From 0651fee27002b02821250d621b88455493a8dea6 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Wed, 10 Jul 2024 13:10:54 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- index.html | 261 ++++++++++-------- ...l-24-output-1.svg => cell-26-output-1.svg} | 122 ++++---- search.json | 6 +- sitemap.xml | 2 +- 5 files changed, 210 insertions(+), 183 deletions(-) rename index_files/figure-revealjs/{cell-24-output-1.svg => cell-26-output-1.svg} (68%) diff --git a/.nojekyll b/.nojekyll index c8bfcb5..93221d7 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -a77169d8 \ No newline at end of file +5b97d39c \ No newline at end of file diff --git a/index.html b/index.html index 58f7b3e..3a66cc7 100644 --- a/index.html +++ b/index.html @@ -423,8 +423,9 @@

Why another named array package?

  • Geospatial data
  • @@ -436,8 +437,8 @@

    Why another named array package?

    -
    -

    Dimensions

    +
    +

    Concepts: Dimensions

    @@ -475,7 +476,6 @@

    Dimension are wrappers


    -
    “Standard” dimensions (90% of spatial data):

    X, Y, Z, Ti
    @@ -483,37 +483,58 @@

    Dimension are wrappers

    (X, Y, Z, Ti)
    -


    -Arbitrary dimensions (everything else)

    +
    +

    Arbitrary dimensions (everything else)

    Dim{:name}
    Dim{:name}
    +
    +

    Lookups

    • Hold lookup values along a dimension
      • -
    • AbstracArrays with lookup traits like Points or Intervals
      • -
    • Sampled and Categorical are the most common
      • +
    • And traits like Points or Intervals
    • mostly detected automatically in array constructors
      • -
    • advanced topic - see the docs!
    +
    +

    You can define them manually when you need to:

    +
    +
    using DimensionalData.Lookups
+l = Sampled(1:10; sampling=Intervals(Start()), order=ForwardOrdered(), span=Regular())
    +
    +
    Sampled{Int64} ForwardOrdered Regular Intervals{Start}
+wrapping: 1:10
    +
    +
    +


    +

    +
    +
    Sampled <: AbstractSampled <: Aligned <: Lookup <: AbstractVector
    +
    +
    false
    +
    +
    +
    -
    -

    DimArray <: AbstractDimArray

    - +
    +

    DimArray Constructors

    +
      +
    • DimArray <: AbstractDimArray <: AbstractArray
      • +

    1 dimensional


    -
    -
    A = DimArray([1, 2, 3], X([:a, :b, :c]))
    +
    +
    A = DimArray([1, 2, 3], X([:a, :b, :c]))
    ╭─────────────────────────────╮
 │ 3-element DimArray{Int64,1} │
@@ -529,8 +550,8 @@ 
    1 dimensional
    
 
    
 
    N dimensional
    
 
    With standard dimensions in a Tuple:
    
-
    
-
    A = DimArray(rand(3, 4), (X([:a, :b, :c]), Y(10.0:10:40.0)))
    
+
    
+
    A = DimArray(rand(3, 4), (X([:a, :b, :c]), Y(10.0:10:40.0)))
    
 
    
 
    ╭─────────────────────────╮
 │ 3×4 DimArray{Float64,2} │
@@ -538,18 +559,18 @@ 
    N dimensional
    
   ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
   → Y Sampled{Float64} 10.0:10.0:40.0 ForwardOrdered Regular Points
 └───────────────────────────────────────────────────────────────────┘
- ↓ →  10.0       20.0        30.0       40.0
-  :a   0.241505   0.0282003   0.462848   0.337448
-  :b   0.370021   0.6171      0.588097   0.499839
-  :c   0.842621   0.509002    0.762115   0.143347
    
+ ↓ →  10.0       20.0       30.0        40.0
+  :a   0.981993   0.513781   0.0573086   0.133294
+  :b   0.597572   0.938506   0.687245    0.831078
+  :c   0.243421   0.560166   0.854638    0.592527

    With arbitrary Dim dimensions, in a NamedTuple:

    -
    -
    DimArray(rand(3, 4), (a=[:a, :b, :c], b=10.0:10:40.0))
    +
    +
    DimArray(rand(3, 4), (a=[:a, :b, :c], b=10.0:10:40.0))
    ╭─────────────────────────╮
 │ 3×4 DimArray{Float64,2} │
@@ -557,18 +578,18 @@ 
    N dimensional
    
   ↓ a Categorical{Symbol} [:a, :b, :c] ForwardOrdered,
   → b Sampled{Float64} 10.0:10.0:40.0 ForwardOrdered Regular Points
 └───────────────────────────────────────────────────────────────────┘
- ↓ →  10.0        20.0        30.0       40.0
-  :a   0.427305    0.633447    0.151995   0.998212
-  :b   0.0411998   0.983792    0.546416   0.0624934
-  :c   0.796127    0.0912018   0.415789   0.990061
    + ↓ → 10.0 20.0 30.0 40.0 + :a 0.819396 0.894635 0.768049 0.093845 + :b 0.427051 0.650875 0.0872013 0.500716 + :c 0.215033 0.896086 0.922141 0.876665

    Shorthands: rand, fill, zeros, ones

    -
    -
    rand(X(6), Y(10:2:20))
    +
    +
    rand(X(6), Y(10:2:20))
    ╭─────────────────────────╮
 │ 6×6 DimArray{Float64,2} │
@@ -576,28 +597,30 @@ 
    N dimensional
    
   ↓ X,
   → Y Sampled{Int64} 10:2:20 ForwardOrdered Regular Points
 └──────────────────────────────────────────────────────────┘
- 10          12         14          16         18          20
-  0.0775471   0.981323   0.324893    0.925181   0.229754    0.747415
-  0.847143    0.570464   0.434556    0.78922    0.578236    0.304476
-  0.36445     0.762368   0.120009    0.821311   0.73623     0.814217
-  0.752036    0.513882   0.143335    0.472154   0.400025    0.178112
-  0.129693    0.542092   0.0298521   0.260005   0.247613    0.35124
-  0.0366645   0.891387   0.384627    0.270451   0.0363059   0.310466
    + 10 12 14 16 18 20 + 0.487087 0.972023 0.633484 0.662478 0.928678 0.976848 + 0.0559741 0.543368 0.311436 0.716113 0.565865 0.938829 + 0.150337 0.507311 0.451984 0.803921 0.647749 0.84485 + 0.738515 0.444562 0.707928 0.339974 0.592118 0.284115 + 0.688352 0.974059 0.928612 0.296633 0.221053 0.883975 + 0.943116 0.752221 0.169269 0.0503815 0.758892 0.316402
    -
    -

    DimStack <: AbstractDimStack

    - +
    +

    DimStack Constructors

    +
      +
    • DimStack <: AbstractDimStack
      • +

    Layers with the same dimensions


    -
    -
    ds = X([:a, :b, :c]), Ti(10.0:10:40.0)
-S = DimStack((layer1=rand(Float32, 3, 4), layer2=zeros(Bool, 3, 4)), ds)
    +
    +
    ds = X([:a, :b, :c]), Ti(10.0:10:40.0)
+S = DimStack((layer1=rand(Float32, 3, 4), layer2=zeros(Bool, 3, 4)), ds)
    ╭──────────────╮
 │ 3×4 DimStack │
@@ -615,9 +638,9 @@ 
    Layers with the same dimensions
    
 
    Layers with different dimensions:
    
 

    
 
    
-
    
-
    x, ti = X([:a, :b, :c]), Ti(10.0:10:40.0)
-DimStack((twodims=rand(Float32, x, ti), onedim=zeros(Bool, x)))
    
+
    
+
    x, ti = X([:a, :b, :c]), Ti(10.0:10:40.0)
+DimStack((twodims=rand(Float32, x, ti), onedim=zeros(Bool, x)))
    
 
    
 
    ╭──────────────╮
 │ 3×4 DimStack │
@@ -634,30 +657,30 @@ 
    Layers with different dimensions:
    
 
    
 
    Named indexing
    
 
    DimArray:
    
-
    
-
    using BenchmarkTools
-@btime $A[3, 4]        # Base Julia Array syntax
-@btime $A[Y(4), X(3)]  # Dimension wrappers
-@btime $A[Y=4, X=3]    # Keyword syntax
    
+
    
+
    using BenchmarkTools
+@btime $A[3, 4]        # Base Julia Array syntax
+@btime $A[Y(4), X(3)]  # Dimension wrappers
+@btime $A[Y=4, X=3]    # Keyword syntax
    
 
    
 
      3.095 ns (0 allocations: 0 bytes)
   3.095 ns (0 allocations: 0 bytes)
   3.095 ns (0 allocations: 0 bytes)
    
 
    
 
    
-
    0.14334711233192854
    
+
    0.5925272105664855
    
 
    
 
    
 
    DimStack:
    
-
    
-
    @btime $S[Ti(4), X(3)] # Dimension wrappers
-@btime $S[Ti=4, X=3]   # Keyword syntax
    
+
    
+
    @btime $S[Ti(4), X(3)] # Dimension wrappers
+@btime $S[Ti=4, X=3]   # Keyword syntax
    
 
    
-
      3.406 ns (0 allocations: 0 bytes)
-  3.706 ns (0 allocations: 0 bytes)
    
+
      3.145 ns (0 allocations: 0 bytes)
+  3.406 ns (0 allocations: 0 bytes)
    
 
    
 
    
-
    (layer1 = 0.15278113f0, layer2 = false)
    
+
    (layer1 = 0.33329403f0, layer2 = false)
    
 
    
 
    
 
    
@@ -674,15 +697,15 @@ 
    At
    
 

    
 Find exact or approximate matches
    
 
    
-
    
-
    A = DimArray([1, 2, 3, 4], (X([10.0, 20.0, 40.0, 80.0])))
-A[X(At(80.0))]
    
+
    
+
    A = DimArray([1, 2, 3, 4], (X([10.0, 20.0, 40.0, 80.0])))
+A[X(At(80.0))]
    
 
    
 
    4
    
 
    
 
    
-
    
-
    A[X(At(80.09; atol=0.1))]
    
+
    
+
    A[X(At(80.09; atol=0.1))]
    
 
    
 
    4
    
 
    
@@ -693,8 +716,8 @@ 
    Near
    
 

    
 Find the closest match
    
 
    
-
    
-
    A[X(Near(85))]
    
+
    
+
    A[X(Near(85))]
    
 
    
 
    4
    
 
    
@@ -705,12 +728,12 @@ 
    Contains
    
 

    
 Find the interval that contains a value
    
 
    
-
    
-
    # Define a DimArray with Intervals lookup
-using DimensionalData.Lookups
-A = DimArray(100:100:9900, X(1.0:1.0:99.0; sampling=Intervals(Start())))
-# Index with Contains
-A[X(Contains(9.5))]
    
+
    
+
    # Define a DimArray with Intervals lookup
+using DimensionalData.Lookups
+A = DimArray(100:100:9900, X(1.0:1.0:99.0; sampling=Intervals(Start())))
+# Index with Contains
+A[X(Contains(9.5))]
    
 
    
 
    900
    
 
    
@@ -721,8 +744,8 @@ 
    .. (an IntervalSets.jl Interval)
    
 

    
 Select data inside an interval
    
 
    
-
    
-
    A[X=9.5 .. 15]
    
+
    
+
    A[X=9.5 .. 15]
    
 
    
 
    ╭─────────────────────────────╮
 │ 5-element DimArray{Int64,1} │
@@ -742,8 +765,8 @@ 
    Where
    
 

    
 Make dimensional queries
    
 
    
-
    
-
    A[X=Where(isodd)]
    
+
    
+
    A[X=Where(isodd)]
    
 
    
 
    ╭──────────────────────────────╮
 │ 50-element DimArray{Int64,1} │
@@ -772,65 +795,67 @@ 
    Plotting

    Plots.jl

    -
    -
    using Plots
-Plots.scatter(rand(X([:a, :b, :c, :d])))
    +
    +
    using Plots
+Plots.scatter(rand(X([:a, :b, :c, :d])))
    - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + +

    Makie.jl

    -
    -
    using CairoMakie, Distributions
-Makie.heatmap(rand(Normal(), X(100:10:200), Y([:a, :b, :c])))
    +
    +
    using CairoMakie, Distributions
+Makie.heatmap(rand(Normal(), X(100:10:200), Y([:a, :b, :c])))
    -
    +

    Integrations

    @@ -841,16 +866,18 @@

    Integrations

    Some packages building on DimensionalData.jl

    +

    Thanks

    diff --git a/index_files/figure-revealjs/cell-24-output-1.svg b/index_files/figure-revealjs/cell-26-output-1.svg similarity index 68% rename from index_files/figure-revealjs/cell-24-output-1.svg rename to index_files/figure-revealjs/cell-26-output-1.svg index 7504a61..27ca151 100644 --- a/index_files/figure-revealjs/cell-24-output-1.svg +++ b/index_files/figure-revealjs/cell-26-output-1.svg @@ -2,43 +2,43 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -52,80 +52,80 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/search.json b/search.json index a2b7530..fd88f31 100644 --- a/search.json +++ b/search.json @@ -4,7 +4,7 @@ "href": "index.html#why-another-named-array-package", "title": "DimensionalData.jl", "section": "Why another named array package?", - "text": "Why another named array package?\n\n\nGeospatial data\n\nnamed dimensions and lookup values are ubiquitous\nthere are a lot of possible lookup configurations\nmulti-array datasets are common (with shared dimensions)\n\n\n\n\nxarray" + "text": "Why another named array package?\n\n\nGeospatial data\n\nnamed dimensions and lookup values are ubiquitous\nselecting spatial and temporal subsets is ubiquitous\nthere are a lot of possible lookup configurations\nmulti-array datasets are also common\n\n\n\n\nxarray" }, { "objectID": "index.html#dimension-are-wrappers", @@ -25,7 +25,7 @@ "href": "index.html#n-dimensional", "title": "DimensionalData.jl", "section": "N dimensional", - "text": "N dimensional\nWith standard dimensions in a Tuple:\n\nA = DimArray(rand(3, 4), (X([:a, :b, :c]), Y(10.0:10:40.0)))\n\n╭─────────────────────────╮\n│ 3×4 DimArray{Float64,2} │\n├─────────────────────────┴─────────────────────────────────── dims ┐\n ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,\n → Y Sampled{Float64} 10.0:10.0:40.0 ForwardOrdered Regular Points\n└───────────────────────────────────────────────────────────────────┘\n ↓ → 10.0 20.0 30.0 40.0\n :a 0.241505 0.0282003 0.462848 0.337448\n :b 0.370021 0.6171 0.588097 0.499839\n :c 0.842621 0.509002 0.762115 0.143347" + "text": "N dimensional\nWith standard dimensions in a Tuple:\n\nA = DimArray(rand(3, 4), (X([:a, :b, :c]), Y(10.0:10:40.0)))\n\n╭─────────────────────────╮\n│ 3×4 DimArray{Float64,2} │\n├─────────────────────────┴─────────────────────────────────── dims ┐\n ↓ X Categorical{Symbol} [:a, :b, :c] ForwardOrdered,\n → Y Sampled{Float64} 10.0:10.0:40.0 ForwardOrdered Regular Points\n└───────────────────────────────────────────────────────────────────┘\n ↓ → 10.0 20.0 30.0 40.0\n :a 0.981993 0.513781 0.0573086 0.133294\n :b 0.597572 0.938506 0.687245 0.831078\n :c 0.243421 0.560166 0.854638 0.592527" }, { "objectID": "index.html#layers-with-the-same-dimensions", @@ -95,7 +95,7 @@ "href": "index.html#some-packages-building-on-dimensionaldata.jl", "title": "DimensionalData.jl", "section": "Some packages building on DimensionalData.jl", - "text": "Some packages building on DimensionalData.jl\n\nRasters.jl\nYAXArrays.jl\nClimateBase.jl\nAstroImages.jl\nArviz.jl\nDynamicGrids.jl\nPyramidScheme.jl\nJump.jl (extension)" + "text": "Some packages building on DimensionalData.jl\n\n\nRasters.jl\nYAXArrays.jl\nClimateBase.jl\nAstroImages.jl\nPyramidScheme.jl\nArviz.jl\nJuMP.jl (extension)\nDynamicGrids.jl" }, { "objectID": "index.html#thanks", diff --git a/sitemap.xml b/sitemap.xml index b9adb74..3731ff8 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,6 +2,6 @@ https://rafaqz.github.io/JuliaCon2024-DD/index.html - 2024-07-09T23:43:09.216Z + 2024-07-10T13:02:41.726Z