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diff --git a/previews/PR856/assets/cljjmmh.wy-ul34S.svg b/previews/PR856/assets/cljjmmh.wy-ul34S.svg
new file mode 100644
index 000000000..4e7ed3e6f
--- /dev/null
+++ b/previews/PR856/assets/cljjmmh.wy-ul34S.svg
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diff --git a/previews/PR856/assets/cuda.md.DRQ0bs8j.js b/previews/PR856/assets/cuda.md.DRQ0bs8j.js
new file mode 100644
index 000000000..99c69b101
--- /dev/null
+++ b/previews/PR856/assets/cuda.md.DRQ0bs8j.js
@@ -0,0 +1,28 @@
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"CUDA & GPUs","description":"","frontmatter":{},"headers":[],"relativePath":"cuda.md","filePath":"cuda.md","lastUpdated":null}'),t={name:"cuda.md"};function l(k,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`CUDA & GPUs Running regular julia code on GPUs is one of the most amazing things about the language. DimensionalData.jl leans into this as much as possible.
julia using DimensionalData, CUDA
+
+# Create a Float32 array to use on the GPU
+A = rand (Float32, X ( 1.0 : 1000.0 ), Y ( 1.0 : 2000.0 ))
+
+# Move the parent data to the GPU with \`modify\` and the \`CuArray\` constructor:
+cuA = modify (CuArray, A)
The result of a GPU broadcast is still a DimArray
:
julia julia > cuA2 = cuA .* 2
+╭───────────────────────────────╮
+│ 1000 × 2000 DimArray{Float32, 2 } │
+├───────────────────────────────┴────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0 : 1.0 : 1000.0 ForwardOrdered Regular Points,
+ → Y Sampled{Float64} 1.0 : 1.0 : 2000.0 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────────────┘
+ ↓ → 1.0 2.0 3.0 4.0 … 1998.0 1999.0 2000.0
+ 1.0 1.69506 1.28405 0.989952 0.900394 1.73623 1.30427 1.98193
+ 2.0 1.73591 0.929995 0.665742 0.345501 0.162919 1.81708 0.702944
+ 3.0 1.24575 1.80455 1.78028 1.49097 0.45804 0.224375 0.0197492
+ 4.0 0.374026 1.91495 1.17645 0.995683 0.835288 1.54822 0.487601
+ 5.0 1.17673 0.0557598 0.183637 1.90645 … 0.88058 1.23788 1.59705
+ 6.0 1.57019 0.215049 1.9155 0.982762 0.906838 0.1076 0.390081
+ ⋮ ⋱
+ 995.0 1.48275 0.40409 1.37963 1.66622 0.462981 1.4492 1.26917
+ 996.0 1.88869 1.86174 0.298383 0.854739 … 0.778222 1.42151 1.75568
+ 997.0 1.88092 1.87436 0.285965 0.304688 1.32669 0.0599431 0.134186
+ 998.0 1.18035 1.61025 0.352614 1.75847 0.464554 1.90309 1.30923
+ 999.0 1.40584 1.83056 0.0804518 0.177423 1.20779 1.95217 0.881149
+ 1000.0 1.41334 0.719974 0.479126 1.92721 0.0649391 0.642908 1.07277
But the data is on the GPU:
julia julia > typeof ( parent (cuA2))
+CuArray{Float32, 2 , CUDA . Mem . DeviceBuffer}
GPU Integration goals DimensionalData.jl has two GPU-related goals:
Work seamlessly with Base
Julia broadcasts and other operations that already work on GPU.
Work as arguments to custom GPU kernel functions.
This means any AbstractDimArray
must be automatically moved to the GPU and its fields converted to GPU-friendly forms whenever required, using Adapt.jl .
The array data must convert to the correct GPU array backend when Adapt.adapt(dimarray)
is called.
All DimensionalData.jl objects, except the actual parent array, need to be immutable isbits
or convertible to them. This is one reason DimensionalData.jl uses rebuild
and a functional style, rather than in-place modification of fields.
Symbols need to be moved to the type system, so Name{:layer_name}()
replaces :layer_name
.
Metadata dictionaries need to be stripped, as they are often too difficult to convert and not needed on GPU.
As an example, DynamicGrids.jl uses AbstractDimArray
for auxiliary model data that are passed into KernelAbstractions.jl / CUDA.jl kernels.
`,13)]))}const g=i(t,[["render",l]]);export{C as __pageData,g as default};
diff --git a/previews/PR856/assets/cuda.md.DRQ0bs8j.lean.js b/previews/PR856/assets/cuda.md.DRQ0bs8j.lean.js
new file mode 100644
index 000000000..99c69b101
--- /dev/null
+++ b/previews/PR856/assets/cuda.md.DRQ0bs8j.lean.js
@@ -0,0 +1,28 @@
+import{_ as i,c as a,a4 as n,o as h}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"CUDA & GPUs","description":"","frontmatter":{},"headers":[],"relativePath":"cuda.md","filePath":"cuda.md","lastUpdated":null}'),t={name:"cuda.md"};function l(k,s,p,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`CUDA & GPUs Running regular julia code on GPUs is one of the most amazing things about the language. DimensionalData.jl leans into this as much as possible.
julia using DimensionalData, CUDA
+
+# Create a Float32 array to use on the GPU
+A = rand (Float32, X ( 1.0 : 1000.0 ), Y ( 1.0 : 2000.0 ))
+
+# Move the parent data to the GPU with \`modify\` and the \`CuArray\` constructor:
+cuA = modify (CuArray, A)
The result of a GPU broadcast is still a DimArray
:
julia julia > cuA2 = cuA .* 2
+╭───────────────────────────────╮
+│ 1000 × 2000 DimArray{Float32, 2 } │
+├───────────────────────────────┴────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0 : 1.0 : 1000.0 ForwardOrdered Regular Points,
+ → Y Sampled{Float64} 1.0 : 1.0 : 2000.0 ForwardOrdered Regular Points
+└────────────────────────────────────────────────────────────────────┘
+ ↓ → 1.0 2.0 3.0 4.0 … 1998.0 1999.0 2000.0
+ 1.0 1.69506 1.28405 0.989952 0.900394 1.73623 1.30427 1.98193
+ 2.0 1.73591 0.929995 0.665742 0.345501 0.162919 1.81708 0.702944
+ 3.0 1.24575 1.80455 1.78028 1.49097 0.45804 0.224375 0.0197492
+ 4.0 0.374026 1.91495 1.17645 0.995683 0.835288 1.54822 0.487601
+ 5.0 1.17673 0.0557598 0.183637 1.90645 … 0.88058 1.23788 1.59705
+ 6.0 1.57019 0.215049 1.9155 0.982762 0.906838 0.1076 0.390081
+ ⋮ ⋱
+ 995.0 1.48275 0.40409 1.37963 1.66622 0.462981 1.4492 1.26917
+ 996.0 1.88869 1.86174 0.298383 0.854739 … 0.778222 1.42151 1.75568
+ 997.0 1.88092 1.87436 0.285965 0.304688 1.32669 0.0599431 0.134186
+ 998.0 1.18035 1.61025 0.352614 1.75847 0.464554 1.90309 1.30923
+ 999.0 1.40584 1.83056 0.0804518 0.177423 1.20779 1.95217 0.881149
+ 1000.0 1.41334 0.719974 0.479126 1.92721 0.0649391 0.642908 1.07277
But the data is on the GPU:
julia julia > typeof ( parent (cuA2))
+CuArray{Float32, 2 , CUDA . Mem . DeviceBuffer}
GPU Integration goals DimensionalData.jl has two GPU-related goals:
Work seamlessly with Base
Julia broadcasts and other operations that already work on GPU.
Work as arguments to custom GPU kernel functions.
This means any AbstractDimArray
must be automatically moved to the GPU and its fields converted to GPU-friendly forms whenever required, using Adapt.jl .
The array data must convert to the correct GPU array backend when Adapt.adapt(dimarray)
is called.
All DimensionalData.jl objects, except the actual parent array, need to be immutable isbits
or convertible to them. This is one reason DimensionalData.jl uses rebuild
and a functional style, rather than in-place modification of fields.
Symbols need to be moved to the type system, so Name{:layer_name}()
replaces :layer_name
.
Metadata dictionaries need to be stripped, as they are often too difficult to convert and not needed on GPU.
As an example, DynamicGrids.jl uses AbstractDimArray
for auxiliary model data that are passed into KernelAbstractions.jl / CUDA.jl kernels.
`,13)]))}const g=i(t,[["render",l]]);export{C as __pageData,g as default};
diff --git a/previews/PR856/assets/dimarrays.md.DJPyWkGa.js b/previews/PR856/assets/dimarrays.md.DJPyWkGa.js
new file mode 100644
index 000000000..60af96bc3
--- /dev/null
+++ b/previews/PR856/assets/dimarrays.md.DJPyWkGa.js
@@ -0,0 +1,259 @@
+import{_ as d,c as p,a4 as l,G as h,w as e,B as k,o as g,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),r={name:"dimarrays.md"};function o(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`DimArrays DimArray
s are wrappers for other kinds of AbstractArray
that add named dimension lookups.
Here we define a Matrix
of Float64
, and give it X
and Y
dimensions
julia julia > using DimensionalData
+
+julia > A = rand ( 5 , 10 )
5×10 Matrix{Float64}:
+ 0.827655 0.0102884 0.688175 0.254555 … 0.628275 0.517329 0.886959
+ 0.223602 0.737979 0.996807 0.124594 0.0959042 0.628507 0.893267
+ 0.0392779 0.792885 0.249616 0.519235 0.0315486 0.113704 0.997572
+ 0.451879 0.846175 0.373575 0.891743 0.700389 0.958811 0.68136
+ 0.689712 0.0927459 0.765773 0.273573 0.688928 0.761347 0.596077
julia julia > da = DimArray (A, (X, Y))
╭───────────────────────────╮
+│ 5 × 10 DimArray{Float64, 2} │
+├───────────────────────────┴────────────────────────────── dims ┐
+ ↓ X , → Y
+└────────────────────────────────────────────────────────────────┘
+ 0.827655 0.0102884 0.688175 0.254555 … 0.628275 0.517329 0.886959
+ 0.223602 0.737979 0.996807 0.124594 0.0959042 0.628507 0.893267
+ 0.0392779 0.792885 0.249616 0.519235 0.0315486 0.113704 0.997572
+ 0.451879 0.846175 0.373575 0.891743 0.700389 0.958811 0.68136
+ 0.689712 0.0927459 0.765773 0.273573 0.688928 0.761347 0.596077
We can access a value with the same dimension wrappers:
julia julia > da[ Y ( 1 ), X ( 2 )]
There are shortcuts for creating DimArray
:
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"│ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"5"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Int64, 2}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," fill"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," │")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"├──────────────────────────────┴─────────────────────────── dims ┐")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," ↓ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},", "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"→ "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"Y")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"├────────────────────────────────────────────────────── metadata ┤")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Dict{Any, Any}()")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└────────────────────────────────────────────────────────────────┘")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")])])])],-1)])),_:1})]),_:1}),a[6]||(a[6]=l(`Constructing DimArray with arbitrary dimension names For arbitrary names, we can use the Dim{:name}
dims by using Symbol
s, and indexing with keywords:
julia julia > da1 = DimArray ( rand ( 5 , 5 ), ( :a , :b ))
╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
and get a value, here another smaller DimArray
:
julia julia > da1[a = 3 , b = 1 : 3 ]
╭────────────────────────────────╮
+│ 3-element DimArray{Float64, 1} │
+├────────────────────────────────┴ dims ┐
+ ↓ b
+└─────────────────────────────────┘
+ 0.910981
+ 0.503823
+ 0.488029
Dimensional Indexing When used for indexing, dimension wrappers free us from knowing the order of our objects axes. These are the same:
julia julia > da[ X ( 2 ), Y ( 1 )] == da[ Y ( 1 ), X ( 2 )]
We also can use Tuples
of dimensions, like CartesianIndex
, but they don't have to be in order of consecutive axes.
julia julia > da2 = rand ( X ( 10 ), Y ( 7 ), Z ( 5 ))
╭─────────────────────────────╮
+│ 10 × 7 × 5 DimArray{Float64, 3} │
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Z
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 0.41173 0.0789906 0.655936 0.762722 0.56273 0.0160205 0.640693
+ 0.228294 0.687236 0.345046 0.749573 0.949769 0.783195 0.591606
+ 0.258576 0.89609 0.00337686 0.99118 0.169845 0.159817 0.784693
+ 0.295346 0.768639 0.754468 0.0738813 0.675126 0.968484 0.243035
+ 0.254627 0.411529 0.629352 0.285205 0.18989 0.891663 0.597808
+ 0.435719 0.0450386 0.0531003 0.385452 0.322612 0.667691 0.906601
+ 0.88841 0.571658 0.991676 0.677848 0.697976 0.971252 0.776593
+ 0.284399 0.196524 0.119937 0.493258 0.342919 0.866772 0.0990347
+ 0.192286 0.765808 0.502499 0.404773 0.0623616 0.627002 0.184813
+ 0.762199 0.159233 0.769884 0.74428 0.606279 0.351096 0.268379
julia julia > da2[( X ( 3 ), Z ( 5 ))]
╭────────────────────────────────╮
+│ 7-element DimArray{Float64, 1} │
+├────────────────────────────────┴ dims ┐
+ ↓ Y
+└─────────────────────────────────┘
+ 0.644748
+ 0.493072
+ 0.316833
+ 0.372311
+ 0.313185
+ 0.494267
+ 0.705582
We can index with Vector
of Tuple{Vararg(Dimension}}
like vectors of CartesianIndex
. This will merge the dimensions in the tuples:
julia julia > inds = [( X ( 3 ), Z ( 5 )), ( X ( 7 ), Z ( 4 )), ( X ( 8 ), Z ( 2 ))]
3-element Vector{Tuple{X{Int64}, Z{Int64}}}:
+ ( ↓ X 3 , → Z 5 )
+ ( ↓ X 7 , → Z 4 )
+ ( ↓ X 8 , → Z 2 )
╭──────────────────────────╮
+│ 7 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ Y ,
+ → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X , → Z
+└──────────────────────────────────────────────────────────────────────────────┘
+ (3, 5) (7, 4) (8, 2)
+ 0.644748 0.510196 0.528138
+ 0.493072 0.925624 0.142957
+ 0.316833 0.485321 0.708081
+ 0.372311 0.676945 0.406221
+ 0.313185 0.0379776 0.6778
+ 0.494267 0.287739 0.155826
+ 0.705582 0.996358 0.638336
DimIndices
can be used like CartesianIndices
but again, without the constraint of consecutive dimensions or known order.
julia julia > da2[ DimIndices ( dims (da2, (X, Z))), Y ( 3 )]
╭───────────────────────────╮
+│ 10 × 5 DimArray{Float64, 2} │
+├───────────────────────────┴────────────────────────────── dims ┐
+ ↓ X , → Z
+└────────────────────────────────────────────────────────────────┘
+ 0.655936 0.853835 0.870583 0.0578313 0.971794
+ 0.345046 0.17597 0.638072 0.136127 0.962808
+ 0.00337686 0.378395 0.0314382 0.310753 0.316833
+ 0.754468 0.188864 0.614012 0.883048 0.191049
+ 0.629352 0.190726 0.338669 0.105539 0.925844
+ 0.0531003 0.876115 0.945147 0.873096 0.342887
+ 0.991676 0.702956 0.281077 0.485321 0.798621
+ 0.119937 0.708081 0.0363983 0.247755 0.527261
+ 0.502499 0.641023 0.0104608 0.10233 0.635425
+ 0.769884 0.37821 0.881533 0.535933 0.454033
The Dimension
indexing layer sits on top of regular indexing and can not be combined with it! Regular indexing specifies order, so doesn't mix well with our dimensions.
Mixing them will throw an error:
ERROR: ArgumentError: invalid index: X{Int64}(3) of type X{Int64}
Begin End indexing julia julia > da1[X = Begin + 1 , Y = End]
┌ Warning: (X, Y) dims were not found in object.
+└ @ DimensionalData.Dimensions ~/work/DimensionalData.jl/DimensionalData.jl/src/Dimensions/primitives.jl:846
+╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
It also works in ranges, even with basic math:
julia julia > da1[X = Begin : Begin + 1 , Y = Begin + 1 : End - 1 ]
┌ Warning: (X, Y) dims were not found in object.
+└ @ DimensionalData.Dimensions ~/work/DimensionalData.jl/DimensionalData.jl/src/Dimensions/primitives.jl:846
+╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
In base julia the keywords begin
and end
can be used to index the first or last element of an array. But this doesn't work when named indexing is used. Instead you can use the types Begin
and End
.
Indexing
Indexing AbstractDimArray
s works with getindex
, setindex!
and view
. The result is still an AbstracDimArray
, unless using all single Int
or Selector
s that resolve to Int
inside Dimension
.
dims
keywords In many Julia functions like, size
or sum
, you can specify the dimension along which to perform the operation as an Int
. It is also possible to do this using Dimension
types with AbstractDimArray
:
julia julia > da5 = rand ( X ( 3 ), Y ( 4 ), Ti ( 5 ))
╭────────────────────────────╮
+│ 3 × 4 × 5 DimArray{Float64, 3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 0.573147 0.206406 0.916702 0.481184
+ 0.0760848 0.94412 0.48817 0.758865
+ 0.0992684 0.917457 0.811947 0.0641884
julia julia > sum (da5; dims = Ti)
╭────────────────────────────╮
+│ 3 × 4 × 1 DimArray{Float64, 3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 2.89248 1.86986 2.12435 1.86194
+ 1.29325 2.76668 2.64923 2.70488
+ 1.11475 3.10914 3.02098 2.35189
Dims keywords
Methods where dims, dim types, or Symbol
s can be used to indicate the array dimension:
size
, axes
, firstindex
, lastindex
cat
, reverse
, dropdims
reduce
, mapreduce
sum
, prod
, maximum
, minimum
mean
, median
, extrema
, std
, var
, cor
, cov
permutedims
, adjoint
, transpose
, Transpose
mapslices
, eachslice
Indexing with Dimension
s has no runtime cost. Let's benchmark it:
julia julia > using BenchmarkTools
+
+julia > da4 = ones ( X ( 3 ), Y ( 3 ))
╭──────────────────────────╮
+│ 3 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ X , → Y
+└────────────────────────────────────────────────────────────────┘
+ 1.0 1.0 1.0
+ 1.0 1.0 1.0
+ 1.0 1.0 1.0
julia julia > @benchmark $ da4[ X ( 1 ), Y ( 2 )]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range ( min … max ): 3.095 ns … 29.175 ns ┊ GC ( min … max ): 0.00% … 0.00%
+ Time ( median ): 3.106 ns ┊ GC ( median ): 0.00%
+ Time ( mean ± σ ): 3.193 ns ± 0.654 ns ┊ GC ( mean ± σ ): 0.00% ± 0.00%
+
+ █ ▁
+ █ ▅ ▁ ▁ ▃ ▁ ▁ ▁ ▃ ▃ ▃ ▃ ▃ ▄ ▃ ▁ ▄ ▁ ▁ ▁ ▃ ▁ ▄ ▆ ▁ ▁ ▁ ▃ ▁ ▄ ▃ ▃ ▄ ▄ ▄ ▄ ▅ ▅ ▅ ▅ ▄ ▅ ▅ ▄ ▅ ▅ ▆ ▅ ▆ ▆ ▆ ▇ ▇ █ ▇ ▇ ▆ ▆ █
+ 3.1 ns Histogram: log( frequency ) by time 5.09 ns <
+
+ Memory estimate : 0 bytes , allocs estimate : 0 .
the same as accessing the parent array directly:
julia julia > @benchmark parent ( $ da4)[ 1 , 2 ]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range ( min … max ): 3.095 ns … 20.027 ns ┊ GC ( min … max ): 0.00% … 0.00%
+ Time ( median ): 3.106 ns ┊ GC ( median ): 0.00%
+ Time ( mean ± σ ): 3.118 ns ± 0.296 ns ┊ GC ( mean ± σ ): 0.00% ± 0.00%
+
+ █
+ ▃ ▁ ▅ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▅ ▁ ▁ █ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▂ ▁ ▃ ▂
+ 3.1 ns Histogram: frequency by time 3.12 ns <
+
+ Memory estimate : 0 bytes , allocs estimate : 0 .
`,52))])}const C=d(r,[["render",o]]);export{u as __pageData,C as default};
diff --git a/previews/PR856/assets/dimarrays.md.DJPyWkGa.lean.js b/previews/PR856/assets/dimarrays.md.DJPyWkGa.lean.js
new file mode 100644
index 000000000..60af96bc3
--- /dev/null
+++ b/previews/PR856/assets/dimarrays.md.DJPyWkGa.lean.js
@@ -0,0 +1,259 @@
+import{_ as d,c as p,a4 as l,G as h,w as e,B as k,o as g,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const u=JSON.parse('{"title":"DimArrays","description":"","frontmatter":{},"headers":[],"relativePath":"dimarrays.md","filePath":"dimarrays.md","lastUpdated":null}'),r={name:"dimarrays.md"};function o(y,a,f,c,b,E){const t=k("PluginTabsTab"),n=k("PluginTabs");return g(),p("div",null,[a[5]||(a[5]=l(`DimArrays DimArray
s are wrappers for other kinds of AbstractArray
that add named dimension lookups.
Here we define a Matrix
of Float64
, and give it X
and Y
dimensions
julia julia > using DimensionalData
+
+julia > A = rand ( 5 , 10 )
5×10 Matrix{Float64}:
+ 0.827655 0.0102884 0.688175 0.254555 … 0.628275 0.517329 0.886959
+ 0.223602 0.737979 0.996807 0.124594 0.0959042 0.628507 0.893267
+ 0.0392779 0.792885 0.249616 0.519235 0.0315486 0.113704 0.997572
+ 0.451879 0.846175 0.373575 0.891743 0.700389 0.958811 0.68136
+ 0.689712 0.0927459 0.765773 0.273573 0.688928 0.761347 0.596077
julia julia > da = DimArray (A, (X, Y))
╭───────────────────────────╮
+│ 5 × 10 DimArray{Float64, 2} │
+├───────────────────────────┴────────────────────────────── dims ┐
+ ↓ X , → Y
+└────────────────────────────────────────────────────────────────┘
+ 0.827655 0.0102884 0.688175 0.254555 … 0.628275 0.517329 0.886959
+ 0.223602 0.737979 0.996807 0.124594 0.0959042 0.628507 0.893267
+ 0.0392779 0.792885 0.249616 0.519235 0.0315486 0.113704 0.997572
+ 0.451879 0.846175 0.373575 0.891743 0.700389 0.958811 0.68136
+ 0.689712 0.0927459 0.765773 0.273573 0.688928 0.761347 0.596077
We can access a value with the same dimension wrappers:
julia julia > da[ Y ( 1 ), X ( 2 )]
There are shortcuts for creating DimArray
:
`,11)),h(n,null,{default:e(()=>[h(t,{label:"DimArray"},{default:e(()=>a[0]||(a[0]=[i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," A "),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," rand"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"5"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},", "),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},")")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"5×10 Matrix{Float64}:")]),s(`
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"│ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"5"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"10"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Int64, 2}"),i("span",{style:{"--shiki-light":"#00afaf","--shiki-dark":"#00afaf"}}," fill"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," │")]),s(`
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└────────────────────────────────────────────────────────────────┘")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," 7 7 7 7 7 7 7 7 7 7")])])])],-1)])),_:1})]),_:1}),a[6]||(a[6]=l(`Constructing DimArray with arbitrary dimension names For arbitrary names, we can use the Dim{:name}
dims by using Symbol
s, and indexing with keywords:
julia julia > da1 = DimArray ( rand ( 5 , 5 ), ( :a , :b ))
╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
and get a value, here another smaller DimArray
:
julia julia > da1[a = 3 , b = 1 : 3 ]
╭────────────────────────────────╮
+│ 3-element DimArray{Float64, 1} │
+├────────────────────────────────┴ dims ┐
+ ↓ b
+└─────────────────────────────────┘
+ 0.910981
+ 0.503823
+ 0.488029
Dimensional Indexing When used for indexing, dimension wrappers free us from knowing the order of our objects axes. These are the same:
julia julia > da[ X ( 2 ), Y ( 1 )] == da[ Y ( 1 ), X ( 2 )]
We also can use Tuples
of dimensions, like CartesianIndex
, but they don't have to be in order of consecutive axes.
julia julia > da2 = rand ( X ( 10 ), Y ( 7 ), Z ( 5 ))
╭─────────────────────────────╮
+│ 10 × 7 × 5 DimArray{Float64, 3} │
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Z
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 0.41173 0.0789906 0.655936 0.762722 0.56273 0.0160205 0.640693
+ 0.228294 0.687236 0.345046 0.749573 0.949769 0.783195 0.591606
+ 0.258576 0.89609 0.00337686 0.99118 0.169845 0.159817 0.784693
+ 0.295346 0.768639 0.754468 0.0738813 0.675126 0.968484 0.243035
+ 0.254627 0.411529 0.629352 0.285205 0.18989 0.891663 0.597808
+ 0.435719 0.0450386 0.0531003 0.385452 0.322612 0.667691 0.906601
+ 0.88841 0.571658 0.991676 0.677848 0.697976 0.971252 0.776593
+ 0.284399 0.196524 0.119937 0.493258 0.342919 0.866772 0.0990347
+ 0.192286 0.765808 0.502499 0.404773 0.0623616 0.627002 0.184813
+ 0.762199 0.159233 0.769884 0.74428 0.606279 0.351096 0.268379
julia julia > da2[( X ( 3 ), Z ( 5 ))]
╭────────────────────────────────╮
+│ 7-element DimArray{Float64, 1} │
+├────────────────────────────────┴ dims ┐
+ ↓ Y
+└─────────────────────────────────┘
+ 0.644748
+ 0.493072
+ 0.316833
+ 0.372311
+ 0.313185
+ 0.494267
+ 0.705582
We can index with Vector
of Tuple{Vararg(Dimension}}
like vectors of CartesianIndex
. This will merge the dimensions in the tuples:
julia julia > inds = [( X ( 3 ), Z ( 5 )), ( X ( 7 ), Z ( 4 )), ( X ( 8 ), Z ( 2 ))]
3-element Vector{Tuple{X{Int64}, Z{Int64}}}:
+ ( ↓ X 3 , → Z 5 )
+ ( ↓ X 7 , → Z 4 )
+ ( ↓ X 8 , → Z 2 )
╭──────────────────────────╮
+│ 7 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ Y ,
+ → XZ MergedLookup{Tuple{Int64, Int64}} [(3, 5), (7, 4), (8, 2)] ↓ X , → Z
+└──────────────────────────────────────────────────────────────────────────────┘
+ (3, 5) (7, 4) (8, 2)
+ 0.644748 0.510196 0.528138
+ 0.493072 0.925624 0.142957
+ 0.316833 0.485321 0.708081
+ 0.372311 0.676945 0.406221
+ 0.313185 0.0379776 0.6778
+ 0.494267 0.287739 0.155826
+ 0.705582 0.996358 0.638336
DimIndices
can be used like CartesianIndices
but again, without the constraint of consecutive dimensions or known order.
julia julia > da2[ DimIndices ( dims (da2, (X, Z))), Y ( 3 )]
╭───────────────────────────╮
+│ 10 × 5 DimArray{Float64, 2} │
+├───────────────────────────┴────────────────────────────── dims ┐
+ ↓ X , → Z
+└────────────────────────────────────────────────────────────────┘
+ 0.655936 0.853835 0.870583 0.0578313 0.971794
+ 0.345046 0.17597 0.638072 0.136127 0.962808
+ 0.00337686 0.378395 0.0314382 0.310753 0.316833
+ 0.754468 0.188864 0.614012 0.883048 0.191049
+ 0.629352 0.190726 0.338669 0.105539 0.925844
+ 0.0531003 0.876115 0.945147 0.873096 0.342887
+ 0.991676 0.702956 0.281077 0.485321 0.798621
+ 0.119937 0.708081 0.0363983 0.247755 0.527261
+ 0.502499 0.641023 0.0104608 0.10233 0.635425
+ 0.769884 0.37821 0.881533 0.535933 0.454033
The Dimension
indexing layer sits on top of regular indexing and can not be combined with it! Regular indexing specifies order, so doesn't mix well with our dimensions.
Mixing them will throw an error:
ERROR: ArgumentError: invalid index: X{Int64}(3) of type X{Int64}
Begin End indexing julia julia > da1[X = Begin + 1 , Y = End]
┌ Warning: (X, Y) dims were not found in object.
+└ @ DimensionalData.Dimensions ~/work/DimensionalData.jl/DimensionalData.jl/src/Dimensions/primitives.jl:846
+╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
It also works in ranges, even with basic math:
julia julia > da1[X = Begin : Begin + 1 , Y = Begin + 1 : End - 1 ]
┌ Warning: (X, Y) dims were not found in object.
+└ @ DimensionalData.Dimensions ~/work/DimensionalData.jl/DimensionalData.jl/src/Dimensions/primitives.jl:846
+╭──────────────────────────╮
+│ 5 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ a , → b
+└────────────────────────────────────────────────────────────────┘
+ 0.601474 0.337576 0.770316 0.21149 0.121375
+ 0.113873 0.511078 0.201362 0.716257 0.253984
+ 0.910981 0.503823 0.488029 0.0130048 0.121186
+ 0.436293 0.789198 0.32806 0.361921 0.830187
+ 0.426888 0.46788 0.724709 0.15163 0.996398
In base julia the keywords begin
and end
can be used to index the first or last element of an array. But this doesn't work when named indexing is used. Instead you can use the types Begin
and End
.
Indexing
Indexing AbstractDimArray
s works with getindex
, setindex!
and view
. The result is still an AbstracDimArray
, unless using all single Int
or Selector
s that resolve to Int
inside Dimension
.
dims
keywords In many Julia functions like, size
or sum
, you can specify the dimension along which to perform the operation as an Int
. It is also possible to do this using Dimension
types with AbstractDimArray
:
julia julia > da5 = rand ( X ( 3 ), Y ( 4 ), Ti ( 5 ))
╭────────────────────────────╮
+│ 3 × 4 × 5 DimArray{Float64, 3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 0.573147 0.206406 0.916702 0.481184
+ 0.0760848 0.94412 0.48817 0.758865
+ 0.0992684 0.917457 0.811947 0.0641884
julia julia > sum (da5; dims = Ti)
╭────────────────────────────╮
+│ 3 × 4 × 1 DimArray{Float64, 3} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X , → Y , ↗ Ti
+└──────────────────────────────────────────────────────────────────────────────┘
+[ : , : , 1 ]
+ 2.89248 1.86986 2.12435 1.86194
+ 1.29325 2.76668 2.64923 2.70488
+ 1.11475 3.10914 3.02098 2.35189
Dims keywords
Methods where dims, dim types, or Symbol
s can be used to indicate the array dimension:
size
, axes
, firstindex
, lastindex
cat
, reverse
, dropdims
reduce
, mapreduce
sum
, prod
, maximum
, minimum
mean
, median
, extrema
, std
, var
, cor
, cov
permutedims
, adjoint
, transpose
, Transpose
mapslices
, eachslice
Indexing with Dimension
s has no runtime cost. Let's benchmark it:
julia julia > using BenchmarkTools
+
+julia > da4 = ones ( X ( 3 ), Y ( 3 ))
╭──────────────────────────╮
+│ 3 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴─────────────────────────────── dims ┐
+ ↓ X , → Y
+└────────────────────────────────────────────────────────────────┘
+ 1.0 1.0 1.0
+ 1.0 1.0 1.0
+ 1.0 1.0 1.0
julia julia > @benchmark $ da4[ X ( 1 ), Y ( 2 )]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range ( min … max ): 3.095 ns … 29.175 ns ┊ GC ( min … max ): 0.00% … 0.00%
+ Time ( median ): 3.106 ns ┊ GC ( median ): 0.00%
+ Time ( mean ± σ ): 3.193 ns ± 0.654 ns ┊ GC ( mean ± σ ): 0.00% ± 0.00%
+
+ █ ▁
+ █ ▅ ▁ ▁ ▃ ▁ ▁ ▁ ▃ ▃ ▃ ▃ ▃ ▄ ▃ ▁ ▄ ▁ ▁ ▁ ▃ ▁ ▄ ▆ ▁ ▁ ▁ ▃ ▁ ▄ ▃ ▃ ▄ ▄ ▄ ▄ ▅ ▅ ▅ ▅ ▄ ▅ ▅ ▄ ▅ ▅ ▆ ▅ ▆ ▆ ▆ ▇ ▇ █ ▇ ▇ ▆ ▆ █
+ 3.1 ns Histogram: log( frequency ) by time 5.09 ns <
+
+ Memory estimate : 0 bytes , allocs estimate : 0 .
the same as accessing the parent array directly:
julia julia > @benchmark parent ( $ da4)[ 1 , 2 ]
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
+ Range ( min … max ): 3.095 ns … 20.027 ns ┊ GC ( min … max ): 0.00% … 0.00%
+ Time ( median ): 3.106 ns ┊ GC ( median ): 0.00%
+ Time ( mean ± σ ): 3.118 ns ± 0.296 ns ┊ GC ( mean ± σ ): 0.00% ± 0.00%
+
+ █
+ ▃ ▁ ▅ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▅ ▁ ▁ █ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▁ ▂ ▁ ▃ ▂
+ 3.1 ns Histogram: frequency by time 3.12 ns <
+
+ Memory estimate : 0 bytes , allocs estimate : 0 .
`,52))])}const C=d(r,[["render",o]]);export{u as __pageData,C as default};
diff --git a/previews/PR856/assets/dimensions.md.Ck2DEYHG.js b/previews/PR856/assets/dimensions.md.Ck2DEYHG.js
new file mode 100644
index 000000000..e8038d0ca
--- /dev/null
+++ b/previews/PR856/assets/dimensions.md.Ck2DEYHG.js
@@ -0,0 +1,3 @@
+import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BnKqFO9u.js";const c=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"dimensions.md","filePath":"dimensions.md","lastUpdated":null}'),t={name:"dimensions.md"};function h(l,s,p,k,d,o){return n(),a("div",null,s[0]||(s[0]=[e(`Dimensions Dimensions are "wrapper types" that can be used to wrap any object to associate it with a named dimension.
X
, Y
, Z
, Ti
are predefined as types:
julia julia > using DimensionalData
+
+julia > X ( 1 )
julia julia > X ( 1 ), Y ( 2 ), Z ( 3 )
You can also create Dim
dimensions with any name:
julia julia > Dim{:a} ( 1 ), Dim{:b} ( 1 )
The wrapped value can be retrieved with val
:
DimensionalData.jl uses Dimensions
everywhere:
Dimension
s are returned from dims
to specify the names of the dimensions of an object
They can wrap Lookups
to associate the lookups with those names
To index into these objects, they can wrap indices like Int
or a Selector
This symmetry means we can ignore how data is organized, and label and access it by name, letting DD work out the details for us.
Dimensions are defined in the Dimensions
submodule, and some Dimension-specific methods can be brought into scope with:
julia using DimensionalData . Dimensions
`,18)]))}const g=i(t,[["render",h]]);export{c as __pageData,g as default};
diff --git a/previews/PR856/assets/dimensions.md.Ck2DEYHG.lean.js b/previews/PR856/assets/dimensions.md.Ck2DEYHG.lean.js
new file mode 100644
index 000000000..e8038d0ca
--- /dev/null
+++ b/previews/PR856/assets/dimensions.md.Ck2DEYHG.lean.js
@@ -0,0 +1,3 @@
+import{_ as i,c as a,a4 as e,o as n}from"./chunks/framework.BnKqFO9u.js";const c=JSON.parse('{"title":"Dimensions","description":"","frontmatter":{},"headers":[],"relativePath":"dimensions.md","filePath":"dimensions.md","lastUpdated":null}'),t={name:"dimensions.md"};function h(l,s,p,k,d,o){return n(),a("div",null,s[0]||(s[0]=[e(`Dimensions Dimensions are "wrapper types" that can be used to wrap any object to associate it with a named dimension.
X
, Y
, Z
, Ti
are predefined as types:
julia julia > using DimensionalData
+
+julia > X ( 1 )
julia julia > X ( 1 ), Y ( 2 ), Z ( 3 )
You can also create Dim
dimensions with any name:
julia julia > Dim{:a} ( 1 ), Dim{:b} ( 1 )
The wrapped value can be retrieved with val
:
DimensionalData.jl uses Dimensions
everywhere:
Dimension
s are returned from dims
to specify the names of the dimensions of an object
They can wrap Lookups
to associate the lookups with those names
To index into these objects, they can wrap indices like Int
or a Selector
This symmetry means we can ignore how data is organized, and label and access it by name, letting DD work out the details for us.
Dimensions are defined in the Dimensions
submodule, and some Dimension-specific methods can be brought into scope with:
julia using DimensionalData . Dimensions
`,18)]))}const g=i(t,[["render",h]]);export{c as __pageData,g as default};
diff --git a/previews/PR856/assets/diskarrays.md.jSt2cdAu.js b/previews/PR856/assets/diskarrays.md.jSt2cdAu.js
new file mode 100644
index 000000000..3f7735010
--- /dev/null
+++ b/previews/PR856/assets/diskarrays.md.jSt2cdAu.js
@@ -0,0 +1 @@
+import{_ as r,c as e,a4 as t,o as i}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),l={name:"diskarrays.md"};function s(n,a,o,c,p,d){return i(),e("div",null,a[0]||(a[0]=[t('DiskArrays.jl compatibility DiskArrays.jl enables lazy, chunked application of:
broadcast
reductions
iteration
generators
zip
as well as caching chunks in RAM via DiskArrays.cache(dimarray)
.
It is rarely used directly, but is present in most disk and cloud based spatial data packages in julia, including: ArchGDAL.jl , NetCDF.jl , Zarr.jl , NCDatasets.jl , GRIBDatasets.jl and CommonDataModel.jl .
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diff --git a/previews/PR856/assets/diskarrays.md.jSt2cdAu.lean.js b/previews/PR856/assets/diskarrays.md.jSt2cdAu.lean.js
new file mode 100644
index 000000000..3f7735010
--- /dev/null
+++ b/previews/PR856/assets/diskarrays.md.jSt2cdAu.lean.js
@@ -0,0 +1 @@
+import{_ as r,c as e,a4 as t,o as i}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"DiskArrays.jl compatibility","description":"","frontmatter":{},"headers":[],"relativePath":"diskarrays.md","filePath":"diskarrays.md","lastUpdated":null}'),l={name:"diskarrays.md"};function s(n,a,o,c,p,d){return i(),e("div",null,a[0]||(a[0]=[t('DiskArrays.jl compatibility DiskArrays.jl enables lazy, chunked application of:
broadcast
reductions
iteration
generators
zip
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It is rarely used directly, but is present in most disk and cloud based spatial data packages in julia, including: ArchGDAL.jl , NetCDF.jl , Zarr.jl , NCDatasets.jl , GRIBDatasets.jl and CommonDataModel.jl .
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diff --git a/previews/PR856/assets/extending_dd.md.B1ibmsju.js b/previews/PR856/assets/extending_dd.md.B1ibmsju.js
new file mode 100644
index 000000000..f8d61bc30
--- /dev/null
+++ b/previews/PR856/assets/extending_dd.md.B1ibmsju.js
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AbstractDimArray
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AbstractDimStack
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Interfaces.jl interface testing DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface
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diff --git a/previews/PR856/assets/extending_dd.md.B1ibmsju.lean.js b/previews/PR856/assets/extending_dd.md.B1ibmsju.lean.js
new file mode 100644
index 000000000..f8d61bc30
--- /dev/null
+++ b/previews/PR856/assets/extending_dd.md.B1ibmsju.lean.js
@@ -0,0 +1,55 @@
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AbstractDimArray
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AbstractDimStack
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Lookup
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dims
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dims
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Dimension
axes Dimensions returned from dims
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dims
keywords To any dims
keyword argument that usually requires the dimension I, objects should accept any Dimension
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This is easier than it sounds, calling DD.dims(objs, dims)
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julia rebuild (obj, args ... ) \nrebuild (obj; kw ... )
rebuild
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For Dimension
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Interfaces.jl interface testing DimensionalData defines explicit, testable Interfaces.jl interfaces: DimArrayInterface
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diff --git a/previews/PR856/assets/fznpaoc.Cr0VT91V.svg b/previews/PR856/assets/fznpaoc.Cr0VT91V.svg
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diff --git a/previews/PR856/assets/get_info.md.B5GxBmxM.js b/previews/PR856/assets/get_info.md.B5GxBmxM.js
new file mode 100644
index 000000000..0066b913a
--- /dev/null
+++ b/previews/PR856/assets/get_info.md.B5GxBmxM.js
@@ -0,0 +1,66 @@
+import{_ as d,c as p,a4 as k,G as l,w as e,B as n,o as g,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"Getters","description":"","frontmatter":{},"headers":[],"relativePath":"get_info.md","filePath":"get_info.md","lastUpdated":null}'),r={name:"get_info.md"};function c(o,a,u,y,E,v){const t=n("PluginTabsTab"),h=n("PluginTabs");return g(),p("div",null,[a[24]||(a[24]=k(`Getters DimensionalData.jl defines consistent methods to retrieve information from objects like DimArray
, DimStack
, Tuple
s of Dimension
, Dimension
, and Lookup
.
First, we will define an example DimArray
.
julia using DimensionalData
+using DimensionalData . Lookups
+x, y = X ( 10 :- 1 : 1 ), Y ( 100.0 : 10 : 200.0 )
(↓ X 10:-1:1,
+→ Y 100.0:10.0:200.0)
julia julia > A = rand (x, y)
╭────────────────────────────╮
+│ 10 × 11 DimArray{Float64, 2} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points ,
+ → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 100.0 110.0 120.0 … 190.0 200.0
+ 10 0.616666 0.561953 0.0157247 0.177857 0.856524
+ 9 0.660024 0.395523 0.0109523 0.552838 0.0536623
+ 8 0.112606 0.548479 0.0545071 0.755722 0.830655
+ 7 0.839223 0.0400057 0.702845 0.384116 0.445628
+ 6 0.734452 0.65906 0.506965 … 0.654591 0.44097
+ 5 0.89757 0.748725 0.00633515 0.178022 0.106904
+ 4 0.969026 0.0042232 0.63959 0.423538 0.556096
+ 3 0.106472 0.274713 0.589536 0.805743 0.390342
+ 2 0.861535 0.167465 0.507581 0.799964 0.292431
+ 1 0.245499 0.742732 0.095824 … 0.90203 0.86858
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diff --git a/previews/PR856/assets/get_info.md.B5GxBmxM.lean.js b/previews/PR856/assets/get_info.md.B5GxBmxM.lean.js
new file mode 100644
index 000000000..0066b913a
--- /dev/null
+++ b/previews/PR856/assets/get_info.md.B5GxBmxM.lean.js
@@ -0,0 +1,66 @@
+import{_ as d,c as p,a4 as k,G as l,w as e,B as n,o as g,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"Getters","description":"","frontmatter":{},"headers":[],"relativePath":"get_info.md","filePath":"get_info.md","lastUpdated":null}'),r={name:"get_info.md"};function c(o,a,u,y,E,v){const t=n("PluginTabsTab"),h=n("PluginTabs");return g(),p("div",null,[a[24]||(a[24]=k(`Getters DimensionalData.jl defines consistent methods to retrieve information from objects like DimArray
, DimStack
, Tuple
s of Dimension
, Dimension
, and Lookup
.
First, we will define an example DimArray
.
julia using DimensionalData
+using DimensionalData . Lookups
+x, y = X ( 10 :- 1 : 1 ), Y ( 100.0 : 10 : 200.0 )
(↓ X 10:-1:1,
+→ Y 100.0:10.0:200.0)
julia julia > A = rand (x, y)
╭────────────────────────────╮
+│ 10 × 11 DimArray{Float64, 2} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Int64} 10:-1:1 ReverseOrdered Regular Points ,
+ → Y Sampled{Float64} 100.0:10.0:200.0 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 100.0 110.0 120.0 … 190.0 200.0
+ 10 0.616666 0.561953 0.0157247 0.177857 0.856524
+ 9 0.660024 0.395523 0.0109523 0.552838 0.0536623
+ 8 0.112606 0.548479 0.0545071 0.755722 0.830655
+ 7 0.839223 0.0400057 0.702845 0.384116 0.445628
+ 6 0.734452 0.65906 0.506965 … 0.654591 0.44097
+ 5 0.89757 0.748725 0.00633515 0.178022 0.106904
+ 4 0.969026 0.0042232 0.63959 0.423538 0.556096
+ 3 0.106472 0.274713 0.589536 0.805743 0.390342
+ 2 0.861535 0.167465 0.507581 0.799964 0.292431
+ 1 0.245499 0.742732 0.095824 … 0.90203 0.86858
`,7)),l(h,null,{default:e(()=>[l(t,{label:"dims"},{default:e(()=>a[0]||(a[0]=[i("p",null,[i("code",null,"dims"),s(" retrieves dimensions from any object that has them.")],-1),i("p",null,[s("What makes it so useful is that you can filter which dimensions you want, and specify in what order, using any "),i("code",null,"Dimension"),s(", "),i("code",null,"Type{Dimension}"),s(" or "),i("code",null,"Symbol"),s(".")],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," dims"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(A)")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"("),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"↓ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Sampled{Int64} "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"10:-1:1"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ReverseOrdered"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Regular"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Points"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},",")]),s(`
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diff --git a/previews/PR856/assets/groupby.md.CQSjcbUC.js b/previews/PR856/assets/groupby.md.CQSjcbUC.js
new file mode 100644
index 000000000..0a223aa05
--- /dev/null
+++ b/previews/PR856/assets/groupby.md.CQSjcbUC.js
@@ -0,0 +1,487 @@
+import{_ as p,c as d,a4 as t,G as e,w as l,j as i,a as s,B as n,o as r}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"Group By","description":"","frontmatter":{},"headers":[],"relativePath":"groupby.md","filePath":"groupby.md","lastUpdated":null}'),g={name:"groupby.md"};function y(f,a,c,o,u,E){const h=n("PluginTabsTab"),k=n("PluginTabs");return r(),d("div",null,[a[25]||(a[25]=t(`Group By DimensionalData.jl provides a groupby
function for dimensional grouping. This guide covers:
Grouping functions Let's look at the kind of functions that can be used to group DateTime
. Other types will follow the same principles, but are usually simpler.
First, load some packages:
julia using DimensionalData
+using Dates
+using Statistics
+const DD = DimensionalData
Now create a demo DateTime
range
julia julia > tempo = range ( DateTime ( 2000 ), step = Hour ( 1 ), length = 365 * 24 * 2 )
Dates.DateTime("2000-01-01T00:00:00"):Dates.Hour(1):Dates.DateTime("2001-12-30T23:00:00")
Let's see how some common functions work.
The hour
function will transform values to the hour of the day - the integers 0:23
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julia julia > A = rand ( X ( 1 : 0.01 : 2 ), Ti (tempo))
╭────────────────────────────────╮
+│ 101 × 17520 DimArray{Float64, 2} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.01:2.0 ForwardOrdered Regular Points ,
+ → Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Hour(1):Dates.DateTime("2001-12-30T23:00:00") ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 2000-01-01T00:00:00 2000-01-01T01:00:00 … 2001-12-30T23:00:00
+ 1.0 0.654537 0.418968 0.677549
+ 1.01 0.664038 0.674881 0.578183
+ 1.02 0.00832284 0.475569 0.454715
+ 1.03 0.639212 0.616635 0.875994
+ ⋮ ⋱ ⋮
+ 1.96 0.0106725 0.846581 0.757228
+ 1.97 0.585756 0.485119 0.299692
+ 1.98 0.471877 0.889153 0.336768
+ 1.99 0.428951 0.312976 … 0.948798
+ 2.0 0.921012 0.397575 0.0897302
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diff --git a/previews/PR856/assets/groupby.md.CQSjcbUC.lean.js b/previews/PR856/assets/groupby.md.CQSjcbUC.lean.js
new file mode 100644
index 000000000..0a223aa05
--- /dev/null
+++ b/previews/PR856/assets/groupby.md.CQSjcbUC.lean.js
@@ -0,0 +1,487 @@
+import{_ as p,c as d,a4 as t,G as e,w as l,j as i,a as s,B as n,o as r}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"Group By","description":"","frontmatter":{},"headers":[],"relativePath":"groupby.md","filePath":"groupby.md","lastUpdated":null}'),g={name:"groupby.md"};function y(f,a,c,o,u,E){const h=n("PluginTabsTab"),k=n("PluginTabs");return r(),d("div",null,[a[25]||(a[25]=t(`Group By DimensionalData.jl provides a groupby
function for dimensional grouping. This guide covers:
Grouping functions Let's look at the kind of functions that can be used to group DateTime
. Other types will follow the same principles, but are usually simpler.
First, load some packages:
julia using DimensionalData
+using Dates
+using Statistics
+const DD = DimensionalData
Now create a demo DateTime
range
julia julia > tempo = range ( DateTime ( 2000 ), step = Hour ( 1 ), length = 365 * 24 * 2 )
Dates.DateTime("2000-01-01T00:00:00"):Dates.Hour(1):Dates.DateTime("2001-12-30T23:00:00")
Let's see how some common functions work.
The hour
function will transform values to the hour of the day - the integers 0:23
`,12)),e(k,null,{default:l(()=>[e(h,{label:"hour"},{default:l(()=>a[0]||(a[0]=[i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," hour"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},".(tempo)")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"17520-element Vector{Int64}:")]),s(`
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julia julia > A = rand ( X ( 1 : 0.01 : 2 ), Ti (tempo))
╭────────────────────────────────╮
+│ 101 × 17520 DimArray{Float64, 2} │
+├────────────────────────────────┴─────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.01:2.0 ForwardOrdered Regular Points ,
+ → Ti Sampled{Dates.DateTime} Dates.DateTime("2000-01-01T00:00:00"):Dates.Hour(1):Dates.DateTime("2001-12-30T23:00:00") ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 2000-01-01T00:00:00 2000-01-01T01:00:00 … 2001-12-30T23:00:00
+ 1.0 0.654537 0.418968 0.677549
+ 1.01 0.664038 0.674881 0.578183
+ 1.02 0.00832284 0.475569 0.454715
+ 1.03 0.639212 0.616635 0.875994
+ ⋮ ⋱ ⋮
+ 1.96 0.0106725 0.846581 0.757228
+ 1.97 0.585756 0.485119 0.299692
+ 1.98 0.471877 0.889153 0.336768
+ 1.99 0.428951 0.312976 … 0.948798
+ 2.0 0.921012 0.397575 0.0897302
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new file mode 100644
index 000000000..96b431197
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new file mode 100644
index 000000000..abae34641
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+import{_ as e,c as r,a4 as t,o}from"./chunks/framework.BnKqFO9u.js";const p=JSON.parse('{"title":"Integrations","description":"","frontmatter":{},"headers":[],"relativePath":"integrations.md","filePath":"integrations.md","lastUpdated":null}'),s={name:"integrations.md"};function i(l,a,n,d,c,h){return o(),r("div",null,a[0]||(a[0]=[t('Integrations Rasters.jl Rasters.jl extends DimensionalData for geospatial data manipulation, providing file load/save capabilities for a wide range of raster data sources and common GIS tools like polygon rasterization and masking. Raster
types are aware of their crs
and their missingval
(which is often not missing
for performance and storage reasons).
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is a AbstractDimArray
, a RasterStack
is a AbstractDimStack
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are AbstractSampled
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ClimateBase.jl ClimateBase.jl Extends DimensionalData.jl with methods for analysis of climate data.
ArviZ.jl ArviZ.jl Is a Julia package for exploratory analysis of Bayesian models.
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DynamicGrids.jl DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.
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AstroImages.jl AstroImages.jl Provides tools to load and visualise astronomical images. AstroImage
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types are aware of their crs
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YAXArray
is a AbstractDimArray
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ClimateBase.jl ClimateBase.jl Extends DimensionalData.jl with methods for analysis of climate data.
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too, through a package extension.
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DynamicGrids.jl DynamicGrids.jl is a spatial simulation engine, for cellular automata and spatial process models.
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rather than mutable struct
. The only things you can modify in-place are the values of the contained arrays or metadata Dict
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Everything else must be rebuilt and assigned to a variable.
modify
Modify the inner arrays of a AbstractDimArray
or AbstractDimStack
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' :a => "foo"')]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' :b => "bar"')])])])],-1)])),_:1})]),_:1}),a[16]||(a[16]=k('The most common use internally is the arg version on Dimension
. This is very useful in dimension-based algorithms as a way to transform a dimension wrapper from one object to another:
julia julia > rebuild (d, 1 : 10 )
rebuild
applications are listed here. AbstractDimArray
and AbstractDimStack
always accept these keywords or arguments, but those in [ ] brackets may be thrown away if not needed. Keywords in ( ) will error if used where they are not accepted.
Type Keywords Arguments AbstractDimArray
data
, dims
, [refdims
, name
, metadata
]as with kw, in order AbstractDimStack
data
, dims
, [refdims
], layerdims
, [metadata
, layermetadata
]as with kw, in order Dimension
val
val Selector
val
, (atol
)val Lookup
data
, (order
, span
, sampling
, metadata
)keywords only
rebuild
magic rebuild
with keywords will even work on objects DD doesn't know about!
julia julia > nt = (a = 1 , b = 2 )
julia julia > rebuild (nt, a = 99 )
Really, the keyword version is just ConstructionBase.setproperties
underneath, but wrapped so objects can customise the DD interface without changing the more generic ConstructionBase.jl behaviours and breaking e.g. Accessors.jl in the process.
set
set
gives us a way to set the values of the immutable objects in DD, like Dimension
and LookupArray
. Unlike rebuild
it tries its best to do the right thing . You don't have to specify what field you want to set. Just pass in the object you want to be part of the lookup. Usually, there is no possible ambiguity.
set
is still improving. Sometimes it may not do the right thing. If you think this is the case, create a GitHub issue .
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diff --git a/previews/PR856/assets/object_modification.md.RAyAT72y.lean.js b/previews/PR856/assets/object_modification.md.RAyAT72y.lean.js
new file mode 100644
index 000000000..9d51cfbe2
--- /dev/null
+++ b/previews/PR856/assets/object_modification.md.RAyAT72y.lean.js
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+import{_ as d,c as p,a4 as k,G as e,w as l,j as i,a as s,B as n,o as r}from"./chunks/framework.BnKqFO9u.js";const m=JSON.parse('{"title":"Modifying Objects","description":"","frontmatter":{},"headers":[],"relativePath":"object_modification.md","filePath":"object_modification.md","lastUpdated":null}'),g={name:"object_modification.md"};function y(o,a,c,f,u,E){const h=n("PluginTabsTab"),t=n("PluginTabs");return r(),p("div",null,[a[12]||(a[12]=k('Modifying Objects DimensionalData.jl objects are all struct
rather than mutable struct
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modify
Modify the inner arrays of a AbstractDimArray
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+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' :a => "foo"')]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},' :b => "bar"')])])])],-1)])),_:1})]),_:1}),a[16]||(a[16]=k('The most common use internally is the arg version on Dimension
. This is very useful in dimension-based algorithms as a way to transform a dimension wrapper from one object to another:
julia julia > rebuild (d, 1 : 10 )
rebuild
applications are listed here. AbstractDimArray
and AbstractDimStack
always accept these keywords or arguments, but those in [ ] brackets may be thrown away if not needed. Keywords in ( ) will error if used where they are not accepted.
Type Keywords Arguments AbstractDimArray
data
, dims
, [refdims
, name
, metadata
]as with kw, in order AbstractDimStack
data
, dims
, [refdims
], layerdims
, [metadata
, layermetadata
]as with kw, in order Dimension
val
val Selector
val
, (atol
)val Lookup
data
, (order
, span
, sampling
, metadata
)keywords only
rebuild
magic rebuild
with keywords will even work on objects DD doesn't know about!
julia julia > nt = (a = 1 , b = 2 )
julia julia > rebuild (nt, a = 99 )
Really, the keyword version is just ConstructionBase.setproperties
underneath, but wrapped so objects can customise the DD interface without changing the more generic ConstructionBase.jl behaviours and breaking e.g. Accessors.jl in the process.
set
set
gives us a way to set the values of the immutable objects in DD, like Dimension
and LookupArray
. Unlike rebuild
it tries its best to do the right thing . You don't have to specify what field you want to set. Just pass in the object you want to be part of the lookup. Usually, there is no possible ambiguity.
set
is still improving. Sometimes it may not do the right thing. If you think this is the case, create a GitHub issue .
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diff --git a/previews/PR856/assets/ogtyhxw.CCyMSy2O.svg b/previews/PR856/assets/ogtyhxw.CCyMSy2O.svg
new file mode 100644
index 000000000..3f3ffda2d
--- /dev/null
+++ b/previews/PR856/assets/ogtyhxw.CCyMSy2O.svg
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diff --git a/previews/PR856/assets/oqyizkf.DtePuwiC.svg b/previews/PR856/assets/oqyizkf.DtePuwiC.svg
new file mode 100644
index 000000000..f8487477c
--- /dev/null
+++ b/previews/PR856/assets/oqyizkf.DtePuwiC.svg
@@ -0,0 +1,517 @@
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diff --git a/previews/PR856/assets/plots.md.bXr761LU.js b/previews/PR856/assets/plots.md.bXr761LU.js
new file mode 100644
index 000000000..2d816742e
--- /dev/null
+++ b/previews/PR856/assets/plots.md.bXr761LU.js
@@ -0,0 +1,12 @@
+import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.BnKqFO9u.js";const e="/DimensionalData.jl/previews/PR856/assets/cljjmmh.wy-ul34S.svg",h="/DimensionalData.jl/previews/PR856/assets/sxuybmz.D5bwtKy3.svg",n="/DimensionalData.jl/previews/PR856/assets/rjcvept.BqNWAx4q.svg",p="/DimensionalData.jl/previews/PR856/assets/oqyizkf.DtePuwiC.svg",k="/DimensionalData.jl/previews/PR856/assets/ogtyhxw.CCyMSy2O.svg",r="/DimensionalData.jl/previews/PR856/assets/itircyw.CHd-coyf.svg",d="/DimensionalData.jl/previews/PR856/assets/ifocdvs.aA03bjeh.svg",o="/DimensionalData.jl/previews/PR856/assets/fznpaoc.Cr0VT91V.svg",E="/DimensionalData.jl/previews/PR856/assets/yqlixmh.DNNBmrjN.svg",v=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),c={name:"plots.md"};function g(y,s,F,C,u,m){return l(),a("div",null,s[0]||(s[0]=[t(`Plots.jl Plots.jl and Makie.jl functions mostly work out of the box on AbstractDimArray
, although not with the same results - they choose to follow each packages default behaviour as much as possible.
This will plot a line plot with 'a', 'b' and 'c' in the legend, and values 1-10 on the labelled X axis:
Plots.jl support is deprecated, as development is moving to Makie.jl
Makie.jl Makie.jl functions also mostly work with AbstractDimArray
and will permute
and reorder
axes into the right places, especially if X
/Y
/Z
/Ti
dimensions are used.
In Makie a DimMatrix
will plot as a heatmap by default, but it will have labels and axes in the right places:
julia using DimensionalData, CairoMakie
+
+A = rand ( X ( 10 : 10 : 100 ), Y ([ :a , :b , :c ]))
+Makie . plot (A; colormap = :inferno )
Other plots also work, here DD ignores the axis order and instead favours the categorical variable for the X axis:
AlgebraOfGraphics.jl AlgebraOfGraphics.jl is a high-level plotting library built on top of Makie.jl that provides a declarative algebra for creating complex visualizations, similar to ggplot2
's "grammar of graphics" in R. It allows you to construct plots using algebraic operations like *
and +
, making it easy to create sophisticated graphics with minimal code.
Any DimensionalArray
is also a Tables.jl
table, so it can be used with AlgebraOfGraphics.jl
directly. You can indicate columns in mapping
with Symbols directly (like :X
or :Y
), or you can use the Dim
type directly (like X
or Y
)!
Note
If your dimensional array is not named, then you can access the data as the :unnamed
column. Otherwise, the data is accessible by its name.
Let's start with a simple example, and plot a 2-D dimarray as a scatter plot, colored by its value.
julia using DimensionalData, AlgebraOfGraphics, CairoMakie
+
+A = DimArray ( rand ( 10 , 10 ), ( X ( 1 : 10 ), Y ( 1 : 10 )), name = :data )
+
+data (A) * mapping (X, Y; color = :data ) * visual (Scatter) |> draw
Don't restrict yourself to standard visualizations! You can use all of AlgebraOfGraphics' features.
Let's plot each X-slice, faceted in Y:
julia data (A) * mapping (X, :data ; layout = Y => nonnumeric) * visual (Lines) |> draw
This approach is also applicable to DimStack
s, since they also convert to DimTables. Let's see an example here.
We'll construct a DimStack with the :data
layer being our DimArray A
, and an X-only variable :color
that we'll use to color the line.
julia color_vec = DimVector ( 1 : 10 , X)
+ds = DimStack ((; data = A, color = color_vec))
+
+data (ds) * mapping (X, :data ; color = :color , layout = Y => nonnumeric) * visual (Lines) |> draw
Note
If you wish to subset your DimArray, you can't pass selectors like X(1 .. 2)
to AlgebraOfGraphics. Instead, subset the DimArray you pass to data
- this is a very cheap operation.
Test series plots default colormap julia B = rand ( X ( 10 : 10 : 100 ), Y ([ :a , :b , :c , :d , :e , :f , :g , :h , :i , :j ]))
+Makie . series (B)
A different colormap The colormap is controlled by the color
argument, which can take as an input a named colormap, i.e. :plasma
or a list of colours.
julia Makie . series (B; color = :plasma )
julia Makie . series (A; color = [ :red , :blue , :orange ])
with markers julia Makie . series (A; color = [ :red , :blue , :orange ], markersize = 15 )
A lot more is planned for Makie.jl plots in future!
',42)]))}const D=i(c,[["render",g]]);export{v as __pageData,D as default};
diff --git a/previews/PR856/assets/plots.md.bXr761LU.lean.js b/previews/PR856/assets/plots.md.bXr761LU.lean.js
new file mode 100644
index 000000000..2d816742e
--- /dev/null
+++ b/previews/PR856/assets/plots.md.bXr761LU.lean.js
@@ -0,0 +1,12 @@
+import{_ as i,c as a,a4 as t,o as l}from"./chunks/framework.BnKqFO9u.js";const e="/DimensionalData.jl/previews/PR856/assets/cljjmmh.wy-ul34S.svg",h="/DimensionalData.jl/previews/PR856/assets/sxuybmz.D5bwtKy3.svg",n="/DimensionalData.jl/previews/PR856/assets/rjcvept.BqNWAx4q.svg",p="/DimensionalData.jl/previews/PR856/assets/oqyizkf.DtePuwiC.svg",k="/DimensionalData.jl/previews/PR856/assets/ogtyhxw.CCyMSy2O.svg",r="/DimensionalData.jl/previews/PR856/assets/itircyw.CHd-coyf.svg",d="/DimensionalData.jl/previews/PR856/assets/ifocdvs.aA03bjeh.svg",o="/DimensionalData.jl/previews/PR856/assets/fznpaoc.Cr0VT91V.svg",E="/DimensionalData.jl/previews/PR856/assets/yqlixmh.DNNBmrjN.svg",v=JSON.parse('{"title":"Plots.jl","description":"","frontmatter":{},"headers":[],"relativePath":"plots.md","filePath":"plots.md","lastUpdated":null}'),c={name:"plots.md"};function g(y,s,F,C,u,m){return l(),a("div",null,s[0]||(s[0]=[t(`Plots.jl Plots.jl and Makie.jl functions mostly work out of the box on AbstractDimArray
, although not with the same results - they choose to follow each packages default behaviour as much as possible.
This will plot a line plot with 'a', 'b' and 'c' in the legend, and values 1-10 on the labelled X axis:
Plots.jl support is deprecated, as development is moving to Makie.jl
Makie.jl Makie.jl functions also mostly work with AbstractDimArray
and will permute
and reorder
axes into the right places, especially if X
/Y
/Z
/Ti
dimensions are used.
In Makie a DimMatrix
will plot as a heatmap by default, but it will have labels and axes in the right places:
julia using DimensionalData, CairoMakie
+
+A = rand ( X ( 10 : 10 : 100 ), Y ([ :a , :b , :c ]))
+Makie . plot (A; colormap = :inferno )
Other plots also work, here DD ignores the axis order and instead favours the categorical variable for the X axis:
AlgebraOfGraphics.jl AlgebraOfGraphics.jl is a high-level plotting library built on top of Makie.jl that provides a declarative algebra for creating complex visualizations, similar to ggplot2
's "grammar of graphics" in R. It allows you to construct plots using algebraic operations like *
and +
, making it easy to create sophisticated graphics with minimal code.
Any DimensionalArray
is also a Tables.jl
table, so it can be used with AlgebraOfGraphics.jl
directly. You can indicate columns in mapping
with Symbols directly (like :X
or :Y
), or you can use the Dim
type directly (like X
or Y
)!
Note
If your dimensional array is not named, then you can access the data as the :unnamed
column. Otherwise, the data is accessible by its name.
Let's start with a simple example, and plot a 2-D dimarray as a scatter plot, colored by its value.
julia using DimensionalData, AlgebraOfGraphics, CairoMakie
+
+A = DimArray ( rand ( 10 , 10 ), ( X ( 1 : 10 ), Y ( 1 : 10 )), name = :data )
+
+data (A) * mapping (X, Y; color = :data ) * visual (Scatter) |> draw
Don't restrict yourself to standard visualizations! You can use all of AlgebraOfGraphics' features.
Let's plot each X-slice, faceted in Y:
julia data (A) * mapping (X, :data ; layout = Y => nonnumeric) * visual (Lines) |> draw
This approach is also applicable to DimStack
s, since they also convert to DimTables. Let's see an example here.
We'll construct a DimStack with the :data
layer being our DimArray A
, and an X-only variable :color
that we'll use to color the line.
julia color_vec = DimVector ( 1 : 10 , X)
+ds = DimStack ((; data = A, color = color_vec))
+
+data (ds) * mapping (X, :data ; color = :color , layout = Y => nonnumeric) * visual (Lines) |> draw
Note
If you wish to subset your DimArray, you can't pass selectors like X(1 .. 2)
to AlgebraOfGraphics. Instead, subset the DimArray you pass to data
- this is a very cheap operation.
Test series plots default colormap julia B = rand ( X ( 10 : 10 : 100 ), Y ([ :a , :b , :c , :d , :e , :f , :g , :h , :i , :j ]))
+Makie . series (B)
A different colormap The colormap is controlled by the color
argument, which can take as an input a named colormap, i.e. :plasma
or a list of colours.
julia Makie . series (B; color = :plasma )
julia Makie . series (A; color = [ :red , :blue , :orange ])
with markers julia Makie . series (A; color = [ :red , :blue , :orange ], markersize = 15 )
A lot more is planned for Makie.jl plots in future!
',42)]))}const D=i(c,[["render",g]]);export{v as __pageData,D as default};
diff --git a/previews/PR856/assets/rjcvept.BqNWAx4q.svg b/previews/PR856/assets/rjcvept.BqNWAx4q.svg
new file mode 100644
index 000000000..8e5a9ea3b
--- /dev/null
+++ b/previews/PR856/assets/rjcvept.BqNWAx4q.svg
@@ -0,0 +1,305 @@
+
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diff --git a/previews/PR856/assets/selectors.md.CYaOZgVK.js b/previews/PR856/assets/selectors.md.CYaOZgVK.js
new file mode 100644
index 000000000..24e2328e5
--- /dev/null
+++ b/previews/PR856/assets/selectors.md.CYaOZgVK.js
@@ -0,0 +1,258 @@
+import{_ as p,c as d,a4 as k,G as l,w as h,B as n,o as r,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"Selectors","description":"","frontmatter":{},"headers":[],"relativePath":"selectors.md","filePath":"selectors.md","lastUpdated":null}'),g={name:"selectors.md"};function y(f,a,o,c,E,u){const e=n("PluginTabsTab"),t=n("PluginTabs");return r(),d("div",null,[a[11]||(a[11]=k(`Selectors In addition to choosing dimensions by name, we can also select values within them.
First, we can create a DimArray
with lookup values as well as dimension names:
julia using DimensionalData
julia julia > A = rand ( X ( 1.0 : 0.2 : 2.0 ), Y ([ :a , :b , :c ]))
╭──────────────────────────╮
+│ 6 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.2:2.0 ForwardOrdered Regular Points ,
+ → Y Categorical{Symbol} [:a, :b, :c] ForwardOrdered
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → :a :b :c
+ 1.0 0.27736 0.802776 0.621603
+ 1.2 0.444305 0.156538 0.768488
+ 1.4 0.184738 0.226064 0.869012
+ 1.6 0.772277 0.764895 0.101231
+ 1.8 0.711133 0.86273 0.239921
+ 2.0 0.883222 0.748041 0.511313
Then we can use the Selector
to select values from the array:
`,7)),l(t,null,{default:h(()=>[l(e,{label:"At"},{default:h(()=>a[0]||(a[0]=[i("p",null,[s("The "),i("a",{href:"/DimensionalData.jl/previews/PR856/api/lookuparrays#DimensionalData.Dimensions.Lookups.At"},[i("code",null,"At(x)")]),s(" selector gets the index or indices exactly matching the passed in value(s).")],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," A[X"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.2"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), Y"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":c"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},")]")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"0.7684883626668314")])])])],-1),i("p",null,"Or within a tolerance:",-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," A[X"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.99"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.201"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.5"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"; atol"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.05"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},")]")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"╭──────────────────────────╮")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"│ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"3"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"×"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"3"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," DimArray{Float64, 2}"),i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}}," │")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"├──────────────────────────┴───────────────────────────────────────────── dims ┐")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," ↓ "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"X"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Sampled{Float64} "),i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}},"[1.0, 1.2, 1.4]"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Irregular"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," Points"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},",")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," → "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"Y"),i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}}," Categorical{Symbol} "),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}},"[:a, :b, :c]"),i("span",{style:{"--shiki-light":"#808080","--shiki-dark":"#808080"}}," ForwardOrdered")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"└──────────────────────────────────────────────────────────────────────────────┘")]),s(`
+`),i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#ff875f","--shiki-dark":"#ff875f"}}," ↓"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," →"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," :a"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," :b"),i("span",{style:{"--shiki-light":"#0087d7","--shiki-dark":"#0087d7"}}," :c")]),s(`
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julia julia > A = DimArray ( rand ( 7 , 5 ), ( X ( 10 : 10 : 70 ), Y ([ :a , :b , :c , :d , :e ])))
╭──────────────────────────╮
+│ 7 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Int64} 10:10:70 ForwardOrdered Regular Points ,
+ → Y Categorical{Symbol} [:a, :b, :c, :d, :e] ForwardOrdered
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → :a :b :c :d :e
+ 10 0.71372 0.301659 0.229418 0.442111 0.680987
+ 20 0.905616 0.307762 0.228248 0.882656 0.396585
+ 30 0.0928922 0.651538 0.152068 0.683795 0.687921
+ 40 0.441181 0.314906 0.650675 0.570603 0.647225
+ 50 0.621662 0.196478 0.97293 0.929034 0.855976
+ 60 0.72217 0.791844 0.883323 0.21921 0.0575993
+ 70 0.896257 0.758149 0.679453 0.506221 0.771237
This array has a Sampled
lookup with ForwardOrdered
Regular
Points
for X
, and a Categorical
ForwardOrdered
for Y
.
Most lookup types and properties are detected automatically like this from the arrays and ranges used.
Arrays and ranges of String
, Symbol
, and Char
are set to Categorical
lookup.
order
is detected as Unordered
, ForwardOrdered
, or ReverseOrdered
Arrays and ranges of Number
, DateTime
, and other things are set to Sampled
lookups.
order
is detected as Unordered
, ForwardOrdered
, or ReverseOrdered
.
sampling
is set to Points()
unless the values are IntervalSets.Interval
, then Intervals(Center())
is used.
span
is detected as Regular(step(range))
for AbstractRange
and Irregular(nothing, nothing)
for other AbstractArray
, where nothing, nothing
are the unknown outer bounds of the lookup. They are not needed for Points
as the outer values are the outer bounds. But they can be specified manually for Intervals
Empty dimensions or dimension types are assigned NoLookup()
ranges that can't be used with selectors as they hold no values.
DimSelector
We can also index with arrays of selectors DimSelectors
. These are like CartesianIndices
or DimIndices
, but holding the Selectors
At
, Near
, or Contains
.
julia julia > A = rand ( X ( 1.0 : 0.2 : 2.0 ), Y ( 10 : 2 : 20 ))
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.2:2.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Int64} 10:2:20 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10 12 14 16 18 20
+ 1.0 0.0775482 0.263531 0.361536 0.508614 0.89559 0.564657
+ 1.2 0.838572 0.388768 0.44818 0.0300922 0.595409 0.952489
+ 1.4 0.166221 0.0689895 0.499307 0.228081 0.473944 0.747695
+ 1.6 0.11787 0.143478 0.0831822 0.528009 0.631878 0.993001
+ 1.8 0.0905873 0.909826 0.557418 0.229792 0.29205 0.699118
+ 2.0 0.495624 0.810377 0.120845 0.574678 0.371583 0.460775
We can define another array with partly matching indices
julia julia > B = rand ( X ( 1.0 : 0.04 : 2.0 ), Y ( 20 :- 1 : 10 ))
╭────────────────────────────╮
+│ 26 × 11 DimArray{Float64, 2} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.04:2.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Int64} 20:-1:10 ReverseOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 20 19 … 12 11 10
+ 1.0 0.751498 0.988013 0.966133 0.465112 0.227865
+ 1.04 0.241791 0.34589 0.0773793 0.728852 0.462859
+ 1.08 0.412631 0.0143204 0.454134 0.0417586 0.286
+ 1.12 0.300304 0.660657 0.712342 0.806495 0.727464
+ ⋮ ⋱ ⋮
+ 1.84 0.752331 0.491911 0.221313 0.72454 0.00869054
+ 1.88 0.296393 0.00428189 0.951789 0.142406 0.331422
+ 1.92 0.930699 0.777408 0.0661958 0.0737984 0.539197
+ 1.96 0.744107 0.360032 … 0.0699673 0.264001 0.27206
+ 2.0 0.635373 0.533041 0.574526 0.531012 0.503183
And we can simply select values from B
with selectors from A
:
julia julia > B[ DimSelectors (A)]
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} [1.0, 1.2, …, 1.8, 2.0] ForwardOrdered Irregular Points ,
+ → Y Sampled{Int64} [10, 12, …, 18, 20] ReverseOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10 12 14 16 18 20
+ 1.0 0.227865 0.966133 0.40169 0.990114 0.358535 0.751498
+ 1.2 0.235356 0.675459 0.426312 0.768719 0.569347 0.399781
+ 1.4 0.508938 0.166186 0.0811277 0.342499 0.819291 0.998484
+ 1.6 0.573391 0.740107 0.817199 0.690159 0.527254 0.634257
+ 1.8 0.0255364 0.895115 0.519705 0.626382 0.171961 0.6063
+ 2.0 0.503183 0.574526 0.98981 0.450629 0.545522 0.635373
If the lookups aren't aligned, we can use Near
instead of At
, which is like doing a nearest neighbor interpolation:
julia julia > C = rand ( X ( 1.0 : 0.007 : 2.0 ), Y ( 10.0 : 0.9 : 30 ))
╭─────────────────────────────╮
+│ 143 × 23 DimArray{Float64, 2} │
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.007:1.994 ForwardOrdered Regular Points ,
+ → Y Sampled{Float64} 10.0:0.9:29.8 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10.0 10.9 … 28.0 28.9 29.8
+ 1.0 0.341785 0.946655 0.954153 0.0821229 0.828315
+ 1.007 0.275784 0.22401 0.199364 0.485952 0.193179
+ 1.014 0.659836 0.832345 0.672556 0.0760101 0.950007
+ 1.021 0.168617 0.890377 0.258764 0.195897 0.747615
+ ⋮ ⋱
+ 1.966 0.426134 0.713457 0.693866 0.899783 0.166822
+ 1.973 0.601787 0.904115 … 0.0670244 0.42964 0.639039
+ 1.98 0.902689 0.231483 0.18906 0.670627 0.67989
+ 1.987 0.454047 0.92977 0.493784 0.998661 0.276742
+ 1.994 0.846736 0.0923954 0.00258281 0.273104 0.562491
julia julia > C[ DimSelectors (A; selectors = Near)]
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} [1.0, 1.203, …, 1.798, 1.994] ForwardOrdered Irregular Points ,
+ → Y Sampled{Float64} [10.0, 11.8, …, 18.1, 19.9] ForwardOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10.0 11.8 13.6 16.3 18.1 19.9
+ 1.0 0.341785 0.0506805 0.621422 0.775495 0.198058 0.168554
+ 1.203 0.333095 0.953651 0.943783 0.789816 0.215448 0.106973
+ 1.399 0.641266 0.694096 0.0724261 0.603258 0.994395 0.570396
+ 1.602 0.827349 0.662227 0.682734 0.432028 0.707314 0.0542529
+ 1.798 0.192274 0.243368 0.53407 0.513909 0.0154473 0.450775
+ 1.994 0.846736 0.573469 0.0410007 0.513088 0.221908 0.0135194
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
diff --git a/previews/PR856/assets/selectors.md.CYaOZgVK.lean.js b/previews/PR856/assets/selectors.md.CYaOZgVK.lean.js
new file mode 100644
index 000000000..24e2328e5
--- /dev/null
+++ b/previews/PR856/assets/selectors.md.CYaOZgVK.lean.js
@@ -0,0 +1,258 @@
+import{_ as p,c as d,a4 as k,G as l,w as h,B as n,o as r,j as i,a as s}from"./chunks/framework.BnKqFO9u.js";const C=JSON.parse('{"title":"Selectors","description":"","frontmatter":{},"headers":[],"relativePath":"selectors.md","filePath":"selectors.md","lastUpdated":null}'),g={name:"selectors.md"};function y(f,a,o,c,E,u){const e=n("PluginTabsTab"),t=n("PluginTabs");return r(),d("div",null,[a[11]||(a[11]=k(`Selectors In addition to choosing dimensions by name, we can also select values within them.
First, we can create a DimArray
with lookup values as well as dimension names:
julia using DimensionalData
julia julia > A = rand ( X ( 1.0 : 0.2 : 2.0 ), Y ([ :a , :b , :c ]))
╭──────────────────────────╮
+│ 6 × 3 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.2:2.0 ForwardOrdered Regular Points ,
+ → Y Categorical{Symbol} [:a, :b, :c] ForwardOrdered
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → :a :b :c
+ 1.0 0.27736 0.802776 0.621603
+ 1.2 0.444305 0.156538 0.768488
+ 1.4 0.184738 0.226064 0.869012
+ 1.6 0.772277 0.764895 0.101231
+ 1.8 0.711133 0.86273 0.239921
+ 2.0 0.883222 0.748041 0.511313
Then we can use the Selector
to select values from the array:
`,7)),l(t,null,{default:h(()=>[l(e,{label:"At"},{default:h(()=>a[0]||(a[0]=[i("p",null,[s("The "),i("a",{href:"/DimensionalData.jl/previews/PR856/api/lookuparrays#DimensionalData.Dimensions.Lookups.At"},[i("code",null,"At(x)")]),s(" selector gets the index or indices exactly matching the passed in value(s).")],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," A[X"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.2"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"), Y"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},":c"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},")]")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"0.7684883626668314")])])])],-1),i("p",null,"Or within a tolerance:",-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," A[X"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"At"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.99"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.201"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},":"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"1.5"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"; atol"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"0.05"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},")]")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"╭──────────────────────────╮")]),s(`
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It is also not shown in REPL printing.")],-1),i("p",null,[s("Here we create a "),i("a",{href:"/DimensionalData.jl/previews/PR856/api/lookuparrays#DimensionalData.Dimensions.Lookups.NoLookup"},[i("code",null,"NoLookup")]),s(":")],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," l "),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," NoLookup"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"()")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"NoLookup")])])])],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," typeof"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(l)")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"NoLookup{AutoValues}")])])])],-1),i("p",null,"Or even fill in the axis:",-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," l "),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"="),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," NoLookup"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(Base"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"OneTo"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"("),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}},"10"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"))")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"NoLookup")])])])],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#005CC5","--shiki-dark":"#79B8FF"}}," typeof"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"(l)")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292e","--shiki-dark":"#e1e4e8"}},"NoLookup{Base.OneTo{Int64}}")])])])],-1)])),_:1})]),_:1}),a[13]||(a[13]=k(`Lookup autodetection When we define an array, extra properties are detected:
julia julia > A = DimArray ( rand ( 7 , 5 ), ( X ( 10 : 10 : 70 ), Y ([ :a , :b , :c , :d , :e ])))
╭──────────────────────────╮
+│ 7 × 5 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Int64} 10:10:70 ForwardOrdered Regular Points ,
+ → Y Categorical{Symbol} [:a, :b, :c, :d, :e] ForwardOrdered
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → :a :b :c :d :e
+ 10 0.71372 0.301659 0.229418 0.442111 0.680987
+ 20 0.905616 0.307762 0.228248 0.882656 0.396585
+ 30 0.0928922 0.651538 0.152068 0.683795 0.687921
+ 40 0.441181 0.314906 0.650675 0.570603 0.647225
+ 50 0.621662 0.196478 0.97293 0.929034 0.855976
+ 60 0.72217 0.791844 0.883323 0.21921 0.0575993
+ 70 0.896257 0.758149 0.679453 0.506221 0.771237
This array has a Sampled
lookup with ForwardOrdered
Regular
Points
for X
, and a Categorical
ForwardOrdered
for Y
.
Most lookup types and properties are detected automatically like this from the arrays and ranges used.
Arrays and ranges of String
, Symbol
, and Char
are set to Categorical
lookup.
order
is detected as Unordered
, ForwardOrdered
, or ReverseOrdered
Arrays and ranges of Number
, DateTime
, and other things are set to Sampled
lookups.
order
is detected as Unordered
, ForwardOrdered
, or ReverseOrdered
.
sampling
is set to Points()
unless the values are IntervalSets.Interval
, then Intervals(Center())
is used.
span
is detected as Regular(step(range))
for AbstractRange
and Irregular(nothing, nothing)
for other AbstractArray
, where nothing, nothing
are the unknown outer bounds of the lookup. They are not needed for Points
as the outer values are the outer bounds. But they can be specified manually for Intervals
Empty dimensions or dimension types are assigned NoLookup()
ranges that can't be used with selectors as they hold no values.
DimSelector
We can also index with arrays of selectors DimSelectors
. These are like CartesianIndices
or DimIndices
, but holding the Selectors
At
, Near
, or Contains
.
julia julia > A = rand ( X ( 1.0 : 0.2 : 2.0 ), Y ( 10 : 2 : 20 ))
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.2:2.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Int64} 10:2:20 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10 12 14 16 18 20
+ 1.0 0.0775482 0.263531 0.361536 0.508614 0.89559 0.564657
+ 1.2 0.838572 0.388768 0.44818 0.0300922 0.595409 0.952489
+ 1.4 0.166221 0.0689895 0.499307 0.228081 0.473944 0.747695
+ 1.6 0.11787 0.143478 0.0831822 0.528009 0.631878 0.993001
+ 1.8 0.0905873 0.909826 0.557418 0.229792 0.29205 0.699118
+ 2.0 0.495624 0.810377 0.120845 0.574678 0.371583 0.460775
We can define another array with partly matching indices
julia julia > B = rand ( X ( 1.0 : 0.04 : 2.0 ), Y ( 20 :- 1 : 10 ))
╭────────────────────────────╮
+│ 26 × 11 DimArray{Float64, 2} │
+├────────────────────────────┴─────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.04:2.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Int64} 20:-1:10 ReverseOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 20 19 … 12 11 10
+ 1.0 0.751498 0.988013 0.966133 0.465112 0.227865
+ 1.04 0.241791 0.34589 0.0773793 0.728852 0.462859
+ 1.08 0.412631 0.0143204 0.454134 0.0417586 0.286
+ 1.12 0.300304 0.660657 0.712342 0.806495 0.727464
+ ⋮ ⋱ ⋮
+ 1.84 0.752331 0.491911 0.221313 0.72454 0.00869054
+ 1.88 0.296393 0.00428189 0.951789 0.142406 0.331422
+ 1.92 0.930699 0.777408 0.0661958 0.0737984 0.539197
+ 1.96 0.744107 0.360032 … 0.0699673 0.264001 0.27206
+ 2.0 0.635373 0.533041 0.574526 0.531012 0.503183
And we can simply select values from B
with selectors from A
:
julia julia > B[ DimSelectors (A)]
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} [1.0, 1.2, …, 1.8, 2.0] ForwardOrdered Irregular Points ,
+ → Y Sampled{Int64} [10, 12, …, 18, 20] ReverseOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10 12 14 16 18 20
+ 1.0 0.227865 0.966133 0.40169 0.990114 0.358535 0.751498
+ 1.2 0.235356 0.675459 0.426312 0.768719 0.569347 0.399781
+ 1.4 0.508938 0.166186 0.0811277 0.342499 0.819291 0.998484
+ 1.6 0.573391 0.740107 0.817199 0.690159 0.527254 0.634257
+ 1.8 0.0255364 0.895115 0.519705 0.626382 0.171961 0.6063
+ 2.0 0.503183 0.574526 0.98981 0.450629 0.545522 0.635373
If the lookups aren't aligned, we can use Near
instead of At
, which is like doing a nearest neighbor interpolation:
julia julia > C = rand ( X ( 1.0 : 0.007 : 2.0 ), Y ( 10.0 : 0.9 : 30 ))
╭─────────────────────────────╮
+│ 143 × 23 DimArray{Float64, 2} │
+├─────────────────────────────┴────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:0.007:1.994 ForwardOrdered Regular Points ,
+ → Y Sampled{Float64} 10.0:0.9:29.8 ForwardOrdered Regular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10.0 10.9 … 28.0 28.9 29.8
+ 1.0 0.341785 0.946655 0.954153 0.0821229 0.828315
+ 1.007 0.275784 0.22401 0.199364 0.485952 0.193179
+ 1.014 0.659836 0.832345 0.672556 0.0760101 0.950007
+ 1.021 0.168617 0.890377 0.258764 0.195897 0.747615
+ ⋮ ⋱
+ 1.966 0.426134 0.713457 0.693866 0.899783 0.166822
+ 1.973 0.601787 0.904115 … 0.0670244 0.42964 0.639039
+ 1.98 0.902689 0.231483 0.18906 0.670627 0.67989
+ 1.987 0.454047 0.92977 0.493784 0.998661 0.276742
+ 1.994 0.846736 0.0923954 0.00258281 0.273104 0.562491
julia julia > C[ DimSelectors (A; selectors = Near)]
╭──────────────────────────╮
+│ 6 × 6 DimArray{Float64, 2} │
+├──────────────────────────┴───────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} [1.0, 1.203, …, 1.798, 1.994] ForwardOrdered Irregular Points ,
+ → Y Sampled{Float64} [10.0, 11.8, …, 18.1, 19.9] ForwardOrdered Irregular Points
+└──────────────────────────────────────────────────────────────────────────────┘
+ ↓ → 10.0 11.8 13.6 16.3 18.1 19.9
+ 1.0 0.341785 0.0506805 0.621422 0.775495 0.198058 0.168554
+ 1.203 0.333095 0.953651 0.943783 0.789816 0.215448 0.106973
+ 1.399 0.641266 0.694096 0.0724261 0.603258 0.994395 0.570396
+ 1.602 0.827349 0.662227 0.682734 0.432028 0.707314 0.0542529
+ 1.798 0.192274 0.243368 0.53407 0.513909 0.0154473 0.450775
+ 1.994 0.846736 0.573469 0.0410007 0.513088 0.221908 0.0135194
`,22))])}const b=p(g,[["render",y]]);export{C as __pageData,b as default};
diff --git a/previews/PR856/assets/stacks.md.DLwsdzup.js b/previews/PR856/assets/stacks.md.DLwsdzup.js
new file mode 100644
index 000000000..609ac6b96
--- /dev/null
+++ b/previews/PR856/assets/stacks.md.DLwsdzup.js
@@ -0,0 +1,368 @@
+import{_ as n,c as p,a4 as t,G as h,w as l,j as i,a as s,B as d,o as r}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"DimStacks","description":"","frontmatter":{},"headers":[],"relativePath":"stacks.md","filePath":"stacks.md","lastUpdated":null}'),g={name:"stacks.md"};function y(f,a,c,o,u,E){const k=d("PluginTabsTab"),e=d("PluginTabs");return r(),p("div",null,[a[25]||(a[25]=t(`DimStacks An AbstractDimStack
represents a collection of AbstractDimArray
layers that share some or all dimensions. For any two layers, a dimension of the same name must have the identical lookup - in fact, only one is stored for all layers to enforce this consistency.
julia julia > using DimensionalData
+
+julia > x, y = X ( 1.0 : 10.0 ), Y ( 5.0 : 10.0 )
( ↓ X 1.0:1.0:10.0 ,
+→ Y 5.0:1.0:10.0 )
julia julia > st = DimStack ((a = rand (x, y), b = rand (x, y), c = rand (y), d = rand (x)))
╭───────────────╮
+│ 10 × 6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+ :a eltype: Float64 dims: X , Y size: 10 × 6
+ :b eltype: Float64 dims: X , Y size: 10 × 6
+ :c eltype: Float64 dims: Y size: 6
+ :d eltype: Float64 dims: X size: 10
+└──────────────────────────────────────────────────────────────────────────────┘
The behavior of a DimStack
is at times like a NamedTuple
of DimArray
and, at other times, an AbstractArray
of NamedTuple
.
NamedTuple-like indexing `,8)),h(e,null,{default:l(()=>[h(k,{label:"getting layers"},{default:l(()=>a[0]||(a[0]=[i("p",null,[s("Layers can be accessed with "),i("code",null,".name"),s(" or "),i("code",null,"[:name]")],-1),i("div",{class:"language-julia vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"},"julia"),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"julia"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},">"),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}}," st"),i("span",{style:{"--shiki-light":"#D73A49","--shiki-dark":"#F97583"}},"."),i("span",{style:{"--shiki-light":"#24292E","--shiki-dark":"#E1E4E8"}},"a")])])])],-1),i("div",{class:"language- vp-adaptive-theme"},[i("button",{title:"Copy Code",class:"copy"}),i("span",{class:"lang"}),i("pre",{class:"shiki shiki-themes github-light github-dark vp-code",tabindex:"0"},[i("code",null,[i("span",{class:"line"},[i("span",{style:{"--shiki-light":"#959da5","--shiki-dark":"#959da5"}},"╭─────────────────────────────╮")]),s(`
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from all layers is usually measured in nanoseconds, and no slower than manually indexing into each parent array directly.
There are some compilation overheads to this though, and stacks with very many layers can take a long time to compile.
julia julia > using BenchmarkTools
+
+julia > @btime $ st[X = 1 , Y = 4 ]
4.048 ns (0 allocations: 0 bytes)
+(a = 0.5154448003579335, b = 0.7410056618197303, c = 0.04060837507113502, d = 0.43235163202114113)
julia julia > @btime $ st[ 1 , 4 ]
4.047 ns (0 allocations: 0 bytes)
+(a = 0.5154448003579335, b = 0.7410056618197303, c = 0.04060837507113502, d = 0.43235163202114113)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
diff --git a/previews/PR856/assets/stacks.md.DLwsdzup.lean.js b/previews/PR856/assets/stacks.md.DLwsdzup.lean.js
new file mode 100644
index 000000000..609ac6b96
--- /dev/null
+++ b/previews/PR856/assets/stacks.md.DLwsdzup.lean.js
@@ -0,0 +1,368 @@
+import{_ as n,c as p,a4 as t,G as h,w as l,j as i,a as s,B as d,o as r}from"./chunks/framework.BnKqFO9u.js";const b=JSON.parse('{"title":"DimStacks","description":"","frontmatter":{},"headers":[],"relativePath":"stacks.md","filePath":"stacks.md","lastUpdated":null}'),g={name:"stacks.md"};function y(f,a,c,o,u,E){const k=d("PluginTabsTab"),e=d("PluginTabs");return r(),p("div",null,[a[25]||(a[25]=t(`DimStacks An AbstractDimStack
represents a collection of AbstractDimArray
layers that share some or all dimensions. For any two layers, a dimension of the same name must have the identical lookup - in fact, only one is stored for all layers to enforce this consistency.
julia julia > using DimensionalData
+
+julia > x, y = X ( 1.0 : 10.0 ), Y ( 5.0 : 10.0 )
( ↓ X 1.0:1.0:10.0 ,
+→ Y 5.0:1.0:10.0 )
julia julia > st = DimStack ((a = rand (x, y), b = rand (x, y), c = rand (y), d = rand (x)))
╭───────────────╮
+│ 10 × 6 DimStack │
+├───────────────┴──────────────────────────────────────────────────────── dims ┐
+ ↓ X Sampled{Float64} 1.0:1.0:10.0 ForwardOrdered Regular Points ,
+ → Y Sampled{Float64} 5.0:1.0:10.0 ForwardOrdered Regular Points
+├────────────────────────────────────────────────────────────────────── layers ┤
+ :a eltype: Float64 dims: X , Y size: 10 × 6
+ :b eltype: Float64 dims: X , Y size: 10 × 6
+ :c eltype: Float64 dims: Y size: 6
+ :d eltype: Float64 dims: X size: 10
+└──────────────────────────────────────────────────────────────────────────────┘
The behavior of a DimStack
is at times like a NamedTuple
of DimArray
and, at other times, an AbstractArray
of NamedTuple
.
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from all layers is usually measured in nanoseconds, and no slower than manually indexing into each parent array directly.
There are some compilation overheads to this though, and stacks with very many layers can take a long time to compile.
julia julia > using BenchmarkTools
+
+julia > @btime $ st[X = 1 , Y = 4 ]
4.048 ns (0 allocations: 0 bytes)
+(a = 0.5154448003579335, b = 0.7410056618197303, c = 0.04060837507113502, d = 0.43235163202114113)
julia julia > @btime $ st[ 1 , 4 ]
4.047 ns (0 allocations: 0 bytes)
+(a = 0.5154448003579335, b = 0.7410056618197303, c = 0.04060837507113502, d = 0.43235163202114113)
`,7))])}const F=n(g,[["render",y]]);export{b as __pageData,F as default};
diff --git a/previews/PR856/assets/style.Bzb1ePYW.css b/previews/PR856/assets/style.Bzb1ePYW.css
new file mode 100644
index 000000000..c309ff33c
--- /dev/null
+++ b/previews/PR856/assets/style.Bzb1ePYW.css
@@ -0,0 +1 @@
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diff --git a/previews/PR856/assets/sxuybmz.D5bwtKy3.svg b/previews/PR856/assets/sxuybmz.D5bwtKy3.svg
new file mode 100644
index 000000000..db4d909f1
--- /dev/null
+++ b/previews/PR856/assets/sxuybmz.D5bwtKy3.svg
@@ -0,0 +1,194 @@
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diff --git a/previews/PR856/assets/tables.md.waQ0OE1e.js b/previews/PR856/assets/tables.md.waQ0OE1e.js
new file mode 100644
index 000000000..2495a75c8
--- /dev/null
+++ b/previews/PR856/assets/tables.md.waQ0OE1e.js
@@ -0,0 +1,165 @@
+import{_ as n,c as g,a4 as e,G as h,w as t,j as i,a as s,B as d,o as p}from"./chunks/framework.BnKqFO9u.js";const m=JSON.parse('{"title":"Tables and DataFrames","description":"","frontmatter":{},"headers":[],"relativePath":"tables.md","filePath":"tables.md","lastUpdated":null}'),r={name:"tables.md"};function f(o,a,y,c,b,w){const l=d("PluginTabsTab"),k=d("PluginTabs");return p(),g("div",null,[a[6]||(a[6]=e(`Tables and DataFrames Tables.jl provides an ecosystem-wide interface to tabular data in Julia, ensuring interoperability with DataFrames.jl , CSV.jl , and hundreds of other packages that implement the standard.
DimensionalData.jl implements the Tables.jl interface for AbstractDimArray
and AbstractDimStack
. DimStack
layers are unrolled so they are all the same size, and dimensions loop to match the length of the largest layer.
Columns are given the name
of the array or stack layer, and the result of DD.name(dimension)
for Dimension
columns.
Looping of dimensions and stack layers is done lazily , and does not allocate unless collected.
Example julia using DimensionalData
+using Dates
+using DataFrames
Define some dimensions:
julia julia > x, y, c = X ( 1 : 10 ), Y ( 1 : 10 ), Dim{:category} ( 'a' : 'z' )
( ↓ X 1:10 ,
+→ Y 1:10 ,
+↗ category 'a':1:'z' )
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julia using CSV
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+ "8,1,a,0.0019198597596568057,0.798655984630017"
+ "9,1,a,0.44833963865079907,0.40268027828179853"
+ ⋮
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+ "3,10,z,0.5099922507050859,0.07986058669268159"
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diff --git a/previews/PR856/assets/tables.md.waQ0OE1e.lean.js b/previews/PR856/assets/tables.md.waQ0OE1e.lean.js
new file mode 100644
index 000000000..2495a75c8
--- /dev/null
+++ b/previews/PR856/assets/tables.md.waQ0OE1e.lean.js
@@ -0,0 +1,165 @@
+import{_ as n,c as g,a4 as e,G as h,w as t,j as i,a as s,B as d,o as p}from"./chunks/framework.BnKqFO9u.js";const m=JSON.parse('{"title":"Tables and DataFrames","description":"","frontmatter":{},"headers":[],"relativePath":"tables.md","filePath":"tables.md","lastUpdated":null}'),r={name:"tables.md"};function f(o,a,y,c,b,w){const l=d("PluginTabsTab"),k=d("PluginTabs");return p(),g("div",null,[a[6]||(a[6]=e(`Tables and DataFrames Tables.jl provides an ecosystem-wide interface to tabular data in Julia, ensuring interoperability with DataFrames.jl , CSV.jl , and hundreds of other packages that implement the standard.
DimensionalData.jl implements the Tables.jl interface for AbstractDimArray
and AbstractDimStack
. DimStack
layers are unrolled so they are all the same size, and dimensions loop to match the length of the largest layer.
Columns are given the name
of the array or stack layer, and the result of DD.name(dimension)
for Dimension
columns.
Looping of dimensions and stack layers is done lazily , and does not allocate unless collected.
Example julia using DimensionalData
+using Dates
+using DataFrames
Define some dimensions:
julia julia > x, y, c = X ( 1 : 10 ), Y ( 1 : 10 ), Dim{:category} ( 'a' : 'z' )
( ↓ X 1:10 ,
+→ Y 1:10 ,
+↗ category 'a':1:'z' )
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+readlines ( "dimstack.csv" )
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+ "8,1,a,0.0019198597596568057,0.798655984630017"
+ "9,1,a,0.44833963865079907,0.40268027828179853"
+ ⋮
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+ "10,10,z,0.8560905731682101,0.8428656510212863"
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+ DimensionalData.jl
+
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+ Skip to content DimensionalData.jl Julia datasets with named dimensions
High performance named indexing for Julia
Seamlessly integrated with the julia ecosystem Works with most methods that accept a regular Array. If a method accepts numeric indices or dims=X in base, you should be able to use DimensionalData.jl dims.
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+
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diff --git a/previews/PR856/object_modification.html b/previews/PR856/object_modification.html
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index 000000000..44b13e7e9
--- /dev/null
+++ b/previews/PR856/object_modification.html
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+
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+ Modifying Objects | DimensionalData.jl
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\ No newline at end of file
diff --git a/previews/PR856/plots.html b/previews/PR856/plots.html
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index 000000000..149bffad5
--- /dev/null
+++ b/previews/PR856/plots.html
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+ Plots.jl | DimensionalData.jl
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\ No newline at end of file
diff --git a/previews/PR856/selectors.html b/previews/PR856/selectors.html
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+++ b/previews/PR856/selectors.html
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+ Selectors | DimensionalData.jl
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\ No newline at end of file
diff --git a/previews/PR856/siteinfo.js b/previews/PR856/siteinfo.js
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+++ b/previews/PR856/siteinfo.js
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+var DOCUMENTER_CURRENT_VERSION = "previews/PR856";
diff --git a/previews/PR856/stacks.html b/previews/PR856/stacks.html
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+ DimStacks | DimensionalData.jl
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\ No newline at end of file
diff --git a/previews/PR856/tables.html b/previews/PR856/tables.html
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+++ b/previews/PR856/tables.html
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+ Tables and DataFrames | DimensionalData.jl
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\ No newline at end of file
diff --git a/previews/PR856/vp-icons.css b/previews/PR856/vp-icons.css
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--- /dev/null
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\ No newline at end of file