text: var(--vp-c-text-1);--vp-custom-block-danger-bg: var(--vp-c-danger-soft);--vp-custom-block-danger-code-bg: var(--vp-c-danger-soft);--vp-custom-block-caution-border: transparent;--vp-custom-block-caution-text: var(--vp-c-text-1);--vp-custom-block-caution-bg: var(--vp-c-caution-soft);--vp-custom-block-caution-code-bg: var(--vp-c-caution-soft);--vp-custom-block-details-border: var(--vp-custom-block-info-border);--vp-custom-block-details-text: var(--vp-custom-block-info-text);--vp-custom-block-details-bg: var(--vp-custom-block-info-bg);--vp-custom-block-details-code-bg: var(--vp-custom-block-info-code-bg)}:root{--vp-input-border-color: var(--vp-c-border);--vp-input-bg-color: var(--vp-c-bg-alt);--vp-input-switch-bg-color: var(--vp-c-default-soft)}:root{--vp-nav-height: 64px;--vp-nav-bg-color: var(--vp-c-bg);--vp-nav-screen-bg-color: 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mode 100644 index 00000000..d44697f3 Binary files /dev/null and b/previews/PR486/assets/taklqov.CQ9uchq9.jpeg differ diff --git a/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.js b/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.js new file mode 100644 index 00000000..28f7deed --- /dev/null +++ b/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.js @@ -0,0 +1,73 @@ +import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.piKCME0r.js";const l="/YAXArrays.jl/previews/PR486/assets/jkytbph.C-WFBMfk.png",k="/YAXArrays.jl/previews/PR486/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")
+NpY = 3
3

and create some seasonal dummy data

julia
x = repeat(range(0, 2π, length=365), NpY)
+var = @. sin(x) + 0.1 * randn()
julia
fig, ax, obj = lines(t, var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; size = (600,400))
+    )
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig

Define the cube ​

julia
julia> using YAXArrays, DimensionalData
+
+julia> using YAXArrays: YAXArrays as YAX
+
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.55 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
+    ## filterig by month-day
+    monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
+    datesid = unique(monthday)
+    ## number of years
+    NpY = Int(size(monthday,1)/ndays)
+    idx = Int.(zeros(ndays, NpY))
+    ## get the day-month indices for data subsetting
+    for i in 1:ndays
+        idx[i,:] = Int.(findall(x-> x == datesid[i], monthday))
+    end
+    ## compute the mean seasonal cycle
+    mscarray = map(x->var[x], idx)
+    msc = mapslices(mean, mscarray, dims=2)
+    return msc
+end
+
+msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
+  0.03528277758302477
+  0.02345835017256051
+  0.039451611552802975
+  0.09689360224777122
+  0.022312156353890087
+  0.11391077060238619
+  0.08058305464254185
+  0.06159722707791853
+  0.08158557886952912
+  0.14715175267308206
+  ⋮
+ -0.10291543325743235
+ -0.22990067443344916
+ -0.12370988510072528
+ -0.0750790675265741
+ -0.05611581504766607
+ -0.06417594925348342
+ -0.062270000476910094
+ -0.029247418895843032
+ -0.05742042630154354

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle ​

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+    linewidth=2.0, linestyle=:dot,
+    axis = (;  xlabel="Day of Year", ylabel="Variable"),
+    figure=(; size = (600,400))
+    )
+lines!(1:365, var[366:730], label="2022", color=:brown,
+    linewidth=1.5, linestyle=:dash
+    )
+lines!(1:365, msc[:,1]; label="MSC", color=:dodgerblue, linewidth=2.5)
+axislegend()
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
+current_figure()

',21)]))}const F=i(p,[["render",t]]);export{c as __pageData,F as default}; diff --git a/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.lean.js b/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.lean.js new file mode 100644 index 00000000..28f7deed --- /dev/null +++ b/previews/PR486/assets/tutorials_mean_seasonal_cycle.md.DLmkPFUH.lean.js @@ -0,0 +1,73 @@ +import{_ as i,c as a,a2 as n,o as h}from"./chunks/framework.piKCME0r.js";const l="/YAXArrays.jl/previews/PR486/assets/jkytbph.C-WFBMfk.png",k="/YAXArrays.jl/previews/PR486/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")
+NpY = 3
3

and create some seasonal dummy data

julia
x = repeat(range(0, 2π, length=365), NpY)
+var = @. sin(x) + 0.1 * randn()
julia
fig, ax, obj = lines(t, var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; size = (600,400))
+    )
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig

Define the cube ​

julia
julia> using YAXArrays, DimensionalData
+
+julia> using YAXArrays: YAXArrays as YAX
+
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.55 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
+    ## filterig by month-day
+    monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
+    datesid = unique(monthday)
+    ## number of years
+    NpY = Int(size(monthday,1)/ndays)
+    idx = Int.(zeros(ndays, NpY))
+    ## get the day-month indices for data subsetting
+    for i in 1:ndays
+        idx[i,:] = Int.(findall(x-> x == datesid[i], monthday))
+    end
+    ## compute the mean seasonal cycle
+    mscarray = map(x->var[x], idx)
+    msc = mapslices(mean, mscarray, dims=2)
+    return msc
+end
+
+msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
+  0.03528277758302477
+  0.02345835017256051
+  0.039451611552802975
+  0.09689360224777122
+  0.022312156353890087
+  0.11391077060238619
+  0.08058305464254185
+  0.06159722707791853
+  0.08158557886952912
+  0.14715175267308206
+  ⋮
+ -0.10291543325743235
+ -0.22990067443344916
+ -0.12370988510072528
+ -0.0750790675265741
+ -0.05611581504766607
+ -0.06417594925348342
+ -0.062270000476910094
+ -0.029247418895843032
+ -0.05742042630154354

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle ​

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+    linewidth=2.0, linestyle=:dot,
+    axis = (;  xlabel="Day of Year", ylabel="Variable"),
+    figure=(; size = (600,400))
+    )
+lines!(1:365, var[366:730], label="2022", color=:brown,
+    linewidth=1.5, linestyle=:dash
+    )
+lines!(1:365, msc[:,1]; label="MSC", color=:dodgerblue, linewidth=2.5)
+axislegend()
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
+current_figure()

',21)]))}const F=i(p,[["render",t]]);export{c as __pageData,F as default}; diff --git a/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.js b/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.js new file mode 100644 index 00000000..e49b66da --- /dev/null +++ b/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.js @@ -0,0 +1 @@ +import{_ as t,c as a,a2 as r,o}from"./chunks/framework.piKCME0r.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('

Other tutorials ​

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

  • Currently the overview tutorial is located at ESDLTutorials Repository

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

  • the other tutorials are still work in progress.

General overview of the functionality of YAXArrays ​

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

Table-style iteration over YAXArrays ​

Work in progress

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

Combining multiple tiff files into a zarr based datacube ​

',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default}; diff --git a/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.lean.js b/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.lean.js new file mode 100644 index 00000000..e49b66da --- /dev/null +++ b/previews/PR486/assets/tutorials_other_tutorials.md.MTLllTlQ.lean.js @@ -0,0 +1 @@ +import{_ as t,c as a,a2 as r,o}from"./chunks/framework.piKCME0r.js";const c=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),i={name:"tutorials/other_tutorials.md"};function l(s,e,n,h,u,f){return o(),a("div",null,e[0]||(e[0]=[r('

Other tutorials ​

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

  • Currently the overview tutorial is located at ESDLTutorials Repository

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

  • the other tutorials are still work in progress.

General overview of the functionality of YAXArrays ​

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

Table-style iteration over YAXArrays ​

Work in progress

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

Combining multiple tiff files into a zarr based datacube ​

',9)]))}const b=t(i,[["render",l]]);export{c as __pageData,b as default}; diff --git a/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.js b/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.js new file mode 100644 index 00000000..7130f02b --- /dev/null +++ b/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.js @@ -0,0 +1,110 @@ +import{_ as i,c as a,a2 as t,o as n}from"./chunks/framework.piKCME0r.js";const h="/YAXArrays.jl/previews/PR486/assets/taklqov.CQ9uchq9.jpeg",l="/YAXArrays.jl/previews/PR486/assets/cyhvrkj.B7KFIfDV.jpeg",p="/YAXArrays.jl/previews/PR486/assets/mbalbzx.96k_BqPR.jpeg",k="/YAXArrays.jl/previews/PR486/assets/oowdcxc.B7b9FwLj.jpeg",e="/YAXArrays.jl/previews/PR486/assets/zvezrog.B074eX2X.jpeg",r="/YAXArrays.jl/previews/PR486/assets/dzarsbx.C5U_qDue.jpeg",d="/YAXArrays.jl/previews/PR486/assets/frwqpez.DX1O6I5P.jpeg",E="/YAXArrays.jl/previews/PR486/assets/idgplot.Blc9BtwN.jpeg",g="/YAXArrays.jl/previews/PR486/assets/weuosxb.Bcyn0CpL.jpeg",y="/YAXArrays.jl/previews/PR486/assets/xzwnmje.-RBZ8LkA.jpeg",o="/YAXArrays.jl/previews/PR486/assets/ssvwqbb.Cj8iZQLt.jpeg",F="/YAXArrays.jl/previews/PR486/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/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.lean.js b/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.lean.js new file mode 100644 index 00000000..7130f02b --- /dev/null +++ b/previews/PR486/assets/tutorials_plottingmaps.md.D1UYqyCC.lean.js @@ -0,0 +1,110 @@ +import{_ as i,c as a,a2 as t,o as n}from"./chunks/framework.piKCME0r.js";const h="/YAXArrays.jl/previews/PR486/assets/taklqov.CQ9uchq9.jpeg",l="/YAXArrays.jl/previews/PR486/assets/cyhvrkj.B7KFIfDV.jpeg",p="/YAXArrays.jl/previews/PR486/assets/mbalbzx.96k_BqPR.jpeg",k="/YAXArrays.jl/previews/PR486/assets/oowdcxc.B7b9FwLj.jpeg",e="/YAXArrays.jl/previews/PR486/assets/zvezrog.B074eX2X.jpeg",r="/YAXArrays.jl/previews/PR486/assets/dzarsbx.C5U_qDue.jpeg",d="/YAXArrays.jl/previews/PR486/assets/frwqpez.DX1O6I5P.jpeg",E="/YAXArrays.jl/previews/PR486/assets/idgplot.Blc9BtwN.jpeg",g="/YAXArrays.jl/previews/PR486/assets/weuosxb.Bcyn0CpL.jpeg",y="/YAXArrays.jl/previews/PR486/assets/xzwnmje.-RBZ8LkA.jpeg",o="/YAXArrays.jl/previews/PR486/assets/ssvwqbb.Cj8iZQLt.jpeg",F="/YAXArrays.jl/previews/PR486/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/previews/PR486/assets/weuosxb.Bcyn0CpL.jpeg b/previews/PR486/assets/weuosxb.Bcyn0CpL.jpeg new file mode 100644 index 00000000..3dd7cbb7 Binary files /dev/null and b/previews/PR486/assets/weuosxb.Bcyn0CpL.jpeg differ diff --git a/previews/PR486/assets/xzwnmje.-RBZ8LkA.jpeg b/previews/PR486/assets/xzwnmje.-RBZ8LkA.jpeg new file mode 100644 index 00000000..c09363b8 Binary files /dev/null and b/previews/PR486/assets/xzwnmje.-RBZ8LkA.jpeg differ diff --git a/previews/PR486/assets/zvezrog.B074eX2X.jpeg b/previews/PR486/assets/zvezrog.B074eX2X.jpeg new file mode 100644 index 00000000..6636367d Binary files /dev/null and b/previews/PR486/assets/zvezrog.B074eX2X.jpeg differ diff --git a/previews/PR486/development/contribute.html b/previews/PR486/development/contribute.html new file mode 100644 index 00000000..85fc6f10 --- /dev/null +++ b/previews/PR486/development/contribute.html @@ -0,0 +1,30 @@ + + + + + + Contribute to YAXArrays.jl | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Contribute to YAXArrays.jl ​

Pull requests and bug reports are always welcome at the YAXArrays.jl GitHub repository.

Contribute to Documentation ​

Contributing with examples can be done by first creating a new file example here

new file

  • your_new_file.md at docs/src/UserGuide/

Once this is done you need to add a new entry here at the appropriate level.

add entry to docs

Your new entry should look like:

  • { text: 'Your title example', link: '/UserGuide/your_new_file.md' }

Build docs locally ​

If you want to take a look at the docs locally before doing a PR follow the next steps:

Install the dependencies in your system, locate yourself at the docs level folder, then do

sh
npm i

Then simply go to your docs env and activate it, i.e.

sh
docs> julia
+julia> ]
+pkg> activate .

Next, run the scripts. Generate files and build docs by running:

sh
include("make.jl")

Now go to your terminal in the same path docs> and run:

sh
npm run docs:dev

This should ouput http://localhost:5173/YAXArrays.jl/, copy/paste this into your browser and you are all set.

+ + + + \ No newline at end of file diff --git a/previews/PR486/development/contributors.html b/previews/PR486/development/contributors.html new file mode 100644 index 00000000..65b4f299 --- /dev/null +++ b/previews/PR486/development/contributors.html @@ -0,0 +1,28 @@ + + + + + + YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Contributors

Current core contributors

They have taking the lead for the ongoing organizational maintenance and technical direction of , and .

Fabian Gans

Fabian Gans

Geoscientific Programmer

Felix Cremer

Felix Cremer

PhD Candidate in Remote Sensing

Rafael Schouten

Rafael Schouten

Spatial/ecological modelling

Lazaro Alonso

Lazaro Alonso

Scientist. Data Visualization

Our valuable contributors

We appreciate all contributions from the Julia community so that this ecosystem can thrive.

+ + + + \ No newline at end of file diff --git a/previews/PR486/favicon.ico b/previews/PR486/favicon.ico new file mode 100644 index 00000000..80dd3a80 Binary files /dev/null and b/previews/PR486/favicon.ico differ diff --git a/previews/PR486/get_started.html b/previews/PR486/get_started.html new file mode 100644 index 00000000..a79e7e72 --- /dev/null +++ b/previews/PR486/get_started.html @@ -0,0 +1,80 @@ + + + + + + Getting Started | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

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} ┐
+├──────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────── loaded in memory ┤
+  data size: 48.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

Assemble a more complex YAXArray with 4 dimensions, i.e. time, x, y and a variable type:

julia
# axes or dimensions with name and tick values
+axlist = (
+    YAX.time(range(1, 20, length=20)),
+    lon(range(1, 10, length=10)),
+    lat(range(1, 5, length=15)),
+    Variables(["temperature", "precipitation"])
+)
+
+# the actual data matching the dimensions defined in axlist
+data = rand(20, 10, 15, 2)
+
+# metadata about the array
+props = Dict(
+    "origin" => "YAXArrays.jl example",
+    "x" => "longitude",
+    "y" => "latitude",
+);
+
+a2 = YAXArray(axlist, data, props)
┌ 20×10×15×2 YAXArray{Float64, 4} ┐
+├─────────────────────────────────┴────────────────────────────────────── dims ┐
+  ↓ time      Sampled{Float64} 1.0:1.0:20.0 ForwardOrdered Regular Points,
+  → lon       Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  ↗ lat       Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points,
+  ⬔ Variables Categorical{String} ["temperature", "precipitation"] ReverseOrdered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, String} with 3 entries:
+  "y"      => "latitude"
+  "x"      => "longitude"
+  "origin" => "YAXArrays.jl example"
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 46.88 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Get the temperature map at the first point in time:

julia
a2[Variables=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
+ 0.320052  0.500909   0.827727   0.415137  …  0.825496  0.487315  0.0962708
+ 0.209357  0.0833026  0.207967   0.224959     0.309666  0.211397  0.552582
+ 0.507229  0.757781   0.514759   0.973442     0.111379  0.787476  0.0247931
+ 0.879677  0.374138   0.97643    0.748725     0.665274  0.776172  0.666534
+ 0.79472   0.958973   0.0114824  0.274902     0.499743  0.645512  0.622774
+ 0.320869  0.952795   0.547668   0.982108  …  0.80871   0.253383  0.743343
+ 0.496429  0.0436     0.790617   0.233118     0.137114  0.55245   0.716721
+ 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.

+ + + + \ No newline at end of file diff --git a/previews/PR486/hashmap.json b/previews/PR486/hashmap.json new file mode 100644 index 00000000..8591f60c --- /dev/null +++ b/previews/PR486/hashmap.json @@ -0,0 +1 @@ +{"api.md":"CRtEnxW2","development_contribute.md":"CXgVQbV5","development_contributors.md":"Dh50rkWi","get_started.md":"CdXe2EOO","index.md":"N2EASpe3","tutorials_mean_seasonal_cycle.md":"DLmkPFUH","tutorials_other_tutorials.md":"MTLllTlQ","tutorials_plottingmaps.md":"D1UYqyCC","userguide_cache.md":"tsnWjcXo","userguide_chunk.md":"DKasdhoL","userguide_combine.md":"DX6-a-cs","userguide_compute.md":"CUq5TZYp","userguide_convert.md":"CkB9umGg","userguide_create.md":"Bweykjuq","userguide_faq.md":"uhp-zjxe","userguide_group.md":"B_BCz8Qu","userguide_read.md":"CncWl83I","userguide_select.md":"B1gCBPvb","userguide_types.md":"DuodkEtM","userguide_write.md":"Dt-jU2T_"} diff --git a/previews/PR486/index.html b/previews/PR486/index.html new file mode 100644 index 00000000..52598eac --- /dev/null +++ b/previews/PR486/index.html @@ -0,0 +1,36 @@ + + + + + + YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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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("ArchGDAL")
julia
using Pkg
+Pkg.add("NetCDF")
julia
using Pkg
+Pkg.add("Zarr")
julia
using Pkg
+Pkg.add(["GLMakie", "GeoMakie", "AlgebraOfGraphics", "DimensionalData"])
+ + + + \ No newline at end of file diff --git a/previews/PR486/logo.png b/previews/PR486/logo.png new file mode 100644 index 00000000..80dd3a80 Binary files /dev/null and b/previews/PR486/logo.png differ diff --git a/previews/PR486/logo.svg b/previews/PR486/logo.svg new file mode 100644 index 00000000..1d1b362f --- /dev/null +++ b/previews/PR486/logo.svg @@ -0,0 +1,324 @@ + + + +YAXArrays.jl diff --git a/previews/PR486/siteinfo.js b/previews/PR486/siteinfo.js new file mode 100644 index 00000000..9ba5dd31 --- /dev/null +++ b/previews/PR486/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR486"; diff --git a/previews/PR486/tutorials/mean_seasonal_cycle.html b/previews/PR486/tutorials/mean_seasonal_cycle.html new file mode 100644 index 00000000..560822b4 --- /dev/null +++ b/previews/PR486/tutorials/mean_seasonal_cycle.html @@ -0,0 +1,100 @@ + + + + + + Mean Seasonal Cycle for a single pixel | YAXArrays.jl + + + + + + + + + + + + + + + + + +
YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Mean Seasonal Cycle for a single pixel ​

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

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

julia
t =  Date("2021-01-01"):Day(1):Date("2023-12-31")
+NpY = 3
3

and create some seasonal dummy data

julia
x = repeat(range(0, 2π, length=365), NpY)
+var = @. sin(x) + 0.1 * randn()
julia
fig, ax, obj = lines(t, var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; size = (600,400))
+    )
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig

Define the cube ​

julia
julia> using YAXArrays, DimensionalData
+
+julia> using YAXArrays: YAXArrays as YAX
+
+julia> axes = (YAX.Time(t),)
(↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31"))
julia
julia> c = YAXArray(axes, var)
┌ 1095-element YAXArray{Float64, 1} ┐
+├───────────────────────────────────┴──────────────────────────────────── dims ┐
+  ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── loaded in memory ┤
+  data size: 8.55 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
+    ## filterig by month-day
+    monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
+    datesid = unique(monthday)
+    ## number of years
+    NpY = Int(size(monthday,1)/ndays)
+    idx = Int.(zeros(ndays, NpY))
+    ## get the day-month indices for data subsetting
+    for i in 1:ndays
+        idx[i,:] = Int.(findall(x-> x == datesid[i], monthday))
+    end
+    ## compute the mean seasonal cycle
+    mscarray = map(x->var[x], idx)
+    msc = mapslices(mean, mscarray, dims=2)
+    return msc
+end
+
+msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
+  0.03528277758302477
+  0.02345835017256051
+  0.039451611552802975
+  0.09689360224777122
+  0.022312156353890087
+  0.11391077060238619
+  0.08058305464254185
+  0.06159722707791853
+  0.08158557886952912
+  0.14715175267308206
+  ⋮
+ -0.10291543325743235
+ -0.22990067443344916
+ -0.12370988510072528
+ -0.0750790675265741
+ -0.05611581504766607
+ -0.06417594925348342
+ -0.062270000476910094
+ -0.029247418895843032
+ -0.05742042630154354

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle ​

julia
fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+    linewidth=2.0, linestyle=:dot,
+    axis = (;  xlabel="Day of Year", ylabel="Variable"),
+    figure=(; size = (600,400))
+    )
+lines!(1:365, var[366:730], label="2022", color=:brown,
+    linewidth=1.5, linestyle=:dash
+    )
+lines!(1:365, msc[:,1]; label="MSC", color=:dodgerblue, linewidth=2.5)
+axislegend()
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
+current_figure()

+ + + + \ No newline at end of file diff --git a/previews/PR486/tutorials/other_tutorials.html b/previews/PR486/tutorials/other_tutorials.html new file mode 100644 index 00000000..dbddb6cc --- /dev/null +++ b/previews/PR486/tutorials/other_tutorials.html @@ -0,0 +1,28 @@ + + + + + + Other tutorials | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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 ​

+ + + + \ No newline at end of file diff --git a/previews/PR486/tutorials/plottingmaps.html b/previews/PR486/tutorials/plottingmaps.html new file mode 100644 index 00000000..be0f5afa --- /dev/null +++ b/previews/PR486/tutorials/plottingmaps.html @@ -0,0 +1,137 @@ + + + + + + Plotting maps | YAXArrays.jl + + + + + + + + + + + + + + + + + +
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
+
+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.

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