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+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/chunk.html b/v0.5.9/UserGuide/chunk.html new file mode 100644 index 00000000..aa8e18aa --- /dev/null +++ b/v0.5.9/UserGuide/chunk.html @@ -0,0 +1,106 @@ + + + + + + Chunk YAXArrays | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Chunk YAXArrays ​

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

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

Chunking YAXArrays ​

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

And the saved file is also splitted into Chunks.

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

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

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

Chunking Datasets ​

Setchunks can also be applied to a Dataset.

Set Chunks by Axis ​

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

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

Saving...

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

Set chunking by Variable ​

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

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

saving...

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

Set chunking for all variables ​

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

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

saving...

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

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

+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/combine.html b/v0.5.9/UserGuide/combine.html new file mode 100644 index 00000000..2f4a1625 --- /dev/null +++ b/v0.5.9/UserGuide/combine.html @@ -0,0 +1,51 @@ + + + + + + Combine YAXArrays | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Combine YAXArrays ​

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

cat along an existing dimension ​

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

julia
using YAXArrays
+
+first_half = YAXArray((Dim{:time}(1:6),), rand(6))
+second_half = YAXArray((Dim{:time}(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
+│ 12-element YAXArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── file size ┤ 
+  file size: 96.0 bytes
+└───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension ​

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

julia
using YAXArrays
+
+temperature = YAXArray((Dim{:time}(1:6),), rand(6))
+precipitation = YAXArray((Dim{:time}(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Dim{:variable}(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
+│ 6×2 YAXArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time     Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → variable Categorical{String} ["temp", "prep"] ReverseOrdered
+├────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── file size ┤ 
+  file size: 96.0 bytes
+└────────────────────────────────────────────────────────────────┘
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/compute.html b/v0.5.9/UserGuide/compute.html new file mode 100644 index 00000000..bf4218ef --- /dev/null +++ b/v0.5.9/UserGuide/compute.html @@ -0,0 +1,198 @@ + + + + + + Compute YAXArrays | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Compute YAXArrays ​

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

  • Use arithmetics to add or multiply numbers to each element of an array

  • Use map to apply a more complex functions to every element of an array

  • Use mapslices to reduce a dimension, e.g. to get the mean over all time steps

  • Use mapCube to apply complex functions on an array that may change any dimensions

Let's start by creating an example dataset:

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

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.8807643159933959
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

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

Arithmetics ​

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

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

map ​

Apply a function on every element of an array individually:

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

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

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

mapslices ​

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

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

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

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

mapCube ​

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

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

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

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

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

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

julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
+│ 10×15 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571    1.57143     …  4.42857   4.71429   5.0
+  1.0  0.798256  0.920447   0.819421       0.739132  0.566009  0.154204
+  2.0  0.10278   0.770185   0.00308046     0.942654  0.919766  0.416274
+  3.0  0.2639    0.0323228  0.156541       0.359575  0.688907  0.820491
+  ⋮                                     ⋱                      ⋮
+  8.0  0.036138  0.457131   0.712288       0.2466    0.373092  0.292254
+  9.0  0.774003  0.250325   0.971698       0.427578  0.795336  0.712081
+ 10.0  0.144255  0.669878   0.319932    …  0.534257  0.23103   0.808461

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

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

Finally, we can transform the entire raster array:

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

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

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

Distributed Computation ​

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

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

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

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

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

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

julia
using Distributed
+using ClusterManagers
+addprocs(SlurmManager(10))
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/convert.html b/v0.5.9/UserGuide/convert.html new file mode 100644 index 00000000..2b1d24ea --- /dev/null +++ b/v0.5.9/UserGuide/convert.html @@ -0,0 +1,101 @@ + + + + + + Convert YAXArrays | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Convert YAXArrays ​

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

WARNING

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

Convert Base.Array ​

Convert Base.Array to YAXArray:

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

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.718802  0.877205   0.709084   0.191564   …  0.892634  0.891774  0.937715
+ 0.84607   0.269647   0.704815   0.572416      0.339514  0.247657  0.891063
+ 0.459549  0.75834    0.463841   0.577757      0.373326  0.484593  0.0316087
+ 0.371498  0.612681   0.45713    0.0573432     0.965811  0.484103  0.962429
+ 0.76362   0.0480175  0.0760908  0.646361      0.800092  0.527433  0.720119

Convert Raster ​

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

julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
╭────────────────────────────╮
+│ 6×6×25 YAXArray{Float64,3} │
+├────────────────────────────┴────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── file size ┤ 
+  file size: 7.03 KB
+└─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
+│ 6×6×25 Raster{Float64,3} │
+├──────────────────────────┴──────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── raster ┤
+  extent: Extent(X = (25, 30), Y = (25, 30), Ti = (2000, 2024))
+
+└─────────────────────────────────────────────────────────────┘
+[:, :, 1]
+  ↓ →  25          26         27         28          29         30
+ 25     0.60611     0.216595   0.900524   0.0526608   0.293033   0.289807
+ 26     0.883762    0.770476   0.321505   0.785602    0.624963   0.879151
+ 27     0.0401722   0.128882   0.991353   0.781533    0.528217   0.963865
+ 28     0.426561    0.235518   0.245619   0.368147    0.838999   0.84384
+ 29     0.96561     0.488183   0.865925   0.344583    0.47353    0.502084
+ 30     0.188337    0.518762   0.676613   0.480564    0.781488   0.71255

Convert DimArray ​

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

Convert DimArray to YAXArray:

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

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
+│ 5×6 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0        11.0       12.0        13.0        14.0       15.0
+ 1     0.343362    0.189021   0.466312    0.0287024   0.26332    0.616274
+ 2     0.318414    0.858017   0.895233    0.962506    0.72166    0.796383
+ 3     0.0247876   0.981214   0.205551    0.478981    0.715169   0.26791
+ 4     0.653445    0.437749   0.545125    0.440346    0.816282   0.0372525
+ 5     0.533318    0.455567   0.0370893   0.869952    0.76547    0.742225

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/v0.5.9/UserGuide/create.html b/v0.5.9/UserGuide/create.html new file mode 100644 index 00000000..4fb9b2a7 --- /dev/null +++ b/v0.5.9/UserGuide/create.html @@ -0,0 +1,71 @@ + + + + + + Create YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Create YAXArrays and Datasets ​

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

Create a YAXArray ​

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

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

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

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

Create a Dataset ​

julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared Axes: 
+↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+→ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+Variables: 
+a2
+a3
+
+Properties: Dict(:origin => "user guide")
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/faq.html b/v0.5.9/UserGuide/faq.html new file mode 100644 index 00000000..377e6b6a --- /dev/null +++ b/v0.5.9/UserGuide/faq.html @@ -0,0 +1,218 @@ + + + + + + Frequently Asked Questions (FAQ) | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Frequently Asked Questions (FAQ) ​

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

Extract the axes names from a Cube ​

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

WARNING

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

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

INFO

Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.

Obtain values from axes and data from the cube ​

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

These two examples bring the same result

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

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

julia
julia> c[:, :, 1]
╭───────────────────────────╮
+│ 10×10 YAXArray{Float64,2} │
+├───────────────────────────┴────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└────────────────────────────────────────────────────────────┘

How do I concatenate cubes ​

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

Let's create two dummy cubes

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

Now we can concatenate ds1 and ds2:

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

How do I subset a Cube? ​

Let's start by creating a dummy cube. Define the time span of the cube

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

create cube axes

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

assign values to a cube

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

Now we subset the cube by any dimension.

Subset cube by years

julia
julia> ctime = c[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
+│ 10×10×12 YAXArray{Int64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Subset cube by a specific date and date range

julia
julia> ctime2 = c[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
+│ 10×10 YAXArray{Int64,2} │
+├─────────────────────────┴────────────────────────── dims ┐
+  ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└──────────────────────────────────────────────────────────┘
julia
julia> ctime3 = c[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
+│ 10×10×8 YAXArray{Int64,3} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 6.25 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Subset cube by longitude and latitude

julia
julia> clonlat = c[Lon=1 .. 5, Lat=5 .. 10] # check even numbers range, it is ommiting them
╭──────────────────────────╮
+│ 5×6×36 YAXArray{Int64,3} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 8.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

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

Cubes with more than 3 dimensions

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

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

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

How do I use the CubeTable function? ​

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

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

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

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

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

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

In this line we calculate the Mean for each class

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

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

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

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

One variable name ​

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

Multiple variable names ​

julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+a
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+b
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+c
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/group.html b/v0.5.9/UserGuide/group.html new file mode 100644 index 00000000..626e311f --- /dev/null +++ b/v0.5.9/UserGuide/group.html @@ -0,0 +1,227 @@ + + + + + + Group YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Group YAXArrays and Datasets ​

The following examples will use the groupby function to calculate temporal and spatial averages.

julia
using YAXArrays, DimensionalData
+using NetCDF
+using Downloads
+using Dates
+using Statistics

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

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

WARNING

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

julia
axs = dims(ds_o) # get the dimensions
+data = ds_o.data[:,:,:] # read the data
+_FillValue = ds_o.properties["_FillValue"]
+data = replace(data, _FillValue => NaN)
+# create new YAXArray
+ds = YAXArray(axs, data)

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :Ti)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, Ti => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:Ti)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, Ti => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:Ti)
+    mean_g = dropdims.(mean_g, dims=:Ti)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :Ti);
+    weighted_g = dropdims.(weighted_g, dims=:Ti)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :Ti)
+    return mean_g, weighted_g, diff_g, seasons_g
+end

Now, we continue with the groupby operations as usual

julia
julia> g_ds = groupby(ds, Ti => seasons(; start=December))
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,3},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×1 YAXArray
+ :Mar_Apr_May  275×205×1 YAXArray
+ :Jun_Jul_Aug  275×205×1 YAXArray
+ :Sep_Oct_Nov  275×205×1 YAXArray

dropdims ​

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

julia
julia> mean_g = dropdims.(mean_g, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

seasons ​

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

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

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

GroupBy: weight ​

Create a YAXArray for the month length

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

Now group it by season

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

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:Ti)
╭────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Int64,1},1} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1-element YAXArray
+ :Mar_Apr_May  1-element YAXArray
+ :Jun_Jul_Aug  1-element YAXArray
+ :Sep_Oct_Nov  1-element YAXArray

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,1},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray

Verify that the sum per season is 1

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

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,3},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti, → x, ↗ y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9×275×205 YAXArray
+ :Mar_Apr_May  9×275×205 YAXArray
+ :Jun_Jul_Aug  9×275×205 YAXArray
+ :Sep_Oct_Nov  9×275×205 YAXArray

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :Ti);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

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

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

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

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

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

+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/read.html b/v0.5.9/UserGuide/read.html new file mode 100644 index 00000000..dcf67a02 --- /dev/null +++ b/v0.5.9/UserGuide/read.html @@ -0,0 +1,105 @@ + + + + + + Read YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Read YAXArrays and Datasets ​

This section describes how to read files, URLs, and directories into YAXArrays and datasets.

Read Zarr ​

Open a Zarr store as a Dataset:

julia
using YAXArrays
+using Zarr
+path="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
+store = zopen(path, consolidated=true)
+ds = open_dataset(store)
YAXArray Dataset
+Shared Axes: 
+()
+Variables: 
+height
+tas
+  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)

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

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

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

Read NetCDF ​

Open a NetCDF file as a Dataset:

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

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

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

Read GDAL (GeoTIFF, GeoJSON) ​

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

julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+ds = open_dataset(path)
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: 
+Red
+Green
+Blue
+
+Properties: Dict{String, Any}("projection" => "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/select.html b/v0.5.9/UserGuide/select.html new file mode 100644 index 00000000..bc8e552f --- /dev/null +++ b/v0.5.9/UserGuide/select.html @@ -0,0 +1,316 @@ + + + + + + Select YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Select YAXArrays and Datasets ​

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

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

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

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

which is the same as:

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

Select elements ​

Using positional integer indexing:

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

Same but using named indexing:

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

Using special types:

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

Select ranges ​

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

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

Same but using named indexing:

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

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

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

Closed and open intervals ​

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

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

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

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

See tutorials for use cases.

Get a dimension ​

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

julia
collect(tos.lat)
170-element Vector{Float64}:
+ -79.5
+ -78.5
+ -77.5
+ -76.5
+ -75.5
+ -74.5
+ -73.5
+ -72.5
+ -71.5
+ -70.5
+   ⋮
+  81.5
+  82.5
+  83.5
+  84.5
+  85.5
+  86.5
+  87.5
+  88.5
+  89.5

These values are defined as lookups in the package DimensionalData:

julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0

which is equivalent to:

julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0
+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/types.html b/v0.5.9/UserGuide/types.html new file mode 100644 index 00000000..c8815445 --- /dev/null +++ b/v0.5.9/UserGuide/types.html @@ -0,0 +1,24 @@ + + + + + + Types | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Types ​

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

YAXArray ​

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

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

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

Dataset ​

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

(Data) Cube ​

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

Dimension ​

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

+ + + + \ No newline at end of file diff --git a/v0.5.9/UserGuide/write.html b/v0.5.9/UserGuide/write.html new file mode 100644 index 00000000..c793748a --- /dev/null +++ b/v0.5.9/UserGuide/write.html @@ -0,0 +1,62 @@ + + + + + + Write YAXArrays and Datasets | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Write YAXArrays and Datasets ​

Create an example Dataset:

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

Write Zarr ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

Write NetCDF ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

Overwrite a Dataset ​

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

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

DANGER

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

Look at the doc string for more information

# YAXArrays.Datasets.savedataset — Function.

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

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

Warning

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

source


Append to a Dataset ​

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+tos
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+z
+  ↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

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

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Int32}, 10, 20))
╭─────────────────────────────────────────╮
+│ 10×20 YAXArray{Union{Missing, Int32},2} │
+├─────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────┘

and save them as

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

and check that all the values are missing

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

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

INFO

The skeleton argument is also available for savedataset.

+ + + + \ No newline at end of file diff --git a/v0.5.9/api.html b/v0.5.9/api.html new file mode 100644 index 00000000..8b6327d9 --- /dev/null +++ b/v0.5.9/api.html @@ -0,0 +1,36 @@ + + + + + + API Reference | YAXArrays.jl + + + + + + + + + + + + + +
YAXArrays.jl

Appearance


Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

API Reference ​

This section describes all available functions of this package.

Public API ​

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

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

  • the name as a string or symbol.

  • an Axis object

source


# YAXArrays.Cubes — Module.

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

source


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

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

Fields

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

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

  • properties: Metadata properties describing the content of the data

  • chunks: Representation of the chunking of the data

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

source


# YAXArrays.Cubes.caxes — Function.

Returns the axes of a Cube

source


# YAXArrays.Cubes.caxes — Method.
julia
caxes

Embeds Cube inside a new Cube

source


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

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

source


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

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

source


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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source


# YAXArrays.Cubes.subsetcube — Function.

This function calculates a subset of a cube's data

source


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

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

Keyword arguments

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

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

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

source


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

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

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

source


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

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

  • axisdesc: List of input axis names

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

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

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

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

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

source


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

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

source


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

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

source


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

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

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

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

source


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

source


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

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

Keyword arguments

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

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

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

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

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

  • showprog boolean indicating if a ProgressMeter shall be shown

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

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

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

  • kwargs additional keyword arguments are passed to the inner function

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

source


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

source


# YAXArrays.Datasets.Dataset — Method.

Dataset(; properties = Dict{String,Any}, cubes...)

Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...

source


# YAXArrays.Datasets.Cube — Method.
julia
Cube(ds::Dataset; joinname="Variable")

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

source


# YAXArrays.Datasets.open_dataset — Method.

open_dataset(g; driver=:all)

Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.

source


# YAXArrays.Datasets.savecube — Method.
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

  • name:

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

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

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

  • driver: The same setting as backend.

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

source


# YAXArrays.Datasets.savedataset — Method.

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

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

Warning

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

source


# YAXArrays.Datasets.to_dataset — Method.

to_dataset(c;datasetaxis = "Variable", layername = "layer")

Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername

source


Internal API ​

# YAXArrays.YAXDefaults — Constant.

Default configuration for YAXArrays, has the following fields:

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

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

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

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

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

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

source


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

Internal function

Extended Help

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

The Axis description can be:

  • the name as a string or symbol.

  • an Axis object

source


# YAXArrays.getOutAxis — Method.
julia
getOutAxis

source


# YAXArrays.get_descriptor — Method.
julia
get_descriptor(a)

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

source


# YAXArrays.match_axis — Method.
julia
match_axis

Internal function

Extended Help

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

source


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

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

source


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

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

source


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

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

source


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

Internal

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

source


# YAXArrays.DAT.DATConfig — Type.

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

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

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

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

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

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

  • loopcachesize::Vector{Int64}:

  • allow_irregular_chunks::Bool:

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

  • fu::Any: Inner function which is computed

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

  • include_loopvars::Bool:

  • ntr::Any:

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

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

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

source


# YAXArrays.DAT.InputCube — Type.

Internal representation of an input cube for DAT operations

  • cube: The input data

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

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

  • icolon

  • colonperm

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

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

  • window

  • iwindow

  • windowloopinds

  • iall

source


# YAXArrays.DAT.OutputCube — Type.

Internal representation of an output cube for DAT operations

Fields

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

  • cube_unpermuted: The unpermuted output cube

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

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

  • allAxes: List of all the axes of the cube

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

  • innerchunks

  • outtype: Elementtype of the outputcube

source


# YAXArrays.DAT.YAXColumn — Type.
julia
YAXColumn

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

  • inarBC

  • inds

source


# YAXArrays.DAT.cmpcachmisses — Method.

Function that compares two cache miss specifiers by their importance

source


# YAXArrays.DAT.getFrontPerm — Method.

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

source


# YAXArrays.DAT.getLoopCacheSize — Method.

Calculate optimal Cache size to DAT operation

source


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

Internal function

Get the element type for the output cube

source


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

Internal function

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

source


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

Internal function

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

source


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

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

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

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

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

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

  • a DiskArrays.GridChunks object

  • a tuple specifying the chunk size along each dimension

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

source


# YAXArrays.Datasets.collectfromhandle — Method.

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

source


# YAXArrays.Datasets.createdataset — Method.

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

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

Keyword arguments

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

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

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

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

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

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

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

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

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

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

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

source


# YAXArrays.Datasets.getarrayinfo — Method.

Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair

source


# YAXArrays.Datasets.testrange — Method.

Test if data in x can be approximated by a step range

source


+ + + + \ No newline at end of file diff --git a/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.js b/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.js new file mode 100644 index 00000000..a3e2aaba --- /dev/null +++ b/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.js @@ -0,0 +1,83 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),p={name:"UserGuide/chunk.md"},h=n(`

Chunk YAXArrays ​

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

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

Chunking YAXArrays ​

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

And the saved file is also splitted into Chunks.

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

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

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

Chunking Datasets ​

Setchunks can also be applied to a Dataset.

Set Chunks by Axis ​

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

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

Saving...

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

Set chunking by Variable ​

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

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

saving...

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

Set chunking for all variables ​

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

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

saving...

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

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

`,36),l=[h];function t(e,k,r,d,E,g){return i(),a("div",null,l)}const o=s(p,[["render",t]]);export{y as __pageData,o as default}; diff --git a/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.lean.js b/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.lean.js new file mode 100644 index 00000000..65b3d018 --- /dev/null +++ b/v0.5.9/assets/UserGuide_chunk.md.C1am-ZJr.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Chunk YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/chunk.md","filePath":"UserGuide/chunk.md","lastUpdated":null}'),p={name:"UserGuide/chunk.md"},h=n("",36),l=[h];function t(e,k,r,d,E,g){return i(),a("div",null,l)}const o=s(p,[["render",t]]);export{y as __pageData,o as default}; diff --git a/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.js b/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.js new file mode 100644 index 00000000..12c0a8d7 --- /dev/null +++ b/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.js @@ -0,0 +1,28 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const g=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),e={name:"UserGuide/combine.md"},t=n(`

Combine YAXArrays ​

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

cat along an existing dimension ​

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

julia
using YAXArrays
+
+first_half = YAXArray((Dim{:time}(1:6),), rand(6))
+second_half = YAXArray((Dim{:time}(7:12),), rand(6))
+whole_year = cat(first_half, second_half, dims = 1)
╭────────────────────────────────╮
+│ 12-element YAXArray{Float64,1} │
+├────────────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time Sampled{Int64} [1, 2, …, 11, 12] ForwardOrdered Regular Points
+├───────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├──────────────────────────────────────────────────────────── file size ┤ 
+  file size: 96.0 bytes
+└───────────────────────────────────────────────────────────────────────┘

concatenatecubes to a new dimension ​

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

julia
using YAXArrays
+
+temperature = YAXArray((Dim{:time}(1:6),), rand(6))
+precipitation = YAXArray((Dim{:time}(1:6),), rand(6))
+cubes = [temperature,precipitation]
+var_axis = Dim{:variable}(["temp", "prep"])
+combined = concatenatecubes(cubes, var_axis)
╭─────────────────────────╮
+│ 6×2 YAXArray{Float64,2} │
+├─────────────────────────┴──────────────────────────────── dims ┐
+  ↓ time     Sampled{Int64} 1:6 ForwardOrdered Regular Points,
+  → variable Categorical{String} ["temp", "prep"] ReverseOrdered
+├────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── file size ┤ 
+  file size: 96.0 bytes
+└────────────────────────────────────────────────────────────────┘
`,10),p=[t];function l(h,k,r,d,o,c){return a(),i("div",null,p)}const y=s(e,[["render",l]]);export{g as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.lean.js b/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.lean.js new file mode 100644 index 00000000..c8a8de75 --- /dev/null +++ b/v0.5.9/assets/UserGuide_combine.md.DNTC2x-W.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const g=JSON.parse('{"title":"Combine YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/combine.md","filePath":"UserGuide/combine.md","lastUpdated":null}'),e={name:"UserGuide/combine.md"},t=n("",10),p=[t];function l(h,k,r,d,o,c){return a(),i("div",null,p)}const y=s(e,[["render",l]]);export{g as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.js b/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.js new file mode 100644 index 00000000..5fd51cfb --- /dev/null +++ b/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.js @@ -0,0 +1,175 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),t={name:"UserGuide/compute.md"},p=n(`

Compute YAXArrays ​

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

Let's start by creating an example dataset:

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

Modify elements of a YAXArray ​

julia
a[1,2,3]
0.8807643159933959
julia
a[1,2,3] = 42
42
julia
a[1,2,3]
42.0

WARNING

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

Arithmetics ​

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

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

map ​

Apply a function on every element of an array individually:

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

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

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

mapslices ​

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

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

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

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

mapCube ​

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

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

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

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

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

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

julia
DimArray(raster_arr[time = 1])
╭───────────────────────────╮
+│ 10×15 DimArray{Float64,2} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ lon Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────────────────┘
+  ↓ →  1.0       1.28571    1.57143     …  4.42857   4.71429   5.0
+  1.0  0.798256  0.920447   0.819421       0.739132  0.566009  0.154204
+  2.0  0.10278   0.770185   0.00308046     0.942654  0.919766  0.416274
+  3.0  0.2639    0.0323228  0.156541       0.359575  0.688907  0.820491
+  ⋮                                     ⋱                      ⋮
+  8.0  0.036138  0.457131   0.712288       0.2466    0.373092  0.292254
+  9.0  0.774003  0.250325   0.971698       0.427578  0.795336  0.712081
+ 10.0  0.144255  0.669878   0.319932    …  0.534257  0.23103   0.808461

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

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

Finally, we can transform the entire raster array:

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

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

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

Distributed Computation ​

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

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

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

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

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

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

julia
using Distributed
+using ClusterManagers
+addprocs(SlurmManager(10))
`,60),l=[p];function e(h,k,r,d,o,E){return a(),i("div",null,l)}const y=s(t,[["render",e]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.lean.js b/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.lean.js new file mode 100644 index 00000000..ce0e2f08 --- /dev/null +++ b/v0.5.9/assets/UserGuide_compute.md.mKUCMQxi.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Compute YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/compute.md","filePath":"UserGuide/compute.md","lastUpdated":null}'),t={name:"UserGuide/compute.md"},p=n("",60),l=[p];function e(h,k,r,d,o,E){return a(),i("div",null,l)}const y=s(t,[["render",e]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.js b/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.js new file mode 100644 index 00000000..40d15a21 --- /dev/null +++ b/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.js @@ -0,0 +1,78 @@ +import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"},e=i(`

Convert YAXArrays ​

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

WARNING

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

Convert Base.Array ​

Convert Base.Array to YAXArray:

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

Convert YAXArray to Base.Array:

julia
m2 = collect(a.data)
5×10 Matrix{Float64}:
+ 0.718802  0.877205   0.709084   0.191564   …  0.892634  0.891774  0.937715
+ 0.84607   0.269647   0.704815   0.572416      0.339514  0.247657  0.891063
+ 0.459549  0.75834    0.463841   0.577757      0.373326  0.484593  0.0316087
+ 0.371498  0.612681   0.45713    0.0573432     0.965811  0.484103  0.962429
+ 0.76362   0.0480175  0.0760908  0.646361      0.800092  0.527433  0.720119

Convert Raster ​

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

julia
using Rasters
+
+lon, lat = X(25:1:30), Y(25:1:30)
+time = Ti(2000:2024)
+ras = Raster(rand(lon, lat, time))
+a = YAXArray(dims(ras), ras.data)
╭────────────────────────────╮
+│ 6×6×25 YAXArray{Float64,3} │
+├────────────────────────────┴────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────── file size ┤ 
+  file size: 7.03 KB
+└─────────────────────────────────────────────────────────────┘
julia
ras2 = Raster(a)
╭──────────────────────────╮
+│ 6×6×25 Raster{Float64,3} │
+├──────────────────────────┴──────────────────────────── dims ┐
+  ↓ X  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  → Y  Sampled{Int64} 25:1:30 ForwardOrdered Regular Points,
+  ↗ Ti Sampled{Int64} 2000:2024 ForwardOrdered Regular Points
+├─────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────────── raster ┤
+  extent: Extent(X = (25, 30), Y = (25, 30), Ti = (2000, 2024))
+
+└─────────────────────────────────────────────────────────────┘
+[:, :, 1]
+  ↓ →  25          26         27         28          29         30
+ 25     0.60611     0.216595   0.900524   0.0526608   0.293033   0.289807
+ 26     0.883762    0.770476   0.321505   0.785602    0.624963   0.879151
+ 27     0.0401722   0.128882   0.991353   0.781533    0.528217   0.963865
+ 28     0.426561    0.235518   0.245619   0.368147    0.838999   0.84384
+ 29     0.96561     0.488183   0.865925   0.344583    0.47353    0.502084
+ 30     0.188337    0.518762   0.676613   0.480564    0.781488   0.71255

Convert DimArray ​

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

Convert DimArray to YAXArray:

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

Convert YAXArray to DimArray:

julia
dim_arr2 = yaxconvert(DimArray, a)
╭─────────────────────────╮
+│ 5×6 DimArray{Float64,2} │
+├─────────────────────────┴────────────────────────────────── dims ┐
+  ↓ X Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Y Sampled{Float64} 10.0:1.0:15.0 ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+└──────────────────────────────────────────────────────────────────┘
+ ↓ →  10.0        11.0       12.0        13.0        14.0       15.0
+ 1     0.343362    0.189021   0.466312    0.0287024   0.26332    0.616274
+ 2     0.318414    0.858017   0.895233    0.962506    0.72166    0.796383
+ 3     0.0247876   0.981214   0.205551    0.478981    0.715169   0.26791
+ 4     0.653445    0.437749   0.545125    0.440346    0.816282   0.0372525
+ 5     0.533318    0.455567   0.0370893   0.869952    0.76547    0.742225

INFO

At the moment there is no support to save a DimArray directly into disk as a NetCDF or a Zarr file.

`,25),l=[e];function t(r,h,d,k,o,c){return n(),a("div",null,l)}const E=s(p,[["render",t]]);export{y as __pageData,E as default}; diff --git a/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.lean.js b/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.lean.js new file mode 100644 index 00000000..47067440 --- /dev/null +++ b/v0.5.9/assets/UserGuide_convert.md.C3aIECaa.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as n,a4 as i}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Convert YAXArrays","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/convert.md","filePath":"UserGuide/convert.md","lastUpdated":null}'),p={name:"UserGuide/convert.md"},e=i("",25),l=[e];function t(r,h,d,k,o,c){return n(),a("div",null,l)}const E=s(p,[["render",t]]);export{y as __pageData,E as default}; diff --git a/v0.5.9/assets/UserGuide_create.md.Czne1Smy.js b/v0.5.9/assets/UserGuide_create.md.Czne1Smy.js new file mode 100644 index 00000000..40d062d6 --- /dev/null +++ b/v0.5.9/assets/UserGuide_create.md.Czne1Smy.js @@ -0,0 +1,48 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),t={name:"UserGuide/create.md"},e=n(`

Create YAXArrays and Datasets ​

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

Create a YAXArray ​

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

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

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

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

Create a Dataset ​

julia
data3 = rand(30, 10, 15)
+a3 = YAXArray(axlist, data3, properties)
+
+arrays = Dict(:a2 => a2, :a3 => a3)
+ds = Dataset(; properties, arrays...)
YAXArray Dataset
+Shared Axes: 
+↓ time Sampled{Date} Date("2022-01-01"):Dates.Day(1):Date("2022-01-30") ForwardOrdered Regular Points,
+→ lon  Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points,
+↗ lat  Sampled{Float64} 1.0:0.2857142857142857:5.0 ForwardOrdered Regular Points
+Variables: 
+a2
+a3
+
+Properties: Dict(:origin => "user guide")
`,16),p=[e];function l(h,k,r,d,E,g){return i(),a("div",null,p)}const y=s(t,[["render",l]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_create.md.Czne1Smy.lean.js b/v0.5.9/assets/UserGuide_create.md.Czne1Smy.lean.js new file mode 100644 index 00000000..9c9d0dde --- /dev/null +++ b/v0.5.9/assets/UserGuide_create.md.Czne1Smy.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Create YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/create.md","filePath":"UserGuide/create.md","lastUpdated":null}'),t={name:"UserGuide/create.md"},e=n("",16),p=[e];function l(h,k,r,d,E,g){return i(),a("div",null,p)}const y=s(t,[["render",l]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.js b/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.js new file mode 100644 index 00000000..50dbdaf3 --- /dev/null +++ b/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.js @@ -0,0 +1,195 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/bhexshv.Dpye-jN0.jpeg",c=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),e={name:"UserGuide/faq.md"},l=n(`

Frequently Asked Questions (FAQ) ​

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

Extract the axes names from a Cube ​

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

WARNING

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

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

INFO

Also, use DD.rebuild(ax, values) instead of axcopy(ax, values) to copy an axes with the same name but different values.

Obtain values from axes and data from the cube ​

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

These two examples bring the same result

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

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

julia
julia> c[:, :, 1]
╭───────────────────────────╮
+│ 10×10 YAXArray{Float64,2} │
+├───────────────────────────┴────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├───────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└────────────────────────────────────────────────────────────┘

How do I concatenate cubes ​

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

Let's create two dummy cubes

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

Now we can concatenate ds1 and ds2:

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

How do I subset a Cube? ​

Let's start by creating a dummy cube. Define the time span of the cube

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

create cube axes

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

assign values to a cube

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

Now we subset the cube by any dimension.

Subset cube by years

julia
julia> ctime = c[Time=Between(Date(2021,1,1), Date(2021,12,31))]
╭────────────────────────────╮
+│ 10×10×12 YAXArray{Int64,3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-01-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 9.38 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Subset cube by a specific date and date range

julia
julia> ctime2 = c[Time=At(Date("2021-05-01"))]
╭─────────────────────────╮
+│ 10×10 YAXArray{Int64,2} │
+├─────────────────────────┴────────────────────────── dims ┐
+  ↓ Lon Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat Sampled{Int64} 1:10 ForwardOrdered Regular Points
+├──────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└──────────────────────────────────────────────────────────┘
julia
julia> ctime3 = c[Time=Date("2021-05-01") .. Date("2021-12-01")]
╭───────────────────────────╮
+│ 10×10×8 YAXArray{Int64,3} │
+├───────────────────────────┴──────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 1:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2021-05-01"):Dates.Month(1):Date("2021-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 6.25 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Subset cube by longitude and latitude

julia
julia> clonlat = c[Lon=1 .. 5, Lat=5 .. 10] # check even numbers range, it is ommiting them
╭──────────────────────────╮
+│ 5×6×36 YAXArray{Int64,3} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+  ↓ Lon  Sampled{Int64} 1:5 ForwardOrdered Regular Points,
+  → Lat  Sampled{Int64} 5:10 ForwardOrdered Regular Points,
+  ↗ Time Sampled{Date} Date("2020-01-01"):Dates.Month(1):Date("2022-12-01") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 8.44 KB
+└──────────────────────────────────────────────────────────────────────────────┘

How do I apply map algebra? ​

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

Multiplying cubes with only spatio-temporal dimensions

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

Cubes with more than 3 dimensions

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

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

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

How do I use the CubeTable function? ​

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

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

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

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

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

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

In this line we calculate the Mean for each class

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

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

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

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

One variable name ​

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

Multiple variable names ​

julia
keylist = (:a, :b, :c)
+varlist = (YAXArray(rand(10)), YAXArray(rand(10,5)), YAXArray(rand(2,5)))
julia
julia> ds = YAXArrays.Dataset(; (keylist .=> varlist)...)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+a
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points
+b
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
+c
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(5) ForwardOrdered Regular Points
`,86),t=[l];function k(p,d,r,g,y,o){return a(),i("div",null,t)}const f=s(e,[["render",k]]);export{c as __pageData,f as default}; diff --git a/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.lean.js b/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.lean.js new file mode 100644 index 00000000..58fd3a9a --- /dev/null +++ b/v0.5.9/assets/UserGuide_faq.md.9eOXzbWW.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/bhexshv.Dpye-jN0.jpeg",c=JSON.parse('{"title":"Frequently Asked Questions (FAQ)","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/faq.md","filePath":"UserGuide/faq.md","lastUpdated":null}'),e={name:"UserGuide/faq.md"},l=n("",86),t=[l];function k(p,d,r,g,y,o){return a(),i("div",null,t)}const f=s(e,[["render",k]]);export{c as __pageData,f as default}; diff --git a/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.js b/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.js new file mode 100644 index 00000000..6d09ed6f --- /dev/null +++ b/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.js @@ -0,0 +1,204 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/wwlhgjb.CBBZcGwj.png",o=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"},e=n(`

Group YAXArrays and Datasets ​

The following examples will use the groupby function to calculate temporal and spatial averages.

julia
using YAXArrays, DimensionalData
+using NetCDF
+using Downloads
+using Dates
+using Statistics

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

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

Download the data ​

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

WARNING

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

julia
axs = dims(ds_o) # get the dimensions
+data = ds_o.data[:,:,:] # read the data
+_FillValue = ds_o.properties["_FillValue"]
+data = replace(data, _FillValue => NaN)
+# create new YAXArray
+ds = YAXArray(axs, data)

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
+    # calculate weights 
+    tempo = dims(ds, :Ti)
+    month_length = YAXArray((tempo,), daysinmonth.(tempo))
+    g_tempo = groupby(month_length, Ti => seasons(; start=December))
+    sum_days = sum.(g_tempo, dims=:Ti)
+    weights = map(./, g_tempo, sum_days)
+    # unweighted seasons
+    g_ds = groupby(ds, Ti => seasons(; start=December))
+    mean_g = mean.(g_ds, dims=:Ti)
+    mean_g = dropdims.(mean_g, dims=:Ti)
+    # weighted seasons
+    g_dsW = broadcast_dims.(*, weights, g_ds)
+    weighted_g = sum.(g_dsW, dims = :Ti);
+    weighted_g = dropdims.(weighted_g, dims=:Ti)
+    # differences
+    diff_g = map(.-, weighted_g, mean_g)
+    seasons_g = lookup(mean_g, :Ti)
+    return mean_g, weighted_g, diff_g, seasons_g
+end

Now, we continue with the groupby operations as usual

julia
julia> g_ds = groupby(ds, Ti => seasons(; start=December))
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×9 YAXArray
+ :Mar_Apr_May  275×205×9 YAXArray
+ :Jun_Jul_Aug  275×205×9 YAXArray
+ :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,3},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y, ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205×1 YAXArray
+ :Mar_Apr_May  275×205×1 YAXArray
+ :Jun_Jul_Aug  275×205×1 YAXArray
+ :Sep_Oct_Nov  275×205×1 YAXArray

dropdims ​

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

julia
julia> mean_g = dropdims.(mean_g, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

seasons ​

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

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

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

GroupBy: weight ​

Create a YAXArray for the month length

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

Now group it by season

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

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:Ti)
╭────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Int64,1},1} │
+├────────────────────────────────────────────────┴─────────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  1-element YAXArray
+ :Mar_Apr_May  1-element YAXArray
+ :Jun_Jul_Aug  1-element YAXArray
+ :Sep_Oct_Nov  1-element YAXArray

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,1},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9-element YAXArray
+ :Mar_Apr_May  9-element YAXArray
+ :Jun_Jul_Aug  9-element YAXArray
+ :Sep_Oct_Nov  9-element YAXArray

Verify that the sum per season is 1

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

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,3},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ Ti, → x, ↗ y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  9×275×205 YAXArray
+ :Mar_Apr_May  9×275×205 YAXArray
+ :Jun_Jul_Aug  9×275×205 YAXArray
+ :Sep_Oct_Nov  9×275×205 YAXArray

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :Ti);
+
+julia> weighted_g = dropdims.(weighted_g, dims=:Ti)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────╮
+│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
+├──────────────────────────────────────────────────┴───────────────────── dims ┐
+  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{Symbol, Any} with 1 entry:
+  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
+├────────────────────────────────────────────────────────────────── group dims ┤
+  ↓ x, → y
+└──────────────────────────────────────────────────────────────────────────────┘
+ :Dec_Jan_Feb  275×205 YAXArray
+ :Mar_Apr_May  275×205 YAXArray
+ :Jun_Jul_Aug  275×205 YAXArray
+ :Sep_Oct_Nov  275×205 YAXArray

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

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

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

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

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

',62),p=[e];function t(k,d,r,g,y,f){return a(),i("div",null,p)}const c=s(l,[["render",t]]);export{o as __pageData,c as default}; diff --git a/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.lean.js b/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.lean.js new file mode 100644 index 00000000..4abda1d0 --- /dev/null +++ b/v0.5.9/assets/UserGuide_group.md.CR_l0Pkn.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/wwlhgjb.CBBZcGwj.png",o=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"},e=n("",62),p=[e];function t(k,d,r,g,y,f){return a(),i("div",null,p)}const c=s(l,[["render",t]]);export{o as __pageData,c as default}; diff --git a/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.js b/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.js new file mode 100644 index 00000000..1495640a --- /dev/null +++ b/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.js @@ -0,0 +1,82 @@ +import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework.CNxXX0r7.js";const g=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),i={name:"UserGuide/read.md"},e=t(`

Read YAXArrays and Datasets ​

This section describes how to read files, URLs, and directories into YAXArrays and datasets.

Read Zarr ​

Open a Zarr store as a Dataset:

julia
using YAXArrays
+using Zarr
+path="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
+store = zopen(path, consolidated=true)
+ds = open_dataset(store)
YAXArray Dataset
+Shared Axes: 
+()
+Variables: 
+height
+tas
+  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)

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

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

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

Read NetCDF ​

Open a NetCDF file as a Dataset:

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

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

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

Read GDAL (GeoTIFF, GeoJSON) ​

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

julia
using YAXArrays
+using ArchGDAL
+using Downloads: download
+
+path = download("https://github.com/yeesian/ArchGDALDatasets/raw/307f8f0e584a39a050c042849004e6a2bd674f99/gdalworkshop/world.tif", "world.tif")
+ds = open_dataset(path)
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: 
+Red
+Green
+Blue
+
+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\\"]]")
`,21),p=[e];function l(o,r,d,h,u,c){return n(),a("div",null,p)}const q=s(i,[["render",l]]);export{g as __pageData,q as default}; diff --git a/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.lean.js b/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.lean.js new file mode 100644 index 00000000..8d9b0bdd --- /dev/null +++ b/v0.5.9/assets/UserGuide_read.md.Dyyx6z-E.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as n,a4 as t}from"./chunks/framework.CNxXX0r7.js";const g=JSON.parse('{"title":"Read YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/read.md","filePath":"UserGuide/read.md","lastUpdated":null}'),i={name:"UserGuide/read.md"},e=t("",21),p=[e];function l(o,r,d,h,u,c){return n(),a("div",null,p)}const q=s(i,[["render",l]]);export{g as __pageData,q as default}; diff --git a/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.js b/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.js new file mode 100644 index 00000000..393f7832 --- /dev/null +++ b/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.js @@ -0,0 +1,293 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),e={name:"UserGuide/select.md"},t=n(`

Select YAXArrays and Datasets ​

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

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

Select a YAXArray ​

Get the sea surface temperature of the Dataset:

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

which is the same as:

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

Select elements ​

Using positional integer indexing:

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

Same but using named indexing:

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

Using special types:

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

Select ranges ​

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

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

Same but using named indexing:

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

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

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

Closed and open intervals ​

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

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

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

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

See tutorials for use cases.

Get a dimension ​

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

julia
collect(tos.lat)
170-element Vector{Float64}:
+ -79.5
+ -78.5
+ -77.5
+ -76.5
+ -75.5
+ -74.5
+ -73.5
+ -72.5
+ -71.5
+ -70.5
+   ⋮
+  81.5
+  82.5
+  83.5
+  84.5
+  85.5
+  86.5
+  87.5
+  88.5
+  89.5

These values are defined as lookups in the package DimensionalData:

julia
lookup(tos, :lon)
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0

which is equivalent to:

julia
tos.lon.val
Sampled{Float64} ForwardOrdered Regular DimensionalData.Dimensions.Lookups.Points
+wrapping: 1.0:2.0:359.0
`,56),p=[t];function l(h,o,k,d,r,u){return i(),a("div",null,p)}const y=s(e,[["render",l]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.lean.js b/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.lean.js new file mode 100644 index 00000000..35151a4f --- /dev/null +++ b/v0.5.9/assets/UserGuide_select.md.C70Wbwm3.lean.js @@ -0,0 +1 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Select YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/select.md","filePath":"UserGuide/select.md","lastUpdated":null}'),e={name:"UserGuide/select.md"},t=n("",56),p=[t];function l(h,o,k,d,r,u){return i(),a("div",null,p)}const y=s(e,[["render",l]]);export{c as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.js b/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.js new file mode 100644 index 00000000..1c7af9ed --- /dev/null +++ b/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.js @@ -0,0 +1 @@ +import{_ as e,c as a,o as t,a4 as r}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),s={name:"UserGuide/types.md"},o=r('

Types ​

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

YAXArray ​

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

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

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

Dataset ​

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

(Data) Cube ​

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

Dimension ​

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

',12),n=[o];function i(d,l,c,h,m,u){return t(),a("div",null,n)}const f=e(s,[["render",i]]);export{y as __pageData,f as default}; diff --git a/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.lean.js b/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.lean.js new file mode 100644 index 00000000..0beb0f09 --- /dev/null +++ b/v0.5.9/assets/UserGuide_types.md.Cu1CJTMs.lean.js @@ -0,0 +1 @@ +import{_ as e,c as a,o as t,a4 as r}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"Types","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/types.md","filePath":"UserGuide/types.md","lastUpdated":null}'),s={name:"UserGuide/types.md"},o=r("",12),n=[o];function i(d,l,c,h,m,u){return t(),a("div",null,n)}const f=e(s,[["render",i]]);export{y as __pageData,f as default}; diff --git a/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.js b/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.js new file mode 100644 index 00000000..4c764827 --- /dev/null +++ b/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.js @@ -0,0 +1,39 @@ +import{_ as s,c as i,o as a,a4 as t}from"./chunks/framework.CNxXX0r7.js";const u=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),e={name:"UserGuide/write.md"},n=t(`

Write YAXArrays and Datasets ​

Create an example Dataset:

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

Write Zarr ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

Write NetCDF ​

Save a single YAXArray to a directory:

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

Save an entire Dataset to a directory:

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

Overwrite a Dataset ​

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

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

DANGER

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

Look at the doc string for more information

# YAXArrays.Datasets.savedataset — Function.

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

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

Warning

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

source


Append to a Dataset ​

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

julia
ds2 = Dataset(z = YAXArray(rand(10,20,5)))
+savedataset(ds2, path="ds.zarr", backend=:zarr, append=true)
julia
julia> open_dataset("ds.zarr", driver=:zarr)
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+tos
+  ↓ lon Sampled{Float64} 1.0:2.0:359.0 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} -79.5:1.0:89.5 ForwardOrdered Regular Points,
+  ↗ Ti  Sampled{CFTime.DateTime360Day} [CFTime.DateTime360Day(2001-01-16T00:00:00), …, CFTime.DateTime360Day(2002-12-16T00:00:00)] ForwardOrdered Irregular Points
+z
+  ↓ Dim_1 Sampled{Int64} 1:1:10 ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} 1:1:20 ForwardOrdered Regular Points,
+  ↗ Dim_3 Sampled{Int64} 1:1:5 ForwardOrdered Regular Points
+
+Properties: Dict{String, Any}("cmor_version" => 0.96, "references" => "Dufresne et al, Journal of Climate, 2015, vol XX, p 136", "realization" => 1, "contact" => "Sebastien Denvil, sebastien.denvil@ipsl.jussieu.fr", "Conventions" => "CF-1.0", "history" => "YYYY/MM/JJ: data generated; YYYY/MM/JJ+1 data transformed  At 16:37:23 on 01/11/2005, CMOR rewrote data to comply with CF standards and IPCC Fourth Assessment requirements", "table_id" => "Table O1 (13 November 2004)", "source" => "IPSL-CM4_v1 (2003) : atmosphere : LMDZ (IPSL-CM4_IPCC, 96x71x19) ; ocean ORCA2 (ipsl_cm4_v1_8, 2x2L31); sea ice LIM (ipsl_cm4_v", "title" => "IPSL  model output prepared for IPCC Fourth Assessment SRES A2 experiment", "experiment_id" => "SRES A2 experiment"…)

Save Skeleton ​

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

julia
using YAXArrays, Zarr, FillArrays

create the Zeros array

julia
julia> a = YAXArray(Zeros(Union{Missing, Int32}, 10, 20))
╭─────────────────────────────────────────╮
+│ 10×20 YAXArray{Union{Missing, Int32},2} │
+├─────────────────────────────────────────┴────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(10) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(20) ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────── file size ┤
+  file size: 800.0 bytes
+└──────────────────────────────────────────────────────────────────────┘

and save them as

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

and check that all the values are missing

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

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

INFO

The skeleton argument is also available for savedataset.

`,39),l=[n];function h(p,k,r,d,o,g){return a(),i("div",null,l)}const y=s(e,[["render",h]]);export{u as __pageData,y as default}; diff --git a/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.lean.js b/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.lean.js new file mode 100644 index 00000000..25eea681 --- /dev/null +++ b/v0.5.9/assets/UserGuide_write.md.BXB6Ju69.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as t}from"./chunks/framework.CNxXX0r7.js";const u=JSON.parse('{"title":"Write YAXArrays and Datasets","description":"","frontmatter":{},"headers":[],"relativePath":"UserGuide/write.md","filePath":"UserGuide/write.md","lastUpdated":null}'),e={name:"UserGuide/write.md"},n=t("",39),l=[n];function h(p,k,r,d,o,g){return a(),i("div",null,l)}const y=s(e,[["render",h]]);export{u as __pageData,y as default}; diff --git a/v0.5.9/assets/api.md.CYX2AwPT.js b/v0.5.9/assets/api.md.CYX2AwPT.js new file mode 100644 index 00000000..964b4657 --- /dev/null +++ b/v0.5.9/assets/api.md.CYX2AwPT.js @@ -0,0 +1,13 @@ +import{_ as e,c as a,o as s,a4 as i}from"./chunks/framework.CNxXX0r7.js";const y=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"},r=i(`

API Reference ​

This section describes all available functions of this package.

Public API ​

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

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

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

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:

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

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.

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

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

source


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

source


# YAXArrays.Datasets.Dataset — Method.

Dataset(; properties = Dict{String,Any}, cubes...)

Construct a YAXArray Dataset with global attributes properties a and a list of named YAXArrays cubes...

source


# YAXArrays.Datasets.Cube — Method.
julia
Cube(ds::Dataset; joinname="Variable")

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

source


# YAXArrays.Datasets.open_dataset — Method.

open_dataset(g; driver=:all)

Open the dataset at g with the given driver. The default driver will search for available drivers and tries to detect the useable driver from the filename extension.

source


# YAXArrays.Datasets.savecube — Method.
julia
savecube(cube,name::String)

Save a YAXArray to the path.

Extended Help

The keyword arguments are:

source


# YAXArrays.Datasets.savedataset — Method.

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

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

Warning

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

source


# YAXArrays.Datasets.to_dataset — Method.

to_dataset(c;datasetaxis = "Variable", layername = "layer")

Convert a Data Cube into a Dataset. It is possible to treat one of the Cube's axes as a "DatasetAxis" i.e. the cube will be split into different parts that become variables in the Dataset. If no such axis is specified or found, there will only be a single variable in the dataset with the name layername

source


Internal API ​

# YAXArrays.YAXDefaults — Constant.

Default configuration for YAXArrays, has the following fields:

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:

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:

source


# YAXArrays.DAT.InputCube — Type.

Internal representation of an input cube for DAT operations

source


# YAXArrays.DAT.OutputCube — Type.

Internal representation of an output cube for DAT operations

Fields

source


# YAXArrays.DAT.YAXColumn — Type.
julia
YAXColumn

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

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:

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

source


# YAXArrays.Datasets.collectfromhandle — Method.

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

source


# YAXArrays.Datasets.createdataset — Method.

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

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

Keyword arguments

source


# YAXArrays.Datasets.getarrayinfo — Method.

Extract necessary information to create a YAXArrayBase dataset from a name and YAXArray pair

source


# YAXArrays.Datasets.testrange — Method.

Test if data in x can be approximated by a step range

source


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Read n-d array like-data

DiskArrays.jl

Get your chunks!

Named Dimensions

DimensionalData.jl

Select & Index!

Out of memory data

Zarr.jl

Chunkerd, compressed !

Rasterized spatial data

Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

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Contribute to YAXArrays.jl ​

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

Contribute to Documentation ​

Contributing with examples can be done by first creating a new file example here

new file

  • your_new_file.md at docs/src/UserGuide/

Once this is done you need to add a new entry here at the appropriate level.

add entry to docs

Your new entry should look like:

  • { text: 'Your title example', link: '/UserGuide/your_new_file.md' }

Build docs locally ​

If you want to take a look at the docs locally before doing a PR follow the next steps:

Install the dependencies in your system, locate yourself at the docs level folder, then do

sh
npm i

Then simply go to your docs env and activate it, i.e.

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

Next, run the scripts. Generate files and build docs by running:

sh
include("make.jl")

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

sh
npm run docs:dev

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

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index 00000000..4b29b46e --- /dev/null +++ b/v0.5.9/assets/get_started.md.BFiwHvQu.js @@ -0,0 +1,56 @@ +import{_ as s,c as a,o as i,a4 as n}from"./chunks/framework.CNxXX0r7.js";const c=JSON.parse('{"title":"Getting Started","description":"","frontmatter":{},"headers":[],"relativePath":"get_started.md","filePath":"get_started.md","lastUpdated":null}'),t={name:"get_started.md"},p=n(`

Getting Started ​

Installation ​

Install Julia v1.10 or above. YAXArrays.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing

julia
pkg> add YAXArrays

Alternatively, you can also do

julia
import Pkg; Pkg.add("YAXArrays")

Quickstart ​

Create a simple array from random numbers given the size of each dimension or axis:

julia
using YAXArrays
+
+a = YAXArray(rand(2,3))
╭─────────────────────────╮
+│ 2×3 YAXArray{Float64,2} │
+├─────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Dim_1 Sampled{Int64} Base.OneTo(2) ForwardOrdered Regular Points,
+  → Dim_2 Sampled{Int64} Base.OneTo(3) ForwardOrdered Regular Points
+├─────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├────────────────────────────────────────────────────────── file size ┤ 
+  file size: 48.0 bytes
+└─────────────────────────────────────────────────────────────────────┘

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

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

Get the temperature map at the first point in time:

julia
a2[variable=At("temperature"), time=1].data
10×15 view(::Array{Float64, 4}, 1, :, :, 1) with eltype Float64:
+ 0.337213  0.0940597  0.245931   0.593108  …  0.410912   0.845202  0.991819
+ 0.666453  0.307108   0.310839   0.502989     0.688703   0.250541  0.692961
+ 0.83222   0.108861   0.0876353  0.942466     0.420922   0.594469  0.640513
+ 0.760904  0.248297   0.891539   0.155208     0.512095   0.793159  0.00733057
+ 0.77556   0.165091   0.470159   0.696438     0.0346543  0.634795  0.782278
+ 0.989097  0.730623   0.883015   0.681342  …  0.390799   0.18893   0.150286
+ 0.852691  0.947389   0.383056   0.959706     0.256152   0.082471  0.419677
+ 0.117966  0.220938   0.509041   0.534621     0.547611   0.758536  0.53538
+ 0.387676  0.271378   0.207092   0.508053     0.606332   0.116976  0.616855
+ 0.4576    0.248494   0.380606   0.299014     0.0993083  0.476608  0.0380605

Get more details at the select page

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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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: 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n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/qlcuekg.DKzzyLhR.png",l="/YAXArrays.jl/v0.5.9/assets/dngbkrd.BWyMc-wd.png",k="/YAXArrays.jl/v0.5.9/assets/pfadugl.BzXHVv-j.png",C=JSON.parse('{"title":"Mean Seasonal Cycle for a sigle 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"},t=n(`

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
lines(1:length(t), var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; resolution = (600,400))
+    )

Currently makie doesn't support time axis natively, but the following function can do the work for now.

julia
function time_ticks(dates; frac=8)
+    tempo = string.(dates)
+    lentime = length(tempo)
+    slice_dates = range(1, lentime, step=lentime ÷ frac)
+    return slice_dates, tempo[slice_dates]
+end
+xpos, ticks = time_ticks(t; frac=8)

In order to apply the previous output, we split the plotting function into his 3 components, figure, axis and plotted object, namely

julia
fig, ax, obj = lines(1:length(t), var; color = :purple, linewidth=1.25,
+    axis=(; xlabel="Time", ylabel="Variable"),
+    figure = (; resolution = (600,400))
+    )
+ax.xticks = (xpos, ticks)
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig

Define the cube ​

julia
julia> using YAXArrays, DimensionalData
+
+julia> axes = (Dim{:Time}(t),)
↓ Time Date("2021-01-01"):Dates.Day(1):Date("2023-12-31")
julia
julia> c = YAXArray(axes, var)
╭──────────────────────────────────╮
+│ 1095-element YAXArray{Float64,1} │
+├──────────────────────────────────┴───────────────────────────────────── dims ┐
+  ↓ Time Sampled{Date} Date("2021-01-01"):Dates.Day(1):Date("2023-12-31") ForwardOrdered Regular Points
+├──────────────────────────────────────────────────────────────────── metadata ┤
+  Dict{String, Any}()
+├─────────────────────────────────────────────────────────────────── file size ┤
+  file size: 8.55 KB
+└──────────────────────────────────────────────────────────────────────────────┘

Let's calculate the mean seasonal cycle of our dummy variable 'var'

julia
function mean_seasonal_cycle(c; ndays = 365)
+    ## filterig by month-day
+    monthday = map(x->Dates.format(x, "u-d"), collect(c.Time))
+    datesid = unique(monthday)
+    ## number of years
+    NpY = Int(size(monthday,1)/ndays)
+    idx = Int.(zeros(ndays, NpY))
+    ## get the day-month indices for data subsetting
+    for i in 1:ndays
+        idx[i,:] = Int.(findall(x-> x == datesid[i], monthday))
+    end
+    ## compute the mean seasonal cycle
+    mscarray = map(x->var[x], idx)
+    msc = mapslices(mean, mscarray, dims=2)
+    return msc
+end
+
+msc = mean_seasonal_cycle(c);
365×1 Matrix{Float64}:
+  0.09366512417810309
+  0.0029976163697445005
+  0.058082787231993165
+  0.008817893551572739
+  0.14576908612965156
+  0.1887126060709123
+ -0.02013755114254336
+  0.1565850522732394
+  0.09650988709118402
+  0.17150763278070644
+  ⋮
+ -0.07739332020203377
+ -0.04103733597084677
+ -0.11735807220298383
+ -0.13435033028295876
+ -0.08690314217665562
+  0.04885873831645466
+ -0.12650119816363398
+ -0.0022038187028614827
+ -0.05714071592422713

TODO: Apply the new groupby funtion from DD

Plot results: mean seasonal cycle ​

julia
xpos, ticks = time_ticks(t[1:365]; frac=8)
+
+fig, ax, obj = lines(1:365, var[1:365]; label="2021", color=:black,
+    linewidth=2.0, linestyle=:dot,
+    axis = (;  xlabel="Time", 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.xticks = (xpos, ticks)
+ax.xticklabelrotation = π / 4
+ax.xticklabelalign = (:right, :center)
+fig
+current_figure()

',26),e=[t];function E(d,r,g,y,c,F){return a(),i("div",null,e)}const u=s(p,[["render",E]]);export{C as __pageData,u as default}; diff --git a/v0.5.9/assets/tutorials_mean_seasonal_cycle.md.BYi8eCNA.lean.js b/v0.5.9/assets/tutorials_mean_seasonal_cycle.md.BYi8eCNA.lean.js new file mode 100644 index 00000000..600d78c2 --- /dev/null +++ b/v0.5.9/assets/tutorials_mean_seasonal_cycle.md.BYi8eCNA.lean.js @@ -0,0 +1 @@ +import{_ as s,c as i,o as a,a4 as n}from"./chunks/framework.CNxXX0r7.js";const h="/YAXArrays.jl/v0.5.9/assets/qlcuekg.DKzzyLhR.png",l="/YAXArrays.jl/v0.5.9/assets/dngbkrd.BWyMc-wd.png",k="/YAXArrays.jl/v0.5.9/assets/pfadugl.BzXHVv-j.png",C=JSON.parse('{"title":"Mean Seasonal Cycle for a sigle 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"},t=n("",26),e=[t];function E(d,r,g,y,c,F){return a(),i("div",null,e)}const u=s(p,[["render",E]]);export{C as __pageData,u as default}; diff --git a/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.js b/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.js new file mode 100644 index 00000000..99abdc5a --- /dev/null +++ b/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.js @@ -0,0 +1 @@ +import{_ as e,c as t,o as a,a4 as r}from"./chunks/framework.CNxXX0r7.js";const p=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),o={name:"tutorials/other_tutorials.md"},i=r('

Other tutorials ​

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

  • Currently the overview tutorial is located at ESDLTutorials Repository

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

  • the other tutorials are still work in progress.

General overview of the functionality of YAXArrays ​

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

Table-style iteration over YAXArrays ​

Work in progress

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

Combining multiple tiff files into a zarr based datacube ​

',9),l=[i];function s(n,h,u,f,d,c){return a(),t("div",null,l)}const _=e(o,[["render",s]]);export{p as __pageData,_ as default}; diff --git a/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.lean.js b/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.lean.js new file mode 100644 index 00000000..cca88692 --- /dev/null +++ b/v0.5.9/assets/tutorials_other_tutorials.md.C04pN_kv.lean.js @@ -0,0 +1 @@ +import{_ as e,c as t,o as a,a4 as r}from"./chunks/framework.CNxXX0r7.js";const p=JSON.parse('{"title":"Other tutorials","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/other_tutorials.md","filePath":"tutorials/other_tutorials.md","lastUpdated":null}'),o={name:"tutorials/other_tutorials.md"},i=r("",9),l=[i];function s(n,h,u,f,d,c){return a(),t("div",null,l)}const _=e(o,[["render",s]]);export{p as __pageData,_ as default}; diff --git a/v0.5.9/assets/tutorials_plottingmaps.md.CK2P9IMw.js b/v0.5.9/assets/tutorials_plottingmaps.md.CK2P9IMw.js new file mode 100644 index 00000000..e5172599 --- /dev/null +++ b/v0.5.9/assets/tutorials_plottingmaps.md.CK2P9IMw.js @@ -0,0 +1,50 @@ +import{_ as s,c as i,o as a,a4 as h}from"./chunks/framework.CNxXX0r7.js";const n="/YAXArrays.jl/v0.5.9/assets/nphfesj.CVsWGJ9w.jpeg",t="/YAXArrays.jl/v0.5.9/assets/pfsgvyt.vdMllTCc.jpeg",k="/YAXArrays.jl/v0.5.9/assets/gzmukqj.Cgvrw55c.jpeg",C=JSON.parse('{"title":"Plotting maps","description":"","frontmatter":{},"headers":[],"relativePath":"tutorials/plottingmaps.md","filePath":"tutorials/plottingmaps.md","lastUpdated":null}'),l={name:"tutorials/plottingmaps.md"},p=h(`

Plotting maps ​

As test data we use the CMIP6 Scenarios.

julia
using Zarr, YAXArrays, Dates
+using DimensionalData
+using GLMakie, GeoMakie
+using GLMakie.GeometryBasics
+
+store ="gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
"gs://cmip6/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp585/r1i1p1f1/3hr/tas/gn/v20190710/"
julia
julia> g = open_dataset(zopen(store, consolidated=true))
YAXArray Dataset
+Shared Axes:
+()
+Variables:
+height
+tas
+  ↓ lon Sampled{Float64} 0.0:0.9375:359.0625 ForwardOrdered Regular Points,
+  → lat Sampled{Float64} [-89.28422753251364, -88.35700351866494, …, 88.35700351866494, 89.28422753251364] ForwardOrdered Irregular Points,
+  ↗ Ti  Sampled{DateTime} [2015-01-01T03:00:00, …, 2101-01-01T00:00:00] ForwardOrdered Irregular Points
+
+Properties: Dict{String, Any}("initialization_index" => 1, "realm" => "atmos", "variable_id" => "tas", "external_variables" => "areacella", "branch_time_in_child" => 60265.0, "data_specs_version" => "01.00.30", "history" => "2019-07-21T06:26:13Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.", "forcing_index" => 1, "parent_variant_label" => "r1i1p1f1", "table_id" => "3hr"…)
julia
julia> c = g["tas"];

Subset, first time step

julia
julia> ct1_slice = c[Ti = Near(Date("2015-01-01"))];

use lookup to get axis values

julia
lon = lookup(ct1_slice, :lon)
+lat = lookup(ct1_slice, :lat)
+data = ct1_slice.data[:,:];

Heatmap plot ​

julia
GLMakie.activate!()
+
+fig, ax, plt = heatmap(ct1_slice; colormap = :seaborn_icefire_gradient,
+    axis = (; aspect=DataAspect()),
+    figure = (; size = (1200,600), fontsize=24))
+fig

Wintri Projection ​

Some transformations

julia
δlon = (lon[2]-lon[1])/2
+nlon = lon .- 180 .+ δlon
+ndata = circshift(data, (192,1))

and add Coastlines with GeoMakie.coastlines(),

julia
fig = Figure(;size=(1200,600))
+ax = GeoAxis(fig[1,1])
+surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
+cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
+translate!(cl, 0, 0, 1000)
+fig

Moll projection ​

julia
fig = Figure(; size=(1200,600))
+ax = GeoAxis(fig[1,1]; dest = "+proj=moll")
+surface!(ax, nlon, lat, ndata; colormap = :seaborn_icefire_gradient, shading=false)
+cl=lines!(ax, GeoMakie.coastlines(), color = :white, linewidth=0.85)
+translate!(cl, 0, 0, 1000)
+fig

3D sphere plot ​

julia
using Bonito, WGLMakie
+Page(exportable=true, offline=true)
+
+WGLMakie.activate!()
+Makie.inline!(true) # Make sure to inline plots into Documenter output!
+
+ds = replace(ndata, missing =>NaN)
+sphere = uv_normal_mesh(Tesselation(Sphere(Point3f(0), 1), 128))
+
+fig = Figure(backgroundcolor=:grey25, size=(500,500))
+ax = LScene(fig[1,1], show_axis=false)
+mesh!(ax, sphere; color = ds'[end:-1:1,:], shading=false,
+    colormap = :seaborn_icefire_gradient)
+zoom!(ax.scene, cameracontrols(ax.scene), 0.5)
+rotate!(ax.scene, 2.5)
+fig
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Mean Seasonal Cycle for a sigle pixel ​